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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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5 Presentation Tips for Data Analysts – Data Storytelling

When you’re working in analytics, telling a story can be as important as the actual data behind it. Your analytical insights are only as good as the actions that you can take from them, and persuasive presentations are at the core of convincing business people to act. I realize that sounds cliche, but it’s true. Without effective presentation skills, an analyst’s job is incomplete.

At MITRE and at Sestra, I’ve worked with some great engineers, but they have a tendency to put WAY too much information on their slides and to get lost behind the language there. It’s always surprising to me to discover how little education many people receive around effective presentations. I feel lucky that my grad school program focused on these skills, and I draw from some of it’s lessons below.

The most prominent example of this lack of presentation education I experienced was at MITRE when one of the smartest folks on my team presented results of work that he had done. He’s a terrific engineer and was doing brilliant work. However, the presentation was one of the dullest that I’ve ever been in attendance for. Every slide had paragraphs of text, and he mostly read them to us. For 45 minutes. I was even on the more technical side of the audience, and I couldn’t understand most of the terminology. It was a disaster.

To avoid a situation like that, here are 5 quick things to remember when you’re compiling your next presentation.

1) WIIFTA – What’s in it for the audience? – Remember that you’re not giving the presentation for you. It’s for the people who are listening. A typical example of a WIIFTA consideration could be a detailed methodology slide. This slide is important to you – it’s a clever new twist on an analytics technique and without you, your company would never have thought of it. But it’s not about you.

Consider your audience: In a context where you are presenting your findings to a room full of data scientists looking to learn the latest language or technique, this slide would be absolutely appropriate. It’s a clever new way to look at something and this group wants to learn about it. However, in a context where you’re presenting the conclusions of your research to your company’s executives, it’s likely that this slide should minimized or removed completely, because this group doesn’t care. They likely trust that you’ve found a good solution, however you got there, and they just want to know why they should make a certain decision.

Oy, the following slide has some other issues (which we’ll revisit later), but if your audience demands a methodology slide, fine, include one. But if you include this slide when presenting to your boss, you’ve probably already lost her (and this is only slide 3!).

2) BLUF – Bottom Line Up Front. – For most young analysts, this is the single biggest change to your presenting skill set that you can make. Oftentimes, an analyst wants to lay out a bunch of evidence and allow it to walk the audience to a conclusion. This is how your research worked, and it feels like the right way to structure your findings and to tell the story, too. It is not. In a business context, the audience wants to know exactly what they are listening for immediately. This goes for a presentation as a whole, along with individual sections or slides.

Check out this slide from kissmetrics. Even at 10% size, you know exactly what the slide is about. Well done. Most analysts writing this slide would have made the header “Equipment Choices” or some equivalent, but it is so much nicer to know exactly what conclusion you are supposed to draw right away.

3) Seriously. Less Text. If someone is reading your slides they’re not paying attention to you, the presenter. Minimize the amount of reading that they have to do while not listening to you. I recommend trying to keep slides to 1 statement or phrase or conclusion per slide.

Let’s look at that methodology slide again. If you have to include it, because your audience is interested, could you make it less of a giant block of text? Try to reduce this slide to a few key ideas:

4) Make a separate version. If your slides need to stand on their own, that’s understandable – sometimes not everyone can make the live presentation. However, the deck that you use that can stand on its own shouldn’t be the same version that you use when you’re presenting with it. By definition if it can stand on its own, you don’t need to be there and you’ve just made yourself obsolete. Yes, I know. Maintaining two separate versions of the same presentation can be a hassle, but it’s worth the trouble.

The following slides are from a presenstation that I gave recently. Forgive the bad BLUF in the second slide, but it shows how I maintained a different variation for people who missed the in-person presentation.

5) Make use of highlights and callouts to draw the eye. Sometimes you need to show a larger about of information on a slide in order to give context. That’s acceptable, at times, but make sure you’ve simplified it as much as possible. Once you’ve done that, use highlights, or bold, or boxes to draw attention to the important takeaway from the slide.

Look again at the slide from my presentation above. While analytics can be important across the company, this particular presentation was focused on Product Analytics. I wanted to call out the fact that going forward in the presentation, we were focused on that area.

If you keep these 5 things in mind, it’ll help keep your audience engaged with you as you take them through your data story. If you forget, you risk losing them before they get the whole story, and you can’t get your point across.

For some more detailed reading, Avinash Kaushik’s Storytelling with Data article is also a terrific read!

Published by Stephen

I love helping people at all levels of a company use data to inform their decision making. View all posts by Stephen

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Content of this Powerpoint Presentation

Data may come across as a technical term to us but the truth is we analyze and process data in our everyday lives. From calculating the right amount of ingredients for a cup of coffee to giving ETAs of your assigned tasks, data analytics is part and parcel of our lives. Organizations employ data analytics tools to anticipate and achieve success.Identifying the right sources of data is a primary requirement for delivering accurate results and should be conveyed to teams handling these channels. For this, you need Data Analytics PowerPoint Presentation Slides to highlight the key sources of data procurement so that the relevant team will know whom to approach. 

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10 Superb Data Presentation Examples To Learn From

The best way to learn how to present data effectively is to see data presentation examples from the professionals in the field.

We collected superb examples of graphical presentation and visualization of data in statistics, research, sales, marketing, business management, and other areas.

On this page:

How to present data effectively? Clever tips.

• 10 Real-life examples of data presentation with interpretation.

Download the above infographic in PDF

Your audience should be able to walk through the graphs and visualizations easily while enjoy and respond to the story.

[bctt tweet=”Your reports and graphical presentations should not just deliver statistics, numbers, and data. Instead, they must tell a story, illustrate a situation, provide proofs, win arguments, and even change minds.” username=””]

Before going to data presentation examples let’s see some essential tips to help you build powerful data presentations.

1. Keep it simple and clear

The presentation should be focused on your key message and you need to illustrate it very briefly.

Graphs and charts should communicate your core message, not distract from it. A complicated and overloaded chart can distract and confuse. Eliminate anything repetitive or decorative.

2. Pick up the right visuals for the job

A vast number of types of graphs and charts are available at your disposal – pie charts, line and bar graphs, scatter plot , Venn diagram , etc.

Choosing the right type of chart can be a tricky business. Practically, the choice depends on 2 major things: on the kind of analysis you want to present and on the data types you have.

Commonly, when we aim to facilitate a comparison, we use a bar chart or radar chart. When we want to show trends over time, we use a line chart or an area chart and etc.

3. Break the complex concepts into multiple graphics

It’s can be very hard for a public to understand a complicated graphical visualization. Don’t present it as a huge amount of visual data.

Instead, break the graphics into pieces and illustrate how each piece corresponds to the previous one.

4. Carefully choose the colors

Colors provoke different emotions and associations that affect the way your brand or story is perceived. Sometimes color choices can make or break your visuals.

It is no need to be a designer to make the right color selections. Some golden rules are to stick to 3 or 4 colors avoiding full-on rainbow look and to borrow ideas from relevant chart designs.

Another tip is to consider the brand attributes and your audience profile. You will see appropriate color use in the below data presentation examples.

5. Don’t leave a lot of room for words

The key point in graphical data presentation is to tell the story using visuals and images, not words. Give your audience visual facts, not text.

However, that doesn’t mean words have no importance.

A great advice here is to think that every letter is critical, and there’s no room for wasted and empty words. Also, don’t create generic titles and headlines, build them around the core message.

6. Use good templates and software tools

Building data presentation with AI nowadays means using some kind of software programs and templates. There are many available options – from free graphing software solutions to advanced data visualization tools.

Choosing a good software gives you the power to create good and high-quality visualizations. Make sure you are using templates that provides characteristics like colors, fonts, and chart styles.

A small investment of time to research the software options prevents a large loss of productivity and efficiency at the end.

10 Superb data presentation examples 

Here we collected some of the best examples of data presentation made by one of the biggest names in the graphical data visualization software and information research.

These brands put a lot of money and efforts to investigate how professional graphs and charts should look.

1. Sales Stage History  Funnel Chart 

Data is beautiful and this sales stage funnel chart by Zoho Reports prove this. The above funnel chart represents the different stages in a sales process (Qualification, Need Analysis, Initial Offer, etc.) and shows the potential revenue for each stage for the last and this quarter.

The potential revenue for each sales stage is displayed by a different color and sized according to the amount. The chart is very colorful, eye-catching, and intriguing.

2. Facebook Ads Data Presentation Examples

These are other data presentation examples from Zoho Reports. The first one is a stacked bar chart that displays the impressions breakdown by months and types of Facebook campaigns.

Impressions are one of the vital KPI examples in digital marketing intelligence and business. The first graph is designed to help you compare and notice sharp differences at the Facebook campaigns that have the most influence on impression movements.

The second one is an area chart that shows the changes in the costs for the same Facebook campaigns over the months.

The 2 examples illustrate how multiple and complicated data can be presented clearly and simply in a visually appealing way.

3. Sales Opportunity Data Presentation

These two bar charts (stacked and horizontal bar charts) by Microsoft Power Bi are created to track sales opportunities and revenue by region and sales stage.

The stacked bar graph shows the revenue probability in percentage determined by the current sales stage (Lead, Quality, Solution…) over the months. The horizontal bar chart represents the size of the sales opportunity (Small, Medium, Large) according to regions (East, Central, West).

Both graphs are impressive ways for a sales manager to introduce the upcoming opportunity to C-level managers and stakeholders. The color combination is rich but easy to digest.

4. Power 100 Data Visualization 

Want to show hierarchical data? Treemaps can be perfect for the job. This is a stunning treemap example by Infogram.com that shows you who are the most influential industries. As you see the Government is on the top.

This treemap is a very compact and space-efficient visualization option for presenting hierarchies, that gives you a quick overview of the structure of the most powerful industries.

So beautiful way to compare the proportions between things via their area size.

When it comes to best research data presentation examples in statistics, Nielsen information company is an undoubted leader. The above professional looking line graph by Nielsen represent the slowing alcoholic grow of 4 alcohol categories (Beer, Wine, Spirits, CPG) for the period of 12 months.

The chart is an ideal example of a data visualization that incorporates all the necessary elements of an effective and engaging graph. It uses color to let you easily differentiate trends and allows you to get a global sense of the data. Additionally, it is incredibly simple to understand.

6. Digital Health Research Data Visualization Example

Digital health is a very hot topic nowadays and this stunning donut chart by IQVIA shows the proportion of different mobile health apps by therapy area (Mental Health, Diabetes, Kidney Disease, and etc.). 100% = 1749 unique apps.

This is a wonderful example of research data presentation that provides evidence of Digital Health’s accelerating innovation and app expansion.

Besides good-looking, this donut chart is very space-efficient because the blank space inside it is used to display information too.

7. Disease Research Data Visualization Examples

Presenting relationships among different variables is hard to understand and confusing -especially when there is a huge number of them. But using the appropriate visuals and colors, the IQVIA did a great job simplifying this data into a clear and digestible format.

The above stacked bar charts by IQVIA represents the distribution of oncology medicine spendings by years and product segments (Protected Brand Price, Protected Brand Volume, New Brands, etc.).

The chart allows you to clearly see the changes in spendings and where they occurred – a great example of telling a deeper story in a simple way.

8. Textual and Qualitative Data Presentation Example

When it comes to easy to understand and good looking textual and qualitative data visualization, pyramid graph has a top place. To know what is qualitative data see our post quantitative vs qualitative data .

9. Product Metrics Graph Example

If you are searching for excel data presentation examples, this stylish template from Smartsheet can give you good ideas for professional looking design.

The above stacked bar chart represents product revenue breakdown by months and product items. It reveals patterns and trends over the first half of the year that can be a good basis for data-driven decision-making .

10. Supply Chain Data Visualization Example 

This bar chart created by ClicData  is an excellent example of how trends over time can be effectively and professionally communicated through the use of well-presented visualization.

It shows the dynamics of pricing through the months based on units sold, units shipped, and current inventory. This type of graph pack a whole lot of information into a simple visual. In addition, the chart is connected to real data and is fully interactive.

The above data presentation examples aim to help you learn how to present data effectively and professionally.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Data Analyst Roadmap – A Complete Guide

Dreaming of a career where you unlock the secrets hidden within data and drive informed business decisions? Becoming a data analyst could be your perfect path! This comprehensive roadmap unveils everything you need to know about navigating this exciting field. Whether you’re a complete beginner or looking to transition from another role, we’ll guide you through the essential skills, educational paths, and tools you’ll need to master to become a data analyst. Explore practical project ideas, conquer job search strategies, and discover the salary potential that awaits a skilled data analyst . Dive in and prepare to transform your future – your data-driven journey starts here!

Data analyst demand is skyrocketing! This booming field offers amazing salaries, and growth, and welcomes diverse backgrounds. Ready to join the hottest IT trend? Our step-by-step roadmap equips you with the essentials to launch your data analyst career fast . Don’t wait – land your dream job today!

Did you know Bengaluru ranks among the top 3 global data analyst hubs? Have a look at the list:

• San Jose, California
• Bengaluru, India
• Geneva, Switzerland

Get ready to unlock exciting opportunities! Buckle up and let’s connect the dots to your data analyst future.

Join our “ Complete Machine Learning & Data Science Program “ to master data analysis, machine learning algorithms, and real-world projects. Get expert guidance and kickstart your career in data science today!

What is Data Analysis?

The process of collecting and processing the raw data and preparing the required statistics are what data analysts do . Their job responsibilities rotate around this and it’s likely less typical than that of a Data Scientist . However, they play a very major role in taking business decisions (that being taken based on their extracted data and stats) and identifying the pain points of customers that eventually helps businesses in changing their approach for better growth. 

Who is a Data Analyst?

A data analyst is a professional who collects, cleans, analyzes, and communicates insights from data. They work in various industries, helping organizations make informed decisions based on evidence. Here’s a breakdown of their responsibilities:

1. Data Gathering and Cleaning:

• Data analysts collect information from various sources like surveys, website records, financial data, or scientific experiments.
• They then meticulously clean and organize the data, ensuring its accuracy and completeness before further analysis.

2. Data Analysis and Pattern Identification:

• Using statistical methods, programming languages, and data visualization tools, analysts explore and analyze the data.
• They search for patterns, trends, and anomalies, revealing hidden connections and insights within the information.

3. Insight Communication and Reporting:

• A crucial skill for data analysts is translating complex findings into clear, concise, and actionable insights that stakeholders can understand and use.
• This often involves creating reports, presentations, and dashboards that effectively communicate the data’s story.

4. Problem-Solving and Performance Optimization:

• Data analysts go beyond interpreting data; they use it to solve problems and improve processes in various contexts.
• This might involve analyzing customer behavior to optimize marketing campaigns, identifying fraudulent transactions in financial systems, or predicting equipment failures for better maintenance schedules.

What Does a Data Analyst Do?

There might be some questions in your mind like How to become a data Analyst or Is it hard to be a data analyst? Well, to answer all of these, you need to first understand step-by-step before entering this domain. To better clarity, let’s find out the responsibilities in a data analysis job during their day to day schedule.

Responsibilities of Data Analyst

Well, that depends upon the type of organization that you’ll be working on because nowadays every industry is looking out for such professionals despite their size (small-medium-large). Although, some of the key responsibility includes:

• To develop and analyze the report
• To manage master data right (create -> update -> delete)
• To support the data warehousing in inspecting the reporting requirements. 
• To troubleshoot the reporting DB environment and reports.
• Coordinating with developers, and engineers to gather insight for improvement and making modifications for data governance.
• Use of statistical tools to interpret data sets, and to follow any ongoing trend that could be valuable.

Why Data Analyst?

Being a Data Analyst you will be working on real-life problem-solving scenarios and with this fast-paced, evolving technology, the demand for Data Analysts has grown enormously. Moving with this pace of advancement, the competition is growing every day and companies require new methods to compete for their existence and that’s what Data Analysts do. Let’s understand the Data Analysts job in 4 simple ways:

• Being a Data Analyst, you’ll be working closely with the raw data and will generate valuable insights that will help companies to decide their future goals.
• If you’re someone who likes thinking out of the box then you are the perfect fit for this domain. Data Analysts help organizations to work with both business and data closely. This eventually maximizes the output for generating more business values.
• Nevertheless, this field gives you a handsome salary for all levels of expertise. Being a Data Analyst you can earn more than $80k per annum and around 4LPA in India (for starting level).
• According to multiple reports, the demand for Data Analysts job are high VS the supply to the market is comparatively less and that’s one of the reasons why people are shifting their career to Data Science. Till now, there are more than 28,000 job postings available in India and 414,000+ jobs are available worldwide. 

Types of Data Analysts

There are many different types of data analysts, each specializing in a specific area or industry. Here are some of the most common types :-

• Business Intelligence Analysts: Analyze business data for insights, informed decisions, and performance improvement.
• Financial Analysts: Focus on financial data for budgeting, investments, and market trends analysis.
• Healthcare Data Analysts: Work with healthcare data for patient outcomes, operational optimization, and medical research.
• Marketing Analysts: Analyze marketing data for campaign effectiveness, consumer behavior, and market trends.
• Operations Analysts: Optimize processes by analyzing operational data, enhancing efficiency, and reducing costs.
• Sports Analysts: Analyze sports data for performance evaluation, strategy improvement, and player/team assessment.
• Crime Analysts: Analyze crime data for pattern identification, assisting law enforcement in prevention and solving.
• Environmental Data Analysts: Analyze environmental data for ecological trends, climate patterns, and human impact assessment.
• Social Media Analysts: Analyze social media data for user behavior understanding and insights for marketing strategies.
• Economic Analysts: Study economic data for trend understanding, economic condition forecasting, and policy insights.

Can Anyone Become a Data Analyst Without Any Experience?

First, understand this, the field of Data Analyst is not about computer science but about applying computational, analysis, and statistics. This field focuses on working with large datasets and the production of useful insights that helps in solving real-life problems. The whole process starts with a hypothesis that needs to be answered and then involvement in gathering new data to test those hypotheses. There are 2 major categories of Data Analysts: Tech and Non-Tech. Both of them work on different tools and Tech domain professionals are required to possess knowledge of required programming languages too (such as R or Python). 

The working professional should be fluent in statistics so that they can present any given amount of raw data in a well-aligned structure.  

So, the answer to that is YES , anyone can become a Data Analyst if they like working on a real-life problem, are good with statistics, and always thinks out of the box. So, now let’s see how to build a successful data analyst career.

Average salary of a Data Analyst in India

Freshers (Less than 1 Year):

• Average Salary:  ₹3.25 Lakhs per annum (LPA)
• Range:  ₹2.5 LPA – ₹4 LPA
• Focus:  Building foundational skills in data analysis tools, SQL, and basic statistics.

Early Career (1-4 Years):

• Average Salary:  ₹4.94 LPA
• Range:  ₹4 LPA – ₹6 LPA
• Focus:  Refining analytical skills, exploring specific data analysis techniques, and gaining work experience on real-world projects.

Mid-Career (5-9 Years):

• Average Salary:  ₹7.75 LPA
• Range:  ₹6 LPA – ₹10 LPA
• Focus:  Specializing in specific areas like Machine Learning, Big Data, or industry-specific analysis. Taking on leadership roles and managing projects.

Late Career (10-19 Years):

• Average Salary:  ₹10.63 LPA
• Range:  ₹8 LPA – ₹15 LPA
• Focus:  Leading complex data analysis projects, mentoring junior analysts, and providing strategic insights to stakeholders.

Experienced (20+ Years):

• Average Salary:  ₹15 Lakhs and above
• Focus:  Senior leadership roles, managing large teams, and driving data-driven initiatives across the organization. Expertise in cutting-edge data analysis technologies.

How to Become a Data Analyst : Roadmap – Skills Required 

To become a data analyst , it’s essential to develop a strong foundation in mathematics and statistics . These skills form the backbone of data analysis , allowing you to understand and interpret complex datasets. Additionally, proficiency in programming languages like Python , R , or SQL is crucial for manipulating data and performing statistical analysis. Practical experience through real-world projects and certifications can further enhance your skills and make you more competitive in the job market. Continuous learning and staying updated with the latest trends and technologies in data analysis are also key to success in this field.

1. Mathematics and Statistics

Mathematics

• Standard Deviation
• System of Linear Equation
• Solving Linear Equations using Gaussian Elimination
• Row Echelon Form
• Matrix Approximation
• Vector Operations
• Linear Mappings
• Linear Algebra
• Probability
• Mean, Standard Deviation, and Variance — Implementation
• Descriptive and Inferential Statistics
• Probability Theory and Distribution
• Sampling Distribution
• Linear Regression
• Sample Error and True Error
• Bias Vs Variance and Its Trade-Off
• Hypothesis Testing
• Confidence Intervals
• Correlation and Covariance
• Correlation Coefficient
• Covariance Matrix
• Pearson Correlation
• Spearman’s Rank Correlation Measure
• Kendall Rank Correlation Measure
• Robust Correlations

For Tech Domain

Programming languages.

• R Programming

Learning Edge – Also refer to the below-mentioned articles to get the full insight: How to Learn Python in 21 Days? 30 Day of SQL – From Basic to Advanced Level

Required Skills for Data Analysts (Basic)

• Problem Solving
• Database Knowledge
• Data Collection
• Data Cleaning
• Data Visualization
• Communication Skill

To learn more about Database with Python, refer to this article: Python Database Tutorial

Required Tools for Data Analysis (Tech)

For non-tech domain.

• Domain Knowledge: Having general background knowledge of the field/environment in which you’ll be working is mandatory so that accurate methods and tools can be applied.
• Problem-Solving Skill: A data analyst must always be prepared to perform troubleshooting when any issue arises that’s why problem-solving skills become crucial while analyzing data.
• Computer Skills: At any point in time, any query can arise and to figure that out you must know to find out the solution so carrying basic skills related to computers is a ‘must to have’ skill.
• Dashboarding: To properly analyze any given set of data would require you to perform dashboarding. It helps in bringing all data together and displays all key metrics and insights.

Required Tools for Data Analysis (Non-Tech)

Best courses for data analysts.

To make a strong grip in this field will require you to have hands-on practice in some of the most crucial skills, which we’re mentioning below for the best reference:

Career Path of a Data Analyst

Data Analyst Career Path

• Senior Data Scientist 
• VP / Director
• Chief Data Officer / Chief Data Scientist
• Senior Business Analyst
• Analytics / Engagement Manager
• Head of Analytics
• Engagement Manager
• Portfolio Manager
• Group Finance Manager

Future Scope of Data Analyst

Today, Billions of companies are generating data on a daily basis and using it to make crucial business decisions. It helps in deciding their future goals and setting new milestones. We’re living in a world where Data is the new fuel and to make it useful data analysts are required in every sector. The more data – the more requirement and thus it is projected that the market share of data analysts are likely to grow by USD 650+ Billion at a CAGR of above 13% and that’s what makes it among the most sought-after profession in the world. So, the answer is YES, it’s an excellent choice to start your career towards becoming a successful Data Analyst.

In conclusion, data analysis offers a promising career path with high demand and attractive opportunities . Bengaluru ranks among the top cities for data analyst jobs, highlighting the global need for skilled professionals in this field. Data analysts play a crucial role in collecting, analyzing, and communicating insights that drive informed business decisions. Whether you’re just starting or looking to advance your career, mastering skills in mathematics , statistics , and data tools is essential. With the right skills and experience, you can embark on a rewarding journey as a data analyst, contributing to the ever-growing importance of data in shaping the future of businesses worldwide.

How To Become a Data Analyst?

Step guidance to Become a successful Data Analyst: Learn Programming Language first (Python, or R) Build basic skills (such as Problem-solving, DB, Data Wrangling, etc.) Start building a strong knowledge of basic tools (such as MS Excel, Tableau, etc.) Try getting hands-on practice on Real-life projects for better clarity Opt for a relevant certification/course which will add value to your portfolio Start applying for entry-level jobs to begin your journey

What does Data Analyst do?

Data Analysts are responsible for developing and managing reports to solve and project different data. Besides this, a data analyst should have the knowledge and skills to turn any provided raw data into insightful ones that can directly impact any taken business decisions.

What can I do after the 12th to become a data analyst?

If you’re looking to get into the field of Data Analyst, then you must possess a degree in Computer Science or any relevant degree in the field of Statistics, Mathematics, etc. However, it is also to be noted that the necessary candidate should possess some of the necessary skills that are required in becoming a Data Analyst, these are: Problem Solving Strong Communication Skill Knowledge of Database Data Wrangling (Collection, Cleaning & Visualization) MS Excel Tableau Power BI, etc.

What is required to become a data analyst?

To be a data analyst, master SQL, Python, and data visualization. Develop soft skills, explore education options, and build a portfolio for success in this dynamic field.

Is Python required for data analyst?

Python is crucial for data analysts, being the top programming language in demand, easy to learn, versatile, and powerful. Key tools like Jupyter Notebook and Pandas are Python-based, providing a competitive edge. Python skills also lead to higher salaries, with an average of $103,059 compared to $88,244 for those without.

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Understanding data analysis: A beginner's guide

Before data can be used to tell a story, it must go through a process that makes it usable. Explore the role of data analysis in decision-making.

What is data analysis?

Data analysis is the process of gathering, cleaning, and modeling data to reveal meaningful insights. This data is then crafted into reports that support the strategic decision-making process.

Types of data analysis

There are many different types of data analysis. Each type can be used to answer a different question.

Descriptive analytics

Descriptive analytics refers to the process of analyzing historical data to understand trends and patterns. For example, success or failure to achieve key performance indicators like return on investment.

An example of descriptive analytics is generating reports to provide an overview of an organization's sales and financial data, offering valuable insights into past activities and outcomes.

Predictive analytics

Predictive analytics uses historical data to help predict what might happen in the future, such as identifying past trends in data to determine if they’re likely to recur.

Methods include a range of statistical and machine learning techniques, including neural networks, decision trees, and regression analysis.

Diagnostic analytics

Diagnostic analytics helps answer questions about what caused certain events by looking at performance indicators. Diagnostic analytics techniques supplement basic descriptive analysis.

Generally, diagnostic analytics involves spotting anomalies in data (like an unexpected shift in a metric), gathering data related to these anomalies, and using statistical techniques to identify potential explanations.

Cognitive analytics

Cognitive analytics is a sophisticated form of data analysis that goes beyond traditional methods. This method uses machine learning and natural language processing to understand, reason, and learn from data in a way that resembles human thought processes.

The goal of cognitive analytics is to simulate human-like thinking to provide deeper insights, recognize patterns, and make predictions.

Prescriptive analytics

Prescriptive analytics helps answer questions about what needs to happen next to achieve a certain goal or target. By using insights from prescriptive analytics, organizations can make data-driven decisions in the face of uncertainty.

Data analysts performing prescriptive analysis often rely on machine learning to find patterns in large semantic models and estimate the likelihood of various outcomes.

analyticsText analytics

Text analytics is a way to teach computers to understand human language. It involves using algorithms and other techniques to extract information from large amounts of text data, such as social media posts or customer previews.

Text analytics helps data analysts make sense of what people are saying, find patterns, and gain insights that can be used to make better decisions in fields like business, marketing, and research.

The data analysis process

Compiling and interpreting data so it can be used in decision making is a detailed process and requires a systematic approach. Here are the steps that data analysts follow:

1. Define your objectives.

Clearly define the purpose of your analysis. What specific question are you trying to answer? What problem do you want to solve? Identify your core objectives. This will guide the entire process.

2. Collect and consolidate your data.

Gather your data from all relevant sources using  data analysis software . Ensure that the data is representative and actually covers the variables you want to analyze.

3. Select your analytical methods.

Investigate the various data analysis methods and select the technique that best aligns with your objectives. Many free data analysis software solutions offer built-in algorithms and methods to facilitate this selection process.

4. Clean your data.

Scrutinize your data for errors, missing values, or inconsistencies using the cleansing features already built into your data analysis software. Cleaning the data ensures accuracy and reliability in your analysis and is an important part of data analytics.

5. Uncover valuable insights.

Delve into your data to uncover patterns, trends, and relationships. Use statistical methods, machine learning algorithms, or other analytical techniques that are aligned with your goals. This step transforms raw data into valuable insights.

6. Interpret and visualize the results.

Examine the results of your analyses to understand their implications. Connect these findings with your initial objectives. Then, leverage the visualization tools within free data analysis software to present your insights in a more digestible format.

7. Make an informed decision.

Use the insights gained from your analysis to inform your next steps. Think about how these findings can be utilized to enhance processes, optimize strategies, or improve overall performance.

By following these steps, analysts can systematically approach large sets of data, breaking down the complexities and ensuring the results are actionable for decision makers.

The importance of data analysis

Data analysis is critical because it helps business decision makers make sense of the information they collect in our increasingly data-driven world. Imagine you have a massive pile of puzzle pieces (data), and you want to see the bigger picture (insights). Data analysis is like putting those puzzle pieces together—turning that data into knowledge—to reveal what’s important.

Whether you’re a business decision maker trying to make sense of customer preferences or a scientist studying trends, data analysis is an important tool that helps us understand the world and make informed choices.

Primary data analysis methods

Quantitative analysis

Quantitative analysis deals with numbers and measurements (for example, looking at survey results captured through ratings). When performing quantitative analysis, you’ll use mathematical and statistical methods exclusively and answer questions like ‘how much’ or ‘how many.’ 

Qualitative analysis

Qualitative analysis is about understanding the subjective meaning behind non-numerical data. For example, analyzing interview responses or looking at pictures to understand emotions. Qualitative analysis looks for patterns, themes, or insights, and is mainly concerned with depth and detail.

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