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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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Data presentation: A comprehensive guide

Learn how to create data presentation effectively and communicate your insights in a way that is clear, concise, and engaging.

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

Whether you're an experienced presenter or just starting, this guide will help you present your data like a pro.

We'll dive deep into what data presentation is, why it's crucial, and how to master it. So, let's embark on this data-driven journey together.

What is data presentation?

Data presentation is the art of transforming raw data into a visual format that's easy to understand and interpret. It's like turning numbers and statistics into a captivating story that your audience can quickly grasp. When done right, data presentation can be a game-changer, enabling you to convey complex information effectively.

Why are data presentations important?

Imagine drowning in a sea of numbers and figures. That's how your audience might feel without proper data presentation. Here's why it's essential:

• Clarity : Data presentations make complex information clear and concise.
• Engagement : Visuals, such as charts and graphs, grab your audience's attention.
• Comprehension : Visual data is easier to understand than long, numerical reports.
• Decision-making : Well-presented data aids informed decision-making.
• Impact : It leaves a lasting impression on your audience.

Types of data presentation

Now, let's delve into the diverse array of data presentation methods, each with its own unique strengths and applications. We have three primary types of data presentation, and within these categories, numerous specific visualization techniques can be employed to effectively convey your data.

1. Textual presentation

Textual presentation harnesses the power of words and sentences to elucidate and contextualize your data. This method is commonly used to provide a narrative framework for the data, offering explanations, insights, and the broader implications of your findings. It serves as a foundation for a deeper understanding of the data's significance.

2. Tabular presentation

Tabular presentation employs tables to arrange and structure your data systematically. These tables are invaluable for comparing various data groups or illustrating how data evolves over time. They present information in a neat and organized format, facilitating straightforward comparisons and reference points.

3. Graphical presentation

Graphical presentation harnesses the visual impact of charts and graphs to breathe life into your data. Charts and graphs are powerful tools for spotlighting trends, patterns, and relationships hidden within the data. Let's explore some common graphical presentation methods:

• Bar charts: They are ideal for comparing different categories of data. In this method, each category is represented by a distinct bar, and the height of the bar corresponds to the value it represents. Bar charts provide a clear and intuitive way to discern differences between categories.
• Pie charts: It excel at illustrating the relative proportions of different data categories. Each category is depicted as a slice of the pie, with the size of each slice corresponding to the percentage of the total value it represents. Pie charts are particularly effective for showcasing the distribution of data.
• Line graphs: They are the go-to choice when showcasing how data evolves over time. Each point on the line represents a specific value at a particular time period. This method enables viewers to track trends and fluctuations effortlessly, making it perfect for visualizing data with temporal dimensions.
• Scatter plots: They are the tool of choice when exploring the relationship between two variables. In this method, each point on the plot represents a pair of values for the two variables in question. Scatter plots help identify correlations, outliers, and patterns within data pairs.

The selection of the most suitable data presentation method hinges on the specific dataset and the presentation's objectives. For instance, when comparing sales figures of different products, a bar chart shines in its simplicity and clarity. On the other hand, if your aim is to display how a product's sales have changed over time, a line graph provides the ideal visual narrative.

Additionally, it's crucial to factor in your audience's level of familiarity with data presentations. For a technical audience, more intricate visualization methods may be appropriate. However, when presenting to a general audience, opting for straightforward and easily understandable visuals is often the wisest choice.

In the world of data presentation, choosing the right method is akin to selecting the perfect brush for a masterpiece. Each tool has its place, and understanding when and how to use them is key to crafting compelling and insightful presentations. So, consider your data carefully, align your purpose, and paint a vivid picture that resonates with your audience.

What to include in data presentation

When creating your data presentation, remember these key components:

• Data points : Clearly state the data points you're presenting.
• Comparison : Highlight comparisons and trends in your data.
• Graphical methods : Choose the right chart or graph for your data.
• Infographics : Use visuals like infographics to make information more digestible.
• Numerical values : Include numerical values to support your visuals.
• Qualitative information : Explain the significance of the data.
• Source citation : Always cite your data sources.

How to structure an effective data presentation

Creating a well-structured data presentation is not just important; it's the backbone of a successful presentation. Here's a step-by-step guide to help you craft a compelling and organized presentation that captivates your audience:

1. Know your audience

Understanding your audience is paramount. Consider their needs, interests, and existing knowledge about your topic. Tailor your presentation to their level of understanding, ensuring that it resonates with them on a personal level. Relevance is the key.

2. Have a clear message

Every effective data presentation should convey a clear and concise message. Determine what you want your audience to learn or take away from your presentation, and make sure your message is the guiding light throughout your presentation. Ensure that all your data points align with and support this central message.

3. Tell a compelling story

Human beings are naturally wired to remember stories. Incorporate storytelling techniques into your presentation to make your data more relatable and memorable. Your data can be the backbone of a captivating narrative, whether it's about a trend, a problem, or a solution. Take your audience on a journey through your data.

4. Leverage visuals

Visuals are a powerful tool in data presentation. They make complex information accessible and engaging. Utilize charts, graphs, and images to illustrate your points and enhance the visual appeal of your presentation. Visuals should not just be an accessory; they should be an integral part of your storytelling.

5. Be clear and concise

Avoid jargon or technical language that your audience may not comprehend. Use plain language and explain your data points clearly. Remember, clarity is king. Each piece of information should be easy for your audience to digest.

6. Practice your delivery

Practice makes perfect. Rehearse your presentation multiple times before the actual delivery. This will help you deliver it smoothly and confidently, reducing the chances of stumbling over your words or losing track of your message.

A basic structure for an effective data presentation

Armed with a comprehensive comprehension of how to construct a compelling data presentation, you can now utilize this fundamental template for guidance:

In the introduction, initiate your presentation by introducing both yourself and the topic at hand. Clearly articulate your main message or the fundamental concept you intend to communicate.

Moving on to the body of your presentation, organize your data in a coherent and easily understandable sequence. Employ visuals generously to elucidate your points and weave a narrative that enhances the overall story. Ensure that the arrangement of your data aligns with and reinforces your central message.

As you approach the conclusion, succinctly recapitulate your key points and emphasize your core message once more. Conclude by leaving your audience with a distinct and memorable takeaway, ensuring that your presentation has a lasting impact.

Additional tips for enhancing your data presentation

To take your data presentation to the next level, consider these additional tips:

• Consistent design : Maintain a uniform design throughout your presentation. This not only enhances visual appeal but also aids in seamless comprehension.
• High-quality visuals : Ensure that your visuals are of high quality, easy to read, and directly relevant to your topic.
• Concise text : Avoid overwhelming your slides with excessive text. Focus on the most critical points, using visuals to support and elaborate.
• Anticipate questions : Think ahead about the questions your audience might pose. Be prepared with well-thought-out answers to foster productive discussions.

By following these guidelines, you can structure an effective data presentation that not only informs but also engages and inspires your audience. Remember, a well-structured presentation is the bridge that connects your data to your audience's understanding and appreciation.

Do’s and don'ts on a data presentation

• Use visuals : Incorporate charts and graphs to enhance understanding.
• Keep it simple : Avoid clutter and complexity.
• Highlight key points : Emphasize crucial data.
• Engage the audience : Encourage questions and discussions.
• Practice : Rehearse your presentation.

Don'ts:

• Overload with data : Less is often more; don't overwhelm your audience.
• Fit Unrelated data : Stay on topic; don't include irrelevant information.
• Neglect the audience : Ensure your presentation suits your audience's level of expertise.
• Read word-for-word : Avoid reading directly from slides.
• Lose focus : Stick to your presentation's purpose.

Summarizing key takeaways

• Definition : Data presentation is the art of visualizing complex data for better understanding.
• Importance : Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact.
• Types : Textual, Tabular, and Graphical presentations offer various ways to present data.
• Choosing methods : Select the right method based on data, audience, and purpose.
• Components : Include data points, comparisons, visuals, infographics, numerical values, and source citations.
• Structure : Know your audience, have a clear message, tell a compelling story, use visuals, be concise, and practice.
• Do's and don'ts : Do use visuals, keep it simple, highlight key points, engage the audience, and practice. Don't overload with data, include unrelated information, neglect the audience's expertise, read word-for-word, or lose focus.

1. What is data presentation, and why is it important in 2023?

Data presentation is the process of visually representing data sets to convey information effectively to an audience. In an era where the amount of data generated is vast, visually presenting data using methods such as diagrams, graphs, and charts has become crucial. By simplifying complex data sets, presentation of the data may helps your audience quickly grasp much information without drowning in a sea of chart's, analytics, facts and figures.

2. What are some common methods of data presentation?

There are various methods of data presentation, including graphs and charts, histograms, and cumulative frequency polygons. Each method has its strengths and is often used depending on the type of data you're using and the message you want to convey. For instance, if you want to show data over time, try using a line graph. If you're presenting geographical data, consider to use a heat map.

3. How can I ensure that my data presentation is clear and readable?

To ensure that your data presentation is clear and readable, pay attention to the design and labeling of your charts. Don't forget to label the axes appropriately, as they are critical for understanding the values they represent. Don't fit all the information in one slide or in a single paragraph. Presentation software like Prezent and PowerPoint can help you simplify your vertical axis, charts and tables, making them much easier to understand.

4. What are some common mistakes presenters make when presenting data?

One common mistake is trying to fit too much data into a single chart, which can distort the information and confuse the audience. Another mistake is not considering the needs of the audience. Remember that your audience won't have the same level of familiarity with the data as you do, so it's essential to present the data effectively and respond to questions during a Q&A session.

5. How can I use data visualization to present important data effectively on platforms like LinkedIn?

When presenting data on platforms like LinkedIn, consider using eye-catching visuals like bar graphs or charts. Use concise captions and e.g., examples to highlight the single most important information in your data report. Visuals, such as graphs and tables, can help you stand out in the sea of textual content, making your data presentation more engaging and shareable among your LinkedIn connections.

Create your data presentation with prezent

Prezent can be a valuable tool for creating data presentations. Here's how Prezent can help you in this regard:

• Time savings : Prezent saves up to 70% of presentation creation time, allowing you to focus on data analysis and insights.
• On-brand consistency : Ensure 100% brand alignment with Prezent's brand-approved designs for professional-looking data presentations.
• Effortless collaboration : Real-time sharing and collaboration features make it easy for teams to work together on data presentations.
• Data storytelling : Choose from 50+ storylines to effectively communicate data insights and engage your audience.
• Personalization : Create tailored data presentations that resonate with your audience's preferences, enhancing the impact of your data.

In summary, Prezent streamlines the process of creating data presentations by offering time-saving features, ensuring brand consistency, promoting collaboration, and providing tools for effective data storytelling. Whether you need to present data to clients, stakeholders, or within your organization, Prezent can significantly enhance your presentation-making process.

So, go ahead, present your data with confidence, and watch your audience be wowed by your expertise.

Thank you for joining us on this data-driven journey. Stay tuned for more insights, and remember, data presentation is your ticket to making numbers come alive!

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10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 05 Apr 2024 • 11 min read

There are different ways of presenting data, so which one is suited you the most? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

• What are Methods of Data Presentations?
• #1 – Tabular

#2 – Text

#3 – pie chart, #4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

• 5 Mistakes to Avoid
• Best Method of Data Presentation

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quiz and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should set your session with open-ended questions , to avoid dead-communication!

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

What is chart presentation?

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should use charts for presentation?

You should use charts to ensure your contents and visual look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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When and how should you use data in a presentation?

The answer is that you should use figures and numbers whenever they give the best evidence to back up your argument, or to tell your story. But how to present that data is more difficult.

Many people are not interested in tables of numbers, and may struggle to understand graphs. How can you help walk them through the data?

This page is designed to help you to answer that question by setting out some simple rules for presenting data.

Remember that You Are Telling Your Audience a Story

All presentations are basically story-telling opportunities.

Human beings have been hard-wired, over millions of years of evolution, to enjoy and respond to stories. It’s best to work with it, not fight it, because if you tell your audience a story, they are likely to listen much more carefully, and also move towards a logical conclusion: the insight to which you are trying to lead them.

Once you understand this, the issue of using data falls into place: it is to provide evidence of how your story unfolds.

Use Data to Tell the Story

You are not presenting data as such, you are using data to help you to tell your story in a more meaningful way.

This means that whenever you are required to present data, you should be asking yourself:

‘ What is the story in this data? ’,

‘ How best can I tell this story to my audience? ’

A Picture Tells a Thousand Words

90% of the information sent to the brain is visual and over 90% of all human communication is visual. Processing text requires our brains to work much harder than when processing images. In fact, the brain can process pictorial information 60,000 times faster than written information.

There is considerable truth in the saying ‘a picture tells a thousand words’ . It may not be literally a thousand, but it is often much easier to use a picture than to describe numerical information in words.

The data itself may be vitally important, but without a visual presentation of that data, its impact (and therefore your message) may be lost.

There are many people in the world who do not find it easy to understand numbers.

There are also many people who will simply switch off if you show them figures in a table. But if you present data in a graph or pie chart, you make a pictorial representation of the data. It makes the numbers much easier to understand. Trends and proportions become more obvious.

Consider this set of data:

Even for the highly numerate, the immediate point is only that there are lot more sales in the first quarter. You would have to do some adding up and dividing to work out the relationships between the four numbers. It also requires much more concentration to read and absorb the information in this format.

Now consider the same data in a pie chart:

It is immediately and shiningly obvious, even for those who struggle with numbers, that more than half of all sales were in the first quarter, and that over 75% were in the first two quarters.

What’s more, nobody is going to be straining from the back of the room to read your figures. You really can see a lot more from a picture.

But, and this is important, make sure that the graph is a good one.

Check that your graph or chart is visually appealing, that all the labels are clear, and that you have used an appropriate type of graph or chart. Poor graph-making is always obvious and can lead to confusion. Your message will also have much more impact if you choose the right type of graph or chart.

For more about this, see our page on Graphs and Charts .

KISS: Keep It Simple, Stupid!

When you’re good at statistics, it’s very tempting to do some really whizzy analysis. And once you’ve done that, you really want to show everyone how clever you are, and how much work you’ve done.

But does it really help to make your point?

Then don’t present it.

In the (relatively rare) cases when you actually need some really whizzy analysis, you then need to ask yourself whether everyone will understand it. And, in these days of presentations being posted on the internet, will the casual reader of your slides understand it later?

Once again, if the answer is ‘probably not’, then don’t use it.

Leave It Out...

If you can’t summarise your analysis in one or two brief and clear sentences, then don’t include it.

It also follows that if you don’t need to include data to make your point, then it may be best not to do so. A slide that is likely to be misunderstood or produce confusion is worse than no slide at all. So cut out all unnecessary data and focus on what you really need  to tell your story .

Remember KISS: Keep It Simple, Stupid.

Highlight the Main Features to Draw Out the Insights

We’re not suggesting that you should ‘ dumb down ’ your presentation, but there is no harm in highlighting the key features, as well as cutting out unnecessary data.

Suppose once again that you are using the sales figures from the last four quarters. You want to show the actual figures. Why not use a highlighting tool to emphasise that the first quarter is more than half?

With PowerPoint and other presentation software, you can make each circle appear separately, as you make your point and discuss the insights.

A little creative use of the technology can help you to highlight certain figures, and once again, make the story clearer.

Take-home message

Paradoxically, your presentation of any data should be designed to move the conversation away from the data and into the insight and action that should result from it.

In other words:

‘What happened there?’

‘What are we going to do about it?’

If you look at your presentation, data and all, and it’s not clear how you would get from the data to the insight and then the action, it’s probably a good idea to look at it again.

Remember, it’s the story that matters… and then what happens as a result.

Continue to: Writing Your Presentation Working with Visual Aids

See also: What is Your Story? How to Identify Your Story from Raw Data Crisis Communications Presenting to Large Groups Simple Statistical Analysis

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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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4 Presenting Data

• Published: July 2009
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Data presentation can greatly influence audiences. This chapter reviews principles and approaches for presenting data, focusing on whether data needs to be used. Data can presented using words alone (e.g., metaphors or narratives), numbers (e.g., tables), symbols (e.g., bar charts or line graphs), or some combination that integrates these methods. Although new software packages and advanced techniques are available, visual symbols that can most readily and effectively communicate public health data are pie charts, bar charts, line graphs, icons/icon arrays, visual scales, and maps. Perceptual cues, especially proximity, continuation, and closure, influence how people process information. Contextual cues help enhance meaning by providing sufficient context to help audiences better understand data. Effective data presentation depends upon articulating the purpose for communicating, understanding audiences and context, and developing storylines to be communicated, taking into account the need to present data ethically and in a manner easily understood.

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Thank you. payment completed., you will receive an email from us to confirm your registration, please click the link in the email to activate your account., there was error during payment, orcid profile found in public registry, download history, the power of visuals: tips for presenting data with tables and figures.

• 21 November, 2023

In academic writing, inclusion of figures and tables brings data into life. They turn complex data into a comprehensible narrative that is easy to follow. But, you may wonder, what sets figures apart from tables, and how do you strike the perfect balance in their presentation?

The Need for Tables and Figures

Imagine a paper without figures and tables. The research might be there, but it would lack the factor that makes it interesting and easy to understand. Scientific tables and figures efficiently present extensive statistical data in a condensed format. Due to their accessibility, readers often find it convenient to glance through these tables and figures, gaining a preliminary understanding of the study before delving into the complete manuscript. At the initial review phase, as well as upon publication, figures and tables provide a swift summary of the research findings for both journal editors and reviewers. It is crucial to emphasise that tables and figures contribute meaningfully to the manuscript only when they strike a balance between being concise and sufficiently descriptive.

Tips for Effective Data Presentation Using Tables and Figures:

Determining the appropriate number of figures and tables for a research paper is essential for effective communication. Here are some key considerations:

i. Purposeful Selection:

• Choose tables or figures based on the nature of your data. 

• Tables are suitable for presenting precise values and relationships, while figures are effective for visualising trends, patterns, or comparisons.

ii. Overlap Considerations:

• Avoiding overlap between figures and tables, as well as limiting overlap with the text, is crucial. 

• Each visual element should have its designated space to ensure clarity and prevent confusion. 

• Overlapping figures and tables can hinder the reader’s ability to focus on individual components. Similarly, too much overlap with the text can distract readers from the main narrative, so it's important to strike a balance and ensure a clear visual hierarchy.

iii. Formatting Requirements:

• Journals often set limits on the number of figures and tables allowed. 

• Authors should view these limitations as guidelines aimed at maintaining a balance between conciseness and informativeness. 

• Exceeding the prescribed limit may compromise the effectiveness of the presentation. Quality should be prioritised over quantity to enhance the overall impact of the visual elements.

iv. Consistency is Key:

• Maintain consistency in style and formatting throughout your visual elements. 

• This includes using the same color schemes, symbols, and fonts for better coherence.

v. Supplementary Figures and Tables:

• Supplementary figures and tables serve as additional resources to provide in-depth information. 

• While not integral to the main narrative, they offer further context or details. 

• Authors should use supplementary material judiciously, ensuring that each additional figure or table contributes value without overwhelming the reader. 

• Striking a balance is essential, with supplementary material serving a specific purpose rather than being overly abundant.

Components of Effective Tables

1. Caption:

• Purpose: Clearly states what the table represents.

• Content: Provides a concise summary or explanation of the table's content.

• Placement: Positioned above the table for quick reference.

2. Headings:

• Clarity: Clearly labels each column or row.

• Consistency: Maintains a consistent style throughout the table.

• Informative: Conveys essential information about the data presented.

3. Body Cells:

• Data Accuracy: Contains accurate and precise numerical information.

• Organisation: Presents data logically, following a clear structure.

• Formatting: Adheres to a consistent format for numerical values, including units.

4. Footnotes:

• Explanation: Provides additional information or clarifications for specific entries.

• Conciseness: Keeps footnotes brief and relevant.

• Position: Placed below the table for easy reference.

Components of Effective Figures

• Description: Summarises the main purpose or findings of the figure.

• Completeness: Offers enough information for readers to understand the figure without relying on the main text.

• Clarity: Ensures that the visual representation is clear and easy to interpret. Consider the size, resolution, and the image’s overall visual attractiveness.

• Accuracy: Accurately reflects the data or information being presented.

• Relevance: Aligns with the key points highlighted in the caption.

3. Legends:

• Clarity: Clearly explains symbols, colors, or any other elements used in the figure.

• Conciseness: Provides necessary information without unnecessary details.

• Placement: Located strategically to avoid cluttering the figure.

4. Axis Labels:

• Precision: Clearly labels x and y-axes in graphs or any other relevant axes.

• Units: Includes units of measurement to avoid ambiguity.

• Orientation: Ensures that labels are easily readable and not crowded.

5. Data Points:

• Differentiation: Clearly distinguishes between various data points.

• Consistency: Maintains a consistent style for data points throughout the figure.

• Highlighting: Uses markers or colors to emphasise key data, if applicable.

6. Trend Lines or Bars:

• Interpretability: Ensures that trend lines or bars are easily understood.

• Context: Places trend lines or bars in relation to the overall figure.

• Consistency: Follows a consistent style if multiple trends are represented.

Navigating Common Pitfalls and Implementing Actionable Tips for Authors

Lack of context is a prevalent issue, where figures or tables are presented without sufficient contextual information, making interpretation challenging. Providing clear captions and additional explanations in the text can remedy this issue. Misleading scaling is a potential pitfall, with authors manipulating scales to exaggerate or minimise trends. To avoid misinterpretation, it's important to present data accurately and communicate the scale used clearly. Overemphasis on aesthetics at the expense of clarity and accuracy is another pitfall to be cautious about. Balancing aesthetics with functionality ensures that visuals effectively communicate the intended message. Finally, excessive detail in visuals can hinder comprehension. Authors should highlight essential information, using supplementary materials for additional details while keeping the main visuals concise.

Improving the quality of tables and figures involves actionable strategies. Authors should start by prioritizing information, identifying key messages, and focusing on the most relevant data. Testing interpretability by seeking feedback from colleagues or peers helps authors refine visuals for a broader audience. Simplifying complex data and choosing appropriate visualisation types are additional strategies to enhance understanding. Consistent and accurate labeling throughout visuals, paying attention to units of measurement, abbreviations, and other details, ensures clarity. By avoiding common pitfalls and implementing these actionable tips, authors can significantly enhance the quality and effectiveness of their tables and figures, turning them into valuable tools for conveying research findings.

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Tips on using figures and tables

Charlesworth Author Services 02/11/2016 00:00:00

How to write about Tables and Figures to provide Data Commentary in your article

15/11/2022 00:00:00

How do I present data in an academic article?

Charlesworth Author Services 17/08/2020 00:00:00

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10 Data Presentation Examples For Strategic Communication

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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It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.

2. Column chart

It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.

3. Pie Charts

Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.

4. Bar charts

A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.

5. Histograms

It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.

6. Box plots

Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.

Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.

All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.

Importance of Data Presentation

Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.

Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.

If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.

There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.

To impress the higher management and top brass of a firm, effective presentation of data is needed.

Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.

Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-

• Many consumers or higher authorities are interested in the interpretation of data, not the raw data itself. Therefore, after the analysis of the data, users should represent the data with a visual aspect for better understanding and knowledge.
• The user should not overwhelm the audience with a number of slides of the presentation and inject an ample amount of texts as pictures that will speak for themselves.
• Data presentation often happens in a nutshell with each department showcasing their achievements towards company growth through a graph or a histogram.
• Providing a brief description would help the user to attain attention in a small amount of time while informing the audience about the context of the presentation
• The inclusion of pictures, charts, graphs and tables in the presentation help for better understanding the potential outcomes.
• An effective presentation would allow the organization to determine the difference with the fellow organization and acknowledge its flaws. Comparison of data would assist them in decision making.

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Data Presentation, Pictorial, Bar charts

Class: Basic 5

Subject: Mathematics

Topic: Data Presentation

Data presentation is the representation of data or information for easy reading and interpretations.

Data can be presented in pictograms, bar graphs or frequency tables.

A pictogram is the representation of data using pictures. For example, the following are types of vehicle that passes through a street in an hour.

🚲🚲🚲 = 3 bicycles

🚕🚕🚕🚕🚕 = 5 cars

🐪🐪🐪🐪🐪🐪🐪 = 7 camels

🚌🚌🚌🚌🚌 = 4 buses

🚚🚚🚚🚚🚚🚚 = 6 lorries

It is a way of representing information pictorially, using rectangular bars of equal widths. The spaces between the bars are the same. The height of the bar represents the number of times the event occurs. A bar graph can be horizontal or vertical.

The graph below shows the number of pupils who like different fruits.

Evaluation:

1.) The table below shows how Pupil’s in class 5 of Island Builders Baptist School travel to school:

Method of travel. No. of pupils

1) Draw a bar graph to show the information above:

2.) What is the most preferred method of travel?

3) How many more Pupil’s travel by car than bus?

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Home » Primary Data – Types, Methods and Examples

Primary Data – Types, Methods and Examples

Table of Contents

Primary Data

Definition:

Primary Data refers to data that is collected firsthand by a researcher or a team of researchers for a specific research project or purpose. It is original information that has not been previously published or analyzed, and it is gathered directly from the source or through the use of data collection methods such as surveys, interviews, observations, and experiments.

Types of Primary Data

Types of Primary Data are as follows:

Surveys are one of the most common types of primary data collection methods. They involve asking a set of standardized questions to a sample of individuals or organizations, usually through a questionnaire or an online form.

Interviews involve asking open-ended or structured questions to a sample of individuals or groups in person, over the phone, or through video conferencing. They can be conducted in a one-on-one setting or in a focus group.

Observations

Observations involve systematically recording the behavior or activities of individuals or groups in a natural or controlled setting. This type of data collection is often used in fields such as anthropology, sociology, and psychology.

Experiments

Experiments involve manipulating one or more variables and observing the effects on an outcome of interest. They are commonly used in scientific research to establish cause-and-effect relationships.

Case studies

Case studies involve in-depth analysis of a particular individual, group, or organization. They typically involve collecting a variety of data, including interviews, observations, and documents.

Action research

Action research involves collecting data to improve a specific practice or process within an organization or community. It often involves collaboration between researchers and practitioners.

Formats of Primary Data

Some common formats for primary data collection include:

• Textual data : This includes written responses to surveys or interviews, as well as written notes from observations.
• Numeric data: Numeric data includes data collected through structured surveys or experiments, such as ratings, rankings, or test scores.
• Audio data : Audio data includes recordings of interviews, focus groups, or other discussions.
• Visual data: Visual data includes photographs or videos of events, behaviors, or phenomena being studied.
• Sensor data: Sensor data includes data collected through electronic sensors, such as temperature readings, GPS data, or motion data.
• Biological data : Biological data includes data collected through biological samples, such as blood, urine, or tissue samples.

Primary Data Analysis Methods

There are several methods that can be used to analyze primary data collected from research, including:

• Descriptive statistics: Descriptive statistics involve summarizing and describing the characteristics of the data collected, such as mean, median, mode, and standard deviation.
• Inferential statistics: Inferential statistics involve making inferences about a population based on a sample of data. This can include techniques such as hypothesis testing and confidence intervals.
• Qualitative analysis: Qualitative analysis involves analyzing non-numerical data, such as textual data from interviews or observations, to identify themes, patterns, or trends.
• Content analysis: Content analysis involves analyzing textual data to identify and categorize specific words or phrases, allowing researchers to identify themes or patterns in the data.
• Coding : Coding involves categorizing data into specific categories or themes, allowing researchers to identify patterns and relationships in the data.
• Data visualization : Data visualization involves creating graphs, charts, and other visual representations of data to help researchers identify patterns and relationships in the data.

Primary Data Gathering Guide

Here are some general steps to guide you in gathering primary data:

• Define your research question or problem: Clearly define the purpose of your research and the specific questions you want to answer.
• Determine the data collection method : Decide which primary data collection method(s) will be most appropriate to answer your research question or problem.
• Develop a data collection instrument : If you are using surveys or interviews, create a structured questionnaire or interview guide to ensure that you ask the same questions of all participants.
• Identify your target population : Identify the group of individuals or organizations that will provide the data you need to answer your research question or problem.
• Recruit participants: Use various methods to recruit participants, such as email, social media, or advertising.
• Collect the data : Conduct your survey, interview, observation, or experiment, ensuring that you follow your data collection instrument.
• Verify the data : Check the data for completeness, accuracy, and consistency. Resolve any missing data or errors.
• Analyze the data: Use appropriate statistical or qualitative analysis techniques to interpret the data.
• Draw conclusions: Use the results of your analysis to answer your research question or problem.
• Communicate your findings : Share your results through a written report, presentation, or publication.

Examples of Primary Data

Some real-time examples of primary data are:

• Customer surveys: When a company collects data through surveys or questionnaires, they are gathering primary data. For example, a restaurant might ask customers to rate their dining experience.
• Market research : Companies may conduct primary research to understand consumer trends or market demand. For instance, a company might conduct interviews or focus groups to gather information about consumer preferences.
• Scientific experiments: Scientists may gather primary data through experiments, such as observing the behavior of animals or testing new drugs on human subjects.
• Traffic counts: Traffic engineers might collect primary data by monitoring the flow of cars on a particular road to determine how to improve traffic flow.
• Consumer behavior : Companies may use primary data to track consumer behavior, such as how customers use a product or interact with a website.
• Social media analytics : Companies can collect primary data by analyzing social media metrics such as likes, comments, and shares to understand how their customers are engaging with their brand.

Applications of Primary Data

Primary data is useful in a wide range of applications, including research, business, and government. Here are some specific applications of primary data:

• Research : Primary data is essential for conducting scientific research, such as in fields like psychology, sociology, and biology. Researchers collect primary data through experiments, surveys, and observations.
• Marketing : Companies use primary data to understand customer needs and preferences, track consumer behavior, and develop marketing strategies. This data is typically collected through surveys, focus groups, and other market research methods.
• Business planning : Primary data can inform business decisions such as product development, pricing strategies, and expansion plans. For example, a company may gather primary data on the buying habits of its customers to decide what products to offer and how to price them.
• Public policy: Primary data is used by government agencies to develop and evaluate public policies. For example, a city government might use primary data on traffic patterns to decide where to build new roads or improve public transportation.
• Education : Primary data is used in education to evaluate student performance, identify areas of need, and develop curriculum. Teachers may gather primary data through assessments, observations, and surveys to improve their teaching methods and help students succeed.
• Healthcare : Primary data is used by healthcare professionals to diagnose and treat illnesses, track patient outcomes, and develop new treatments. Doctors and researchers collect primary data through medical tests, clinical trials, and patient surveys.
• Environmental management: Primary data is used to monitor and manage natural resources and the environment. For example, scientists and environmental managers collect primary data on water quality, air quality, and biodiversity to develop policies and programs aimed at protecting the environment.
• Product testing: Companies use primary data to test new products before they are released to the market. This data is collected through surveys, focus groups, and product testing sessions to evaluate the effectiveness and appeal of the product.
• Crime prevention : Primary data is used by law enforcement agencies to identify crime hotspots, track criminal activity, and develop crime prevention strategies. Police departments may collect primary data through crime reports, surveys, and community meetings to better understand the needs and concerns of the community.
• Disaster response: Primary data is used by emergency responders and disaster management agencies to assess the impact of disasters and develop response plans. This data is collected through surveys, interviews, and observations to identify the needs of affected populations and allocate resources accordingly.

Purpose of Primary Data

The purpose of primary data is to gather information directly from the source, without relying on secondary sources or pre-existing data. This data is collected through research methods such as surveys, interviews, experiments, and observations. Primary data is valuable because it is tailored to the specific research question or problem at hand and is collected with a specific purpose in mind. Some of the main purposes of primary data include:

• To answer research questions: Researchers use primary data to answer specific research questions, such as understanding consumer preferences, evaluating the effectiveness of a program, or testing a hypothesis.
• To gather original information : Primary data provides new and original information that is not available from other sources. This data can be used to make informed decisions, develop new products, or design new programs.
• To tailor research methods: Primary data collection methods can be customized to fit the research question or problem. This allows researchers to gather the most relevant and accurate information possible.
• To control the quality of data: Researchers have greater control over the quality of primary data, as they can design and implement the data collection methods themselves. This reduces the risk of errors or biases that may be present in secondary data sources.
• To address specific populations : Primary data can be collected from specific populations, such as customers, patients, or students. This allows researchers to gather data that is directly relevant to their research question or problem.

When to use Primary Data

Primary data should be used when the specific information required for a research question or problem cannot be obtained from existing data sources. Here are some situations where primary data would be appropriate to use:

• When no secondary data is available: Primary data should be collected when there is no existing data available that addresses the research question or problem.
• When the available secondary data is not relevant: Existing secondary data may not be specific or relevant enough to address the research question or problem at hand.
• When the research requires specific information : Primary data collection allows researchers to gather information that is tailored to their specific research question or problem.
• When the research requires a specific population: Primary data can be collected from specific populations, such as customers, patients, or employees, to provide more targeted and relevant information.
• When the research requires control over the data collection process: Primary data allows researchers to have greater control over the data collection process, which can ensure the data is of high quality and relevant to the research question or problem.
• When the research requires current or up-to-date information: Primary data collection can provide more current and up-to-date information than existing secondary data sources.

Characteristics of Primary Data

Primary data has several characteristics that make it unique and valuable for research purposes. These characteristics include:

• Originality : Primary data is collected for a specific research question or problem and is not previously published or available in any other source.
• Relevance : Primary data is collected to directly address the research question or problem at hand and is therefore highly relevant to the research.
• Accuracy : Primary data collection methods can be designed to ensure the data is accurate and reliable, reducing the risk of errors or biases.
• Timeliness: Primary data is collected in real-time or near real-time, providing current and up-to-date information for the research.
• Specificity : Primary data can be collected from specific populations, such as customers, patients, or employees, providing targeted and relevant information.
• Control : Researchers have greater control over the data collection process, allowing them to ensure the data is collected in a way that is most relevant to the research question or problem.
• Cost : Primary data collection can be more expensive than using existing secondary data sources, as it requires resources such as personnel, equipment, and materials.

Advantages of Primary Data

There are several advantages of using primary data in research. These include:

• Specificity : Primary data collection can be tailored to the specific research question or problem, allowing researchers to gather the most relevant and targeted information possible.
• Control : Researchers have greater control over the data collection process, which can ensure the data is of high quality and relevant to the research question or problem.
• Timeliness : Primary data is collected in real-time or near real-time, providing current and up-to-date information for the research.
• Flexibility : Primary data collection methods can be adjusted or modified during the research process to ensure the most relevant and useful data is collected.
• Greater depth : Primary data collection methods, such as interviews or focus groups, can provide more in-depth and detailed information than existing secondary data sources.
• Potential for new insights : Primary data collection can provide new and unexpected insights into a research question or problem, which may not have been possible using existing secondary data sources.

Limitations of Primary Data

While primary data has several advantages, it also has some limitations that researchers need to be aware of. These limitations include:

• Time-consuming: Primary data collection can be time-consuming, especially if the research requires collecting data from a large sample or a specific population.
• Limited generalizability: Primary data is collected from a specific population, and therefore its generalizability to other populations may be limited.
• Potential bias: Primary data collection methods can be subject to biases, such as social desirability bias or interviewer bias, which can affect the accuracy and reliability of the data.
• Potential for errors: Primary data collection methods can be prone to errors, such as data entry errors or measurement errors, which can affect the accuracy and reliability of the data.
• Ethical concerns: Primary data collection methods, such as interviews or surveys, may raise ethical concerns related to confidentiality, privacy, and informed consent.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Open access
• Published: 17 April 2024

Burden of antimicrobial prescribing in primary care attributable to sore throat: a retrospective cohort study of patient record data

• Kylie S Carville 1 , 2   na1 ,
• Niamh Meagher 2   na1 ,
• Yara-Natalie Abo 3 ,
• Jo-Anne Manski-Nankervis 4 ,
• James Fielding 1 , 2 ,
• Andrew Steer 5 ,
• Jodie McVernon 1 , 2   na2 &
• David J Price 2 , 6   na2  

BMC Primary Care volume  25 , Article number:  117 ( 2024 ) Cite this article

Metrics details

Reducing antibiotic use in Australia, and the subsequent impact on antimicrobial resistance, requires multiple, sustained approaches with appropriate resources and support. Additional strategies to reduce antibiotic prescribing include effective vaccines, against pathogens such as Streptococcus pyogenes , the most common bacterial cause of sore throat. As part of efforts towards assessing the benefits of introducing new strategies to reduce antimicrobial prescribing, we aimed to determine the burden of antimicrobial prescribing for sore throat in general practice.

General practice activity data from 2013 – 2017 derived from the first 8 practices participating in the ‘Primary Care Audit, Teaching and Research Open Network’ (Patron) program were analysed according to reason for visit (upper respiratory tract infection, URTI, or sore throat) and antibiotic prescription. The main outcome measures were percentage of sore throat or URTI presentations with antibiotic prescription by age.

A total of 722,339 visits to general practice were made by 65,449 patients; 5.7% of visits were for URTI with 0.8% meeting the more specific criteria for sore throat. 66.1% of sore throat visits and 36.2% of URTI visits resulted in antibiotic prescription. Penicillin, the recommended antibiotic for sore throat when indicated, was the antibiotic of choice in only 52.9% of sore throat cases prescribed antibiotics. Broader spectrum antibiotics were prescribed more frequently in older age groups.

Conclusions

Frequency of antibiotic prescribing for sore throat is high and broad, despite Australian Therapeutic guideline recommendations. Multiple, sustained interventions to reduce prescribing, including availability of effective S. pyogenes vaccines that could reduce the incidence of streptococcal pharyngitis, could obviate the need to prescribe antibiotics and support ongoing efforts to promote antimicrobial stewardship.

Peer Review reports

Control of Streptococcus pyogenes (Strep A) diseases, that directly affect >750 million people and cause more than 500,000 deaths each year [ 1 ], is a high priority. Strep A has a disproportionately high burden among the First Nations peoples of Australia and Canada, the Pacific region, sub-Saharan Africa, south central Asia, and settings of disadvantage worldwide [ 2 , 3 ]. However, a significant burden of disease is distributed across all settings and life stages [ 4 , 5 ]. Infections range from common superficial skin infections (>150 million prevalent cases globally) and pharyngitis (sore throat, >600 million/year), to life-threatening invasive disease (>600,000/year) with a considerable surge in incidence of invasive Strep A noted in USA, Europe and Australia after pandemic years 2020-2021 [ 6 , 7 ]. Post-infectious complications include acute rheumatic fever (ARF) leading to rheumatic heart disease (RHD, prevalence ~34 million), and glomerulonephritis [ 8 ]. A vaccine against Strep A would reduce the varied burden of disease, and the World Health Organization (WHO) has prioritised Strep A as a priority pathogen for accelerated vaccine development [ 9 ]. A key factor is establishing an economic case considering the full scope of costs and benefits, including the potential to reduce the secondary burden of antimicrobial prescribing for suspected Strep A infection, and the consequent contribution to antibiotic resistance.

In Australia, the burden of Strep A disease is distributed across every level of the health system. Direct and indirect costs associated with common but less severe syndromes such as sore throat manifest through primary care and hospital visits, work and school absences, antibiotic use and misuse, leading to antimicrobial resistance [ 10 , 11 ]. Whilst Strep A disease most commonly manifests as pharyngitis, sore throat is most often attributable to viral pathogens. Strep A causes an estimated 10–20% of sore throat cases, and is usually self-limiting [ 12 ]. It is difficult to clinically distinguish Streptococcal pharyngitis from viral causes of sore throat, although the latter is more likely to be associated with upper respiratory tract infection (URTI) symptoms such as cough, hoarse voice, and nasal congestion. A risk factor-based approach to prescribing antibiotics for suspected streptococcal pharyngitis and tonsillitis is advised in the Australian Therapeutic Guidelines [ 13 ]. Antibiotic prescription is recommended only for patients 2 to 25 years from populations with high incidence of rheumatic fever, and patients with existing rheumatic heart disease, scarlet fever, or very severe symptoms including severe throat pain, dysphagia or need for hospitalisation. Despite these recommendations and national efforts targeted at reducing antibiotic prescriptions, several studies have shown high incidence of antibiotic prescribing for URTI and sore throat [ 14 , 15 , 16 ]. Treatment guidelines for sore throat vary substantially across countries and regions [ 17 ] with a lower threshold for investigation and treatment of Strep A pharyngitis recommended in some countries such as the USA [ 18 ], further increasing the global burden of antimicrobial prescribing.

This study measured the relative frequency of URTI, sore throat and pharyngitis presentations among different age groups in a sample of general practice (GP) clinics in the state of Victoria (population 6,500,000), and frequency and type of antibiotic prescribed, to quantify antimicrobial prescribing and potential vaccine benefits.

Extraction of patient data

URTI presentations to the first eight GP clinics participating in the ‘Primary Care Audit, Teaching and Research Open Network’ (Patron) program were retrospectively reviewed. Patron is a research initiative of The University of Melbourne’s Department of General Practice Data for Decisions Program, that aims to make better use of existing primary care data to improve knowledge, medical education, policy, and the way medical care is delivered [ 19 ].

Data extraction was performed by the Patron team with data de-identified at the source of extraction. Data were extracted for patients who had a current medical record (i.e., were not marked inactive as a result of death, moving clinics, or not attending for a set period of time) at the time of data extraction (May 2019) and who had visited one of the eight practices over a five-year period, 2013 to 2017. All visit records were selected for extraction, including clinical and administrative records. Non-clinical records were subsequently excluded if they were interactions that were: 1) not conducted by a GP, or; 2) unless specifically recorded as telehealth, occurred via telephone, email, or SMS, or were listed as a non-visit. The reason for visit field was only extracted if terms associated with URTI were listed as the reason for presentation. Reasons which were not associated with URTI were not extracted, and so in these instances the reason for visit field was blank. All prescription records from 2013 and 2017 were extracted separately and linked to visit records using visit ID or date.

Coding of variables of interest

Reason for visit was categorised into three levels with increasing specificity for sore throat caused by Strep A.

The free text terms in the reason for visit variable used to extract potential URTI presentations are listed in Additional file 1 . These terms were broad in nature in order to capture as many relevant records as possible and included those used for sore throat (as defined below), variations on the term URTI and pathogen/disease names that cause URTI. Extracted records were then reviewed in consultation with clinical experts to determine a shortlist of exclusion terms (Additional file 1 ), which were applied to reduce misclassification of visits as being related to URTI.

• Sore throat

Any visit reason including the terms “sore throat”, “throat infection”, “tonsillitis”, “pharyngitis”, “epiglottitis”, “tonsillar abscess”, “strep” or “scarlet fever”.

Streptococcal pharyngitis

Any visit reason including the terms “strep” or “scarlet fever”.

Antibiotic prescriptions

Antibiotic prescriptions associated with a clinical visit were identified in prescription records. Coding for any antibiotic was defined using the Anatomical Therapeutic Chemical (ATC) and Australian Medicines Terminology (AMT) codes obtained from publicly available data on the Pharmaceutical Benefits Scheme (PBS) website [ 20 ]. Antibiotics that may be prescribed for sore throat in practice were identified from the Australian Therapeutic Guidelines and with advice of experts (Additional file 2 ). Recommended antibiotics for sore throat according to the Australian Therapeutic Guidelines are: penicillin (including phenoxymethylpenicillin, benzathine benzylpenicillin, procaine benzylpenicillin) as first line, or cefalexin or azithromycin depending on degree of penicillin allergy [ 13 ]. Amoxicillin is also listed as a recommended antibiotic in the Infectious Disease Society of America guideline on management of Streptococcal pharyngitis [ 21 ] or an alternative antibiotic in the Australian Therapeutic Guidelines in cases where children are unable to tolerate the liquid formulation of phenoxymethylpenicillin. Antibiotics are not generally recommended for URTI.

Data analysis

Patient demographics, visit patterns and patient numbers were summarised, overall and by clinic. The overall burden of URTI, sore throat, and pharyngitis was quantified by the number and proportion of clinical visits, and the number and proportion of patients presenting at least once with these conditions of interest. These data were also examined by age group, sex, season, clinic, and year.

Prescription of antibiotics was summarised as the frequency and proportion of URTI and sore throat presentation for which any antibiotic was prescribed. Among clinical visits associated with an antibiotic prescription, the frequency and proportion that received: 1) any Australian Therapeutic Guidelines recommended antibiotic; 2) each individual Australian Therapeutic Guidelines recommended antibiotic, and; 3) each commonly prescribed non-recommended antibiotic. Antibiotic prescribing practices were also examined by age group, sex, season, clinic, and year.

Repeat presentations

Repeat presentations for the same episode of disease were defined as the overall number and proportion of visits for URTI or sore throat that occurred within a pre-specified timeframe, defined as either within 1) 7 days, or; 2) 28 days of another visit for the same presentation. Repeat visits may represent cases of chronic sore throat, which in turn could contribute to increased antibiotic prescription in this cohort. To understand the impact of repeat presentations, the frequency and proportion of Australian Therapeutic Guidelines recommended and non-recommended antibiotic prescribing at repeat visits for sore throat or URTI was reported.

Data cleaning, linkage of data tables, analysis and creation of figures was performed using Stata version 16.0 and R version 4.2.2 [ 22 , 23 ].

Presentations

From 2013 to 2017 a total of 722,339 clinical visits were made across the eight practices by 65,449 patients in the cohort, with an average of five visits per patient (interquartile range [IQR]: 2, 13). Most (714,755, 99.0%) visits were recorded as occurring in the GP clinic, with other visits occurring at other locations such as aged care facilities or homes. The median age of patients was 32 years (IQR: 20, 48) and 55.0% were female (35,991), with similar age and sex distributions across the clinics apart from one clinic with majority male presentations (Table 1 ). Six clinics were metropolitan and two were regional.

Over the five years, 5.7% of all visits had URTI recorded as the reason for visit, with 0.8% of all visits recording the more specific terms for sore throat (Table 2 ). There were very few presentations coded as streptococcal pharyngitis ( n =73) — the majority of these were recorded in 2017 ( n =59) from two clinics. Therefore, the category of streptococcal pharyngitis is not further described. Just under 7% of patients had ever visited a clinic for sore throat over 2013–2017, and 30% of patients for URTI (Table 2 ).

The yearly proportion of presentations recorded as URTI and sore throat remained constant. Across years, a median of 19.7% (IQR: 18.1%, 19.9%) of patients ever presented with an URTI; 3.4% (IQR: 2.8%, 3.7%) with sore throat. There was little difference in URTI or sore throat presentations amongst males and females over the five years (Table 2 ).

Sore throat presentations were most common in school-aged children, with 11.3% of those aged 5 to 14 years presenting at least once for sore throat (Fig. 1 ). Visits for URTI were highest amongst those aged under 5 years, accounting for 17.4% of all visits in this age group (Fig. 1 ). The median proportion of children under 5 years who presented for URTI at least once in a year was 39.6% (IQR: 37.9%, 39.8%). Presentations peaked in winter, when 0.9% of all presentations were for sore throat and 7.8% for all URTI (compared with 0.7% and 3.7%, respectively, in summer months).

Total visits presenting with, and patients who presented at least once with A upper respiratory tract infection (URTI) and B sore throat, from 2013–2017 by age group.

Prescriptions

In total, there were 544,343 records of prescriptions, of which 491,086 (90.2%) were able to be linked to a clinical visit. Unmatched prescriptions were largely linked to non-clinical entries in patient records, such as phone calls to administrative staff, of which only 565 and 107 were related to URTI or sore throat, respectively. Of the linked prescriptions, 83,938 (17.1%) were for antibiotics. Overall, 11.6% of all GP visits were associated with an antibiotic prescription (Table 3 ). URTI was recorded as the reason for the visit in 15,057 (17.9%) of presentations where antibiotics were prescribed. There were 3,876 antibiotic prescriptions for the more specific category of sore throat, which accounted for 4.6% of all antibiotic prescriptions.

Two thirds (66.1%) of patients presenting with sore throat were prescribed antibiotics, compared to 36.2% of patients presenting with URTI. Of the antibiotics prescribed for sore throat, the most common was penicillin (52.9%; phenoxymethylpenicillin 43.5% and benzylpenicillin 9.5%), followed by amoxicillin (23.1%). Over half (57.3%) of the prescriptions were for antibiotics recommended in the Australian guidelines. Of the antibiotic prescriptions for URTI, amoxicillin was prescribed most commonly (39.5%), with less use of penicillin (14.7%).

Antibiotic prescription for sore throat did not differ according to sex and was highest in children aged under 5 years, for whom 75% of presentations for sore throat resulted in antibiotic prescription (Fig. 2 ). Penicillin was the most commonly prescribed antibiotic in most age groups: 56.2% (276/491) of children under 5 years and 60.6% (411/678) of children aged 5 to 14 years who received an antibiotic were prescribed penicillin, along with 53.6% (1,272/2,374) of those 15 to 49 years, but only 27.9% (93/333) of those aged over 50 years. Those aged over 50 years who received an antibiotic were prescribed amoxicillin in 114 (34.2%) of 333 cases compared with approximately 20% of younger age groups, and received more amoxicillin clavulanic acid and roxithromycin (8.7% and 8.4% of prescriptions respectively, compared with 2% or less in children under 15 years) (Fig. 2 ).

A Proportion of presentations for sore throat and upper respiratory tract infections (URTI) from 2013–2017 which received an antibiotic prescription, by age group; B Proportion of antibiotic prescriptions for sore throat from 2013–2017 which were recommended antibiotics or amoxicillin, by age group.

The proportion of visits where an antibiotic was prescribed for an URTI increased with increasing age, doubling from 20.7% among children aged less than 5 years to over 40% for those aged over 15 years (Fig. 2 ). The majority of prescribing for children aged under 5 years with an URTI was amoxicillin (55.5%, 1,020/1,839). People aged over 50 years with an URTI mostly received amoxicillin (34.8%, 1,347/3,874), amoxicillin clavulanic acid (14.4%, 559/3874), and roxithromycin (12.0%, 466/3,874).

Prescription of any antibiotic was variable across different clinics regardless of location. For sore throat presentations, 57.5% to 81.5% of visits received any antibiotic. For URTI presentations it was 27.6% to 44.3% of visits. Of the two clinics prescribing the most antibiotics for sore throat (both over 80%), one prescribed the lowest proportion of recommended antibiotics (16.4%; 14.6% penicillin), while the other prescribed a higher proportion (64.4%; 58.3% penicillin) consistent with most other clinics, which ranged between 47.3% and 67.5%. Prescription of penicillin was relatively constant over the five-year period of the study, however, prescription of amoxicillin increased from 12.8% of all prescriptions for sore throat in 2013 to 24.6% in 2017.

Of all clinical visits for sore throat, there were 283 and 440 instances of multiple presentations for the same condition within 7- and 28-day timeframes, respectively (Table 4. ). This included a total of 308 (5.3%) visits in 7 days and 518 (8.8%) visits in 28 days. For URTI, there were 2,533 episodes of multiple visits within 7 days and 4,449 episodes of multiple visits within 28-days. The majority of repeat presentations for sore throat were limited to two visits (7 days: two visits were 91.5% of repeat visits [ n =259]; 28 days: two visits were 85.2% of repeat visits [ n =375]). The proportion of repeat presentations limited to two visits was similar for URTI (7 days: 90.1% [ n =2,283], and 81.7% [ n =3,636]).

Antibiotics were prescribed at least once for 86.6% and 87.5% of all episodes of multiple visits for sore throat within 7 and 28 days, respectively. The majority of these repeat visits received at least one Australian Therapeutic Guidelines recommended antibiotic (62.5% for the 7-day timeframe and 59.3% for the 28-day timeframe). The pattern of antibiotic prescription for the first two visits for sore throat within 28 days is also characterised in Fig. 3 . At each visit, patients were characterised as receiving: i) an antibiotic recommended for treatment of sore throat, as indicated by Australian Therapeutic Guidelines; ii) amoxicillin; iii) any other non-recommended antibiotic, or; iv) no antibiotic prescription. Antibiotic prescribing was higher at the first visit (319 [72.5%]) than the second visit (193 [43.9%]). Prescription rates did not change from the second visit when considering subsequent repeat visits for sore throat (35/78 [44.9%]). Recommended antibiotics were also more common at the first visit compared to the second, comprising 205 (64.3%) and 96 (49.7%) of all prescriptions at the first and second visits respectively.

Alluvial plot of antibiotic prescribing outcomes of the first two repeat visits for 440 episodes of sore throat within 28 days of first presentation from 2013–2017. Patients were categorised by whether they received: i) an antibiotic recommended for treatment of sore throat, as indicated by Australian Therapeutic Guidelines; ii) amoxicillin; iii) any other non-recommended antibiotic, or; iv) no antibiotic prescription.

We reviewed a large dataset of 722,339 clinical visits to eight GP practices in Victoria over a five-year period to determine frequency of antibiotic prescriptions for sore throat and URTI. Antibiotics were prescribed in 66.1% of cases of sore throat, and 36.2% cases of URTI. Penicillin, the antibiotic of choice where indicated, represented 53% of antibiotic prescriptions for sore throat. Even in the setting of suspected bacterial pharyngitis, antibiotics are only recommended in select cases [ 13 ]. Yearly national surveys of general practice activity have estimated that 19% to 40% of tonsillitis/pharyngitis cases may require antibiotic treatment based on Australian Therapeutic Guidelines criteria for antibiotic prescription [ 15 ].

Presentations recorded as sore throat were most common among those aged 5 to 15 years, consistent with the known peak incidence of streptococcal pharyngitis occurring in school aged children [ 3 ]. A bacterial cause is less likely in adults: Strep A is cultured in 15% to 36% of children compared to 5% to 17% of adults with sore throat [ 12 ]. Despite this, little difference was seen in prescribing in 5 to 15 year-olds compared with those less than 5 years and 15 to 49 years of age. Furthermore, there was substantial antibiotic prescribing for sore throat in those aged 50 years or older in this data (53.7%). A cohort study of GP trainees in 2010 – 2012 reported less antibiotic prescribing for sore throat for those aged over 50 years (7.4%). [ 16 ] This could in part be due to differences in prescribing practices by seniority [ 24 ], which could not be assessed in this study as GP level data was not available, although it is likely a range of career stages are represented in our data. These contrasting results highlight the diversity of antibiotic prescribing practices in primary care. These differences may be attributable to the specific patient populations served by clinics included or the differences in the methods used to quantify antibiotic prescribing in either study.

Although this study found high levels of antibiotic prescribing overall, antibiotic type was more likely to follow the Australian Therapeutic Guidelines for children than older adults. People aged over 50 years were more likely to be prescribed broader spectrum antibiotics for sore throat, with amoxicillin prescribed for 34.2% of sore throat presentations in those over 50 compared to 21.5% to 23.4% in the younger age groups. Older age groups, aged 15 to 49 and over 50 years, were more likely to receive antibiotics for URTI (42.7% and 43.5% of presentations, respectively) relative to children (20.7% of presentations in those aged under 5 and 28.6% of presentations in the 5 to 14 age group). Only 32% of those aged over 50 years received an Australian Therapeutic Guideline recommended antibiotic for sore throat. Another smaller study using Patron data showed 440/795 (55.3%) prescriptions for pharyngitis or tonsillitis in individuals aged 12 years or older were recommended by the Australian Therapeutic Guidelines, noting that this study used more specific diagnostic terms. [ 25 ]

The antibiotic prescription rate for sore throat and URTI from 2013 – 2017 in our study is consistent with previous Australian studies, where antibiotics were prescribed for 21.6% of URTI presentations (2010 – 2012) [ 16 ], and 71.5% of sore throat presentations (2010 – 2014) [ 14 ], but not as high as for the more specific diagnosis of acute pharyngitis or tonsillitis (94%, 2010 – 2015) [ 15 ]. It is possible that current antibiotic prescribing practices have changed since 2017, however, high antibiotic prescribing in primary care continues to be reported nationally [ 26 ]. Repeat presentations for sore throat do not appear to be the driver of increased antibiotic prescribing observed in this study: only 5.3% of visits were repeat visits within a 7 day window, and; among repeat clinical visits for sore throat, antibiotics were prescribed at a higher rate at the first visit (72.5%), than at the second (43.9%) or subsequent (44.9%) visits for sore throat, potentially indicating that patients with repeat visits had more severe illnesses at the outset.

There are limitations in this analysis. The “Reason for visit” field is not always completed in electronic medical records, and may not include all reasons why a patient presented, thus URTI and sore throat presentations may be an underestimate. Moreover, attribution of prescribing to any given presenting symptom or syndrome is limited to inference in the absence of a stated indication, and we could not identify where amoxicillin was prescribed in young children for improved tolerability. The cohort comprised of patients with a current medical record at the time of record extraction (May 2019), thus patients who may have attended these clinics earlier in the study period, but were not active in May 2019, were not included. This skews the data towards more recent presentations; however, this is not expected to be a notable issue as presentations for sore throat and URTI were constant across the time, and similar to other data sources. The Patron network was in its infancy at the time this study was initiated, and only a small number of clinics were available for inclusion. These clinics may not be representative of broader prescribing practices across primary care settings. Finally, the Australian Therapeutic Guidelines is one of multiple published criteria available in Australia [ 27 ]. While widely used, access to these guidelines is subscription-based and so not universally available to Australian GPs. For example, any GPs using the Infectious Disease Society of America guidelines may be prescribing amoxicillin [ 21 ], which may explain the high use of this antibiotic in our cohort. In addition, more specific guidelines exist which are more applicable for remote areas and/or Aboriginal and Torres Strait Islander peoples.

Reasons for antibiotic prescription in primary care are multifactorial, including fear of rare sequelae [ 28 ]. Lowering antibiotic prescribing rates is challenging. Our study did not include data regarding specific reasons for prescription, or sufficient details to understand risk factor profiles that may lead to increased prescribing under Australian guidelines, such as for ARF/RHD, although the prescribing rate was consistent across urban and regional clinics included in this cohort, and the overall annual incidence of ARF and RHD in Victoria is low [ 29 ]. The data also did not capture the performance of or results of investigations (e.g., throat culture). Australian guidelines do not place emphasis on performing investigations for sore throat and rapid point-of-care tests are not widely available, with throat culture potentially performed in 18 – 37% of cases [ 30 ]. Even when available, rapid point-of-care testing was only associated with a 16% absolute reduction in the rate of antibiotic prescribing for sore throat in children in the United States [ 31 ]. A Cochrane review of interventions to improve antibiotic prescribing in ambulatory care found multi‐faceted interventions combining physician, patient and public education were most successful, but only one of four studies demonstrated a sustained reduction in the incidence of antibiotic‐resistant bacteria [ 32 ]. Such interventions are costly and resource-intensive: one intervention in 56 GP practices in Australia reduced antibiotic prescriptions by only 7% [ 33 ], consistent with other reports of suboptimal or unsustained reductions as a result of education or antimicrobial stewardship interventions [ 31 , 34 , 35 ].

This study has demonstrated high, often broad-spectrum, antibiotic prescribing for sore throat in a large sample of GP visits. Reports of suboptimal reductions in unnecessary antibiotic prescribing as a result of costly, multi-faceted interventions suggests that additional strategies are required, such as effective vaccines, alongside ongoing GP and community education and clinical decision support tools. Further work to understand the drivers for antibiotic prescribing is crucial to determine how resources can best be allocated to the wide spectrum of interventions aimed at improving antimicrobial stewardship in the GP clinic. Reducing the demonstrated burden of prescribing for sore throat and URTI through a multifaceted approach that includes vaccination could help combat the ongoing global health threat of antimicrobial resistance [ 31 ].

Availability of data and materials

The data that support the findings of this study are available from Patron but restrictions apply to the availability of these data, which were used under agreement for the current study, and so are not publicly available. Authors had full access to the data (including statistical reports and tables).

Abbreviations

Australian Medicines Terminology

Acute rheumatic fever

Anatomical Therapeutic Chemical

General practice

Interquartile range

Primary Care Audit, Teaching and Research Open Network

Rheumatic heart disease

Pharmaceutical Benefits Scheme

Upper respiratory tract infection

World Health Organization

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Acknowledgements

This research used de-identified patient data from the Patron primary care data repository (extracted from consenting general practices), which has been created and is operated by the Department of General Practice, University of Melbourne ( www.gp.unimelb.edu.au/datafordecisions ).

We gratefully acknowledge the contribution of Rod James via assistance with definitions and his knowledge of antibiotic prescribing.

This pilot work was supported by an internal seed funding research grant from MCRI. The funder had no role in the study, and researchers were independent from the funder.

Author information

Kylie S Carville and Niamh Meagher contributed equally to the work.

Jodie McVernon and David J Price contributed equally to the work.

Authors and Affiliations

Doherty Epidemiology, Victorian Infectious Diseases Reference Laboratory, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia

Kylie S Carville, James Fielding & Jodie McVernon

Department of Infectious Diseases, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia

Kylie S Carville, Niamh Meagher, James Fielding, Jodie McVernon & David J Price

Department of Microbiology, Infection Prevention and Control, The Royal Children’s Hospital Melbourne, Melbourne, Victoria, Australia

Yara-Natalie Abo

Department of General Practice and Primary Care, The University of Melbourne, Melbourne, Victoria, Australia

Jo-Anne Manski-Nankervis

Centre for International Child Health, Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia

Andrew Steer

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

David J Price

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Contributions

JM and AS conceived the study. KC, JMN, and JF developed the proposal and acquired data. KC, NM and DJP analysed and interpreted data. YA assessed the literature. KC, NM and YA drafted the initial manuscript. All authors revised and approved the final manuscript.

Corresponding author

Correspondence to Niamh Meagher .

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Ethics approval and consent to participate.

Informed consent was waived by The University of Melbourne Human Research Ethics Committee for the Data for Decisions program, consistent with the National Health and Medical Research Council’s guidelines (Ethics ID: 1647396).(19) Use of Patron data for this study was also approved by the University of Melbourne Human Research Ethics Committee (Ethics ID: 1750534). All data extraction and analyses were carried out in accordance with relevant guidelines and regulations.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Carville, K.S., Meagher, N., Abo, YN. et al. Burden of antimicrobial prescribing in primary care attributable to sore throat: a retrospective cohort study of patient record data. BMC Prim. Care 25 , 117 (2024). https://doi.org/10.1186/s12875-024-02371-y

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Received : 07 September 2023

Accepted : 08 April 2024

Published : 17 April 2024

DOI : https://doi.org/10.1186/s12875-024-02371-y

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