data analysis tools for research

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

Create a Free Account

Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection  methods, and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

LEARN ABOUT: Average Order Value

QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

MORE LIKE THIS

Customer Experience Automation: Benefits and Best Tools

Apr 1, 2024

7 Best Market Segmentation Tools in 2024

In-App Feedback Tools: How to Collect, Uses & 14 Best Tools

Mar 29, 2024

11 Best Customer Journey Analytics Software in 2024

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

The 11 Best Data Analytics Tools for Data Analysts in 2024

As the field of data analytics evolves, the range of available data analysis tools grows with it. If you’re considering a career in the field, you’ll want to know: Which data analysis tools do I need to learn?

In this post, we’ll highlight some of the key data analytics tools you need to know and why. From open-source tools to commercial software, you’ll get a quick overview of each, including its applications, pros, and cons. What’s even better, a good few of those on this list contain AI data analytics tools , so you’re at the forefront of the field as 2024 comes around.

We’ll start our list with the must-haves, then we’ll move onto some of the more popular tools and platforms used by organizations large and small. Whether you’re preparing for an interview, or are deciding which tool to learn next, by the end of this post you’ll have an idea how to progress.

If you’re only starting out, then CareerFoundry’s free data analytics short course will help you take your first steps.

Here are the data analysis tools we’ll cover:

• Microsoft Excel
• Jupyter Notebook
• Apache Spark
• Google Cloud AutoML
• Microsoft Power BI

How to choose a data analysis tool

Data analysis tools faq.

So, let’s get into the list then!

1.  Microsoft Excel

Excel at a glance:

• Type of tool: Spreadsheet software.
• Availability : Commercial.
• Mostly used for: Data wrangling and reporting.
• Pros: Widely-used, with lots of useful functions and plug-ins.
• Cons: Cost, calculation errors, poor at handling big data.

Excel: the world’s best-known spreadsheet software. What’s more, it features calculations and graphing functions that are ideal for data analysis.

Whatever your specialism, and no matter what other software you might need, Excel is a staple in the field. Its invaluable built-in features include pivot tables (for sorting or totaling data) and form creation tools.

It also has a variety of other functions that streamline data manipulation. For instance, the CONCATENATE function allows you to combine text, numbers, and dates into a single cell. SUMIF lets you create value totals based on variable criteria, and Excel’s search function makes it easy to isolate specific data.

It has limitations though. For instance, it runs very slowly with big datasets and tends to approximate large numbers, leading to inaccuracies. Nevertheless, it’s an important and powerful data analysis tool, and with many plug-ins available, you can easily bypass Excel’s shortcomings. Get started with these ten Excel formulas that all data analysts should know .

Python at a glance:

• Type of tool: Programming language.
• Availability: Open-source, with thousands of free libraries.
• Used for: Everything from data scraping to analysis and reporting.
• Pros: Easy to learn, highly versatile, widely-used.
• Cons: Memory intensive—doesn’t execute as fast as some other languages.

  A programming language with a wide range of uses, Python is a must-have for any data analyst. Unlike more complex languages, it focuses on readability, and its general popularity in the tech field means many programmers are already familiar with it.

Python is also extremely versatile; it has a huge range of resource libraries suited to a variety of different data analytics tasks. For example, the NumPy and pandas libraries are great for streamlining highly computational tasks, as well as supporting general data manipulation.

Libraries like Beautiful Soup and Scrapy are used to scrape data from the web, while Matplotlib is excellent for data visualization and reporting. Python’s main drawback is its speed—it is memory intensive and slower than many languages. In general though, if you’re building software from scratch, Python’s benefits far outweigh its drawbacks. You can learn more about Python in our full guide .

R at a glance:

• Availability: Open-source.
• Mostly used for: Statistical analysis and data mining.
• Pros: Platform independent, highly compatible, lots of packages.
• Cons: Slower, less secure, and more complex to learn than Python.

R, like Python, is a popular open-source programming language. It is commonly used to create statistical/data analysis software.

R’s syntax is more complex than Python and the learning curve is steeper. However, it was built specifically to deal with heavy statistical computing tasks and is very popular for data visualization. A bit like Python, R also has a network of freely available code, called CRAN (the Comprehensive R Archive Network), which offers 10,000+ packages.

It integrates well with other languages and systems (including big data software) and can call on code from languages like C, C++, and FORTRAN. On the downside, it has poor memory management, and while there is a good community of users to call on for help, R has no dedicated support team. But there is an excellent R-specific integrated development environment (IDE) called RStudio , which is always a bonus!

4.  Jupyter Notebook

Jupyter Notebook at a glance:

• Type of tool: Interactive authoring software.
• Mostly used for: Sharing code, creating tutorials, presenting work.
• Pros: Great for showcasing, language-independent.
• Cons: Not self-contained, nor great for collaboration.

Jupyter Notebook is an open-source web application that allows you to create interactive documents. These combine live code, equations, visualizations, and narrative text.

Imagine something a bit like a Microsoft word document, only far more interactive, and designed specifically for data analytics! As a data analytics tool, it’s great for showcasing work: Jupyter Notebook runs in the browser and supports over 40 languages, including Python and R. It also integrates with big data analysis tools, like Apache Spark (see below) and offers various outputs from HTML to images, videos, and more.

But as with every tool, it has its limitations. Jupyter Notebook documents have poor version control, and tracking changes is not intuitive. This means it’s not the best place for development and analytics work (you should use a dedicated IDE for these) and it isn’t well suited to collaboration.

Since it isn’t self-contained, this also means you have to provide any extra assets (e.g. libraries or runtime systems) to anybody you’re sharing the document with. But for presentation and tutorial purposes, it remains an invaluable data science and data analytics tool.

5.  Apache Spark

Apache Spark at a glance:

• Type of tool: Data processing framework
• Availability: Open-source
• Mostly used for: Big data processing, machine learning
• Pros: Fast, dynamic, easy to use
• Cons: No file management system, rigid user interface

Apache Spark is a software framework that allows data analysts and data scientists to quickly process vast data sets. It was first developed in 2012, it’s designed to analyze unstructured big data, Spark distributes computationally heavy analytics tasks across many computers.

While other similar frameworks exist (for example, Apache Hadoop ) Spark is exceptionally fast. By using RAM rather than local memory, it is around 100x faster than Hadoop. That’s why it’s often used for the development of data-heavy machine learning models .

It even has a library of machine learning algorithms, MLlib , including classification, regression, and clustering algorithms, to name a few. On the downside, consuming so much memory means Spark is computationally expensive. It also lacks a file management system, so it usually needs integration with other software, i.e. Hadoop.

6. Google Cloud AutoML

Google Cloud AutoML at a glance:

• Type of tool: Machine learning platform
• Availability:  Cloud-based, commercial
• Mostly used for:  Automating machine learning tasks
• Pros: Allows analysts with limited coding experience to build and deploy ML models , skipping lots of steps
• Cons:  Can be pricey for large-scale projects, lacks some flexibility

A serious proposition for data analysts and scientists in 2024 is Google Cloud’s AutoML tool. With the hype around generative AI in 2023 set to roll over into the next year, tools like AutoML but the capability to create machine learning models into your own hands.

Google Cloud AutoML contains a suite of tools across categories from structured data to language translation, image and video classification. As more and more organizations adopt machine learning, there will be a growing demand for data analysts who can use AutoML tools to automate their work easily.

SAS at a glance:

• Type of tool: Statistical software suite
• Availability: Commercial
• Mostly used for: Business intelligence, multivariate, and predictive analysis
• Pros: Easily accessible, business-focused, good user support
• Cons: High cost, poor graphical representation

SAS (which stands for Statistical Analysis System) is a popular commercial suite of business intelligence and data analysis tools. It was developed by the SAS Institute in the 1960s and has evolved ever since. Its main use today is for profiling customers, reporting, data mining, and predictive modeling. Created for an enterprise market, the software is generally more robust, versatile, and easier for large organizations to use. This is because they tend to have varying levels of in-house programming expertise.

But as a commercial product, SAS comes with a hefty price tag. Nevertheless, with cost comes benefits; it regularly has new modules added, based on customer demand. Although it has fewer of these than say, Python libraries, they are highly focused. For instance, it offers modules for specific uses such as anti-money laundering and analytics for the Internet of Things.

8. Microsoft Power BI

Power BI at a glance:

• Type of tool: Business analytics suite.
• Availability: Commercial software (with a free version available).
• Mostly used for: Everything from data visualization to predictive analytics.  
• Pros: Great data connectivity, regular updates, good visualizations.
• Cons: Clunky user interface, rigid formulas, data limits (in the free version).

At less than a decade old, Power BI is a relative newcomer to the market of data analytics tools. It began life as an Excel plug-in but was redeveloped in the early 2010s as a standalone suite of business data analysis tools. Power BI allows users to create interactive visual reports and dashboards , with a minimal learning curve. Its main selling point is its great data connectivity—it operates seamlessly with Excel (as you’d expect, being a Microsoft product) but also text files, SQL server, and cloud sources, like Google and Facebook analytics.

It also offers strong data visualization but has room for improvement in other areas. For example, it has quite a bulky user interface, rigid formulas, and the proprietary language (Data Analytics Expressions, or ‘DAX’) is not that user-friendly. It does offer several subscriptions though, including a free one. This is great if you want to get to grips with the tool, although the free version does have drawbacks—the main limitation being the low data limit (around 2GB).

Tableau at a glance:

• Type of tool: Data visualization tool.
• Availability: Commercial.
• Mostly used for: Creating data dashboards and worksheets.
• Pros: Great visualizations, speed, interactivity, mobile support.
• Cons: Poor version control, no data pre-processing.

If you’re looking to create interactive visualizations and dashboards without extensive coding expertise, Tableau is one of the best commercial data analysis tools available. The suite handles large amounts of data better than many other BI tools, and it is very simple to use. It has a visual drag and drop interface (another definite advantage over many other data analysis tools). However, because it has no scripting layer, there’s a limit to what Tableau can do. For instance, it’s not great for pre-processing data or building more complex calculations.

While it does contain functions for manipulating data, these aren’t great. As a rule, you’ll need to carry out scripting functions using Python or R before importing your data into Tableau. But its visualization is pretty top-notch, making it very popular despite its drawbacks. Furthermore, it’s mobile-ready. As a data analyst , mobility might not be your priority, but it’s nice to have if you want to dabble on the move! You can learn more about Tableau in this post .

KNIME at a glance:

• Type of tool: Data integration platform.
• Mostly used for: Data mining and machine learning.
• Pros: Open-source platform that is great for visually-driven programming.
• Cons: Lacks scalability, and technical expertise is needed for some functions.

Last on our list is KNIME (Konstanz Information Miner), an open-source, cloud-based, data integration platform. It was developed in 2004 by software engineers at Konstanz University in Germany. Although first created for the pharmaceutical industry, KNIME’s strength in accruing data from numerous sources into a single system has driven its application in other areas. These include customer analysis, business intelligence, and machine learning.

Its main draw (besides being free) is its usability. A drag-and-drop graphical user interface (GUI) makes it ideal for visual programming. This means users don’t need a lot of technical expertise to create data workflows. While it claims to support the full range of data analytics tasks, in reality, its strength lies in data mining. Though it offers in-depth statistical analysis too, users will benefit from some knowledge of Python and R. Being open-source, KNIME is very flexible and customizable to an organization’s needs—without heavy costs. This makes it popular with smaller businesses, who have limited budgets.

Now that we’ve checked out all of the data analysis tools, let’s see how to choose the right one for your business needs.

11. Streamlit

• Type of tool:  Python library for building web applications
• Availability:  Open-source
• Mostly used for:  Creating interactive data visualizations and dashboards
• Pros: Easy to use, can create a wide range of graphs, charts, and maps, can be deployed as web apps
• Cons: Not as powerful as Power BI or Tableau, requires a Python installation

Sure we mentioned Python itself as a tool earlier and introduced a few of its libraries, but Streamlit is definitely one data analytics tool to watch in 2024, and to consider for your own toolkit.

Essentially, Streamlit is an open-source Python library for building interactive and shareable web apps for data science and machine learning projects. It’s a pretty new tool on the block, but is already one which is getting attention from data professionals looking to create visualizations easily!

Alright, so you’ve got your data ready to go, and you’re looking for the perfect tool to analyze it with. How do you find the one that’s right for your organization?

First, consider that there’s no one singular data analytics tool that will address all the data analytics issues you may have. When looking at this list, you may look at one tool for most of your needs, but require the use of a secondary tool for smaller processes.

Second, consider the business needs of your organization and figure out exactly who will need to make use of the data analysis tools. Will they be used primarily by fellow data analysts or scientists, non-technical users who require an interactive and intuitive interface—or both? Many tools on this list will cater to both types of user.

Third, consider the tool’s data modeling capabilities. Does the tool have these capabilities, or will you need to use SQL or another tool to perform data modeling prior to analysis?

Fourth—and finally!—consider the practical aspect of price and licensing. Some of the options are totally free or have some free-to-use features (but will require licensing for the full product). Some data analysis tools will be offered on a subscription or licencing basis. In this case, you may need to consider the number of users required or—if you’re looking on solely a project-to-project basis—the potential length of the subscription.

In this post, we’ve explored some of the most popular data analysis tools currently in use. The key thing to takeaway is that there’s no one tool that does it all. A good data analyst has wide-ranging knowledge of different languages and software.

CareerFoundry’s own data expert, Tom Gadsby, explains which data analytics tools are best for specific processes in the following short video:

If you found a tool on this list that you didn’t know about, why not research more? Play around with the open-source data analysis tools (they’re free, after all!) and read up on the rest.

At the very least, it helps to know which data analytics tools organizations are using. To learn more about the field, start our free 5-day data analytics short course .

For more industry insights, check out the following:

• The 7 most useful data analysis methods and techniques
• How to build a data analytics portfolio
• Get started with SQL: A cheatsheet

What are data analytics tools?

Data analytics tools are software and apps that help data analysts collect, clean, analyze, and visualize data. These tools are used to extract insights from data that can be used to make informed business decisions.

What is the most used tool by data analysts?

Microsoft Excel continues to be the most widely used tool by data analysts for data wrangling and reporting. Big reasons are that it provides a user-friendly interface for data manipulation, calculations, and data viz.

Is SQL a data analysis tool?

Yes. SQL is a specialized programming language for managing and querying data in relational databases. Data analysts use SQL to extract and analyze data from databases, which can then be used to generate insights and reports.

Which tool is best to analyse data?

It depends on what you want to do with the data and the context. Some of the most popular and versatile tools are included in this article, namely Python, SQL, MS Excel, and Tableau.

Your browser is out of date, please update it.

Essential Data Analyst Tools Discover a List of The 17 Best Data Analysis Software & Tools On The Market

Top 17 software & tools for data analysts (2023).

Table of Content 1) What are data analyst tools? 2) The best 17 data analyst tools for 2023 3) Key takeaways & guidance

To be able to perform data analysis at the highest level possible, analysts and data professionals will use software that will ensure the best results in several tasks from executing algorithms, preparing data, generating predictions, and automating processes, to standard tasks such as visualizing and reporting on the data. Although there are many of these solutions on the market, data analysts must choose wisely in order to benefit their analytical efforts. That said, in this article, we will cover the best data analyst tools and name the key features of each based on various types of analysis processes. But first, we will start with a basic definition and a brief introduction.

1) What Are Data Analyst Tools?

Data analyst tools is a term used to describe software and applications that data analysts use in order to develop and perform analytical processes that help companies to make better, informed business decisions while decreasing costs and increasing profits.

In order to make the best possible decision on which software you need to choose as an analyst, we have compiled a list of the top data analyst tools that have various focus and features, organized in software categories, and represented with an example of each. These examples have been researched and selected using rankings from two major software review sites: Capterra and G2Crowd . By looking into each of the software categories presented in this article, we selected the most successful solutions with a minimum of 15 reviews between both review websites until November 2022. The order in which these solutions are listed is completely random and does not represent a grading or ranking system.

2) What Tools Do Data Analysts Use?

To make the most out of the infinite number of software that is currently offered on the market, we will focus on the most prominent tools needed to be an expert data analyst. The image above provides a visual summary of all the areas and tools that will be covered in this insightful post. These data analysis tools are mostly focused on making analysts lives easier by providing them with solutions that make complex analytical tasks more efficient. Like this, they get more time to perform the analytical part of their job. Let’s get started with business intelligence tools.

1. Business intelligence tools

BI tools are one of the most represented means of performing data analysis. Specializing in business analytics, these solutions will prove to be beneficial for every data analyst that needs to analyze, monitor, and report on important findings. Features such as self-service, predictive analytics, and advanced SQL modes make these solutions easily adjustable to every level of knowledge, without the need for heavy IT involvement. By providing a set of useful features, analysts can understand trends and make tactical decisions. Our data analytics tools article wouldn’t be complete without business intelligence, and datapine is one example that covers most of the requirements both for beginner and advanced users. This all-in-one tool aims to facilitate the entire analysis process from data integration and discovery to reporting.

KEY FEATURES:

Visual drag-and-drop interface to build SQL queries automatically, with the option to switch to, advanced (manual) SQL mode

Powerful predictive analytics features, interactive charts and dashboards, and automated reporting

AI-powered alarms that are triggered as soon as an anomaly occurs or a goal is met

datapine is a popular business intelligence software with an outstanding rating of 4.8 stars in Capterra and 4.6 stars in G2Crowd. It focuses on delivering simple, yet powerful analysis features into the hands of beginners and advanced users in need of a fast and reliable online data analysis solution for all analysis stages. An intuitive user interface will enable you to simply drag-and-drop your desired values into datapine’s Analyzer and create numerous charts and graphs that can be united into an interactive dashboard. If you’re an experienced analyst, you might want to consider the SQL mode where you can build your own queries or run existing codes or scripts. Another crucial feature is the predictive analytics forecast engine that can analyze data from multiple sources which can be previously integrated with their various data connectors. While there are numerous predictive solutions out there, datapine provides simplicity and speed at its finest. By simply defining the input and output of the forecast based on specified data points and desired model quality, a complete chart will unfold together with predictions.

We should also mention robust artificial intelligence that is becoming an invaluable assistant in today’s analysis processes. Neural networks, pattern recognition, and threshold alerts will alarm you as soon as a business anomaly occurs or a previously set goal is met so you don’t have to manually analyze large volumes of data – the data analytics software does it for you. Access your data from any device with an internet connection, and share your findings easily and securely via dashboards or customized reports for anyone that needs quick answers to any type of business question.

2. Statistical Analysis Tools

Next in our list of data analytics tools comes a more technical area related to statistical analysis. Referring to computation techniques that often contain a variety of statistical techniques to manipulate, explore, and generate insights, there exist multiple programming languages to make (data) scientists’ work easier and more effective. With the expansion of various languages that are today present on the market, science has its own set of rules and scenarios that need special attention when it comes to statistical data analysis and modeling. Here we will present one of the most popular tools for a data analyst – Posit (previously known as RStudio or R programming). Although there are other languages that focus on (scientific) data analysis, R is particularly popular in the community.

POSIT (R-STUDIO)

An ecosystem of more than 10 000 packages and extensions for distinct types of data analysis

Statistical analysis, modeling, and hypothesis testing (e.g. analysis of variance, t test, etc.)

Active and communicative community of researchers, statisticians, and scientists

Posit , formerly known as RStudio, is one of the top data analyst tools for R and Python. Its development dates back to 2009 and it’s one of the most used software for statistical analysis and data science, keeping an open-source policy and running on a variety of platforms, including Windows, macOS and Linux. As a result of the latest rebranding process, some of the famous products on the platform will change their names, while others will stay the same. For example, RStudio Workbench and RStudio Connect will now be known as Posit Workbench and Posit Connect respectively. On the other side, products like RStudio Desktop and RStudio Server will remain the same. As stated on the software’s website, the rebranding happened because the name RStudio no longer reflected the variety of products and languages that the platform currently supports.

Posit is by far the most popular integrated development environment (IDE) out there with 4,7 stars on Capterra and 4,5 stars on G2Crowd. Its capabilities for data cleaning, data reduction, and data analysis report output with R markdown, make this tool an invaluable analytical assistant that covers both general and academic data analysis. It is compiled of an ecosystem of more than 10 000 packages and extensions that you can explore by categories, and perform any kind of statistical analysis such as regression, conjoint, factor cluster analysis, etc. Easy to understand for those that don’t have a high-level of programming skills, Posit can perform complex mathematical operations by using a single command. A number of graphical libraries such as ggplot and plotly make this language different than others in the statistical community since it has efficient capabilities to create quality visualizations.

Posit was mostly used in the academic area in the past, today it has applications across industries and large companies such as Google, Facebook, Twitter, and Airbnb, among others. Due to an enormous number of researchers, scientists, and statisticians using it, the tool has an extensive and active community where innovative technologies and ideas are presented and communicated regularly.

3. QUALITATIVE DATA ANALYSIS TOOLS

Naturally, when we think about data, our mind automatically takes us to numbers. Although much of the extracted data might be in a numeric format, there is also immense value in collecting and analyzing non-numerical information, especially in a business context. This is where qualitative data analysis tools come into the picture. These solutions offer researchers, analysts, and businesses the necessary functionalities to make sense of massive amounts of qualitative data coming from different sources such as interviews, surveys, e-mails, customer feedback, social media comments, and much more depending on the industry. There is a wide range of qualitative analysis software out there, the most innovative ones rely on artificial intelligence and machine learning algorithms to make the analysis process faster and more efficient. Today, we will discuss MAXQDA, one of the most powerful QDA platforms in the market.

The possibility to mark important information using codes, colors, symbols or emojis

AI-powered audio transcription capabilities such as speed and rewind controls, speaker labels, and others

Possibility to work with multiple languages and scripts thanks to Unicode support

Founded in 1989 “by researchers, for researchers”, MAXQDA is a qualitative data analysis software for Windows and Mac that assists users in organizing and interpreting qualitative data from different sources with the help of innovative features. Unlike some other solutions on the same range, MAXQDA supports a wide range of data sources and formats. Users can import traditional text data from interviews, focus groups, web pages, and YouTube or Twitter comments, as well as various types of multimedia data such as videos or audio files. Paired to that, the software also offers a Mixed Methods tool which allows users to use both qualitative and quantitative data for a more complete analytics process. This level of versatility has earned MAXQDA worldwide recognition for many years. The tool has a positive 4.6 stars rating in Capterra and a 4.5 in G2Crowd.

Amongst its most valuable functions, MAXQDA offers users the capability of setting different codes to mark their most important data and organize it in an efficient way. Codes can be easily generated via drag & drop and labeled using colors, symbols, or emojis. Your findings can later be transformed, automatically or manually, into professional visualizations and exported in various readable formats such as PDF, Excel, or Word, among others.

4. General-purpose programming languages

Programming languages are used to solve a variety of data problems. We have explained R and statistical programming, now we will focus on general ones that use letters, numbers, and symbols to create programs and require formal syntax used by programmers. Often, they’re also called text-based programs because you need to write software that will ultimately solve a problem. Examples include C#, Java, PHP, Ruby, Julia, and Python, among many others on the market. Here we will focus on Python and we will present PyCharm as one of the best tools for data analysts that have coding knowledge as well.

Intelligent code inspection and completion with error detection, code fixes, and automated code refractories

Built-in developer tools for smart debugging, testing, profiling, and deployment

Cross-technology development supporting JavaScript, CoffeeScript, HTML/CSS, Node.js, and more

PyCharm is an integrated development environment (IDE) by JetBrains designed for developers that want to write better, more productive Python code from a single platform. The tool, which is successfully rated with 4.7 stars on Capterra and 4.6 in G2Crowd, offers developers a range of essential features including an integrated visual debugger, GUI-based test runner, integration with major VCS and built-in database tools, and much more. Amongst its most praised features, the intelligent code assistance provides developers with smart code inspections highlighting errors and offering quick fixes and code completions.

PyCharm supports the most important Python implementations including Python 2.x and 3.x, Jython, IronPython, PyPy and Cython, and it is available in three different editions. The Community version, which is free and open-sourced, the Professional paid version, including all advanced features, and the Edu version which is also free and open-sourced for educational purposes. Definitely, one of the best Python data analyst tools in the market.

5. SQL consoles

Our data analyst tools list wouldn’t be complete without SQL consoles. Essentially, SQL is a programming language that is used to manage/query data held in relational databases, particularly effective in handling structured data as a database tool for analysts. It’s highly popular in the data science community and one of the analyst tools used in various business cases and data scenarios. The reason is simple: as most of the data is stored in relational databases and you need to access and unlock its value, SQL is a highly critical component of succeeding in business, and by learning it, analysts can offer a competitive advantage to their skillset. There are different relational (SQL-based) database management systems such as MySQL, PostgreSQL, MS SQL, and Oracle, for example, and by learning these data analysts’ tools would prove to be extremely beneficial to any serious analyst. Here we will focus on MySQL Workbench as the most popular one.

MySQL Workbench

A unified visual tool for data modeling, SQL development, administration, backup, etc.

Instant access to database schema and objects via the Object Browser

SQL Editor that offers color syntax highlighting, reuse of SQL snippets, and execution history

MySQL Workbench is used by analysts to visually design, model, and manage databases, optimize SQL queries, administer MySQL environments, and utilize a suite of tools to improve the performance of MySQL applications. It will allow you to perform tasks such as creating and viewing databases and objects (triggers or stored procedures, e.g.), configuring servers, and much more. You can easily perform backup and recovery as well as inspect audit data. MySQL Workbench will also help in database migration and is a complete solution for analysts working in relational database management and companies that need to keep their databases clean and effective. The tool, which is very popular amongst analysts and developers, is rated 4.6 stars in Capterra and 4.5 in G2Crowd.

6. Standalone predictive analytics tools

Predictive analytics is one of the advanced techniques, used by analysts that combine data mining, machine learning, predictive modeling, and artificial intelligence to predict future events, and it deserves a special place in our list of data analysis tools as its popularity has increased in recent years with the introduction of smart solutions that enabled analysts to simplify their predictive analytics processes. You should keep in mind that some BI tools we already discussed in this list offer easy to use, built-in predictive analytics solutions but, in this section, we focus on standalone, advanced predictive analytics that companies use for various reasons, from detecting fraud with the help of pattern detection to optimizing marketing campaigns by analyzing consumers’ behavior and purchases. Here we will list a data analysis software that is helpful for predictive analytics processes and helps analysts to predict future scenarios.

IBM SPSS PREDICTIVE ANALYTICS ENTERPRISE

A visual predictive analytics interface to generate predictions without code

Can be integrated with other IBM SPSS products for a complete analysis scope

Flexible deployment to support multiple business scenarios and system requirements

IBM SPSS Predictive Analytics provides enterprises with the power to make improved operational decisions with the help of various predictive intelligence features such as in-depth statistical analysis, predictive modeling, and decision management. The tool offers a visual interface for predictive analytics that can be easily used by average business users with no previous coding knowledge, while still providing analysts and data scientists with more advanced capabilities. Like this, users can take advantage of predictions to inform important decisions in real time with a high level of certainty.

Additionally, the platform provides flexible deployment options to support multiple scenarios, business sizes and use cases. For example, for supply chain analysis or cybercrime prevention, among many others. Flexible data integration and manipulation is another important feature included in this software. Unstructured and structured data, including text data, from multiple sources, can be analyzed for predictive modeling that will translate into intelligent business outcomes.

As a part of the IBM product suite, users of the tool can take advantage of other solutions and modules such as the IBM SPSS Modeler, IBM SPSS Statistics, and IMB SPSS Analytic Server for a complete analytical scope. Reviewers gave the software a 4.5 star rating on Capterra and 4.2 on G2Crowd.

7. Data modeling tools

Our list of data analysis tools wouldn’t be complete without data modeling. Creating models to structure the database, and design business systems by utilizing diagrams, symbols, and text, ultimately represent how the data flows and is connected in between. Businesses use data modeling tools to determine the exact nature of the information they control and the relationship between datasets, and analysts are critical in this process. If you need to discover, analyze, and specify changes in information that is stored in a software system, database or other application, chances are your skills are critical for the overall business. Here we will show one of the most popular data analyst software used to create models and design your data assets.

erwin data modeler (DM)

Automated data model generation to increase productivity in analytical processes

Single interface no matter the location or the type of the data

5 different versions of the solution you can choose from and adjust based on your business needs

erwin DM works both with structured and unstructured data in a data warehouse and in the cloud. It’s used to “find, visualize, design, deploy and standardize high-quality enterprise data assets,” as stated on their official website. erwin can help you reduce complexities and understand data sources to meet your business goals and needs. They also offer automated processes where you can automatically generate models and designs to reduce errors and increase productivity. This is one of the tools for analysts that focus on the architecture of the data and enable you to create logical, conceptual, and physical data models.

Additional features such as a single interface for any data you might possess, no matter if it’s structured or unstructured, in a data warehouse or the cloud makes this solution highly adjustable for your analytical needs. With 5 versions of the erwin data modeler, their solution is highly adjustable for companies and analysts that need various data modeling features. This versatility is reflected in its positive reviews, gaining the platform an almost perfect 4.8 star rating on Capterra and 4.3 stars in G2Crowd.

8. ETL tools

ETL is a process used by companies, no matter the size, across the world, and if a business grows, chances are you will need to extract, load, and transform data into another database to be able to analyze it and build queries. There are some core types of ETL tools for data analysts such as batch ETL, real-time ETL, and cloud-based ETL, each with its own specifications and features that adjust to different business needs. These are the tools used by analysts that take part in more technical processes of data management within a company, and one of the best examples is Talend.

Collecting and transforming data through data preparation, integration, cloud pipeline designer

Talend Trust Score to ensure data governance and resolve quality issues across the board

Sharing data internally and externally through comprehensive deliveries via APIs

Talend is a data integration platform used by experts across the globe for data management processes, cloud storage, enterprise application integration, and data quality. It’s a Java-based ETL tool that is used by analysts in order to easily process millions of data records and offers comprehensive solutions for any data project you might have. Talend’s features include (big) data integration, data preparation, cloud pipeline designer, and stitch data loader to cover multiple data management requirements of an organization. Users of the tool rated it with 4.2 stars in Capterra and 4.3 in G2Crowd. This is an analyst software extremely important if you need to work on ETL processes in your analytical department.

Apart from collecting and transforming data, Talend also offers a data governance solution to build a data hub and deliver it through self-service access through a unified cloud platform. You can utilize their data catalog, inventory and produce clean data through their data quality feature. Sharing is also part of their data portfolio; Talend’s data fabric solution will enable you to deliver your information to every stakeholder through a comprehensive API delivery platform. If you need a data analyst tool to cover ETL processes, Talend might be worth considering.

9. Automation Tools

As mentioned, the goal of all the solutions present on this list is to make data analysts lives easier and more efficient. Taking that into account, automation tools could not be left out of this list. In simple words, data analytics automation is the practice of using systems and processes to perform analytical tasks with almost no human interaction. In the past years, automation solutions have impacted the way analysts perform their jobs as these tools assist them in a variety of tasks such as data discovery, preparation, data replication, and more simple ones like report automation or writing scripts. That said, automating analytical processes significantly increases productivity, leaving more time to perform more important tasks. We will see this more in detail through Jenkins one of the leaders in open-source automation software.

Popular continuous integration (CI) solution with advanced automation features such as running code in multiple platforms

Job automations to set up customized tasks can be scheduled or based on a specific event

Several job automation plugins for different purposes such as Jenkins Job Builder, Jenkins Job DLS or Jenkins Pipeline DLS

Developed in 2004 under the name Hudson, Jenkins is an open-source CI automation server that can be integrated with several DevOps tools via plugins. By default, Jenkins assists developers to automate parts of their software development process like building, testing, and deploying. However, it is also highly used by data analysts as a solution to automate jobs such as running codes and scripts daily or when a specific event happened. For example, run a specific command when new data is available.

There are several Jenkins plugins to generate jobs automatically. For example, the Jenkins Job Builder plugin takes simple descriptions of jobs in YAML or JSON format and turns them into runnable jobs in Jenkins’s format. On the other side, the Jenkins Job DLS plugin provides users with the capabilities to easily generate jobs from other jobs and edit the XML configuration to supplement or fix any existing elements in the DLS. Lastly, the Pipeline plugin is mostly used to generate complex automated processes.

For Jenkins, automation is not useful if it’s not tight to integration. For this reason, they provide hundreds of plugins and extensions to integrate Jenkins with your existing tools. This way, the entire process of code generation and execution can be automated at every stage and in different platforms - leaving you enough time to perform other relevant tasks. All the plugins and extensions from Jenkins are developed in Java meaning the tool can also be installed in any other operator that runs on Java. Users rated Jenkins with 4.5 stars in Capterra and 4.4 stars in G2Crowd.

10. DOCUMENT SHARING TOOLS

As an analyst working with programming, it is very likely that you have found yourself in the situation of having to share your code or analytical findings with others. Rather you want someone to look into your code for errors or provide any other kind of feedback to your work, a document sharing tool is the way to go. These solutions enable users to share interactive documents which can contain live code and other multimedia elements for a collaborative process. Below, we will present Jupyter Notebook, one of the most popular and efficient platforms for this purpose.

JUPYTER NOTEBOOK

Supports 40 programming languages including Python, R, Julia, C++, and more

Easily share notebooks with others via email, Dropbox, GitHub and Jupyter Notebook Viewer

In-browser editing for code, with automatic syntax highlighting, indentation, and tab completion

Jupyter Notebook is an open source web based interactive development environment used to generate and share documents called notebooks, containing live codes, data visualizations, and text in a simple and streamlined way. Its name is an abbreviation of the core programming languages it supports: Julia, Python, and R and, according to its website, it has a flexible interface that enables users to view, execute and share their code all in the same platform. Notebooks allow analysts, developers, and anyone else to combine code, comments, multimedia, and visualizations in an interactive document that can be easily shared and reworked directly in your web browser.

Even though it works by default on Python, Jupyter Notebook supports over 40 programming languages and it can be used in multiple scenarios. Some of them include sharing notebooks with interactive visualizations, avoiding the static nature of other software, live documentation to explain how specific Python modules or libraries work, or simply sharing code and data files with others. Notebooks can be easily converted into different output formats such as HTML, LaTeX, PDF, and more. This level of versatility has earned the tool 4.7 stars rating on Capterra and 4.5 in G2Crowd.

11. Unified data analytics engines

If you work for a company that produces massive datasets and needs a big data management solution, then unified data analytics engines might be the best resolution for your analytical processes. To be able to make quality decisions in a big data environment, analysts need tools that will enable them to take full control of their company’s robust data environment. That’s where machine learning and AI play a significant role. That said, Apache Spark is one of the data analysis tools on our list that supports big-scale data processing with the help of an extensive ecosystem.

Apache Spark

High performance: Spark owns the record in the large-scale data processing

A large ecosystem of data frames, streaming, machine learning, and graph computation

Perform Exploratory Analysis on petabyte-scale data without the need for downsampling

Apache Spark was originally developed by UC Berkeley in 2009 and since then, it has expanded across industries and companies such as Netflix, Yahoo, and eBay that have deployed Spark, processed petabytes of data and proved that Apache is the go-to solution for big data management, earning it a positive 4.2 star rating in both Capterra and G2Crowd. Their ecosystem consists of Spark SQL, streaming, machine learning, graph computation, and core Java, Scala, and Python APIs to ease the development. Already in 2014, Spark officially set a record in large-scale sorting. Actually, the engine can be 100x faster than Hadoop and this is one of the features that is extremely crucial for massive volumes of data processing.

You can easily run applications in Java, Python, Scala, R, and SQL while more than 80 high-level operators that Spark offers will make your data transformation easy and effective. As a unified engine, Spark comes with support for SQL queries, MLlib for machine learning and GraphX for streaming data that can be combined to create additional, complex analytical workflows. Additionally, it runs on Hadoop, Kubernetes, Apache Mesos, standalone or in the cloud and can access diverse data sources. Spark is truly a powerful engine for analysts that need support in their big data environment.

12. Spreadsheet applications

Spreadsheets are one of the most traditional forms of data analysis. Quite popular in any industry, business or organization, there is a slim chance that you haven’t created at least one spreadsheet to analyze your data. Often used by people that don’t have high technical abilities to code themselves, spreadsheets can be used for fairly easy analysis that doesn’t require considerable training, complex and large volumes of data and databases to manage. To look at spreadsheets in more detail, we have chosen Excel as one of the most popular in business.

Part of the Microsoft Office family, hence, it’s compatible with other Microsoft applications

Pivot tables and building complex equations through designated rows and columns

Perfect for smaller analysis processes through workbooks and quick sharing

With 4.8 stars rating in Capterra and 4.7 in G2Crowd, Excel needs a category on its own since this powerful tool has been in the hands of analysts for a very long time. Often considered a traditional form of analysis, Excel is still widely used across the globe. The reasons are fairly simple: there aren’t many people who have never used it or come across it at least once in their career. It’s a fairly versatile data analyst tool where you simply manipulate rows and columns to create your analysis. Once this part is finished, you can export your data and send it to the desired recipients, hence, you can use Excel as a reporting tool as well. You do need to update the data on your own, Excel doesn’t have an automation feature similar to other tools on our list. Creating pivot tables, managing smaller amounts of data and tinkering with the tabular form of analysis, Excel has developed as an electronic version of the accounting worksheet to one of the most spread tools for data analysts.

A wide range of functionalities accompany Excel, from arranging to manipulating, calculating and evaluating quantitative data to building complex equations and using pivot tables, conditional formatting, adding multiple rows and creating charts and graphs – Excel has definitely earned its place in traditional data management.

13. Industry-specific analytics tools

While there are many data analysis tools on this list that are used in various industries and are applied daily in analysts’ workflow, there are solutions that are specifically developed to accommodate a single industry and cannot be used in another. For that reason, we have decided to include of one these solutions on our list, although there are many others, industry-specific data analysis programs and software. Here we focus on Qualtrics as one of the leading research software that is used by over 11000 world’s brands and has over 2M users across the globe as well as many industry-specific features focused on market research.

5 main experience features: design, customer, brand, employee, and product

Additional research services by their in-house experts

Advanced statistical analysis with their Stats iQ analysis tool

Qualtrics is a software for data analysis that is focused on experience management (XM) and is used for market research by companies across the globe. The tool, which has a positive 4.8 stars rating on Capterra and 4.4 in G2Crowd, offers 5 product pillars for enterprise XM which include design, customer, brand, employee, and product experiences, as well as additional research services performed by their own experts. Their XM platform consists of a directory, automated actions, Qualtrics iQ tool, and platform security features that combine automated and integrated workflows into a single point of access. That way, users can refine each stakeholder’s experience and use their tool as an “ultimate listening system.”

Since automation is becoming increasingly important in our data-driven age, Qualtrics has also developed drag-and-drop integrations into the systems that companies already use such as CRM, ticketing, or messaging, while enabling users to deliver automatic notifications to the right people. This feature works across brand tracking and product feedback as well as customer and employee experience. Other critical features such as the directory where users can connect data from 130 channels (including web, SMS, voice, video, or social), and Qualtrics iQ to analyze unstructured data will enable users to utilize their predictive analytics engine and build detailed customer journeys. If you’re looking for a data analytic software that needs to take care of market research of your company, Qualtrics is worth the try.

14. Data science platforms

Data science can be used for most software solutions on our list, but it does deserve a special category since it has developed into one of the most sought-after skills of the decade. No matter if you need to utilize preparation, integration or data analyst reporting tools, data science platforms will probably be high on your list for simplifying analytical processes and utilizing advanced analytics models to generate in-depth data science insights. To put this into perspective, we will present RapidMiner as one of the top data analyst software that combines deep but simplified analysis.

A comprehensive data science and machine learning platform with 1500+ algorithms and functions

Possible to integrate with Python and R as well as support for database connections (e.g. Oracle)

Advanced analytics features for descriptive and prescriptive analytics

RapidMiner , which was just acquired by Altair in 2022 as a part of their data analytics portfolio, is a tool used by data scientists across the world to prepare data, utilize machine learning, and model operations in more than 40 000 organizations that heavily rely on analytics in their operations. By unifying the entire data science cycle, RapidMiner is built on 5 core platforms and 3 automated data science products that help in the design and deployment of analytics processes. Their data exploration features such as visualizations and descriptive statistics will enable you to get the information you need while predictive analytics will help you in cases such as churn prevention, risk modeling, text mining, and customer segmentation.

With more than 1500 algorithms and data functions, support for 3rd party machine learning libraries, integration with Python or R, and advanced analytics, RapidMiner has developed into a data science platform for deep analytical purposes. Additionally, comprehensive tutorials and full automation, where needed, will ensure simplified processes if your company requires them, so you don’t need to perform manual analysis. All these positive traits have earned the tool a positive 4.4 stars rating on Capterra and 4.6 stars in G2Crowd. If you’re looking for analyst tools and software focused on deep data science management and machine learning, then RapidMiner should be high on your list.

15. DATA CLEANSING PLATFORMS

The amount of data being produced is only getting bigger, hence, the possibility of it involving errors. To help analysts avoid these errors that can damage the entire analysis process is that data cleansing solutions were developed. These tools help in preparing the data by eliminating errors, inconsistencies, and duplications enabling users to extract accurate conclusions from it. Before cleansing platforms were a thing, analysts would manually clean the data, this is also a dangerous practice since the human eye is prompt to error. That said, powerful cleansing solutions have proved to boost efficiency and productivity while providing a competitive advantage as data becomes reliable. The cleansing software we picked for this section is a popular solution named OpenRefine.

Data explorer to clean “messy” data using transformations, facets, and clustering, among others

Transform data to the format you desire, for example, turn a list into a table by importing the file into OpenRefine

Includes a large list of extensions and plugins to link and extend datasets with various web services

Previously known as Google Refine, OpenRefine is a Java-based open-source desktop application for working with large sets of data that needs to be cleaned. The tool, with ratings of 4.0 stars in Capterra and 4.6 in G2Crowd, also enables users to transform their data from one format to another and extend it with web services and external data. OpenRefine has a similar interface to the one of spreadsheet applications and can handle CSV file formats, but all in all, it behaves more as a database. Upload your datasets into the tool and use their multiple cleaning features that will let you spot anything from extra spaces to duplicated fields.

Available in more than 15 languages, one of the main principles of OpenRefine is privacy. The tool works by running a small server on your computer and your data will never leave that server unless you decide to share it with someone else.

16. DATA MINING TOOLS

Next, in our insightful list of data analyst tools we are going to touch on data mining. In short, data mining is an interdisciplinary subfield of computer science that uses a mix of statistics, artificial intelligence and machine learning techniques and platforms to identify hidden trends and patterns in large, complex data sets. To do so, analysts have to perform various tasks including data classification, cluster analysis, association analysis, regression analysis, and predictive analytics using professional data mining software. Businesses rely on these platforms to anticipate future issues and mitigate risks, make informed decisions to plan their future strategies, and identify new opportunities to grow. There are multiple data mining solutions in the market at the moment, most of them relying on automation as a key feature. We will focus on Orange, one of the leading mining software at the moment.

Visual programming interface to easily perform data mining tasks via drag and drop

Multiple widgets offering a set of data analytics and machine learning functionalities

Add-ons for text mining and natural language processing to extract insights from text data

Orange is an open source data mining and machine learning tool that has existed for more than 20 years as a project from the University of Ljubljana. The tool offers a mix of data mining features, which can be used via visual programming or Python Scripting, as well as other data analytics functionalities for simple and complex analytical scenarios. It works under a “canvas interface” in which users place different widgets to create a data analysis workflow. These widgets offer different functionalities such as reading the data, inputting the data, filtering it, and visualizing it, as well as setting machine learning algorithms for classification and regression, among other things.

What makes this software so popular amongst others in the same category is the fact that it provides beginners and expert users with a pleasant usage experience, especially when it comes to generating swift data visualizations in a quick and uncomplicated way. Orange, which has 4.2 stars ratings on both Capterra and G2Crowd, offers users multiple online tutorials to get them acquainted with the platform. Additionally, the software learns from the user’s preferences and reacts accordingly, this is one of their most praised functionalities.

17. Data visualization platforms

Data visualization has become one of the most indispensable elements of data analytics tools. If you’re an analyst, there is probably a strong chance you had to develop a visual representation of your analysis or utilize some form of data visualization at some point. Here we need to make clear that there are differences between professional data visualization tools often integrated through already mentioned BI tools, free available solutions as well as paid charting libraries. They’re simply not the same. Also, if you look at data visualization in a broad sense, Excel and PowerPoint also have it on offer, but they simply cannot meet the advanced requirements of a data analyst who usually chooses professional BI or data viz tools as well as modern charting libraries, as mentioned. We will take a closer look at Highcharts as one of the most popular charting libraries on the market.

Interactive JavaScript library compatible with all major web browsers and mobile systems like Android and iOS

Designed mostly for a technical-based audience (developers)

WebGL-powered boost module to render millions of datapoints directly in the browser

Highcharts is a multi-platform library that is designed for developers looking to add interactive charts to web and mobile projects. With a promising 4.6 stars rating in Capterra and 4.5 in G2Crowd, this charting library works with any back-end database and data can be given in CSV, JSON, or updated live. They also feature intelligent responsiveness that fits the desired chart into the dimensions of the specific container but also places non-graph elements in the optimal location automatically.

Highcharts supports line, spline, area, column, bar, pie, scatter charts and many others that help developers in their online-based projects. Additionally, their WebGL-powered boost module enables you to render millions of datapoints in the browser. As far as the source code is concerned, they allow you to download and make your own edits, no matter if you use their free or commercial license. In essence, Basically, Highcharts is designed mostly for the technical target group so you should familiarize yourself with developers’ workflow and their JavaScript charting engine. If you’re looking for a more easy to use but still powerful solution, you might want to consider an online data visualization tool like datapine.

3) Key Takeaways & Guidance

We have explained what are data analyst tools and given a brief description of each to provide you with the insights needed to choose the one (or several) that would fit your analytical processes the best. We focused on diversity in presenting tools that would fit technically skilled analysts such as R Studio, Python, or MySQL Workbench. On the other hand, data analysis software like datapine cover needs both for data analysts and business users alike so we tried to cover multiple perspectives and skill levels.

We hope that by now you have a clearer perspective on how modern solutions can help analysts perform their jobs more efficiently in a less prompt to error environment. To conclude, if you want to start an exciting analytical journey and test a professional BI analytics software for yourself, you can try datapine for a 14-day trial , completely free of charge and with no hidden costs.

Take advantage of modern BI software features today!

Top 24 tools for data analysis and how to decide between them

Data analysis is a core practice of modern businesses. Choosing the right data analytics tool is challenging, as no tool fits every need. To help you determine which data analysis tool best fits your organization, let's examine the important factors for choosing between them and then look at some of the most popular options on the market today.

There are a few things to take care of before evaluating the available tools. You should first understand the types of data your enterprise wants to analyze, and, by extension, your data integration requirements. In addition, before you can begin analyzing data, you'll need to select data sources and the tables and columns within them, and replicate them to a data warehouse to create a single source of truth for analytics. You'll want to assess data security and data governance as well. If data is shared between departments, for example, there should be access control and permission systems to protect sensitive information.

How to choose a data analysis tool

Once you have data ready, you can try analyzing it using different tools. How do you find one that's a good fit for your company? Start by considering your organization's business needs and learning who will be using your analytics tool. Will it be used by sophisticated data analysts and data scientists, by nontechnical users who need an intuitive interface, or should it suit both kinds of users? Some platforms provide an interactive experience for iterating on code development — typically using SQL — while others focus more on point-and-click analysis for less technical users. The tool should also provide support for visualizations relevant to your enterprise .

Consider a tool's data modeling capabilities. Some support a semantic layer or can perform data modeling themselves. If you want to use one that doesn't, you'll have to use SQL or a tool like dbt to model your data prior to analysis.

Finally, consider price and licensing. Some offerings are free, while others charge licensing or subscription fees. The most expensive tools are not necessarily the most feature-complete, and users should not ignore the many robust free solutions available.

Try Stitch to get data for your favorite analytics tool today

Now that you know what factors to look for in a data analysis tool, let's jump into the list. We'll start with discussing the eight platforms in the Visionaries band of Gartner's Magic Quadrant for Analytics and Business Intelligence Platforms before covering other popular options.

1. Microsoft Power BI

Microsoft Power BI is a top business intelligence platform with support for dozens of data sources. It allows users to create and share reports, visualizations, and dashboards. Users can combine a group of dashboards and reports into a Power BI app for simple distribution. Power BI also allows users to build automated machine learning models and integrates with Azure Machine Learning.

2. SAP BusinessObjects

SAP BusinessObjects provides a suite of business intelligence applications for data discovery, analysis, and reporting. The tools are aimed at less technical business users, but they're also capable of performing complex analysis. BusinessObjects integrates with Microsoft Office products, allowing business analysts to quickly go back and forth between applications such as Excel and BusinessObjects reports. It also allows for self-service predictive analytics .

Sisense is a data analytics platform aimed at helping both technical developers and business analysts process and visualize all of their business data. It boasts a large collection of drag-and-drop tools and provides interactive dashboards for collaboration. A unique aspect of the Sisense platform is its custom In-Chip technology, which optimizes computation to utilize CPU caching rather than slower RAM. For some workflows, this can lead to 10–100x faster computation.

4. TIBCO Spotfire

TIBCO Spotfire is a data analytics platform that provides natural language search and AI-powered data insights. It's a comprehensive visualization tool that can publish reports to both mobile and desktop applications. Spotfire also provides point-and-click tools for building predictive analytics models.

5. Thoughtspot

Thoughtspot is an analytics platform that allows users to explore data from various types of sources through reports and natural language searches. Its AI system, SpotIQ, finds insights automatically to help users uncover patterns they didn't know to look for. The platform also allows users to automatically join tables from different data sources to help break down data silos .

Qlik provides a self-service data analytics and business intelligence platform that supports both cloud and on-premises deployment. The tool boasts strong support for data exploration and discovery by technical and nontechnical users alike. Qlik supports many types of charts that users can customize with both embedded SQL and drag-and-drop modules.

7. SAS Business Intelligence

SAS Business Intelligence provides a suite of applications for self-service analytics. It has many built-in collaboration features, such as the ability to push reports to mobile applications. While SAS Business Intelligence is a comprehensive and flexible platform, it can be more expensive than some of its competitors. Larger enterprises may find it worth the price due to its versatility.

Tableau is a data visualization and analytics platform that allows users to create reports and share them across desktop and mobile platforms, within a browser, or embedded in an application. It can run on the cloud or on-premises. Much of the Tableau platform runs on top of its core query language, VizQL. This translates drag-and-drop dashboard and visualization components into efficient back-end queries and minimizes the need for end-user performance optimizations. However, Tableau lacks support for advanced SQL queries.

9. Google Data Studio

Google Data Studio is a free dashboarding and data visualization tool that automatically integrates with most other Google applications, such as Google Analytics , Google Ads, and Google BigQuery . Thanks to its integration with other Google services, Data Studio is great for those who need to analyze their Google data. For instance, marketers can build dashboards for their Google Ads and Analytics data to better understand customer conversion and retention. Data Studio can work with data from a variety of other sources as well, provided that the data is first replicated to BigQuery using a data pipeline like Stitch.

Redash is a lightweight and cost-effective tool for querying data sources and building visualizations. The code is open source, and an affordable hosted version is available for organizations that want to get started fast. The core of Redash is the query editor, which provides a simple interface for writing queries, exploring schemas, and managing integrations. Query results are cached within Redash and users can schedule updates to run automatically.

11. Periscope Data

Periscope Data — now owned by Sisense — is a business intelligence platform that supports integrations for a variety of popular data warehouses and databases. Technical analysts can transform data using SQL, Python, or R, and less technical users can easily create and share dashboards. Periscope Data also boasts a number of security certifications, such as HIPAA-HITECH.

12. Metabase

Metabase is a free, open source analytics and business intelligence tool. Metabase allows users to "ask questions" about data, which is a way for nontechnical users to use a point-and-click interface for query construction. This works well for simple filtering and aggregations; more technical users can go straight to raw SQL for more complex analysis. Metabase also has the ability to push analytics results to external systems like Slack.

13. Jupyter Notebook

Jupyter Notebook is a free, open source web application that can be run in a browser or on desktop platforms after installation using the Anaconda platform or Python’s package manager, pip. It allows developers to create reports with data and visualizations from live code. The system supports more than 40 programming languages. Jupyter Notebook — formerly IPython Notebook — was originally programmed using Python, and allows developers to make use of the wide range of Python packages for analytics and visualizations. The tool has a wide developer community using other languages as well.

14. IBM Cognos

IBM Cognos is a business intelligence platform that features built-in AI tools to reveal insights hidden in data and explain them in plain English. Cognos also has automated data preparation tools to automatically cleanse and aggregate data sources, which allows for quickly integrating and experimenting with data sources for analysis.

15. Chartio

Chartio is a self-service business intelligence system that integrates with various data warehouses and allows for easy import of files such as spreadsheets. Chartio has a unique visual representation of SQL that allows for point-and-click construction of queries, which lets business analysts who aren't familiar with SQL syntax modify and experiment with queries without having to dig into the language.

Let Stitch increase your data flexibility and accessibility today

Free 14-day trial. No credit card required

Mode is an analytics platform focused on giving data scientists an easy and iterative environment. It provides an interactive SQL editor and notebook environment for analysis, along with visualization and collaboration tools for less technical users. Mode has a unique data engine called Helix that streams data from external databases and stores it in memory to allow for fast and interactive analysis. It supports in-memory analysis of up to 10GB of data.

KNIME — short for the Konstanz Information Miner — is a free, open source data analytics platform that supports data integration , processing, visualization, and reporting. It plugs in machine learning and data mining libraries with minimal or no programming requirements. KNIME is great for data scientists who need to integrate and process data for machine learning and other statistical models but don't necessarily have strong programming skills. The graphical interface allows for point-and-click analysis and modeling.

Looker is a cloud-based business intelligence and data analytics platform. It features automatic data model generation that scans data schemas and infers relationships between tables and data sources. Data engineers can modify the generated models through a built-in code editor.

19. RapidMiner

RapidMiner provides all the technology users need to integrate, clean, and transform data before they run predictive analytics and statistical models. Users can perform nearly all of this through a simple graphical interface. RapidMiner can also be extended using R and Python scripts, and numerous third-party plugins are available through the company's marketplace. However, the product is heavily optimized for its graphical interface so that analysts can prepare data and run models on their own.

Domo provides more than 1,000 built-in integrations — called connectors — that allow users to transfer data to and from on-premises and cloud external systems. Domo also supports building custom apps that integrate with the platform, which allows developers to extend the system with immediate access to the connectors and visualization tools. Domo comes as a single platform that includes a data warehouse and ETL software, so businesses that already have their own data warehouse and data pipeline set up may want to look elsewhere.

21. Oracle Analytics Cloud

Oracle Analytics Cloud is a suite of cloud business intelligence and analytics applications. It's focused on helping large enterprises transition their legacy systems to a modern cloud platform. Users can take advantage of its wide range of analytics features to do everything from producing simple visualizations to using machine learning algorithms to obtain insights from data.

R is an open source programming language and computing environment with a focus on statistics and graphical data visualization. R features numerous graphical tools and over 15,000 open source packages available, including many for loading, manipulating, modeling, and visualizing data. The environment allows technical analysts with programming skills to build almost any type of data analysis, but users without those programming skills should look elsewhere.

Python is an open source, high-level programming language that's often used by technical analysts and data scientists. It now boasts more worldwide developers than Java and has more than 200,000 available packages. Python can handle many different analyses on its own, and can integrate with third-party packages for machine learning and data visualization. Popular data visualization packages include Matplotlib , Plotly , and Seaborn . Python is also used as a programming interface to other analytics systems.

Microsoft Excel is the most common tool used for manipulating spreadsheets and building analyses. With decades of development behind it, Excel can support almost any standard analytics workflow and is extendable through its native programming language, Visual Basic. Excel is suitable for simple analysis, but it is not suited for analyzing big data — it has a limit of around 1 million rows — and it does not have good support for collaboration or versioning. Enterprises should consider more modern cloud-based analytics platforms for large and collaborative analyses.

Using data analysis tools with Stitch

Data analysis tools work best with accessible data centralized in a data warehouse. Stitch is a simple data pipeline that that can populate your preferred data warehouse for fast and easy analytics using more than 100 data sources. Try Stitch for free today .

Give Stitch a try, on us

Stitch streams all of your data directly to your analytics warehouse.

Set up in minutes Unlimited data volume during trial

11 Best Data Analysis Software for Research [2023]

5 Best Reference Management Software for Research [FREE]

7 Best Survey Tools for Research [2023]

Leave a comment cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Anaesth
• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

• Hire a PhD Guide
• Guidance Process
• PhD Topic and Proposal Help
• PhD Thesis Chapters Writing
• PhD Literature Review Writing Help
• PhD Research Methodology Chapter Help
• Questionnaire Design for PhD Research
• PhD Statistical Analysis Help
• Qualitative Analysis Help for PhD Research
• Software Implementation Help for PhD Projects
• Enhance the Quality of Your PhD Thesis with Professional Thesis Editing Services
• Journal Paper Publication Assistance
• Addressing Comments, Revisions in PhD Thesis
• PhD Thesis Defence Preparation

Ethical research guidance and consulting services for PhD candidates since 2008

Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 12 unexplored data analysis tools for qualitative research.

Welcome to our guide on 5 lesser-known tools for studying information in a different way – specifically designed for understanding and interpreting data in qualitative research. Data analysis tools for qualitative research are specialized instruments designed to interpret non-numerical data, offering insights into patterns, themes, and relationships.

These tools enable researchers to uncover meaning from qualitative information, enhancing the depth and understanding of complex phenomena in fields such as social sciences, psychology, and humanities.

In the world of research, there are tools tailored for qualitative data analysis that can reveal hidden insights. This blog explores these tools, showcasing their unique features and advantages compared to the more commonly used quantitative analysis tools.

Whether you’re a seasoned researcher or just starting out, we aim to make these tools accessible and highlight how they can add depth and accuracy to your analysis. Join us as we uncover these innovative approaches, offering practical solutions to enhance your experience with qualitative research.

Tool 1:MAXQDA Analytics Pro

MAXQDA Analytics Pro emerges as a game-changing tool for qualitative data analysis, offering a seamless experience that goes beyond the capabilities of traditional quantitative tools.

Here’s how MAXQDA stands out in the world of qualitative research:

Advanced Coding and Text Analysis: MAXQDA empowers researchers with advanced coding features and text analysis tools, enabling the exploration of qualitative data with unprecedented depth. Its intuitive interface allows for efficient categorization and interpretation of textual information.

Intuitive Interface for Effortless Exploration: The user-friendly design of MAXQDA makes it accessible for researchers of all levels. This tool streamlines the process of exploring qualitative data, facilitating a more efficient and insightful analysis compared to traditional quantitative tools.

Uncovering Hidden Narratives: MAXQDA excels in revealing hidden narratives within qualitative data, allowing researchers to identify patterns, themes, and relationships that might be overlooked by conventional quantitative approaches. This capability adds a valuable layer to the analysis of complex phenomena.

In the landscape of qualitative data analysis tools, MAXQDA Analytics Pro is a valuable asset, providing researchers with a unique set of features that enhance the depth and precision of their analysis. Its contribution extends beyond the confines of quantitative analysis tools, making it an indispensable tool for those seeking innovative approaches to qualitative research.

Tool 2: Quirkos

Quirkos , positioned as data analysis software, shines as a transformative tool within the world of qualitative research.

Here’s why Quirkos is considered among the best for quality data analysis: Visual Approach for Enhanced Understanding: Quirkos introduces a visual approach, setting it apart from conventional analysis software. This unique feature aids researchers in easily grasping and interpreting qualitative data, promoting a more comprehensive understanding of complex information.

User-Friendly Interface: One of Quirkos’ standout features is its user-friendly interface. This makes it accessible to researchers of various skill levels, ensuring that the tool’s benefits are not limited to experienced users. Its simplicity adds to the appeal for those seeking the best quality data analysis software.

Effortless Pattern Identification: Quirkos simplifies the process of identifying patterns within qualitative data. This capability is crucial for researchers aiming to conduct in-depth analysis efficiently.

The tool’s intuitive design fosters a seamless exploration of data, making it an indispensable asset in the world of analysis software. Quirkos, recognized among the best quality data analysis software, offers a visual and user-friendly approach to qualitative research. Its ability to facilitate effortless pattern identification positions it as a valuable asset for researchers seeking optimal outcomes in their data analysis endeavors.

Tool 3: Provalis Research WordStat

Provalis Research WordStat stands out as a powerful tool within the world of qualitative data analysis tools, offering unique advantages for researchers engaged in qualitative analysis:

WordStat excels in text mining, providing researchers with a robust platform to delve into vast amounts of textual data. This capability enhances the depth of qualitative analysis, setting it apart in the landscape of tools for qualitative research.

Specializing in content analysis, WordStat facilitates the systematic examination of textual information. Researchers can uncover themes, trends, and patterns within qualitative data, contributing to a more comprehensive understanding of complex phenomena.

WordStat seamlessly integrates with qualitative research methodologies, providing a bridge between quantitative and qualitative analysis. This integration allows researchers to harness the strengths of both approaches, expanding the possibilities for nuanced insights.

In the domain of tools for qualitative research, Provalis Research WordStat emerges as a valuable asset. Its text mining capabilities, content analysis expertise, and integration with qualitative research methodologies collectively contribute to elevating the qualitative analysis experience for researchers.

Tool 4: ATLAS.ti

ATLAS.ti proves to be a cornerstone in the world of qualitative data analysis tools, offering distinctive advantages that enhance the qualitative analysis process:

Multi-Faceted Data Exploration: ATLAS.ti facilitates in-depth exploration of textual, graphical, and multimedia data. This versatility enables researchers to engage with diverse types of qualitative information, broadening the scope of analysis beyond traditional boundaries.

Collaboration and Project Management: The tool excels in fostering collaboration among researchers and project management. This collaborative aspect sets ATLAS.ti apart, making it a comprehensive solution for teams engaged in qualitative research endeavors.

User-Friendly Interface: ATLAS.ti provides a user-friendly interface, ensuring accessibility for researchers of various skill levels. This simplicity in navigation enhances the overall qualitative analysis experience, making it an effective tool for both seasoned researchers and those new to data analysis tools. In the landscape of tools for qualitative research, ATLAS.ti emerges as a valuable ally. Its multi-faceted data exploration, collaboration features, and user-friendly interface collectively contribute to enriching the qualitative analysis journey for researchers seeking a comprehensive and efficient solution.

Tool 5: NVivo Transcription

NVivo Transcription emerges as a valuable asset in the world of data analysis tools, seamlessly integrating transcription services with qualitative research methodologies:

Efficient Transcription Services: NVivo Transcription offers efficient and accurate transcription services, streamlining the process of converting spoken words into written text. This feature is essential for researchers engaged in qualitative analysis, ensuring a solid foundation for subsequent exploration.

Integration with NVivo Software: The tool seamlessly integrates with NVivo software, creating a synergistic relationship between transcription and qualitative analysis. Researchers benefit from a unified platform that simplifies the organization and analysis of qualitative data, enhancing the overall research workflow.

Comprehensive Qualitative Analysis: NVivo Transcription contributes to comprehensive qualitative analysis by providing a robust foundation for understanding and interpreting audio and video data. Researchers can uncover valuable insights within the transcribed content, enriching the qualitative analysis process.

In the landscape of tools for qualitative research, NVivo Transcription plays a crucial role in bridging the gap between transcription services and qualitative analysis. Its efficient transcription capabilities, integration with NVivo software, and support for comprehensive qualitative analysis make it a valuable tool for researchers seeking a streamlined and effective approach to handling qualitative data.

Tool 6: Dedoose

Web-Based Accessibility: Dedoose’s online platform allows PhD researchers to conduct qualitative data analysis from anywhere, promoting flexibility and collaboration.

Mixed-Methods Support: Dedoose accommodates mixed-methods research, enabling the integration of both quantitative and qualitative data for a comprehensive analysis.

Multi-Media Compatibility: The tool supports various data formats, including text, audio, and video, facilitating the analysis of diverse qualitative data types.

Collaborative Features: Dedoose fosters collaboration among researchers, providing tools for shared coding, annotation, and exploration of qualitative data.

Organized Data Management: PhD researchers benefit from Dedoose’s organizational features, streamlining the coding and retrieval of data for a more efficient analysis process.

Tool 7: HyperRESEARCH

HyperRESEARCH caters to various qualitative research methods, including content analysis and grounded theory, offering a flexible platform for PhD researchers.

The software simplifies the coding and retrieval of data, aiding researchers in organizing and analyzing qualitative information systematically.

HyperRESEARCH allows for detailed annotation of text, enhancing the depth of qualitative analysis and providing a comprehensive understanding of the data.

The tool provides features for visualizing relationships within data, aiding researchers in uncovering patterns and connections in qualitative content.

HyperRESEARCH facilitates collaborative research efforts, promoting teamwork and shared insights among PhD researchers.

Tool 8: MAXQDA Analytics Plus

Advanced Collaboration:  

MAXQDA Analytics Plus enhances collaboration for PhD researchers with teamwork support, enabling multiple researchers to work seamlessly on qualitative data analysis.

Extended Visualization Tools:  

The software offers advanced data visualization features, allowing researchers to create visual representations of qualitative data patterns for a more comprehensive understanding.

Efficient Workflow:  

MAXQDA Analytics Plus streamlines the qualitative analysis workflow, providing tools that facilitate efficient coding, categorization, and interpretation of complex textual information.

Deeper Insight Integration:  

Building upon MAXQDA Analytics Pro, MAXQDA Analytics Plus integrates additional features for a more nuanced qualitative analysis, empowering PhD researchers to gain deeper insights into their research data.

User-Friendly Interface:  

The tool maintains a user-friendly interface, ensuring accessibility for researchers of various skill levels, contributing to an effective and efficient data analysis experience.

Tool 9: QDA Miner

Versatile Data Analysis: QDA Miner supports a wide range of qualitative research methodologies, accommodating diverse data types, including text, images, and multimedia, catering to the varied needs of PhD researchers.

Coding and Annotation Tools: The software provides robust coding and annotation features, facilitating a systematic organization and analysis of qualitative data for in-depth exploration.

Visual Data Exploration: QDA Miner includes visualization tools for researchers to analyze data patterns visually, aiding in the identification of themes and relationships within qualitative content.

User-Friendly Interface: With a user-friendly interface, QDA Miner ensures accessibility for researchers at different skill levels, contributing to a seamless and efficient qualitative data analysis experience.

Comprehensive Analysis Support: QDA Miner’s features contribute to a comprehensive analysis, offering PhD researchers a tool that integrates seamlessly into their qualitative research endeavors.

Tool 10: NVivo

NVivo supports diverse qualitative research methodologies, allowing PhD researchers to analyze text, images, audio, and video data for a comprehensive understanding.

The software aids researchers in organizing and categorizing qualitative data systematically, streamlining the coding and analysis process.

NVivo seamlessly integrates with various data formats, providing a unified platform for transcription services and qualitative analysis, simplifying the overall research workflow.

NVivo offers tools for visual representation, enabling researchers to create visual models that enhance the interpretation of qualitative data patterns and relationships.

NVivo Transcription integration ensures efficient handling of audio and video data, offering PhD researchers a comprehensive solution for qualitative data analysis.

Tool 11: Weft QDA

Open-Source Affordability: Weft QDA’s open-source nature makes it an affordable option for PhD researchers on a budget, providing cost-effective access to qualitative data analysis tools.

Simplicity for Beginners: With a straightforward interface, Weft QDA is user-friendly and ideal for researchers new to qualitative data analysis, offering basic coding and text analysis features.

Ease of Use: The tool simplifies the process of coding and analyzing qualitative data, making it accessible to researchers of varying skill levels and ensuring a smooth and efficient analysis experience.

Entry-Level Solution: Weft QDA serves as a suitable entry-level option, introducing PhD researchers to the fundamentals of qualitative data analysis without overwhelming complexity.

Basic Coding Features: While being simple, Weft QDA provides essential coding features, enabling researchers to organize and explore qualitative data effectively.

Tool 12: Transana

Transana specializes in the analysis of audio and video data, making it a valuable tool for PhD researchers engaged in qualitative studies with rich multimedia content.

The software streamlines the transcription process, aiding researchers in converting spoken words into written text, providing a foundation for subsequent qualitative analysis.

Transana allows for in-depth exploration of multimedia data, facilitating coding and analysis of visual and auditory aspects crucial to certain qualitative research projects.

With tools for transcribing and coding, Transana assists PhD researchers in organizing and categorizing qualitative data, promoting a structured and systematic approach to analysis.

Researchers benefit from Transana’s capabilities to uncover valuable insights within transcribed content, enriching the qualitative analysis process with a focus on visual and auditory dimensions.

Final Thoughts

In wrapping up our journey through 5 lesser-known data analysis tools for qualitative research, it’s clear these tools bring a breath of fresh air to the world of analysis. MAXQDA Analytics Pro, Quirkos, Provalis Research WordStat, ATLAS.ti, and NVivo Transcription each offer something unique, steering away from the usual quantitative analysis tools.

They go beyond, with MAXQDA’s advanced coding, Quirkos’ visual approach, WordStat’s text mining, ATLAS.ti’s multi-faceted data exploration, and NVivo Transcription’s seamless integration.

These tools aren’t just alternatives; they are untapped resources for qualitative research. As we bid adieu to the traditional quantitative tools, these unexplored gems beckon researchers to a world where hidden narratives and patterns are waiting to be discovered.

They don’t just add to the toolbox; they redefine how we approach and understand complex phenomena. In a world where research is evolving rapidly, these tools for qualitative research stand out as beacons of innovation and efficiency.

PhDGuidance is a website that provides customized solutions for PhD researchers in the field of qualitative analysis. They offer comprehensive guidance for research topics, thesis writing, and publishing. Their team of expert consultants helps researchers conduct copious research in areas such as social sciences, humanities, and more, aiming to provide a comprehensive understanding of the research problem.

PhDGuidance offers qualitative data analysis services to help researchers study the behavior of participants and observe them to analyze for the research work. They provide both manual thematic analysis and using NVivo for data collection. They also offer customized solutions for research design, data collection, literature review, language correction, analytical tools, and techniques for both qualitative and quantitative research projects.

Frequently Asked Questions

• What is the best free qualitative data analysis software?

When it comes to free qualitative data analysis software, one standout option is RQDA. RQDA, an open-source tool, provides a user-friendly platform for coding and analyzing textual data. Its compatibility with R, a statistical computing language, adds a layer of flexibility for those familiar with programming. Another notable mention is QDA Miner Lite, offering basic qualitative analysis features at no cost. While these free tools may not match the advanced capabilities of premium software, they serve as excellent starting points for individuals or small projects with budget constraints.

2. Which software is used to Analyse qualitative data?

For a more comprehensive qualitative data analysis experience, many researchers turn to premium tools like NVivo, MAXQDA, or ATLAS.ti. NVivo, in particular, stands out due to its user-friendly interface, robust coding capabilities, and integration with various data types, including audio and visual content. MAXQDA and ATLAS.ti also offer advanced features for qualitative data analysis, providing researchers with tools to explore, code, and interpret complex qualitative information effectively.

3. How can I Analyse my qualitative data?

Analyzing qualitative data involves a systematic approach to make sense of textual, visual, or audio information. Here’s a general guide:

Data Familiarization: Understand the context and content of your data through thorough reading or viewing.

Open Coding: Begin with open coding, identifying and labeling key concepts without preconceived categories.

Axial Coding: Organize codes into broader categories, establishing connections and relationships between them.

Selective Coding: Focus on the most significant codes, creating a narrative that tells the story of your data.

Constant Comparison: Continuously compare new data with existing codes to refine categories and ensure consistency.

Use of Software: Employ qualitative data analysis software, such as NVivo or MAXQDA, to facilitate coding, organization, and interpretation.

4. Is it worth using NVivo for qualitative data analysis?

The use of NVivo for qualitative data analysis depends on the specific needs of the researcher and the scale of the project. NVivo is worth considering for its versatility, user-friendly interface, and ability to handle diverse data types. It streamlines the coding process, facilitates collaboration, and offers in-depth analytical tools. However, its cost may be a consideration for individuals or smaller research projects. Researchers with complex data sets, especially those involving multimedia content, may find NVivo’s advanced features justify the investment.

5. What are the tools used in quantitative data analysis?

Quantitative data analysis relies on tools specifically designed to handle numerical data. Some widely used tools include:

SPSS (Statistical Package for the Social Sciences): A statistical software suite that facilitates data analysis through descriptive statistics, regression analysis, and more. Excel: Widely used for basic quantitative analysis, offering functions for calculations, charts, and statistical analysis.

R and RStudio: An open-source programming language and integrated development environment used for statistical computing and graphics.

Python with Pandas and NumPy: Python is a versatile programming language, and Pandas and NumPy are libraries that provide powerful tools for data manipulation and analysis.

STATA: A software suite for data management and statistical analysis, widely used in various fields.

Hence, the choice of qualitative data analysis software depends on factors like project scale, budget, and specific requirements. Free tools like RQDA and QDA Miner Lite offer viable options for smaller projects, while premium software such as NVivo, MAXQDA, and ATLAS.ti provide advanced features for more extensive research endeavors. When it comes to quantitative data analysis, SPSS, Excel, R, Python, and STATA are among the widely used tools, each offering unique strengths for numerical data interpretation. Ultimately, the selection should align with the researcher’s goals and the nature of the data being analyzed.

Recent Posts

• How to Choose Well Matched Research Methodologies in PhD in 2024 – 25 Research Methodology January 16, 2024
• 5 Different Types of Research Methodology for 2024 PhD Research January 9, 2024
• 12 UNEXPLORED Data Analysis Tools for Qualitative Research Qualitative Analysis January 4, 2024
• Separating Myth from Reality: The Scientific Rigor of Qualitative Research Topic and Proposal March 7, 2023
• PhD Guidance: How We Aid Your Preparation for PhD Thesis Defence PhD Thesis September 8, 2022
• Data Analysis
• PhD Research
• Qualitative Analysis
• Research Methodology
• Topic and Proposal

REQUEST CALL BACK

Quick links.

• PhD Guidance Maharashtra Trail
• Synopsis and Thesis Assistance
• Privacy Policy
• Terms of use
• Schedule Your Consultation Now

Information

• Geo Polymer for road construction
• Machine Learning for Image processing applications
• IoT and automation
• Concrete strength with changing flyash percentage
• Purchase regret prediction with Deep Learning
• Low Power VLSI
• Antenna design using HFSS
• PhD Planner

CONTACT DETAILS

• 022 4971 0935 (20 Lines)
• 0091 93102 29971
• [email protected]
• Copyright © 2008-2024 PhD Guidance All Rights Reserved.

Data Analysis Techniques in Research – Methods, Tools & Examples

By Varun Saharawat | January 22, 2024

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

For Latest Tech Related Information, Join Our Official Free Telegram Group : PW Skills Telegram Group

Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

Data Mining Architecture: Components, Types & Techniques

Comprehensive Data Analytics Syllabus: Courses and Curriculum

Google Data Analytics Professional Certificate Review, Cost, Eligibility 2023

Learn / Guides / Qualitative data analysis guide

Back to guides

10 best qualitative data analysis tools

A lot of teams spend a lot of time collecting qualitative customer experience data—but how do you make sense of it, and how do you turn insights into action?

Qualitative data analysis tools help you make sense of customer feedback so you can focus on improving the user and product experience and creating customer delight.

Last updated

Reading time.

This chapter of Hotjar's qualitative data analysis (QDA) guide covers the ten best QDA tools that will help you make sense of your customer insights and better understand your users.

Collect qualitative customer data with Hotjar

Use Hotjar’s Surveys and Feedback widget to collect user insights and better understand your customers.

10 tools for qualitative data analysis 

Qualitative data analysis involves gathering, structuring, and interpreting contextual data to identify key patterns and themes in text, audio, and video.

Qualitative data analysis software automates this process, allowing you to focus on interpreting the results—and make informed decisions about how to improve your product—rather than wading through pages of often subjective, text-based data.

Pro tip: before you can analyze qualitative data, you need to gather it. 

One way to collect qualitative customer insights is to place Hotjar Surveys on key pages of your site . Surveys make it easy to capture voice-of-the-customer (VoC) feedback about product features, updated designs, and customer satisfaction—or to perform user and market research.

Need some ideas for your next qualitative research survey? Check out our Hotjar Survey Templates for inspiration.

Example product discovery questions from Hotjar’s bank of survey templates

1. Cauliflower

Cauliflower is a no-code qualitative data analysis tool that gives researchers, product marketers, and developers access to AI-based analytics without dealing with complex interfaces.

How Cauliflower analyzes qualitative data

Cauliflower’s AI-powered analytics help you understand the differences and similarities between different pieces of customer feedback. Ready-made visualizations help identify themes in customers’ words without reading through every review, and make it easy to:

Analyze customer survey data and answers to open-ended questions

Process and understand customer reviews

Examine your social media channels

Identify and prioritize product testing initiatives

Visualize results and share them with your team

One of Cauliflower’s customers says, “[Cauliflower is] great for visualizing the output, particularly finding relevant patterns in comparing breakouts and focussing our qualitative analysis on the big themes emerging.”

NVivo is one of the most popular qualitative data analysis tools on the market—and probably the most expensive. It’s a more technical solution than Cauliflower, and requires more training. NVivo is best for tech-savvy customer experience and product development teams at mid-sized companies and enterprises.

How NVivo analyzes qualitative data

NVivo’s Transcription tool transcribes and analyzes audio and video files from recorded calls—like sales calls, customer interviews, and product demos—and lets you automatically transfer text files into NVivo for further analysis to:

Find recurring themes in customer feedback

Analyze different types of qualitative data, like text, audio, and video

Code and visualize customer input

Identify market gaps based on qualitative and consumer-focused research

Dylan Hazlett from Adial Pharmaceuticals says, “ We needed a reliable software to perform qualitative text analysis. The complexity and features of [Nvivo] have created great value for our team.”

3. ​​Quirkos

Quirkos is a simple and affordable qualitative data analysis tool. Its text analyzer identifies common keywords within text documents to help businesses quickly and easily interpret customer reviews and interviews.

How Quirkos analyzes qualitative data

Quirkos displays side-by-side comparison views to help you understand the difference between feedback shared by different audience groups (by age group, location, gender, etc.). You can also use it to:

Identify keywords and phrases in survey responses and customer interviews

Visualize customer insights

Collaborate on projects

Color code texts effortlessly

One of Quirkos's users says, “ The interface is intuitive, easy to use, and follows quite an intuitive method of assigning codes to documents.”

4. Qualtrics

Qualtrics is a sophisticated experience management platform. The platform offers a range of tools, but we’ll focus on Qualtrics CoreXM here.  

Qualtrics CoreXM lets you collect and analyze insights to remove uncertainty from product development. It helps validate product ideas, spot gaps in the market, and identify broken product experiences, and the tool uses predictive intelligence and analytics to put your customer opinion at the heart of your decision-making.

How Qualtrics analyzes qualitative data

Qualtrics helps teams streamline multiple processes in one interface. You can gather and analyze qualitative data, then immediately share results and hypotheses with stakeholders. The platform also allows you to:

Collect customer feedback through various channels

Understand emotions and sentiment behind customers’ words

Predict what your customers will do next

Act immediately based on the results provided through various integrations

A user in project management shares, “The most useful part of Qualtrics is the depth of analytics you receive on your surveys, questionnaires, and other tools. In real-time, as you develop your surveys, you are given insights into how your data can be analyzed. It is designed to help you get the data you need without asking unnecessary questions.”

5. Dovetail

Dovetail is a customer research platform for growing businesses. It offers three core tools: Playback, Markup, and Backstage. For qualitative data analysis, you’ll need Markup.

Markup offers tools for transcription and analysis of all kinds of qualitative data, and is a great way to consolidate insights.

How Dovetail analyzes qualitative data

Dovetail’s charts help you easily quantify qualitative data. If you need to present your findings to the team, the platform makes it easy to loop in your teammates, manage access rights, and collaborate through the interface. You can:

Transcribe recordings automatically

Discover meaningful patterns in textual data

Highlight and tag customer interviews

Run sentiment analysis

Collaborate on customer research through one interface

Kathryn Rounding , Senior Product Designer at You Need A Budget, says, “Dovetail is a fantastic tool for conducting and managing qualitative research. It helps bring all your research planning, source data, analysis, and reporting together, so you can not only share the final results but all the supporting work that helped you get there.”

6. Thematic

Thematic's AI-driven text feedback analysis platform helps you understand what your customers are saying—and why they’re saying it.

How Thematic analyzes qualitative data

Thematic helps you connect feedback from different channels, uncover themes in customer experience data, and run sentiment analysis—all to make better product decisions. Thematic is helpful when you need to:

Analyze unstructured feedback data from across channels

Discover relationships and patterns in feedback

Reveal emerging trends in customer feedback

Split insights by customer segment

Use resulting data in predictive analytics

Emma Glazer , Director of Marketing at DoorDash, says, “Thematic empowers us with information to help make the right decisions, and I love seeing themes as they emerge. We get real-time signals on issues our customers are experiencing and early feedback on new features they love. I love looking at the week-over-week breakdowns and comparing segments of our audience (market, tenure, etc.) Thematic helps me understand what’s driving our metrics and what steps we need to take next.” 

Delve is cloud-based qualitative data analysis software perfect for coding large volumes of textual data, and is best for analyzing long-form customer interviews.

How Delve analyzes qualitative data

Delve helps reveal the core themes and narratives behind transcripts from sales calls and customer interviews. It also helps to:

Find, group, and refine themes in customer feedback

Analyze long-form customer interviews

Categorize your data by code, pattern, and demographic information

Perform thematic analysis, narrative analysis, and grounded theory analysis

One Delve user says, “Using Delve, it is easier to focus just on coding to start, without getting sidetracked analyzing what I am reading. Once coding is finished, the selected excerpts are already organized based on my own custom outline and I can begin analyzing right away, rather than spending time organizing my notes before I can begin the analysis and writing process.”

8. ATLAS.ti

ATLAS.ti is a qualitative data analysis tool that brings together customer and product research data. It has a range of helpful features for marketers, product analysts, UX professionals, and product designers.

How ATLAS.ti analyzes qualitative data

ATLAS.ti helps product teams collect, structure, and evaluate user feedback before realizing new product ideas. To enhance your product design process with ATLAS.ti, you can:

Generate qualitative insights from surveys

Apply any method of qualitative research

Analyze open-ended questions and standardized surveys

Perform prototype testing

Visualize research results with charts

Collaborate with your team through a single platform

One of the ATLAS.ti customers shares,“ATLAS.ti is innovating in the handling of qualitative data. It gives the user total freedom and the possibility of connecting with other software, as it has many export options.” 

MAXQDA is a data analysis software that can analyze and organize a wide range of data, from handwritten texts, to video recordings, to Tweets.

How MAXQDA analyzes qualitative data

MAWQDA organizes your customer interviews and turns the data into digestible statistics by enabling you to:

Easily transcribe audio or video interviews

Structure standardized and open-ended survey responses

Categorize survey data

Combine qualitative and quantitative methods to get deeper insights into customer data

Share your work with team members

One enterprise-level customer says MAXQDA has “lots of useful features for analyzing and reporting interview and survey data. I really appreciated how easy it was to integrate SPSS data and conduct mixed-method research. The reporting features are high-quality and I loved using Word Clouds for quick and easy data representation.”

10. MonkeyLearn

MonkeyLearn is no-code analytics software for CX and product teams.

How MonkeyLearn analyzes qualitative data

MonkeyLearn automatically sorts, visualizes, and prioritizes customer feedback with its AI-powered algorithms. Along with organizing your data into themes, the tool will split it by intent—allowing you to promptly distinguish positive reviews from issues and requests and address them immediately.

One MonkeyLearn user says, “I like that MonkeyLearn helps us pull data from our tickets automatically and allows us to engage with our customers properly. As our tickets come in, the AI classifies data through keywords and high-end text analysis. It highlights specific text and categorizes it for easy sorting and processing.”

The next step in automating qualitative data analysis 

Qualitative data analysis tools help you uncover actionable insights from customer feedback, reviews, interviews, and survey responses—without getting lost in data.

But there's no one tool to rule them all: each solution has specific functionality, and your team might need to use the tools together depending on your objectives.

With the right qualitative data analysis software, you can make sense of what your customers really want and create better products for them, achieving customer delight and loyalty.

FAQs about qualitative data analysis software

What is qualitative data analysis software.

Qualitative data analysis software is technology that compiles and organizes contextual, non-quantifiable data, making it easy to interpret qualitative customer insights and information.

Which software is used for qualitative data analysis?

The best software used for qualitative data analysis is:

Cauliflower

MonkeyLearn

Is NVivo the only tool for qualitative data analysis?

NVivo isn’t the only tool for qualitative data analysis, but it’s one of the best (and most popular) software providers for qualitative and mixed-methods research.

QDA examples

Previous chapter

Guide index

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

Prevent plagiarism. Run a free check.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Is this article helpful?

Other students also liked, writing strong research questions | criteria & examples.

• What Is a Research Design | Types, Guide & Examples
• Data Collection | Definition, Methods & Examples

More interesting articles

• Between-Subjects Design | Examples, Pros, & Cons
• Cluster Sampling | A Simple Step-by-Step Guide with Examples
• Confounding Variables | Definition, Examples & Controls
• Construct Validity | Definition, Types, & Examples
• Content Analysis | Guide, Methods & Examples
• Control Groups and Treatment Groups | Uses & Examples
• Control Variables | What Are They & Why Do They Matter?
• Correlation vs. Causation | Difference, Designs & Examples
• Correlational Research | When & How to Use
• Critical Discourse Analysis | Definition, Guide & Examples
• Cross-Sectional Study | Definition, Uses & Examples
• Descriptive Research | Definition, Types, Methods & Examples
• Ethical Considerations in Research | Types & Examples
• Explanatory and Response Variables | Definitions & Examples
• Explanatory Research | Definition, Guide, & Examples
• Exploratory Research | Definition, Guide, & Examples
• External Validity | Definition, Types, Threats & Examples
• Extraneous Variables | Examples, Types & Controls
• Guide to Experimental Design | Overview, Steps, & Examples
• How Do You Incorporate an Interview into a Dissertation? | Tips
• How to Do Thematic Analysis | Step-by-Step Guide & Examples
• How to Write a Literature Review | Guide, Examples, & Templates
• How to Write a Strong Hypothesis | Steps & Examples
• Inclusion and Exclusion Criteria | Examples & Definition
• Independent vs. Dependent Variables | Definition & Examples
• Inductive Reasoning | Types, Examples, Explanation
• Inductive vs. Deductive Research Approach | Steps & Examples
• Internal Validity in Research | Definition, Threats, & Examples
• Internal vs. External Validity | Understanding Differences & Threats
• Longitudinal Study | Definition, Approaches & Examples
• Mediator vs. Moderator Variables | Differences & Examples
• Mixed Methods Research | Definition, Guide & Examples
• Multistage Sampling | Introductory Guide & Examples
• Naturalistic Observation | Definition, Guide & Examples
• Operationalization | A Guide with Examples, Pros & Cons
• Population vs. Sample | Definitions, Differences & Examples
• Primary Research | Definition, Types, & Examples
• Qualitative vs. Quantitative Research | Differences, Examples & Methods
• Quasi-Experimental Design | Definition, Types & Examples
• Questionnaire Design | Methods, Question Types & Examples
• Random Assignment in Experiments | Introduction & Examples
• Random vs. Systematic Error | Definition & Examples
• Reliability vs. Validity in Research | Difference, Types and Examples
• Reproducibility vs Replicability | Difference & Examples
• Reproducibility vs. Replicability | Difference & Examples
• Sampling Methods | Types, Techniques & Examples
• Semi-Structured Interview | Definition, Guide & Examples
• Simple Random Sampling | Definition, Steps & Examples
• Single, Double, & Triple Blind Study | Definition & Examples
• Stratified Sampling | Definition, Guide & Examples
• Structured Interview | Definition, Guide & Examples
• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

What is your plagiarism score?

6 Best AI Tools for Data Analysts (April 2024)

Unite.AI is committed to rigorous editorial standards. We may receive compensation when you click on links to products we review. Please view our affiliate disclosure .

Table Of Contents

Data analysis is now one of the core functions within any data-driven organization. It enables companies to convert raw data into useful insights that can drive better decision-making processes. The best part about data analytics is that there are many tools on the market for both professionals and those with a limited background in the field. These tools help you visualize, analyze, and track data so you can derive insights needed to achieve your business goals. 

AI in Analytics

AI is the driving force behind any effective data analytics strategy. It is a powerful, efficient, and approachable way to process data. 

Artificial intelligence examines massive amounts of data to find trends and patterns that can be used to derive insights for improving business processes. AI also helps streamline data analysis by funneling all data into one solution, enabling users to have a complete overview of the data. When AI and data are combined for Predictive AI, users can develop forecasts and analyze certain scenarios to determine chances of success. 

AI-powered data analysis tools are key for any organization looking to succeed in this data-driven world.

Here is a look at the 5 best AI tools for data analysts: 

1. Julius AI

Julius AI is an intelligent data analyst tool that interprets, analyzes, and visualizes complex data in an intuitive, user-friendly manner. Its power lies in its ability to make data analysis accessible and actionable, even for those who aren't data scientists or statisticians.

They support any data file format, including but not limited to Spreadsheets (.xls, .xlsx, .xlsm, .xlsb, .csv), Google Sheets, and Postgres databases.

After linking a data source, you can analyze it with natural language prompting on the Chat page — try asking for insights or directing Julius to create a visualization.

This tool is best for easy of use and simple projects.

Here are some of the advantages of Tableau: 

• Link to a source directly in the chat interface.
• Analyze spreadsheets with multiple tabs.
•  Strict access-control, as each user only has access to their own data.
• Easy to use.

Read our Julius AI Review or visit Julius AI .

2. Microsoft Power BI

Another top AI tool for data analysis is Microsoft Power BI, which is a highly useful business intelligence platform that enables users to sort through their data and visualize it for insights. The platform allows users to import data from nearly any source, and they can begin building reports and dashboards right away. 

Microsoft Power BI also enables users to build machine learning models and utilize other AI-powered features to analyze data. It supports multiple integrations, such as a native Excel integration and an integration with Azure Machine Learning. If an enterprise already uses microsoft tools, Power BI can be easily implemented for data reporting, data visualization, and for building dashboards. 

Here are some of the advantages of Microsoft Power BI: 

• Integrates seamlessly with existing applications.
• Creates personalized dashboards. 
• Helps publish secure reports.
• No memory and speed constraints. 

Another great option for data analysts is Polymer, which is a robust AI tool that offers a powerful AI to transform data into a streamlined, flexible, and powerful database. Similar to other great AI tools, one of the best aspects of Polymer is that it doesn't require any coding. 

The tool relies on AI to analyze data and improve users’ understanding of it. Polymer achieves all of this without a long onboarding process. All a user has to do is upload their spreadsheet to the platform to instantly transform it into a streamlined database that can then be explored for insights. 

Polymer prides itself on being the only tool that makes a user’s spreadsheets “searchable, intelligent, and interactive instantly.” The tool is used by a wide range of professionals, including data analysts, digital marketers, content creators, and more. 

Here are some of the advantages of Polymer: 

• Robust AI tool that transforms data into a database. 
• Doesn’t require any coding.
• Analyzes data and improves users’ understanding. 
• Makes spreadsheets searchable and interactive. 

Nearing the end of our list of 5 best AI tools for data analysts is Akkio, which is a business analytics and forecasting tool for users to analyze their data and predict potential outcomes. The tool is aimed at beginners and is ideal for users wanting to get started with their data. 

The AI tool enables users to upload their dataset and select the variable that they want to predict, which helps Akkio build a neural network around that variable. It is highly useful for predictive analysis, marketing, and sales. Like many of the other top tools on this list, Akkio doesn’t require any prior coding experience. 

Akkio uses 80 percent of the uploaded data as training data, and the other 20 percent is used as validation data. Rather than predicting results, the AI tool offers an accuracy rating for the models and pulls out false positives. 

Here are some of the advantages of Akkio: 

• No-code machine learning platform.
• Great for beginners looking to get started with data.
• Build neural network around selected variables.
• Accuracy rating for the models.

5. MonkeyLearn

Closing out our list of 5 best AI tools for data analytics is MonkeyLearn, which is yet another no-coding platform that uses AI data analysis features to help users visualize and rearrange their data. 

MonkeyLearn includes multiple AI-powered text analysis tools that instantly analyze and visualize data to the user’s needs. It can also be used to set up text classifiers and text extractors, which help automatically sort data according to topic or intent, as well as extract product features or user data. 

With its reliance on machine learning to automate business workflows and analyze text, MonkeyLearn can save hours of manual data processing. One of the features most liked by its users is MonkeyLearn’s ability to pull data from tickets automatically as they come in. It classifies data through keywords and high-end text analysis, and highlights specific text and categorizes it for easy sorting and processing. 

Here are some of the advantages of MonkeyLearn: 

• Classifies text in labels in a simple way.
• Makes it easy to clean, organize, and visualize feedback. 
• No coding required. 
• Saves hours by automating business workflows and analyzing text. 

Another top tool is Tableau, which is an analytics and data visualization platform that enables users to interact with their data. One of the top selling points of Tableau is that it doesn't require any knowledge of coding. With Tableau, users can create reports and share them across desktop and mobile platforms. 

The data analytics tool supports data visualization and analytics to create reports that can be shared within a browser or embedded in an application. All of this can take place while Tableau is run on either the cloud or on-premise. 

The query language that the Tableau platform runs on is called VizQL, which translates drag-and-drop dashboard and visualization components into back-end queries. It also requires little need for end-user performance optimization. 

• Supports complex computations, data blending, and dashboarding. 
• Quickly create interactive visualizations. 
• Ease of implementation
• Handles large amounts of data. 

7 Best AI Software Development Tools

5 Best Machine Learning (AI) Programming Languages

Alex McFarland is a tech writer who covers the latest developments in artificial intelligence. He has worked with AI startups and publications across the globe.

You may like

10 Best Data Cleaning Tools (April 2024)

10 Best Databases for Machine Learning & AI

10 Best Python Libraries for Data Science

10 “Best” AI Tools for Business (April 2024)

10 Best AI Tools for Social Media (April 2024)

10 Best Machine Learning Algorithms

Recent Posts

• Beyond Generative AI: Building a Comprehensive and Scalable Digital Infrastructure
• Brian Tolkin, Head of Product, at Opendoor – Interview Series
• MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
• Groundbreaking Biomimetic Olfactory Chips Use AI to Enable Robots to Smell
• Gartner Data & Analytics Summit São Paulo: Mercado Livre’s AI and Data Democratization in Brazil

• Trending Blogs
• Geeksforgeeks NEWS
• Geeksforgeeks Blogs
• Tips & Tricks
• Website & Apps
• ChatGPT Blogs
• ChatGPT News
• ChatGPT Tutorial
• How to Make Money With AI in 2024 - Secret Revealed
• How to Earn $10000 Per Month Through Side Gigs : Answered by ChatGPT
• ChatGPT Tips & Tricks: 5 Secret Tips That Can Boost Your ChatGPT Game to God Mode
• YouTube Summary with ChatGPT - Best AI Powered Chrome Extensions
• DragGAN AI Editing Tool : AI powered Image Tool
• 10 Secret AI Websites You Should Know (But No One Told You About)
• Level up your ChatGPT Game with OpenAI's Free Course on Prompt Engineering for Developers
• WhatsApp Introduces Chat Lock To Enhance Your Privacy
• Microsoft brings Bing Chat AI Widget to Android and iOS users
• Hottest Job of 2023 with 2Cr Salary - AI Prompt Engineering
• Google to Delete Inactive Accounts Starting December
• Amazon Lays off 500 Employees in India, Tech Layoffs Continue in Q2
• Google Bard Can Now Generate And Debug Code
• ONDC is Destroying Swiggy-Zomato and People are Happy About It!
• AI Could Replace 80% of Jobs in Near Future, Expert Warns
• Gmail Introduces Blue Checkmarks To Boost Email Security
• Discord Removes Four-Digit Numbers from Usernames, Citing User Feedback
• Reddit Launches New Features To Simplify Content Sharing Across Social Media Platforms
• Google Loses "Father of AI" as Geoffrey Hinton Quits Google Over Chatbot Concerns

10 Best AI Tools for Data Analysts in 2024

Do you want to analyze data in a better and easier way? You must know the best AI tools for Data Analysts . The world of today is dynamic and data-driven. Because of this, businesses are continually looking for ways to exploit their massive data sets. Data analysis has evolved significantly since the rise of AI. That’s why data analysis is more productive, accurate, and insightful than ever. Learn about the best AI tools for Data Analysts and how they may change your data analysis.

In this article, we’ll talk about the top 10 best AI tools for data analysts in 2024.

Top 10 AI Tools for Data Analysts in 2024

Microsoft power bi, monkeylearn, tips to select the best alternative to data analyst ai tool, faqs – best ai tools for data analysts in 2024.

AI and ML have changed data analysis, which is continually evolving. In 2024, many AI solutions promised faster, simpler, and more powerful data analysis. Let’s examine 2024’s ten best AI tools for Data Analysts.

Tableau will be one of the best AI tools for data analysts in 2024 . Businesses and individuals seeking a Generative AI Tool for Data Analysis that simplifies complex data through ready-to-use dashboards without scripting should utilize Tableau.

• Get instant access to the best analytical tools.
• You can easily group data points to make research more accessible.
• Better info trust, visibility, and findability.
• The way your data can help you with natural language.
• Tableau gives users AI-driven insights to improve analysis.
• Improves workflow by streamlining data handling.
• Previous data versions are unavailable.
• Tableau is a costly tool for big companies.
• The pricing plan starts from $12/month.

Link : https://www.tableau.com/

Polymer, a generative AI data analysis tool , is known for its usability. This is one of the best AI tools for data analysts ; this program can create visualizations and presentations without writing.

• Monitor market fluctuations to make informed decisions.
• Prepare long-term firm expansion goals through strategic planning.
• Profitability analysis evaluates financial performance to maximize profits.
• Check out the most crucial KPIs to ensure smooth operations.
• Polymer simplifies data analysis, saving time.
• Polymer helps individuals make smart decisions by automating pattern detection and providing deep data insights.
• Polymer’s sophisticated features may require training for data analytics novices.
• Users should examine price plans because advanced functionality may cost more.
• Starter – $25/month.
• Pro – $50/month.
• Teams – $125/month.
• Enterprise – Contact sales for a custom plan.

Link : https://www.polymersearch.com/

An intuitive drag-and-drop structure speeds up data processing in RapidMiner, one of the best AI tools for data analysts , a versatile data mining platform for all skill levels. Extensive integration and machine learning help data teams access, load, and analyze text, image, and audio files throughout the analytics cycle.

• API integration streamlines workflow management.
• Advanced access controls safeguard data and comply with requirements.
• Real-time notifications bring actionable data.
• Tools for cooperative decision-making and research.
• RapidMiner simplifies data analysis with different system integrations.
• Advanced analytics from machine learning helps companies make decisions.
• Cheaper plans may restrict the use or functionality of analytics tools by membership tiers.
• The interface is straightforward; however, data analysis beginners may need to understand its complete potential.
• Professional – $7,500/month.
• Enterprise – $15,000/month.
• All Hub – $54,000/month.

Link : https://docs.rapidminer.com/

Microsoft Power BI , a vital business intelligence application, is one of the best AI tools for data analysts . Power BI, a renowned data analysis application, creates comprehensive reports and dashboards from several data sources.

• Connect to several data sources effortlessly.
• Microsoft-integrated Power BI, one of the best 10 AI tools in data analytics in 2024 free , makes sensible connections between information.
• Change and model data with Power Query and Power Pivot Access.
• Custom data visualizations make data entertaining and relevant.
• It streamlines data reporting, visualization, and dashboard development in current frameworks.
• Dashboard customization improves the experience and provides targeted insights.
• This free AI Data Analytics includes many capabilities; however, some subscriptions limit sophisticated analytics.
• Power BI may take time to understand, like any complex data analysis tool.
• Free: Free with limited features .
• Pro – £8.20/month.
• Premium – £16.40 per user/month.

Link : https://powerbi.microsoft.com/

Julius AI is one of the best AI tools for data analysts , offering customized data analysis tools and a web-based solution. Intended for CSV file analysis, users can upload their files to the platform for AI to analyze.

• Data visualization, including simple charts and graphs.
• Simple and accessible user interface for data exploration and analysis.
• Forecasting uses predictive analytics to predict trends.
• Creating unique statistical models with reliable findings.
• Julius AI simplifies data analysis for non-technical users.
• Users have complete control over their data sources, ensuring data security and confidentiality.
• While Julius AI simplifies data processing, it may need more advanced features for extensive analyses.
• Julius AI visualization is a simple project but may need help with complicated data processing.
• Free Plan – Free with limited features.
• Basic Plan – $17.99 /month.
• Essential Plan – $37.99/month.
• Pro Plan – $49.99/month.

Link : https://julius.ai/

Monkey Learn, one of the best AI tools for data analysts , is a 2014 San Francisco-based Generative AI Tool for Data Analysis . It’s adaptable and utilized for text and data analysis; the main function is to extract insights from unstructured text.

• No-code text Analytics simplifies client feedback visualization.
• Choose pre-built or custom machine learning models to develop your models.
• Company Blueprints provides customized text analysis models and dashboards for various company needs.
• Data visualization involves creating simple charts and visualizations from textual data.
• Great for text analysis.
• Does not require code.
• Efficiency beyond text data is limited.
• Quality utilized effectiveness.
• Available for free
• MonkeyLearn API – $299/10k queries/month.
• MonkeyLearn Studio : Tell MonkeyLearn about pricing.

Link: https://monkeylearn.com/

PyTorch, one of the best AI tools for data analysts , is a groundbreaking deep-learning framework that supports complex models in natural language processing and image recognition. Developers get a broad set of tools and frameworks from major cloud service providers.

• You can compute tensors for analytics.
• Better GPU acceleration speeds data processing.
• Automatic differentiation simplifies deep neural network training.
• Flexible data mining tools with robust machine learning.
• Developing and testing complex neural network topologies is easy with PyTorch’s interface.
• It efficiently distributes models across platforms and devices with ONNX and TorchScript.
• PyTorch’s flexibility and dynamic nature may lengthen deep learning.
• PyTorch is one of the top 10 data analysis tools for research and prototyping, although high-performance and scaled production may take more work.

Pricing : Free

Link : https://pytorch.org/

Akkio, one of the best AI tools for data analysts , is a groundbreaking AI platform for data analysts seeking seamless business analytics and forecasting. It lets users analyze datasets and predict outcomes, making it ideal for data analysis beginners.

• Future-predicting analytical methodologies.
• Prioritize with a list for better job management.
• This top free AI tool for data analysis has lead scoring that helps find buyers.
• Forecasting sales and income.
• Akkio’s simple interface and lack of use make it ideal for beginners in data customization.
• Clarity on model correctness helps users trust platform analytics.
• With lower-priced subscription services, analytics features may be limited, limiting data exploration.
• Akkio is capable but might use more features to meet varied analytical demands.
• Basic – $49/month.
• Professional – $99/month.
• Organization Build-On Package – $999/month.
• Organization Dedicated Server – $1,999/month.

Link: https://www.akkio.com/

DataRobot, one of the best AI tools for data analysts , transforms machine learning model creation and deployment for data scientists and ML engineers. Its automatic machine-learning capabilities speed up model experimentation and operationalization, decreasing human intervention.

• Automatic feature data type detection.
• Datarobot provides simple statistics for each trait.
• Creating frequent number histograms and charts.
• Manually changing variable types helps freedom.
• The flagship product, DataRobot AI Cloud, allows non-coders to build machine learning models.
• By automating repetitive tasks, DataRobot enhances the productivity of data scope rationalization.
• Specific pricing options may restrict advanced analytics and capability.
• DataRobot simplifies machine learning, yet some users may need the complete experience.

Link : https://www.datarobot.com/

Sisense – Generative AI Tools for Data Analysis

Sisense is one of the best AI tools for data analysts , and it is an Israeli company that makes Generative AI Tools for Data Analysis . The well-known periscope data is now part of this tool.

• Sisense data visualization simplifies complex data.
• Manage massive data sets professionally.
• Clean data quickly for focussed analysis.
• Find ideas by thoroughly exploring data.
• Sisense cuts down on the need to use several tools.
• It can grow according to the needs of the business.
• It is hard to learn and use its more advanced tools.
• There aren’t many native connections.
• Basic – $25000/year.

Link : https://www.sisense.com/

Here, we discuss tips for selecting the best AI tools for data analysts .

• Look at various tools’ features for scheduling posts, data, content curation, and audience engagement.
• Check pricing to ensure options meet your budget and offer the required features.
• To simplify your work, check if it connects to social media, marketing, and CRM applications.

Related Articles Top 10 AI Tools for Data Analysis Top 10 Data Analytics Tools in 2024 10 Best Generative AI Tools for Data Analysis in 2024

In 2024, the best AI tools for data analysts are exciting, with cutting-edge solutions to satisfy their shifting needs. These tools have advanced data processing, analysis, visualization, and prediction functions. Analysts can rapidly and accurately extract meaningful data from vast and complex datasets.

Machine learning platforms and natural language processing tools each have characteristics that help people make better decisions and build enterprises. In this data-driven world, analysts may expect more advanced and specialized tools to help them accomplish their work and visualize possibilities as AI improves.

What are the most significant features of the best AI tools for Data Analysts?

Look for technologies with powerful machine learning, simple data visualization, strong data purification, and boundless analytics.

How might the best AI tools for data analysts simplify data analysis?

AI automates data cleansing and pattern specialization. They also provide real-time insights and predictive analytics to reduce analysis time.

Can non-technical individuals analyze data with the best AI tools for data analysts?

Many AI technologies now have simple interfaces and don’t require much code. So, even non-visualization individuals can utilize them for basic research and insights.

How do you use the best AI tools for data analysts to analyze data safely?

Use tools with robust encryption, obey Privacy rules like GDPR and CCPA, and have stringent access restrictions to prevent unauthorized usage of data analysis.

What are the best AI tools for Data Analysts to verify data?

AI tools verify data using outlier detection, statistical analysis, and validation. To ensure utilization ability, they offer data lineage monitoring and version utilization.

Please Login to comment...

• Trending News
• 10 Best Free Social Media Management and Marketing Apps for Android - 2024
• 10 Best Customer Database Software of 2024
• How to Delete Whatsapp Business Account?
• Discord vs Zoom: Select The Efficienct One for Virtual Meetings?
• 30 OOPs Interview Questions and Answers (2024)

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

TechRepublic

Account information.

Share with Your Friends

10 Best Reporting Tools and Software of 2024

Your email has been sent

• Best for comprehensive data integration: Zoho Analytics
• Best for task-based reporting: Asana
• Best for high-level project reporting: Hive
• Best for data-driven decision-making: Google Looker
• Best for customizable project reporting: Wrike
• Best for visual project tracking: monday.com
• Best for all-in-one project management: ClickUp
• Best for agile project management: Jira Software
• Best for data visualization: Tableau
• Best for Microsoft ecosystem integration: Power BI

Reporting tools and software are crucial to teams, especially in terms of project management as they provide a structured way to track progress, identify risks and make informed decisions. They offer a sweeping view of project health that helps managers to not only pinpoint areas of concern but also identify successes. With effective reporting, an organization gets transparency and ensures its stakeholders are aligned, which plays a part in making projects successful since everyone involved has access to the same information and insights. We’ve analyzed 10 top reporting tools and software worth your consideration.

Top reporting software: Comparison table

Zoho analytics: best for comprehensive data integration.

Zoho Analytics is a reporting tool that excels at aggregating data from a wide array of sources as it connects with over 250 data sources, including files, feeds, databases and cloud services. Its comprehensive suite of reporting options includes charts, pivot tables, summary views, tabular views and more. Zoho Analytics also offers an intuitive drag-and-drop interface to further simplify the report creation process and make it accessible for users of varying skill levels.

Zoho Analytics offers plans starting at $22 per month for the Basic plan, while the Standard, Premium and Enterprise plans cost $45, $112 and $445 per month, respectively, when billed annually. There’s also a Custom plan for prospective users to share their requirements.

• Extensive data integration from over 250 sources.
• Data preparation and management tools for accurate analysis.
• A wide array of visualization options for insightful reporting ( Figure A ).
• AI and ML-powered augmented analytics for guided insights.

Integrations

Zoho Analytics’s integrations include Zoho CRM, Salesforce CRM, Microsoft Dynamics CRM, HubSpot CRM and Zoho Bigin.

• Comprehensive data integration capabilities.
• Wide range of visualization tools.
• Advanced augmented analytics features.
• May be complex for beginners.
• Customization can require a learning curve.

Why we chose Zoho Analytics

We selected Zoho Analytics for its broad range of reporting capabilities and user-friendly design. Its ability to present data in various visual formats makes analysis flexible and insightful and caters to diverse reporting needs as well as a wide variety of users.

Learn more about other Zoho products, like Zoho Projects and Zoho Vault .

Asana: Best for task-based reporting

Asana simplifies project management with its Universal Reporting feature, which provides teams with a clear overview of task progress and project health. Its visual reporting format is designed for easy interpretation, meaning that users at all levels within an organization can easily access and use Asana.

Asana’s paid plans include the Premium plan at $10.99 per user per month, billed annually, and the Business plan at $24.99 per user per month. Its Enterprise plan’s pricing hasn’t been listed publicly.

• Visual and intuitive reporting tools for task and project tracking ( Figure B ).
• Goal tracking to align daily tasks with strategic objectives.
• Real-time updates to keep teams informed on project progress.
• A variety of highly customizable charts.

Asana’s top integrations include Microsoft Teams, Slack, the Asana for Gmail add-on, Asana for Adobe Creative Cloud and Google Calendar.

• User-friendly reporting and task management.
• Effective goal alignment features.
• Wide range of integrations.
• Limited depth in analytical features.
• Real-time analytics are somewhat restricted.

Why we chose Asana

We simply selected Asana for its user-friendly approach to task-based reporting. Asana is also highly effective when it comes to aligning tasks with organizational goals.

For more information, check out our full Asana review .

Hive: Best for high-level project reporting

Hive is recognized for its high-level reporting capabilities, offering a suite of options for a variety of project management use cases. With features like goals, analytics dashboards and timesheet reporting, Hive provides a comprehensive tool for gaining visibility and gathering insights into projects.

Hive has two premium plans atop a free plan. Teams at $12 per user per month when billed annually and $18 when billed monthly, and Enterprise, whose prices aren’t publicly listed.

• Goals for setting, tracking and monitoring goals across teams.
• Analytics dashboards to showcase project status, project breakdowns and more.
• Timesheets reporting to analyze data across timesheets.
• Multiple views like Portfolio, Summary, Table, Kanban and more ( Figure C ).

Hive’s top integrations include Google Calendar, Gmail, Google Sheets, Google Drive and Slack.

• Customizable high-level reporting options.
• Variety of views for different reporting needs.
• Efficient project and action management features.
• May require initial setup time to customize views.
• Some advanced features might be available only on higher-tier plans.

Why we chose Hive

We selected Hive for its versatile high-level reporting options and customizable views. They bring a flexible and comprehensive overview to projects.

For more information, check out our full Hive review .

Google Looker: Best for data-driven decision-making

A rather different entry from most tools on this list, Google Looker stands as a unified business intelligence platform that excels at turning data into actionable insights. It offers self-service BI that allows users to access, analyze and act on up-to-date, trusted data. As a reporting tool, Looker offers reliable data experiences at scale and empowers users with real-time insights.

Looker has a 30-day free trial, and its Standard plan costs $5,000 per month. For an annual quote, as well as quotes for the Enterprise and Embed plans, contact Google sales.

• Embedded analytics and applications for enhanced data experiences.
• Data modeling to unify business metrics across teams and applications.
• Real-time insights to empower users with up-to-date information.
• An extensive template gallery for templates on many of Google’s applications ( Figure D ).

Looker offers extensive integration capabilities, including BigQuery, Spanner, Cloud SQL and Cloud Storage.

• Unified platform for all BI needs.
• Real-time insights for up-to-date decision-making.
• Extensive integration capabilities with data sources.
• Pricing transparency could be improved.
• May require a learning curve to fully utilize advanced features.

Why we chose Google Looker

Google Looker’s reporting capabilities can be seen particularly through its embedded analytics and real-time insights. It easily unifies business metrics across teams and applications. It’s also a great tool for users predominantly using applications in the Google ecosystem.

Wrike: Best for customizable project reporting

Wrike stands out for its highly customizable reporting features. This flexibility, combined with Wrike’s thorough resource management and advanced analytics, makes Wrike competent enough to provide detailed insights into project performance and resource allocation and flexible enough to adapt to various workflows.

Wrike has five plans: the ones with prices listed are the Free plan, Team plan at $9.80 per user per month and Business plan at $24.80 per user per month. The Enterprise and Pinnacle plans’ pricing plans aren’t publicly listed.

• Customizable reports for tailored project insights ( Figure E ).
• Resource management to monitor progress and identify risks.
• Advanced analytics for deep visibility into project performance.

Wrike’s top integrations include Jira, GitHub, Google Sheets, Azure DevOps and HubSpot.

• Highly customizable reporting options.
• Comprehensive project and resource monitoring.
• Advanced analytics capabilities.
• Customization options may require time to master.
• Extensive features can be overwhelming for newcomers.

Why we chose Wrike

Wrike has robust reporting capabilities and customizable features, which give users the flexibility and depth needed to gain extensive insights into their projects and resources.

For more information, check out our full Wrike review .

monday.com: Best for visual project tracking

monday.com is a favorite among teams that love visual task management and prioritize ease of use as it offers a visually intuitive platform for project tracking. Its advanced reporting features, such as stacked charts and workload views, provide a thorough overview of project progress and team capacity. monday.com’s dashboard customization is very flexible; this enables teams to mold their reporting to meet their project needs.

monday has a free plan and a handful of premium plans, namely, Basic at $9 per seat per month, billed annually, or $12 per seat billed monthly; Standard at $12 per seat per month, billed annually, or $14 per seat billed monthly; Pro at $19 per seat per month, billed annually, or $24 per seat billed monthly; and Enterprise, which offers customized pricing.

• Stacked charts for multi-dimensional data analysis.
• Workload views for balanced resource allocation.
• Pivot tables for detailed data breakdowns.
• Customizable dashboards for tailored project insights ( Figure F ).

Some of the best monday.com integrations include GitLab, OneDrive, Todoist, Slack and Microsoft Teams.

• Highly visual and intuitive interface.
• Advanced reporting for comprehensive project insights.
• Flexible dashboard customization.
• Can be overwhelming for new users due to numerous features.
• Some advanced features require higher-tier plans.

Why we chose monday.com

monday.com is a visually intuitive platform and has advanced reporting capabilities. It delivers a balance between visual project tracking and in-depth reporting.

For more information, check out our full monday.com review .

ClickUp: Best for all-in-one project management

ClickUp is recognized for its all-in-one approach to project management, offering a wide range of features from task management to time tracking and goal setting. Its reporting features are designed to provide teams with insights into productivity and project progress, supporting data-driven decision-making. ClickUp’s customizable dashboards and reporting tools allow teams to monitor key metrics and track performance effectively.

ClickUp offers a generous free forever plan alongside three premium tiers: Unlimited at $7 per user per month when billed annually, or $10 per user per month when billed monthly; Business at $12 per user per month when billed annually, or $19 per user per month when billed monthly; and Enterprise that needs prospective users to contact ClickUp for a custom quote.

• Comprehensive dashboards for project overview ( Figure G ).
• Customizable reporting for tailored insights.
• Goal tracking to align efforts with objectives.
• Time tracking to monitor task durations and productivity.

Some of ClickUp’s top integrations include Gmail, Zoom, HubSpot, Make and Google Calendar.

• Versatile all-in-one project management solution.
• Extensive customization options for dashboards and reporting.
• Generous free plan with substantial features.
• Steep learning curve due to feature richness.
• Customization can be time-consuming.

Why we chose ClickUp

We included ClickUp because of its comprehensive feature set and flexibility, offering teams an all-in-one solution for project management and reporting. It proves suitable for a wide range of project types and sizes.

For more information, check out our full ClickUp review .

Jira Software: Best for agile project management

Jira Software is tailored for agile project management with specialized reporting features like sprint reports, burndown charts and velocity charts. These agile-centric reports give teams critical insights into their agile processes to help them optimize workflows and improve sprint planning. It’s worth considering for software development teams and those that follow scrum or kanban frameworks.

Jira offers a free plan for 10 users max. Its premium plans are the Standard plan at about $8.15 per user per month and the Premium plan at about $16 per user per month. It also offers an Enterprise plan that’s billed annually. However, you need to contact Jira for a quote.

• Sprint reports for tracking sprint progress ( Figure H ).
• Burndown charts for visualizing task completion.
• Velocity charts for assessing team performance over sprints.
• Cumulative flow diagrams for Kanban teams.

Jira has extensive integrations with development tools like Bitbucket, Confluence, GitHub, Opsgenie, Jenkins and Dynatrace.

• Tailored for agile project management.
• Comprehensive reporting for scrum and kanban teams.
• Wide range of integrations with development tools.
• Primarily focused on software development teams.
• Can be complex for non-technical users.

Why we chose Jira Software

Jira Software has robust agile reporting features and is capable of providing deep insights into agile project management processes, especially for teams practicing scrum or kanban methodologies.

For more information, check out our full Jira Software review .

Tableau: Best for data visualization

Tableau sets the standard for data visualization, offering a wide range of chart types and interactive dashboards that make complex data understandable at a glance. As reporting software, it offers a user-friendly interface and powerful data handling capabilities for users to create detailed and insightful visual reports.

Tableau’s pricing starts at $15 per user per month, with its highest tier costing $75 per user per month, both billed annually.

• Wide range of visualization options.
• User-friendly interface for non-technical users ( Figure I ).
• Powerful data handling and processing capabilities.

Tableau’s top integrations include Salesforce, Google Analytics, Microsoft Excel, Amazon Redshift and Snowflake.

• Leading data visualization capabilities.
• Intuitive interface for easy use.
• Strong data connectivity options.
• Higher price point compared to some competitors.
• Can require significant resources for large datasets.

Why we chose Tableau

We considered Tableau because of its unparalleled data visualization capabilities and user-friendly interface. It should make it to your shortlist if your teams value both data accessibility and detailed reporting.

For more information, check out our full Tableau review .

Power BI: Best for Microsoft ecosystem integration

Power BI is a key player in the reporting and analytics space, especially for those deeply embedded in the Microsoft ecosystem. Its seamless integration with other Microsoft products, like Excel and Azure, makes it a no-brainer for teams that want compatibility and ease of use with their reporting tools. What makes it a great reporting and analytics tool is its ability to handle large datasets and provide advanced analytics, including AI capabilities and custom visualizations.

Power BI offers a free version, with premium plans starting at $10 per user per month for the Pro plan and $20 per user per month for the Premium plan.

• Seamless integration with Microsoft products.
• Advanced analytics with AI capabilities.
• Custom visualizations for personalized reporting ( Figure J ).

Aside from a variety of tools in the Microsoft ecosystem like Microsoft Office 365, Power BI’s top integrations include Asana, HubSpot, Google Sheets and Salesforce Pardot.

• Strong Microsoft integration.
• Comprehensive analytics and AI features.
• Flexible pricing with a robust free version.
• Can be complex for new users.
• Limited integration outside the Microsoft ecosystem.

Why we chose Power BI

We chose Power BI due to its strong analytics capabilities combined with its seamless integration with tools in the Microsoft ecosystem. It’s a particularly fitting choice for teams that already use Microsoft products.

For more information, check out our full Power BI review .

Key features of reporting software

Real-time analytics.

Real-time analytics allows users to view, assess and analyze data as it flows into the business, which can be displayed on dashboards or reports. With this, users get to make decisions faster since they get instant, descriptive insights from the most current data.

Custom reports

Custom reports save time as they automate the data gathering and report generation processes. After the initial setup, reporting processes can be entirely streamlined, with live data feeds ensuring that any additional requests can be quickly addressed by making changes to existing reports.

Dashboard customization

Dashboard customization is crucial in reporting software as it allows users to set up their reporting environment based on their needs. Custom dashboards can provide in-depth data on various aspects of business operations, illustrating potential revenue and areas where improvements are needed. Businesses can mix and match data sources for a comprehensive view of their digital environment.

Automated reporting

This kind of reporting streamlines the process of generating regular reports and reduces the manual effort required while making sure that stakeholders receive timely updates. Users can schedule report generation and ensure that reports are always current and reflect the latest data.

Data visualization

Data visualization transforms complex datasets into graphical representations, making it easier to understand trends, patterns and outliers. This feature helps to make data more accessible and actionable, which enables users to quickly grasp the insights presented in the data.

How do I choose the best reporting software for my business?

First things first, when it comes to choosing the best reporting software for you, you must match a tool’s capabilities to your needs. For small to medium-sized businesses, tools like Zoho Analytics and ClickUp offer a vast feature set at a more accessible price point, which makes them great options when seeking value without compromising on functionality. Larger enterprises or those with more complex reporting and data analysis needs might lean towards Power BI or Tableau, known for their advanced analytics and integration within larger ecosystems.

Consider the types of reports you need, the data you’re working with and who will be using the tool. For teams that prioritize real-time data and collaboration, monday.com and Asana provide user-friendly interfaces and seamless integration with other productivity tools. On the other hand, if your focus is on in-depth data analysis and visualization, Tableau’s extensive customization options and Power BI’s deep Microsoft integration stand out.

In essence, the best reporting tool is one that not only fits your budget and technical requirements but also grows with your business, adapting to changing needs and helping you make informed decisions based on accurate, up-to-date data.

Methodology

Our approach to identifying the top reporting tools for 2024 involved a detailed examination of each tool’s core features, ease of use, use cases and pricing. This allowed us to provide popular tools that cut across industries, use cases and team sizes. Additionally, we tested the tools where possible to understand how they approached reporting and compared our findings to verified reviews by real users. From this, we got to understand the pros and cons of each tool.

Subscribe to the Project Management Insider Newsletter

Subscribe to Project Management Insider for best practices, reviews and resources. From project scheduling software to project planning apps, stay up to date with the latest in project management tools. Delivered Wednesdays

• The Best Project Management Software and Tools for 2024
• The Best Simple Project Management Software of 2024
• The Best Project Management Certifications in 2024
• Telephone Interview Cheat Sheet: Project Manager

Create a TechRepublic Account

Get the web's best business technology news, tutorials, reviews, trends, and analysis—in your inbox. Let's start with the basics.

* - indicates required fields

Sign in to TechRepublic

Lost your password? Request a new password

Reset Password

Please enter your email adress. You will receive an email message with instructions on how to reset your password.

Check your email for a password reset link. If you didn't receive an email don't forgot to check your spam folder, otherwise contact support .

Welcome. Tell us a little bit about you.

This will help us provide you with customized content.

Want to receive more TechRepublic news?

You're all set.

Thanks for signing up! Keep an eye out for a confirmation email from our team. To ensure any newsletters you subscribed to hit your inbox, make sure to add [email protected] to your contacts list.

Help | Advanced Search

Computer Science > Computation and Language

Title: uni-smart: universal science multimodal analysis and research transformer.

Abstract: In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, making in-depth literature analysis increasingly challenging and time-consuming. The emergence of Large Language Models (LLMs) has offered a new way to address this challenge. Known for their strong abilities in summarizing texts, LLMs are seen as a potential tool to improve the analysis of scientific literature. However, existing LLMs have their own limits. Scientific literature often includes a wide range of multimodal elements, such as molecular structure, tables, and charts, which are hard for text-focused LLMs to understand and analyze. This issue points to the urgent need for new solutions that can fully understand and analyze multimodal content in scientific literature. To answer this demand, we present Uni-SMART (Universal Science Multimodal Analysis and Research Transformer), an innovative model designed for in-depth understanding of multimodal scientific literature. Through rigorous quantitative evaluation across several domains, Uni-SMART demonstrates superior performance over leading text-focused LLMs. Furthermore, our exploration extends to practical applications, including patent infringement detection and nuanced analysis of charts. These applications not only highlight Uni-SMART's adaptability but also its potential to revolutionize how we interact with scientific literature.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

• Open access
• Published: 28 March 2024

Using the consolidated Framework for Implementation Research to integrate innovation recipients’ perspectives into the implementation of a digital version of the spinal cord injury health maintenance tool: a qualitative analysis

• John A Bourke 1 , 2 , 3 ,
• K. Anne Sinnott Jerram 1 , 2 ,
• Mohit Arora 1 , 2 ,
• Ashley Craig 1 , 2 &
• James W Middleton 1 , 2 , 4 , 5  

BMC Health Services Research volume  24 , Article number:  390 ( 2024 ) Cite this article

69 Accesses

Metrics details

Despite advances in managing secondary health complications after spinal cord injury (SCI), challenges remain in developing targeted community health strategies. In response, the SCI Health Maintenance Tool (SCI-HMT) was developed between 2018 and 2023 in NSW, Australia to support people with SCI and their general practitioners (GPs) to promote better community self-management. Successful implementation of innovations such as the SCI-HMT are determined by a range of contextual factors, including the perspectives of the innovation recipients for whom the innovation is intended to benefit, who are rarely included in the implementation process. During the digitizing of the booklet version of the SCI-HMT into a website and App, we used the Consolidated Framework for Implementation Research (CFIR) as a tool to guide collection and analysis of qualitative data from a range of innovation recipients to promote equity and to inform actionable findings designed to improve the implementation of the SCI-HMT.

Data from twenty-three innovation recipients in the development phase of the SCI-HMT were coded to the five CFIR domains to inform a semi-structured interview guide. This interview guide was used to prospectively explore the barriers and facilitators to planned implementation of the digital SCI-HMT with six health professionals and four people with SCI. A team including researchers and innovation recipients then interpreted these data to produce a reflective statement matched to each domain. Each reflective statement prefaced an actionable finding, defined as alterations that can be made to a program to improve its adoption into practice.

Five reflective statements synthesizing all participant data and linked to an actionable finding to improve the implementation plan were created. Using the CFIR to guide our research emphasized how partnership is the key theme connecting all implementation facilitators, for example ensuring that the tone, scope, content and presentation of the SCI-HMT balanced the needs of innovation recipients alongside the provision of evidence-based clinical information.

Conclusions

Understanding recipient perspectives is an essential contextual factor to consider when developing implementation strategies for healthcare innovations. The revised CFIR provided an effective, systematic method to understand, integrate and value recipient perspectives in the development of an implementation strategy for the SCI-HMT.

Trial registration

Peer Review reports

Injury to the spinal cord can occur through traumatic causes (e.g., falls or motor vehicle accidents) or from non-traumatic disease or disorder (e.g., tumours or infections) [ 1 ]. The onset of a spinal cord injury (SCI) is often sudden, yet the consequences are lifelong. The impact of a SCI is devastating, with effects on sensory and motor function, bladder and bowel function, sexual function, level of independence, community participation and quality of life [ 2 ]. In order to maintain good health, wellbeing and productivity in society, people with SCI must develop self-management skills and behaviours to manage their newly acquired chronic health condition [ 3 ]. Given the increasing emphasis on primary health care and community management of chronic health conditions, like SCI, there is a growing responsibility on all parties to promote good health practices and minimize the risks of common health complications in their communities.

To address this need, the Spinal Cord Injury Health Maintenance Tool (SCI-HMT) was co-designed between 2018 and 2023 with people living with SCI and their General Practitioners (GPs) in NSW, Australia [ 4 ] The aim of the SCI-HMT is to support self-management of the most common and arguably avoidable potentially life-threatening complications associated with SCI, such as mental health crises, autonomic dysreflexia, kidney infections and pressure injuries. The SCI-HMT provides comprehensible information with resources about the six highest priority health areas related to SCI (as indicated by people with SCI and GPs) and was developed over two phases. Phase 1 focused on developing a booklet version and Phase 2 focused on digitizing this content into a website and smartphone app [ 4 , 5 ].

Enabling the successful implementation of evidence-based innovations such as the SCI-HMT is inevitably influenced by contextual factors: those dynamic and diverse array of forces within real-world settings working for or against implementation efforts [ 6 ]. Contextual factors often include background environmental elements in which an intervention is situated, for example (but not limited to) demographics, clinical environments, organisational culture, legislation, and cultural norms [ 7 ]. Understanding the wider context is necessary to identify and potentially mitigate various challenges to the successful implementation of those innovations. Such work is the focus of determinant frameworks, which focus on categorising or classing groups of contextual determinants that are thought to predict or demonstrate an effect on implementation effectiveness to better understand factors that might influence implementation outcomes [ 8 ].

One of the most highly cited determinant frameworks is the Consolidated Framework for Implementation Research (CFIR) [ 9 ], which is often posited as an ideal framework for pre-implementation preparation. Originally published in 2009, the CFIR has recently been subject to an update by its original authors, which included a literature review, survey of users, and the creation of an outcome addendum [ 10 , 11 ]. A key contribution from this revision was the need for a greater focus on the place of innovation recipients, defined as the constituency for whom the innovation is being designed to benefit; for example, patients receiving treatment, students receiving a learning activity. Traditionally, innovation recipients are rarely positioned as key decision-makers or innovation implementers [ 8 ], and as a consequence, have not often been included in the application of research using frameworks, such as the CFIR [ 11 ].

Such power imbalances within the intersection of healthcare and research, particularly between those receiving and delivering such services and those designing such services, have been widely reported [ 12 , 13 ]. There are concerted efforts within health service development, health research and health research funding, to rectify this power imbalance [ 14 , 15 ]. Importantly, such efforts to promote increased equitable population impact are now being explicitly discussed within the implementation science literature. For example, Damschroder et al. [ 11 ] has recently argued for researchers to use the CFIR to collect data from innovation recipients, and that, ultimately, “equitable population impact is only possible when recipients are integrally involved in implementation and all key constituencies share power and make decisions together” (p. 7). Indeed, increased equity between key constituencies and partnering with innovation recipients promotes the likelihood of sustainable adoption of an innovation [ 4 , 12 , 14 ].

There is a paucity of work using the updated CFIR to include and understand innovation recipients’ perspectives. To address this gap, this paper reports on a process of using the CFIR to guide the collection of qualitative data from a range of innovation recipients within a wider co-design mixed methods study examining the development and implementation of SCI-HMT. The innovation recipients in our research are people living with SCI and GPs. Guided by the CFIR domains (shown in the supplementary material), we used reflexive thematic analysis [ 16 ]to summarize data into reflective summaries, which served to inform actionable findings designed to improve implementation of the SCI-HMT.

The procedure for this research is multi-stepped and is summarized in Fig.  1 . First, we mapped retrospective qualitative data collected during the development of the SCI-HMT [ 4 ] against the five domains of the CFIR in order to create a semi-structured interview guide (Step 1). Then, we used this interview guide to collect prospective data from health professionals and people with SCI during the development of the digital version of the SCI-HMT (Step 2) to identify implementation barriers and facilitators. This enabled us to interpret a reflective summary statement for each CFIR domain. Lastly, we developed an actionable finding for each domain summary. The first (RESP/18/212) and second phase (2019/ETH13961) of the project received ethical approval from The Northern Sydney Local Health District Human Research Ethics Committee. The reporting of this study was conducted in line with the consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines [ 17 ]. All methods were performed in accordance with the relevant guidelines and regulations.

Procedure of synthesising datasets to inform reflective statements and actionable findings. a Two health professionals had a SCI (one being JAB); b Two co-design researchers had a SCI (one being JAB)

Step one: retrospective data collection and analysis

We began by retrospectively analyzing the data set (interview and focus group transcripts) from the previously reported qualitative study from the development phase of the SCI-HMT [ 4 ]. This analysis was undertaken by two team members (KASJ and MA). KASJ has a background in co-design research. Transcript data were uploaded into NVivo software (Version 12: QSR International Pty Ltd) and a directed content analysis approach [ 18 ] was applied to analyze categorized data a priori according to the original 2009 CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of individuals, and process of implementation) described by Damschroder et al. [ 9 ]. This categorized data were summarized and informed the specific questions of a semi-structured interview guide. The final output of step one was an interview guide with context-specific questions arranged according to the CFIR domains (see supplementary file 1). The interview was tested with two people with SCI and one health professional.

Step two: prospective data collection and analysis

In the second step, semi-structured interviews were conducted by KASJ (with MA as observer) with consenting healthcare professionals who had previously contributed to the development of the SCI-HMT. Healthcare professionals included GPs, Nurse Consultants, Specialist Physiotherapists, along with Health Researchers (one being JAB). In addition, a focus group was conducted with consenting individuals with SCI who had contributed to the SCI-HMT design and development phase. The interview schedule designed in step one above guided data collection in all interviews and the focus group.

The focus group and interviews were conducted online, audio recorded, transcribed verbatim and uploaded to NVivo software (Version 12: QSR International Pty Ltd). All data were subject to reflexive, inductive and deductive thematic analysis [ 16 , 19 ] to better understand participants’ perspectives regarding the potential implementation of the SCI-HMT. First, one team member (KASJ) read transcripts and began a deductive analysis whereby data were organized into CFIR domains-specific dataset. Second, KASJ and JAB analyzed this domain-specific dataset to inductively interpret a reflective statement which served to summarise all participant responses to each domain. The final output of step two was a reflective summary statement for each CFIR domain.

Step three: data synthesis

In the third step we aimed to co-create an actionable finding (defined as tangible alteration that can be made to a program, in this case the SCI-HMT [ 20 ]) based on each domain-specific reflective statement. To achieve this, three codesign researchers (KAS and JAB with one person with SCI from Step 2 (deidentified)) focused on operationalising each reflective statement into a recommended modification for the digital version of the SCI-HMT. This was an iterative process guided by the specific CFIR domain and construct definitions, which we deemed salient and relevant to each reflective statement (see Table  2 for example). Data synthesis involved line by line analysis, group discussion, and repeated refinement of actionable findings. A draft synthesis was shared with SCI-HMT developers (JWM and MA) and refinement continued until consensus was agreed on. The final outputs of step three were an actionable finding related to each reflective statement for each CFIR domain.

The characteristics of both the retrospective and prospective study participants are shown in Table  1 . The retrospective data included data from a total of 23 people: 19 people with SCI and four GPs. Of the 19 people with SCI, 12 participated in semi-structured interviews, seven participated in the first focus group, and four returned to the second focus group. In step 2, four people with SCI participated in a focus group and six healthcare professionals participated in one-on-one semi-structured interviews. Two of the healthcare professionals (a GP and a registrar) had lived experience of SCI, as did one researcher (JAB). All interviews and focus groups were conducted either online or in-person and ranged in length between 60 and 120 min.

In our overall synthesis, we actively interpreted five reflective statements based on the updated CFIR domain and construct definitions by Damschroder et al. [ 11 ]. Table  2 provides a summary of how we linked the updated CFIR domain and construct definitions to the reflective statements. We demonstrate this process of co-creation below, including illustrative quotes from participants. Importantly, we guide readers to the actionable findings related to each reflective statement in Table  2 . Each actionable statement represents an alteration that can be made to a program to improve its adoption into practice.

Participants acknowledged that self-management is a major undertaking and very demanding, as one person with SCI said, “ we need to be informed without being terrified and overwhelmed”. Participants felt the HMT could indeed be adapted, tailored, refined, or reinvented to meet local needs. For example, another person with SCI remarked:

“Education needs to be from the get-go but in bite sized pieces from all quarters when readiness is most apparent… at all time points , [not just as a] a newbie tool or for people with [long-term impairment] ” (person with SCI_02).

Therefore, the SCI-HMT had to balance complexity of content while still being accessible and engaging, and required input from both experts in the field and those with lived experience of SCI, for example, a clinical nurse specialist suggested:

“it’s essential [the SCI-HMT] is written by experts in the field as well as with collaboration with people who have had a, you know, the lived experience of SCI” (healthcare professional_03).

Furthermore, the points of contact with healthcare for a person with SCI can be challenging to navigate and the SCI-HMT has the potential to facilitate a smoother engagement process and improve communication between people with SCI and healthcare services. As a GP suggested:

“we need a tool like this to link to that pathway model in primary health care , [the SCI-HMT] it’s a great tool, something that everyone can read and everyone’s reading the same thing” (healthcare professional_05).

Participants highlighted that the ability of the SCI-HMT to facilitate effective communication was very much dependent on the delivery format. The idea of digitizing the SCI-HMT garnered equal support from people with SCI and health care professionals, with one participant with SCI deeming it to be “ essential” ( person with SCI_01) and a health professional suggesting a “digitalized version will be an advantage for most people” (healthcare professional_02).

Outer setting

There was strong interest expressed by both people with SCI and healthcare professionals in using the SCI-HMT. The fundamental premise was that knowledge is power and the SCI-HMT would have strong utility in post-acute rehabilitation services, as well as primary care. As a person with SCI said,

“ we need to leave the [spinal unit] to return to the community with sufficient knowledge, and to know the value of that knowledge and then need to ensure primary healthcare provider [s] are best informed” (person with SCI_04).

The value of the SCI-HMT in facilitating clear and effective communication and shared decision-making between healthcare professionals and people with SCI was also highlighted, as shown by the remarks of an acute nurse specialist:

“I think this tool is really helpful for the consumer and the GP to work together to prioritize particular tests that a patient might need and what the regularity of that is” (healthcare professional_03).

Engaging with SCI peer support networks to promote the SCI-HMT was considered crucial, as one person with SCI emphasized when asked how the SCI-HMT might be best executed in the community, “…peers, peers and peers” (person with SCI_01). Furthermore, the layering of content made possible in the digitalized version will allow for the issue of approachability in terms of readiness for change, as another person with SCI said:

“[putting content into a digital format] is essential and required and there is a need to put summarized content in an App with links to further web-based information… it’s not likely to be accessed otherwise” (person with SCI_02).

Inner setting

Participants acknowledged that self-management of health and well-being is substantial and demanding. It was suggested that the scope, tone, and complexity of the SCI-HMT, while necessary, could potentially be resisted by people with SCI if they felt overwhelmed, as one person with SCI described:

“a manual that is really long and wordy, like, it’s [a] health metric… they maybe lack the health literacy to, to consume the content then yes, it would impede their readiness for [self-management]” (person with SCI_02).

Having support from their GPs was considered essential, and the HMT could enable GP’s, who are under time pressure, to provide more effective health and advice to their patients, as one GP said:

“We GP’s are time poor, if you realize then when you’re time poor you look quickly to say oh this is a patient tool - how can I best use this?” (healthcare professional_05).

Furthermore, health professional skills may be best used with the synthesis of self-reported symptoms, behaviors, or observations. A particular strength of a digitized version would be its ability to facilitate more streamlined communication between a person with SCI and their primary healthcare providers developing healthcare plans, as an acute nurse specialist reflected, “ I think that a digitalized version is essential with links to primary healthcare plans” (healthcare professional_03).

Efficient communication with thorough assessment is essential to ensure serious health issues are not missed, as findings reinforce that the SCI-HMT is an educational tool, not a replacement for healthcare services, as a clinical nurse specialist commented, “ remember, things will go wrong– people end up very sick and in acute care “ (healthcare professional_02).

The SCI-HMT has the potential to provide a pathway to a ‘hope for better than now’ , a hope to ‘remain well’ and a hope to ‘be happy’ , as the informant with SCI (04) declared, “self-management is a long game, if you’re keeping well, you’ve got that possibility of a good life… of happiness”. Participants with SCI felt the tool needed to be genuine and

“acknowledge the huge amount of adjustment required, recognizing that dealing with SCI issues is required to survive and live a good life” (person with SCI_04).

However, there is a risk that an individual is completely overwhelmed by the scale of the SCI-HMT content and the requirement for lifelong vigilance. Careful attention and planning were paid to layering the information accordingly to support self-management as a ‘long game’, which one person with SCI reflected in following:

“the first 2–3 year [period] is probably the toughest to get your head around the learning stuff, because you’ve got to a stage where you’re levelling out, and you’ve kind of made these promises to yourself and then you realize that there’s no quick fix” (person with SCI_01).

It was decided that this could be achieved by providing concrete examples and anecdotes from people with SCI illustrating that a meaningful, healthy life is possible, and that good health is the bedrock of a good life with SCI.

There was universal agreement that the SCI-HMT is aspirational and that it has the potential to improve knowledge and understanding for people with SCI, their families, community workers/carers and primary healthcare professionals, as a GP remarked:

“[different groups] could just read it and realize, ‘Ahh, OK that’s what that means… when you’re doing catheters. That’s what you mean when you’re talking about bladder and bowel function or skin care” (healthcare professional_04).

Despite the SCI-HMT providing an abundance of information and resources to support self-management, participants identified four gaps: (i) the priority issue of sexuality, including pleasure and identity, as one person with SCI remarked:

“ sexuality is one of the biggest issues that people with SCI often might not speak about that often cause you know it’s awkward for them. So yeah, I think that’s a that’s a serious issue” (person with SCI_03).

(ii) consideration of the taboo nature of bladder and bowel topics for indigenous people, (iii) urgent need to ensure links for SCI-HMT care plans are compatible with patient management systems, and (iv) exercise and leisure as a standalone topic taking account of effects of physical activity, including impact on mental health and wellbeing but more especially for fun.

To ensure longevity of the SCI-HMT, maintaining a partnership between people with SCI, SCI community groups and both primary and tertiary health services is required for liaison with the relevant professional bodies, care agencies, funders, policy makers and tertiary care settings to ensure ongoing education and promotion of SCI-HMT is maintained. For example, delivery of ongoing training of healthcare professionals to both increase the knowledge base of primary healthcare providers in relation to SCI, and to promote use of the tools and resources through health communities. As a community nurse specialist suggested:

“ improving knowledge in the health community… would require digital links to clinical/health management platforms” (healthcare professional_02).

In a similar vein, a GP suggested:

“ our common GP body would have continuing education requirements… especially if it’s online, in particular for the rural, rural doctors who you know, might find it hard to get into the city” (healthcare professional_04).

The successful implementation of evidence-based innovations into practice is dependent on a wide array of dynamic and active contextual factors, including the perspectives of the recipients who are destined to use such innovations. Indeed, the recently updated CFIR has called for innovation recipient perspectives to be a priority when considering contextual factors [ 10 , 11 ]. Understanding and including the perspectives of those the innovation is being designed to benefit can promote increased equity and validation of recipient populations, and potentially increase the adoption and sustainability of innovations.

In this paper, we have presented research using the recently updated CFIR to guide the collection of innovation recipients’ perspectives (including people with SCI and GPs working in the community) regarding the potential implementation barriers and facilitators of the digital version of the SCI-HMT. Collected data were synthesized to inform actionable findings– tangible ways in which the SCI-HMT could be modified according of the domains of the CFIR (e.g., see Keith et al. [ 20 ]). It is important to note that we conducted this research using the original domains of the CFIR [ 9 ] prior to Damschroder et al. publishing the updated CFIR [ 11 ]. However, in our analysis we were able to align our findings to the revised CFIR domains and constructs, as Damschroder [ 11 ] suggests, constructs can “be mapped back to the original CFIR to ensure longitudinal consistency” (p. 13).

One of the most poignant findings from our analyses was the need to ensure the content of the SCI-HMT balanced scientific evidence and clinical expertise with lived experience knowledge. This balance of clinical and experiential knowledge demonstrated genuine regard for lived experience knowledge, and created a more accessible, engaging, useable platform. For example, in the innovation and individual domains, the need to include lived experience quotes was immediately apparent once the perspective of people with SCI was included. It was highlighted that while the SCI-HMT will prove useful to many parties at various stages along the continuum of care following onset of SCI, there will be those individuals that are overwhelmed by the scale of the content. That said, the layering of information facilitated by the digitalized version is intended to provide an ease of navigation through the SCI-HMT and enable a far greater sense of control over personal health and wellbeing. Further, despite concerns regarding e-literacy the digitalized version of the SCI-HMT is seen as imperative for accessibility given the wide geographic diversity and recent COVID pandemic [ 21 ]. While there will be people who are challenged by the technology, the universally acceptable use of the internet is seen as less of a barrier than printed material.

The concept of partnership was also apparent within the data analysis focusing on the outer and inner setting domains. In the outer setting domain, our findings emphasized the importance of engaging with SCI community groups, as well as primary and tertiary care providers to maximize uptake at all points in time from the phase of subacute rehabilitation onwards. While the SCI-HMT is intended for use across the continuum of care from post-acute rehabilitation onwards, it may be that certain modules are more relevant at different times, and could serve as key resources during the hand over between acute care, inpatient rehabilitation and community reintegration.

Likewise, findings regarding the inner setting highlighted the necessity of a productive partnership between GPs and individuals with SCI to address the substantial demands of long-term self-management of health and well-being following SCI. Indeed, support is crucial, especially when self-management is the focus. This is particularly so in individuals living with complex disability following survival after illness or injury [ 22 ], where health literacy has been found to be a primary determinant of successful health and wellbeing outcomes [ 23 ]. For people with SCI, this tool potentially holds the most appeal when an individual is ready and has strong partnerships and supportive communication. This can enable potential red flags to be recognized earlier allowing timely intervention to avert health crises, promoting individual well-being, and reducing unnecessary demands on health services.

While the SCI-HMT is an educational tool and not meant to replace health services, findings suggest the current structure would lead nicely to having the conversation with a range of likely support people, including SCI peers, friends and family, GP, community nurses, carers or via on-line support services. The findings within the process domain underscored the importance of ongoing partnership between innovation implementers and a broad array of innovation recipients (e.g., individuals with SCI, healthcare professionals, family, funding agencies and policy-makers). This emphasis on partnership also addresses recent discussions regarding equity and the CFIR. For example, Damschroder et al. [ 11 ] suggests that innovation recipients are too often not included in the CFIR process, as the CFIR is primarily seen as a tool intended “to collect data from individuals who have power and/or influence over implementation outcomes” (p. 5).

Finally, we feel that our inclusion of innovation recipients’ perspectives presented in this article begins to address the notion of equity in implementation, whereby the inclusion of recipient perspectives in research using the CFIR both validates, and increases, the likelihood of sustainable adoption of evidence-based innovations, such as the SCI-HMT. We have used the CFIR in a pragmatic way with an emphasis on meaningful engagement between the innovation recipients and the research team, heeding the call from Damschroder et al. [ 11 ], who recently argued for researchers to use the CFIR to collect data from innovation recipients. Adopting this approach enabled us to give voice to innovation recipient perspectives and subsequently ensure that the tone, scope, content and presentation of the SCI-HMT balanced the needs of innovation recipients alongside the provision of evidence-based clinical information.

Our research is not without limitations. While our study was successful in identifying a number of potential barriers and facilitators to the implementation of the SCI-HMT, we did not test any implementation strategies to impact determinants, mechanisms, or outcomes. This will be the focus of future research on this project, which will investigate the impact of implementation strategies on outcomes. Focus will be given to the context-mechanism configurations which give rise to particular outcomes for different groups in certain circumstances [ 7 , 24 ]. A second potential concern is the relatively small sample size of participants that may not allow for saturation and generalizability of the findings. However, both the significant impact of secondary health complications for people with SCI and the desire for a health maintenance tool have been established in Australia [ 2 , 4 ]. The aim our study reported in this article was to achieve context-specific knowledge of a small sample that shares a particular mutual experience and represents a perspective, rather than a population [ 25 , 26 ]. We feel our findings can stimulate discussion and debate regarding participant-informed approaches to implementation of the SCI-HMT, which can then be subject to larger-sample studies to determine their generalisability, that is, their external validity. Notably, future research could examine the interaction between certain demographic differences (e.g., gender) of people with SCI and potential barriers and facilitators to the implementation of the SCI-HMT. Future research could also include the perspectives of other allied health professionals working in the community, such as occupational therapists. Lastly, while our research gave significant priority to recipient viewpoints, research in this space would benefit for ensuring innovation recipients are engaged as genuine partners throughout the entire research process from conceptualization to implementation.

Employing the CFIR provided an effective, systematic method for identifying recipient perspectives regarding the implementation of a digital health maintenance tool for people living with SCI. Findings emphasized the need to balance clinical and lived experience perspectives when designing an implementation strategy and facilitating strong partnerships with necessary stakeholders to maximise the uptake of SCI-HMT into practice. Ongoing testing will monitor the uptake and implementation of this innovation, specifically focusing on how the SCI-HMT works for different users, in different contexts, at different stages and times of the rehabilitation journey.

Data availability

The datasets supporting the conclusions of this article are available available upon request and with permission gained from the project Steering Committee.

Abbreviations

spinal cord injury

HMT-Spinal Cord Injury Health Maintenance Tool

Consolidated Framework for Implementation Research

Kirshblum S, Vernon WL. Spinal Cord Medicine, Third Edition. New York: Springer Publishing Company; 2018.

Middleton JW, Arora M, Kifley A, Clark J, Borg SJ, Tran Y, et al. Australian arm of the International spinal cord Injury (Aus-InSCI) Community Survey: 2. Understanding the lived experience in people with spinal cord injury. Spinal Cord. 2022;60(12):1069–79.

Article   PubMed   PubMed Central   Google Scholar  

Craig A, Nicholson Perry K, Guest R, Tran Y, Middleton J. Adjustment following chronic spinal cord injury: determining factors that contribute to social participation. Br J Health Psychol. 2015;20(4):807–23.

Article   PubMed   Google Scholar  

Middleton JW, Arora M, Jerram KAS, Bourke J, McCormick M, O’Leary D, et al. Co-design of the Spinal Cord Injury Health Maintenance Tool to support Self-Management: a mixed-methods Approach. Top Spinal Cord Injury Rehabilitation. 2024;30(1):59–73.

Article   Google Scholar  

Middleton JW, Arora M, McCormick M, O’Leary D. Health maintenance Tool: how to stay healthy and well with a spinal cord injury. A tool for consumers by consumers. 1st ed. Sydney, NSW Australia: Royal Rehab and The University of Sydney; 2020.

Google Scholar  

Nilsen P, Bernhardsson S. Context matters in implementation science: a scoping review of determinant frameworks that describe contextual determinants for implementation outcomes. BMC Health Serv Res. 2019;19(1):189.

Jagosh J. Realist synthesis for Public Health: building an Ontologically Deep understanding of how Programs Work, for whom, and in which contexts. Annu Rev Public Health. 2019;40(1):361–72.

Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci. 2015;10(1):53.

Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4(1):50.

Damschroder LJ, Reardon CM, Opra Widerquist MA, Lowery JC. Conceptualizing outcomes for use with the Consolidated Framework for Implementation Research (CFIR): the CFIR outcomes Addendum. Implement Sci. 2022;17(1):7.

Damschroder LJ, Reardon CM, Widerquist MAO, Lowery JC. The updated Consolidated Framework for Implementation Research based on user feedback. Implement Sci. 2022;17(1):75.

Plamondon K, Ndumbe-Eyoh S, Shahram S. 2.2 Equity, Power, and Transformative Research Coproduction. Research Co-Production in Healthcare2022. p. 34–53.

Verville L, Cancelliere C, Connell G, Lee J, Munce S, Mior S, et al. Exploring clinicians’ experiences and perceptions of end-user roles in knowledge development: a qualitative study. BMC Health Serv Res. 2021;21(1):926.

Gainforth HL, Hoekstra F, McKay R, McBride CB, Sweet SN, Martin Ginis KA, et al. Integrated Knowledge Translation Guiding principles for conducting and Disseminating Spinal Cord Injury Research in Partnership. Arch Phys Med Rehabil. 2021;102(4):656–63.

Langley J, Knowles SE, Ward V. Conducting a Research Coproduction Project. Research Co-Production in Healthcare2022. p. 112– 28.

Braun V, Clarke V. One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology. 2020:1–25.

Tong A, Sainsbury p, Craig J. Consolidated criteria for reporting qulaitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Bengtsson M. How to plan and perform a qualitative study using content analysis. NursingPlus Open. 2016;2:8–14.

Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Res Psychol. 2006;3(2):77–101.

Keith RE, Crosson JC, O’Malley AS, Cromp D, Taylor EF. Using the Consolidated Framework for Implementation Research (CFIR) to produce actionable findings: a rapid-cycle evaluation approach to improving implementation. Implement Science: IS. 2017;12(1):15.

Choukou M-A, Sanchez-Ramirez DC, Pol M, Uddin M, Monnin C, Syed-Abdul S. COVID-19 infodemic and digital health literacy in vulnerable populations: a scoping review. Digit HEALTH. 2022;8:20552076221076927.

PubMed   PubMed Central   Google Scholar  

Daniels N. Just Health: Meeting Health needs fairly. Cambridge University Press; 2007. p. 397.

Parker SM, Stocks N, Nutbeam D, Thomas L, Denney-Wilson E, Zwar N, et al. Preventing chronic disease in patients with low health literacy using eHealth and teamwork in primary healthcare: protocol for a cluster randomised controlled trial. BMJ Open. 2018;8(6):e023239–e.

Salter KL, Kothari A. Using realist evaluation to open the black box of knowledge translation: a state-of-the-art review. Implement Sci. 2014;9(1):115.

Sebele-Mpofu FY. The Sampling Conundrum in qualitative research: can Saturation help alleviate the controversy and alleged subjectivity in Sampling? Int’l J Soc Sci Stud. 2021;9:11.

Malterud K, Siersma VD, Guassora AD. Sample size in qualitative interview studies: guided by Information Power. Qual Health Res. 2015;26(13):1753–60.

Download references

Acknowledgements

Authors of this study would like to thank all the consumers with SCI and healthcare professionals for their invaluable contribution to this project. Their participation and insights have been instrumental in shaping the development of the SCI-HMT. The team also acknowledges the support and guidance provided by the members of the Project Steering Committee, as well as the partner organisations, including NSW Agency for Clinical Innovation, and icare NSW. Author would also like to acknowledge the informant group with lived experience, whose perspectives have enriched our understanding and informed the development of SCI-HMT.

The SCI Wellness project was a collaborative project between John Walsh Centre for Rehabilitation Research at The University of Sydney and Royal Rehab. Both organizations provided in-kind support to the project. Additionally, the University of Sydney and Royal Rehab received research funding from Insurance and Care NSW (icare NSW) to undertake the SCI Wellness Project. icare NSW do not take direct responsibility for any of the following: study design, data collection, drafting of the manuscript, or decision to publish.

Author information

Authors and affiliations.

John Walsh Centre for Rehabilitation Research, Northern Sydney Local Health District, St Leonards, NSW, Australia

John A Bourke, K. Anne Sinnott Jerram, Mohit Arora, Ashley Craig & James W Middleton

The Kolling Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia

Burwood Academy Trust, Burwood Hospital, Christchurch, New Zealand

John A Bourke

Royal Rehab, Ryde, NSW, Australia

James W Middleton

State Spinal Cord Injury Service, NSW Agency for Clinical Innovation, St Leonards, NSW, Australia

You can also search for this author in PubMed   Google Scholar

Contributions

Project conceptualization: KASJ, MA, JWM; project methodology: JWM, MA, KASJ, JAB; data collection: KASJ and MA; data analysis: KASJ, JAB, MA, JWM; writing—original draft preparation: JAB; writing—review and editing: JAB, KASJ, JWM, MA, AC; funding acquisition: JWM, MA. All authors contributed to the revision of the paper and approved the final submitted version.

Corresponding author

Correspondence to John A Bourke .

Ethics declarations

Ethics approval and consent to participate.

The first (RESP/18/212) and second phase (2019/ETH13961) of the project received ethical approval from The Northern Sydney Local Health District Human Research Ethics Committee. All participants provided informed, written consent. All data were to be retained for 7 years (23rd May 2030).

Consent for publication

Not applicable.

Competing interests

MA part salary (from Dec 2018 to Dec 2023), KASJ part salary (July 2021 to Dec 2023) and JAB part salary (Jan 2022 to Aug 2022) was paid from the grant monies. Other authors declare no conflicts of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Bourke, J.A., Jerram, K.A.S., Arora, M. et al. Using the consolidated Framework for Implementation Research to integrate innovation recipients’ perspectives into the implementation of a digital version of the spinal cord injury health maintenance tool: a qualitative analysis. BMC Health Serv Res 24 , 390 (2024). https://doi.org/10.1186/s12913-024-10847-x

Download citation

Received : 14 August 2023

Accepted : 11 March 2024

Published : 28 March 2024

DOI : https://doi.org/10.1186/s12913-024-10847-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Spinal Cord injury
• Self-management
• Innovation recipients
• Secondary health conditions
• Primary health care
• Evidence-based innovations
• Actionable findings
• Consolidated Framework for implementation research

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

We built an AI tool to help set priorities for conservation in Madagascar: What we found

A rtificial Intelligence (AI)—models that process large and diverse datasets and make predictions from them—can have many uses in nature conservation, such as remote monitoring (like the use of camera traps to study animals or plants) or data analysis. Some of these are controversial because AI can be trained to be biased, but others are valuable research tools.

Biologist Daniele Silvestro has developed an AI tool that can help identify conservation and restoration priorities. We asked him to tell us more about how it works and what it offers.

How does your artificial intelligence tool for conservation work?

Artificial intelligence (AI) is a term indicating a broad family of models used to process large and diverse datasets and make predictions from them.

We built a model using biodiversity datasets as well as socioeconomic data. The aim was to identify optimal strategies to conserve nature. Our AI tool, Conservation Area Prioritisation through Artificial Intelligence (Captain), uses a type of AI called reinforcement learning . This is a family of algorithms that optimizes decisions within a dynamic environment.

The tool we built was the result of years of work involving an international team with experience in biology, sustainable economics, math and computer science.

The software we developed can take multiple types of data as input, including biodiversity maps, species ranges, climate and predicted climate change, as well as socioeconomic data such as cost of land and a budget available for conservation action. It then processes this information and, based on a set conservation target (for example, to include all endangered species in a protected area, or to protect as many species as possible) it suggests a conservation policy.

The tool's environment is a simulation of biodiversity, an artificial world with species and individuals that reproduce, migrate and die through time. We use the tool to look for the most appropriate conservation policy.

It works similarly to a video game where the player (called the agent) is the "brains" of our software. The goal of the game is to protect biodiversity and prevent as many species as possible from going extinct within a simulated environment that includes human pressure and climate change.

The agent observes the environment and tries to place protected areas in this environment in the best way. At the end of the game the agent gets a reward for each species it manages to save from extinction. It will have to play the game many times to learn how to best interpret the environment and place the protected areas. After that, the model is trained and can be used with real biodiversity data to identify conservation priorities that should maximize biodiversity protection.

Why did you test the tool in Madagascar? What did you find?

The State of the World's Plants and Fungi report showed that biodiversity is facing unprecedented threats, with as many as 45% of all plant species at risk of extinction. Together with climate change, this is one of the major challenges humanity faces, given our dependence on the natural world for our survival.

In a recent paper we summarized the extent of Madagascar's extraordinary concentration of biodiversity with thousands of species of plants, animals and fungi. The project was led by Hélène Ralimanana of the Royal Botanic Gardens, Kew and Kew Madagascar Conservation Centre.

By applying the Captain tool to a dataset of endemic trees of Madagascar we were able to identify the most important areas for biodiversity protection in the country, for instance the area in the Sava region, where the Marojejy National Park has long been established.

Madagascar already has number of conservation areas and programs. What our experiment shows is that the technology we developed can be used with real-world data. We hope it can guide conservation planning.

Who do you think can use the Captain AI?

We think it can help policy makers, practitioners and companies in guiding conservation and restoration planning. In particular, the software can use diverse types of data in addition to biodiversity data. For instance it can use costs and opportunity costs related to setting up protected or restoration areas. It can also use future climate scenarios.

Is technology alone enough to conserve biodiversity?

Certainly not. Technology can help us by crunching the numbers and disentangling complex data. But there are many aspects of conservation that are not easily quantifiable as numbers. There are aspects of cultural value of land and nature, and social and political issues related to the fair distribution of resources. These are issues for real humans to take into account, rather than artificial intelligence programs.

Technology and science can (and should) assist us in making decisions, but ultimately the protection and conservation of the natural world is and must be in the hands of humans, not software.

This article is republished from The Conversation under a Creative Commons license. Read the original article .

Provided by The Conversation