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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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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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Home Blog Design Understanding Data Presentations (Guide + Examples)

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.

FAQ's on a data presentation

1. what is data presentation, and why is it important in 2024.

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! Sign up for our free trial or book a demo ! ‍

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10 Methods of Data Presentation That Really Work in 2024

Leah Nguyen • 15 July, 2024 • 13 min read

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 and make your presentation as clear as the day? Let's check out these best ways to present data. 💎

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Get any of the above examples as templates. Sign up for free and take what you want from the template library!

Data Presentation - What Is It?

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 to cut 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.

#3 - Pie chart

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.

#4 - Bar 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 data presentation. 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.

#5 - Histogram

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.

#6 - Line graph

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).

#7 - Pictogram graph

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.

#8 - Radar chart

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 .

#9 - Heat map

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

Most US 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.

#10 - Scatter plot

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 quizzes 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 end your session with open-ended questions to see what your participants really think.

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👇

Frequently Asked Questions

What is a 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 the presentation?

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

Why should you use charts for presentation?

You should use charts to ensure your contents and visuals 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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Statistical data presentation

1 Department of Anesthesiology and Pain Medicine, Dongguk University Ilsan Hospital, Goyang, Korea.

Sangseok Lee

2 Department of Anesthesiology and Pain Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea.

Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed, and analyzed. However, no matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers. In this article, the techniques of data and information presentation in textual, tabular, and graphical forms are introduced. Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and qualitative information. A graph is a very effective visual tool as it displays data at a glance, facilitates comparison, and can reveal trends and relationships within the data such as changes over time, frequency distribution, and correlation or relative share of a whole. Text, tables, and graphs for data and information presentation are very powerful communication tools. They can make an article easy to understand, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Moreover, as journal editors and reviewers glance at these presentations before reading the whole article, their importance cannot be ignored.

Introduction

Data are a set of facts, and provide a partial picture of reality. Whether data are being collected with a certain purpose or collected data are being utilized, questions regarding what information the data are conveying, how the data can be used, and what must be done to include more useful information must constantly be kept in mind.

Since most data are available to researchers in a raw format, they must be summarized, organized, and analyzed to usefully derive information from them. Furthermore, each data set needs to be presented in a certain way depending on what it is used for. Planning how the data will be presented is essential before appropriately processing raw data.

First, a question for which an answer is desired must be clearly defined. The more detailed the question is, the more detailed and clearer the results are. A broad question results in vague answers and results that are hard to interpret. In other words, a well-defined question is crucial for the data to be well-understood later. Once a detailed question is ready, the raw data must be prepared before processing. These days, data are often summarized, organized, and analyzed with statistical packages or graphics software. Data must be prepared in such a way they are properly recognized by the program being used. The present study does not discuss this data preparation process, which involves creating a data frame, creating/changing rows and columns, changing the level of a factor, categorical variable, coding, dummy variables, variable transformation, data transformation, missing value, outlier treatment, and noise removal.

We describe the roles and appropriate use of text, tables, and graphs (graphs, plots, or charts), all of which are commonly used in reports, articles, posters, and presentations. Furthermore, we discuss the issues that must be addressed when presenting various kinds of information, and effective methods of presenting data, which are the end products of research, and of emphasizing specific information.

Data Presentation

Data can be presented in one of the three ways:

–as text;

–in tabular form; or

–in graphical form.

Methods of presentation must be determined according to the data format, the method of analysis to be used, and the information to be emphasized. Inappropriately presented data fail to clearly convey information to readers and reviewers. Even when the same information is being conveyed, different methods of presentation must be employed depending on what specific information is going to be emphasized. A method of presentation must be chosen after carefully weighing the advantages and disadvantages of different methods of presentation. For easy comparison of different methods of presentation, let us look at a table ( Table 1 ) and a line graph ( Fig. 1 ) that present the same information [ 1 ]. If one wishes to compare or introduce two values at a certain time point, it is appropriate to use text or the written language. However, a table is the most appropriate when all information requires equal attention, and it allows readers to selectively look at information of their own interest. Graphs allow readers to understand the overall trend in data, and intuitively understand the comparison results between two groups. One thing to always bear in mind regardless of what method is used, however, is the simplicity of presentation.

Values are expressed as mean ± SD. Group C: normal saline, Group D: dexmedetomidine. SBP: systolic blood pressure, DBP: diastolic blood pressure, MBP: mean blood pressure, HR: heart rate. * P < 0.05 indicates a significant increase in each group, compared with the baseline values. † P < 0.05 indicates a significant decrease noted in Group D, compared with the baseline values. ‡ P < 0.05 indicates a significant difference between the groups.

Text presentation

Text is the main method of conveying information as it is used to explain results and trends, and provide contextual information. Data are fundamentally presented in paragraphs or sentences. Text can be used to provide interpretation or emphasize certain data. If quantitative information to be conveyed consists of one or two numbers, it is more appropriate to use written language than tables or graphs. For instance, information about the incidence rates of delirium following anesthesia in 2016–2017 can be presented with the use of a few numbers: “The incidence rate of delirium following anesthesia was 11% in 2016 and 15% in 2017; no significant difference of incidence rates was found between the two years.” If this information were to be presented in a graph or a table, it would occupy an unnecessarily large space on the page, without enhancing the readers' understanding of the data. If more data are to be presented, or other information such as that regarding data trends are to be conveyed, a table or a graph would be more appropriate. By nature, data take longer to read when presented as texts and when the main text includes a long list of information, readers and reviewers may have difficulties in understanding the information.

Table presentation

Tables, which convey information that has been converted into words or numbers in rows and columns, have been used for nearly 2,000 years. Anyone with a sufficient level of literacy can easily understand the information presented in a table. Tables are the most appropriate for presenting individual information, and can present both quantitative and qualitative information. Examples of qualitative information are the level of sedation [ 2 ], statistical methods/functions [ 3 , 4 ], and intubation conditions [ 5 ].

The strength of tables is that they can accurately present information that cannot be presented with a graph. A number such as “132.145852” can be accurately expressed in a table. Another strength is that information with different units can be presented together. For instance, blood pressure, heart rate, number of drugs administered, and anesthesia time can be presented together in one table. Finally, tables are useful for summarizing and comparing quantitative information of different variables. However, the interpretation of information takes longer in tables than in graphs, and tables are not appropriate for studying data trends. Furthermore, since all data are of equal importance in a table, it is not easy to identify and selectively choose the information required.

For a general guideline for creating tables, refer to the journal submission requirements 1) .

Heat maps for better visualization of information than tables

Heat maps help to further visualize the information presented in a table by applying colors to the background of cells. By adjusting the colors or color saturation, information is conveyed in a more visible manner, and readers can quickly identify the information of interest ( Table 2 ). Software such as Excel (in Microsoft Office, Microsoft, WA, USA) have features that enable easy creation of heat maps through the options available on the “conditional formatting” menu.

All numbers were created by the author. SBP: systolic blood pressure, DBP: diastolic blood pressure, MBP: mean blood pressure, HR: heart rate.

Graph presentation

Whereas tables can be used for presenting all the information, graphs simplify complex information by using images and emphasizing data patterns or trends, and are useful for summarizing, explaining, or exploring quantitative data. While graphs are effective for presenting large amounts of data, they can be used in place of tables to present small sets of data. A graph format that best presents information must be chosen so that readers and reviewers can easily understand the information. In the following, we describe frequently used graph formats and the types of data that are appropriately presented with each format with examples.

Scatter plot

Scatter plots present data on the x - and y -axes and are used to investigate an association between two variables. A point represents each individual or object, and an association between two variables can be studied by analyzing patterns across multiple points. A regression line is added to a graph to determine whether the association between two variables can be explained or not. Fig. 2 illustrates correlations between pain scoring systems that are currently used (PSQ, Pain Sensitivity Questionnaire; PASS, Pain Anxiety Symptoms Scale; PCS, Pain Catastrophizing Scale) and Geop-Pain Questionnaire (GPQ) with the correlation coefficient, R, and regression line indicated on the scatter plot [ 6 ]. If multiple points exist at an identical location as in this example ( Fig. 2 ), the correlation level may not be clear. In this case, a correlation coefficient or regression line can be added to further elucidate the correlation.

Bar graph and histogram

A bar graph is used to indicate and compare values in a discrete category or group, and the frequency or other measurement parameters (i.e. mean). Depending on the number of categories, and the size or complexity of each category, bars may be created vertically or horizontally. The height (or length) of a bar represents the amount of information in a category. Bar graphs are flexible, and can be used in a grouped or subdivided bar format in cases of two or more data sets in each category. Fig. 3 is a representative example of a vertical bar graph, with the x -axis representing the length of recovery room stay and drug-treated group, and the y -axis representing the visual analog scale (VAS) score. The mean and standard deviation of the VAS scores are expressed as whiskers on the bars ( Fig. 3 ) [ 7 ].

By comparing the endpoints of bars, one can identify the largest and the smallest categories, and understand gradual differences between each category. It is advised to start the x - and y -axes from 0. Illustration of comparison results in the x - and y -axes that do not start from 0 can deceive readers' eyes and lead to overrepresentation of the results.

One form of vertical bar graph is the stacked vertical bar graph. A stack vertical bar graph is used to compare the sum of each category, and analyze parts of a category. While stacked vertical bar graphs are excellent from the aspect of visualization, they do not have a reference line, making comparison of parts of various categories challenging ( Fig. 4 ) [ 8 ].

A pie chart, which is used to represent nominal data (in other words, data classified in different categories), visually represents a distribution of categories. It is generally the most appropriate format for representing information grouped into a small number of categories. It is also used for data that have no other way of being represented aside from a table (i.e. frequency table). Fig. 5 illustrates the distribution of regular waste from operation rooms by their weight [ 8 ]. A pie chart is also commonly used to illustrate the number of votes each candidate won in an election.

Line plot with whiskers

A line plot is useful for representing time-series data such as monthly precipitation and yearly unemployment rates; in other words, it is used to study variables that are observed over time. Line graphs are especially useful for studying patterns and trends across data that include climatic influence, large changes or turning points, and are also appropriate for representing not only time-series data, but also data measured over the progression of a continuous variable such as distance. As can be seen in Fig. 1 , mean and standard deviation of systolic blood pressure are indicated for each time point, which enables readers to easily understand changes of systolic pressure over time [ 1 ]. If data are collected at a regular interval, values in between the measurements can be estimated. In a line graph, the x-axis represents the continuous variable, while the y-axis represents the scale and measurement values. It is also useful to represent multiple data sets on a single line graph to compare and analyze patterns across different data sets.

Box and whisker chart

A box and whisker chart does not make any assumptions about the underlying statistical distribution, and represents variations in samples of a population; therefore, it is appropriate for representing nonparametric data. AA box and whisker chart consists of boxes that represent interquartile range (one to three), the median and the mean of the data, and whiskers presented as lines outside of the boxes. Whiskers can be used to present the largest and smallest values in a set of data or only a part of the data (i.e. 95% of all the data). Data that are excluded from the data set are presented as individual points and are called outliers. The spacing at both ends of the box indicates dispersion in the data. The relative location of the median demonstrated within the box indicates skewness ( Fig. 6 ). The box and whisker chart provided as an example represents calculated volumes of an anesthetic, desflurane, consumed over the course of the observation period ( Fig. 7 ) [ 9 ].

Three-dimensional effects

Most of the recently introduced statistical packages and graphics software have the three-dimensional (3D) effect feature. The 3D effects can add depth and perspective to a graph. However, since they may make reading and interpreting data more difficult, they must only be used after careful consideration. The application of 3D effects on a pie chart makes distinguishing the size of each slice difficult. Even if slices are of similar sizes, slices farther from the front of the pie chart may appear smaller than the slices closer to the front ( Fig. 8 ).

Drawing a graph: example

Finally, we explain how to create a graph by using a line graph as an example ( Fig. 9 ). In Fig. 9 , the mean values of arterial pressure were randomly produced and assumed to have been measured on an hourly basis. In many graphs, the x- and y-axes meet at the zero point ( Fig. 9A ). In this case, information regarding the mean and standard deviation of mean arterial pressure measurements corresponding to t = 0 cannot be conveyed as the values overlap with the y-axis. The data can be clearly exposed by separating the zero point ( Fig. 9B ). In Fig. 9B , the mean and standard deviation of different groups overlap and cannot be clearly distinguished from each other. Separating the data sets and presenting standard deviations in a single direction prevents overlapping and, therefore, reduces the visual inconvenience. Doing so also reduces the excessive number of ticks on the y-axis, increasing the legibility of the graph ( Fig. 9C ). In the last graph, different shapes were used for the lines connecting different time points to further allow the data to be distinguished, and the y-axis was shortened to get rid of the unnecessary empty space present in the previous graphs ( Fig. 9D ). A graph can be made easier to interpret by assigning each group to a different color, changing the shape of a point, or including graphs of different formats [ 10 ]. The use of random settings for the scale in a graph may lead to inappropriate presentation or presentation of data that can deceive readers' eyes ( Fig. 10 ).

Owing to the lack of space, we could not discuss all types of graphs, but have focused on describing graphs that are frequently used in scholarly articles. We have summarized the commonly used types of graphs according to the method of data analysis in Table 3 . For general guidelines on graph designs, please refer to the journal submission requirements 2) .

Conclusions

Text, tables, and graphs are effective communication media that present and convey data and information. They aid readers in understanding the content of research, sustain their interest, and effectively present large quantities of complex information. As journal editors and reviewers will scan through these presentations before reading the entire text, their importance cannot be disregarded. For this reason, authors must pay as close attention to selecting appropriate methods of data presentation as when they were collecting data of good quality and analyzing them. In addition, having a well-established understanding of different methods of data presentation and their appropriate use will enable one to develop the ability to recognize and interpret inappropriately presented data or data presented in such a way that it deceives readers' eyes [ 11 ].

<Appendix>

Output for presentation.

Discovery and communication are the two objectives of data visualization. In the discovery phase, various types of graphs must be tried to understand the rough and overall information the data are conveying. The communication phase is focused on presenting the discovered information in a summarized form. During this phase, it is necessary to polish images including graphs, pictures, and videos, and consider the fact that the images may look different when printed than how appear on a computer screen. In this appendix, we discuss important concepts that one must be familiar with to print graphs appropriately.

The KJA asks that pictures and images meet the following requirement before submission 3)

“Figures and photographs should be submitted as ‘TIFF’ files. Submit files of figures and photographs separately from the text of the paper. Width of figure should be 84 mm (one column). Contrast of photos or graphs should be at least 600 dpi. Contrast of line drawings should be at least 1,200 dpi. The Powerpoint file (ppt, pptx) is also acceptable.”

Unfortunately, without sufficient knowledge of computer graphics, it is not easy to understand the submission requirement above. Therefore, it is necessary to develop an understanding of image resolution, image format (bitmap and vector images), and the corresponding file specifications.

Resolution is often mentioned to describe the quality of images containing graphs or CT/MRI scans, and video files. The higher the resolution, the clearer and closer to reality the image is, while the opposite is true for low resolutions. The most representative unit used to describe a resolution is “dpi” (dots per inch): this literally translates to the number of dots required to constitute 1 inch. The greater the number of dots, the higher the resolution. The KJA submission requirements recommend 600 dpi for images, and 1,200 dpi 4) for graphs. In other words, resolutions in which 600 or 1,200 dots constitute one inch are required for submission.

There are requirements for the horizontal length of an image in addition to the resolution requirements. While there are no requirements for the vertical length of an image, it must not exceed the vertical length of a page. The width of a column on one side of a printed page is 84 mm, or 3.3 inches (84/25.4 mm ≒ 3.3 inches). Therefore, a graph must have a resolution in which 1,200 dots constitute 1 inch, and have a width of 3.3 inches.

Bitmap and Vector

Methods of image construction are important. Bitmap images can be considered as images drawn on section paper. Enlarging the image will enlarge the picture along with the grid, resulting in a lower resolution; in other words, aliasing occurs. On the other hand, reducing the size of the image will reduce the size of the picture, while increasing the resolution. In other words, resolution and the size of an image are inversely proportionate to one another in bitmap images, and it is a drawback of bitmap images that resolution must be considered when adjusting the size of an image. To enlarge an image while maintaining the same resolution, the size and resolution of the image must be determined before saving the image. An image that has already been created cannot avoid changes to its resolution according to changes in size. Enlarging an image while maintaining the same resolution will increase the number of horizontal and vertical dots, ultimately increasing the number of pixels 5) of the image, and the file size. In other words, the file size of a bitmap image is affected by the size and resolution of the image (file extensions include JPG [JPEG] 6) , PNG 7) , GIF 8) , and TIF [TIFF] 9) . To avoid this complexity, the width of an image can be set to 4 inches and its resolution to 900 dpi to satisfy the submission requirements of most journals [ 12 ].

Vector images overcome the shortcomings of bitmap images. Vector images are created based on mathematical operations of line segments and areas between different points, and are not affected by aliasing or pixelation. Furthermore, they result in a smaller file size that is not affected by the size of the image. They are commonly used for drawings and illustrations (file extensions include EPS 10) , CGM 11) , and SVG 12) ).

Finally, the PDF 13) is a file format developed by Adobe Systems (Adobe Systems, CA, USA) for electronic documents, and can contain general documents, text, drawings, images, and fonts. They can also contain bitmap and vector images. While vector images are used by researchers when working in Powerpoint, they are saved as 960 × 720 dots when saved in TIFF format in Powerpoint. This results in a resolution that is inappropriate for printing on a paper medium. To save high-resolution bitmap images, the image must be saved as a PDF file instead of a TIFF, and the saved PDF file must be imported into an imaging processing program such as Photoshop™(Adobe Systems, CA, USA) to be saved in TIFF format [ 12 ].

1) Instructions to authors in KJA; section 5-(9) Table; https://ekja.org/index.php?body=instruction

2) Instructions to Authors in KJA; section 6-1)-(10) Figures and illustrations in Manuscript preparation; https://ekja.org/index.php?body=instruction

3) Instructions to Authors in KJA; section 6-1)-(10) Figures and illustrations in Manuscript preparation; https://ekja.org/index.php?body=instruction

4) Resolution; in KJA, it is represented by “contrast.”

5) Pixel is a minimum unit of an image and contains information of a dot and color. It is derived by multiplying the number of vertical and horizontal dots regardless of image size. For example, Full High Definition (FHD) monitor has 1920 × 1080 dots ≒ 2.07 million pixel.

6) Joint Photographic Experts Group.

7) Portable Network Graphics.

8) Graphics Interchange Format

9) Tagged Image File Format; TIFF

10) Encapsulated PostScript.

11) Computer Graphics Metafile.

12) Scalable Vector Graphics.

13) Portable Document Format.

Statistical data presentation

• Korean Journal of Anesthesiology 70(3):267

• Dongguk Unversity Ilsan Hospital, Goyang, Republic of Korea

• Inje University, Sanggye Paik Hospital

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Tips for Presenting Statistical Data

Statistician Zone

Welcome to our comprehensive guide on mastering the art of presenting statistical data. In this post, we'll explore essential tips that can transform your data presentation skills. We understand that statistics can be overwhelming, and presenting them in an engaging, understandable way can be challenging. But don't worry, we've got you covered. Whether you're a student, a researcher, or a business professional, this guide will help you present statistical data effectively.

Understanding Your Audience

Knowing your audience is the first step in presenting statistical data effectively. You need to understand their background, their level of knowledge about the topic, and what they expect from your presentation. This understanding will guide you in choosing the right statistical data and the best way to present it.

For instance, if your audience is not familiar with statistical jargon, you should avoid using complex terms and focus on presenting the data in a simple, understandable way. Use visuals to illustrate your points and explain the significance of the data in a language your audience can understand.

On the other hand, if your audience is well-versed in statistics, you can delve deeper into the data. You can use more complex graphs and charts, discuss the methodology used in data collection, and engage your audience in a more technical discussion. Remember, the goal is to communicate effectively with your audience, not to impress them with your statistical prowess.

Choosing the Right Visuals

Visuals play a crucial role in presenting statistical data. They help your audience understand the data quickly and easily. However, not all visuals are created equal. You need to choose the right visual based on the type of data you're presenting and the message you want to convey.

Bar charts, for instance, are great for comparing quantities across different categories. Line graphs, on the other hand, are ideal for showing trends over time. Pie charts can be used to show proportions of a whole, while scatter plots are perfect for showing relationships between two variables.

When creating visuals, keep them simple and uncluttered. Avoid using too many colors or unnecessary decorations that can distract your audience. Also, make sure to label your visuals clearly and provide a brief explanation of what they represent.

Using Clear and Concise Language

When presenting statistical data, it's important to use clear and concise language. Avoid using jargon or complex terms that your audience may not understand. Instead, explain the data in simple terms and focus on the key points you want your audience to remember.

For example, instead of saying "The data shows a statistically significant positive correlation between X and Y", you could say "As X increases, Y also tends to increase". This way, you're not only making the data easier to understand, but you're also highlighting the main takeaway for your audience.

Also, when discussing the results, avoid making absolute statements unless the data supports them. Instead, use phrases like "the data suggests" or "the results indicate" to show that you're interpreting the data, not stating facts.

Telling a Story with Your Data

One of the most effective ways to engage your audience and make your data memorable is by telling a story. Instead of just presenting the numbers, show your audience what those numbers mean. Connect the data to real-world situations or issues that your audience cares about.

For instance, if you're presenting data on climate change, you could start by showing the rising global temperatures over the years. Then, you could relate this data to the increasing frequency of wildfires or the melting of polar ice caps. By doing this, you're not just presenting data, you're telling a story that your audience can relate to and remember.

Remember, the goal of presenting statistical data is not just to inform, but also to persuade and inspire action. By telling a story with your data, you can achieve all these goals.

Practicing Your Presentation

Practice makes perfect, and this is especially true when it comes to presenting statistical data. Before your presentation, take the time to practice. This will help you become more familiar with the data and your visuals, and it will also help you anticipate any questions your audience might have.

When practicing, pay attention to your pacing. You don't want to rush through your presentation, but you also don't want to drag it out. Aim for a pace that allows your audience to absorb the information, but also keeps them engaged.

Also, practice your body language and tone of voice. These non-verbal cues can greatly affect how your audience perceives your presentation. Stand tall, make eye contact, and speak with confidence. Remember, you're not just presenting data, you're also selling an idea.

Handling Questions and Feedback

After your presentation, be prepared to handle questions and feedback from your audience. This is an opportunity for you to clarify any points that your audience may not have understood, and to further discuss the implications of your data.

When answering questions, be honest and straightforward. If you don't know the answer, admit it and offer to find out. Also, be open to feedback. Your audience's insights and perspectives can help you improve your future presentations.

Remember, presenting statistical data is not just about showing numbers. It's about communicating effectively, engaging your audience, and making your data meaningful and memorable.

Wrapping Up: Mastering Data Presentation

Presenting statistical data effectively is an art that requires understanding your audience, choosing the right visuals, using clear language, telling a story, practicing your presentation, and handling questions and feedback. By mastering these skills, you can transform your data presentations from dull and confusing to engaging and memorable. So, start applying these tips today and see the difference they can make in your data presentation skills.

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“Statistics is the grammar of science.” – Karl Pearson

Data and statistics are part of almost every sector and are used to understand and drive results.

These are essential tools to make decisions, answer important questions, summarize big data, recognize patterns, prove theories, etc.

A good presentation gets the backing of data and statistics, but data alone will not guarantee the success of a presentation.

How you choose to present that data either doubles or decimates the impact of your presentation.

While you get weeks working on the charts and numbers, your audience gets only a few minutes to go through the content. So, it becomes all the more imperative that you present it in the most comprehensible way possible for them to understand and remember.

Unfortunately, most of us, at some point, have sat through presentations where the slides didn’t make much sense, and we had to rely on the speaker to know more.

So, take the help of these pointers to turn your complex numbers into interesting information. Let’s begin.

Tips to Deliver Statistics and Analytics in an Impactful Manner

Your presentation might look boring and lengthy if not presented well. Here are some quick tips to make your data lively and impactful.

1. Graphics are the Way to Go

Imagine a slide with a lot of data and numbers presented just like that. How difficult would it be to make sense of it or to read it?

Graphics and visuals are the most powerful way to present numbers. It can make your data easy to understand, livelier, and better accessible to your audience. Graphics and visuals help break down complex and intricate information into readable content.

Keep these tips in mind when using graphics-

Your visuals should not overlap the text and vice versa.

The graphics should be in alignment with your brand and the broader theme of the presentation.

Choose the right graph. For example, a bar graph is apt when you want to compare, and a line graph can be used to depict changes over time.

You can use pre-designed presentation templates featuring relevant graphics and charts to complement your statistical and analytical data and present it in a simple yet trendy way.

Stories and Analogies are Incredibly Powerful

Do you know what mnemonics and memory palaces do? They help you associate the things you want to remember with certain other easy-to-remember things (which you already know).

It took you a lot of time to craft all that information in your presentation, and consider it a bonus if people will remember parts of it later on.

Storytelling is one of the most potent ways to capture attention and aid memory retention. Try to weave a story around the data to help people understand and recollect the data better.

Analogies will help soften the impact of everything technical and non-understandable into something familiar to the audience.

For example, if your presentation is about business growth, you can highlight the increase in numbers with what took you to reach there, i.e., how you improved the website’s visibility, interface, etc.

For more understanding, watch this video displaying a few stunning examples of data storytelling.

3. Highlight Points that Your Data is Illustrating (Inference)

When you present data on the slides , it is not that the numbers hold the real value but the inference drawn from it. Remember to highlight well how the statistics and analytics support your major points.

Don’t leave the decoding part to the audience, or your audience won’t be able to process the relevancy of your argument. When you want to connect the statistic with an inference, make sure the transition is clear with terms such as ‘the numbers show,’ ‘this data proves,’ ‘this figure/chart illustrates,’ etc.

The transitions are critical to bringing everyone’s attention to the most important part of relating to and explaining the conclusions. Not everyone likes to crunch numbers, so highlight the inference in such a way that there is no scope of confusion left for people.

4. Your Data Should be Visible

It sounds obvious, right? But it is a common mistake while placing data on the slides. When you have a lot of information to share, with only so much place on a single slide, it might so happen that some content is aptly visible on a laptop but not so much from a distance in the actual presentation.

To avoid the debacle of having to translate poorly visible numbers and labels, practice your presentation by having people sit as far away as in the actual presentation. Make sure that each slide is clearly visible and readable with all information.

It will also help you align and tweak the material on the slides (keeping only the relevant and required content).

5. Share Only One Piece of Information

When you have a lot of information to share, it becomes an impulse to share everything you know. It is also hard to filter out information that you can exclude from relevant figures. And last, a lot of presenters feel that they are required to share all the information they present - on the slides - as well.

Chaos on the slides with too many details and overuse of the negative space – yes, it will show people the work you have done and the data you have collected, but it will be just that. It will confuse your audience and miss the point for you.

Include data points that significantly support your main argument, and it should be one point on one slide/chart. Enquire yourself what’s the most important learning that you want people to take from that data. Convey that to people.

If you have multiple key points, present each with new visualizations. It will help you demarcate your presentation neatly into understandable chunks and help people remember better. Also, refrain from including unnecessary information that doesn’t directly affect your main point.

6. Use Colors Wisely

Colors will help you differentiate between figures and charts. It can help people figure out the before and after clearly. Presenting the data in black and white wouldn’t be impactful.

Remember to use colors consistently when presenting the same values in a chart . You wouldn’t want your audience confused and draw inaccurate conclusions by highlighting a similar figure in different colors. You can also use brand colors in your presentation to appear more professional.

Another way of using the colors in a user-friendly way is by matching the axis and series colors when you are presenting a dual-axis chart. It will help your audience match the series with the respective axis easily. There are a lot of other ways in which you can use colors to bring coherency and life to your data.

7. You can Present the Data in Stages

Animating your charts will make the data look less intimidating and help people derive more information from the figures. Presenting your data in stages will enhance comprehension and give everyone time to process it properly.

For example, let’s say you are showing the sale of 2 products. You can show the chart in 3 stages by explaining the axes in one, then a chart for the sale of product X as a base (2nd stage), and after that for product Y (3rd stage).

PowerPoint has a chart animation feature that lets you do it by series or category.

The technique will aid you in presenting your data effectively and efficiently.

8. Go Simple

Don’t scare your audience with a barrage of numbers. You have had time to soak in everything you want to tell, but this won’t be the case with people sitting in front. Try to be simple with the data you are presenting. For instance, keep the format of your number simple. Don’t make people count the number of zeroes like 10000 vs. 1000000.

Try to include decimals (skip unnecessary decimals) for numbers that are close to each other in a range of values and not for numbers as far away as 2-90%. If your numbers are within a few percent range of each other, it is important to use decimals.

Another factor that can help simplify your data is keeping the numbers right-aligned always. It can help people scan the numbers (to study), which becomes a little harder in the case of center-aligned numbers.

9. Initiate with the End

Try to start by giving the bottom line up front. Let us explain what it means. Your audience will naturally scan your slide from top to bottom. Your titles should give a clear picture of your chart. Rather than going for vague titles and letting people fumble through the slides to figure out the key message – share a clear title that will immediately let them know what to look for in the slide.

For example, let’s say your chart is about a certain product’s growth over other products. Go for a title that says Product C’s growth over the last quarter. Your audience will automatically start scanning the relevant figures related to this product and save time and effort.

Your slide title should be point specific and reinforce the main point. Try not to go for generic words and phrases serving no functional purpose.

10. Remember to Present to the Audience

One mistake that you can make while presenting statistics and analytics is focusing too much on your slides. After all, even you wouldn’t remember all the figures.

It can be detrimental for you as a presenter, as you would not be able to connect to your audience and might look uncompetitive. Therefore, try to keep your gaze on the audience, for they will be able to understand better when you speak while maintaining eye contact.

You can keep cue cards for your reference and look at your slides here and there while emphasizing a point to the audience.

In a Nutshell

Incorporating data and statistics to add credibility to a presentation is a common practice. And finding relevant data is not difficult either. However, how you choose to present that data will define the impact of your presentation.

Keep these above tips in mind to make your figures speak to the audience efficiently.

They will make your presentation appear crisp and appealing and bring life to your statistics and analysis.

Also, remember your presentation should have a clear take-home message. People should know what they are supposed to do with what you have shared. You can include a clear CTA in your presentation to guide everyone better.

About the Author

Ashish Arora is Co-Founder of SketchBubble, a leading provider of result-driven, professionally built PowerPoint templates .

Travelling the world to gather new creative ideas, he has been working in the digital marketing space since 2007 and has a passion for designing presentations.

Continue to: Presenting Data Top Tips for Effective Presentations

See also: Statistical Analysis: Understanding Statistical Distributions Industries Where Employers Value Data Analytics Skills 7 Things That Can Help You Improve Your Data Collection Skills

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

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

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

Click to jump ahead:

10 Essential data presentation examples + methods you should know

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

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

1. Bar graph

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

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

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

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

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

2. Line graph

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

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

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

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

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

3. Pie chart

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

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

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

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

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

4. Scatter plot

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

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

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

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

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

5. Histogram

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

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

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

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

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

6. Stacked bar chart

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

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

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

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

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

7. Area chart

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

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

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

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

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

8. Tabular presentation

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

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

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

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

9. Textual data

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

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

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

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

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

10. Pictogram

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

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

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

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

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

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

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

1. Title and objective

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

2. Key data points

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

3. Context and significance

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

4. Key takeaways

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

5. Visuals and charts

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

6. Implications or actions

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

7. Q&A and discussion

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

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

Overloading with data

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

Assuming everyone’s on the same page

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

Misleading visuals

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

Not providing context

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

Not citing sources properly

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

Not telling a story

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

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

Ignoring data quality

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

Simplify your visuals

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

Missing the emotional connection

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

Skipping the actionable insights

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

Can you provide some data presentation examples for business reports?

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

What are some creative data presentation examples for academic presentations?

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

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

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

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

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

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

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

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

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

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

What is the difference between data visualization and data presentation?

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

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

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

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

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

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A Guide to Effective Data Presentation

Key objectives of data presentation, charts and graphs for great visuals, storytelling with data, visuals, and text, audiences and data presentation, the main idea in data presentation, storyboarding and data presentation, additional resources, data presentation.

Tools for effective data presentation

Financial analysts are required to present their findings in a neat, clear, and straightforward manner. They spend most of their time working with spreadsheets in MS Excel, building financial models , and crunching numbers. These models and calculations can be pretty extensive and complex and may only be understood by the analyst who created them. Effective data presentation skills are critical for being a world-class financial analyst .

It is the analyst’s job to effectively communicate the output to the target audience, such as the management team or a company’s external investors. This requires focusing on the main points, facts, insights, and recommendations that will prompt the necessary action from the audience.

One challenge is making intricate and elaborate work easy to comprehend through great visuals and dashboards. For example, tables, graphs, and charts are tools that an analyst can use to their advantage to give deeper meaning to a company’s financial information. These tools organize relevant numbers that are rather dull and give life and story to them.

Here are some key objectives to think about when presenting financial analysis:

• Visual communication
• Audience and context
• Charts, graphs, and images
• Focus on important points
• Design principles
• Storytelling
• Persuasiveness

For a breakdown of these objectives, check out Excel Dashboards & Data Visualization course to help you become a world-class financial analyst.

Charts and graphs make any financial analysis readable, easy to follow, and provide great data presentation. They are often included in the financial model’s output, which is essential for the key decision-makers in a company.

The decision-makers comprise executives and managers who usually won’t have enough time to synthesize and interpret data on their own to make sound business decisions. Therefore, it is the job of the analyst to enhance the decision-making process and help guide the executives and managers to create value for the company.

When an analyst uses charts, it is necessary to be aware of what good charts and bad charts look like and how to avoid the latter when telling a story with data.

Examples of Good Charts

As for great visuals, you can quickly see what’s going on with the data presentation, saving you time for deciphering their actual meaning. More importantly, great visuals facilitate business decision-making because their goal is to provide persuasive, clear, and unambiguous numeric communication.

For reference, take a look at the example below that shows a dashboard, which includes a gauge chart for growth rates, a bar chart for the number of orders, an area chart for company revenues, and a line chart for EBITDA margins.

To learn the step-by-step process of creating these essential tools in MS Excel, watch our video course titled “ Excel Dashboard & Data Visualization .”  Aside from what is given in the example below, our course will also teach how you can use other tables and charts to make your financial analysis stand out professionally.

Learn how to build the graph above in our Dashboards Course !

Example of Poorly Crafted Charts

A bad chart, as seen below, will give the reader a difficult time to find the main takeaway of a report or presentation, because it contains too many colors, labels, and legends, and thus, will often look too busy. It also doesn’t help much if a chart, such as a pie chart, is displayed in 3D, as it skews the size and perceived value of the underlying data. A bad chart will be hard to follow and understand.

Aside from understanding the meaning of the numbers, a financial analyst must learn to combine numbers and language to craft an effective story. Relying only on data for a presentation may leave your audience finding it difficult to read, interpret, and analyze your data. You must do the work for them, and a good story will be easier to follow. It will help you arrive at the main points faster, rather than just solely presenting your report or live presentation with numbers.

The data can be in the form of revenues, expenses, profits, and cash flow. Simply adding notes, comments, and opinions to each line item will add an extra layer of insight, angle, and a new perspective to the report.

Furthermore, by combining data, visuals, and text, your audience will get a clear understanding of the current situation,  past events, and possible conclusions and recommendations that can be made for the future.

The simple diagram below shows the different categories of your audience.

  This chart is taken from our course on how to present data .

Internal Audience

An internal audience can either be the executives of the company or any employee who works in that company. For executives, the purpose of communicating a data-filled presentation is to give an update about a certain business activity such as a project or an initiative.

Another important purpose is to facilitate decision-making on managing the company’s operations, growing its core business, acquiring new markets and customers, investing in R&D, and other considerations. Knowing the relevant data and information beforehand will guide the decision-makers in making the right choices that will best position the company toward more success.

External Audience

An external audience can either be the company’s existing clients, where there are projects in progress, or new clients that the company wants to build a relationship with and win new business from. The other external audience is the general public, such as the company’s external shareholders and prospective investors of the company.

When it comes to winning new business, the analyst’s presentation will be more promotional and sales-oriented, whereas a project update will contain more specific information for the client, usually with lots of industry jargon.

Audiences for Live and Emailed Presentation

A live presentation contains more visuals and storytelling to connect more with the audience. It must be more precise and should get to the point faster and avoid long-winded speech or text because of limited time.

In contrast, an emailed presentation is expected to be read, so it will include more text. Just like a document or a book, it will include more detailed information, because its context will not be explained with a voice-over as in a live presentation.

When it comes to details, acronyms, and jargon in the presentation, these things depend on whether your audience are experts or not.

Every great presentation requires a clear “main idea”. It is the core purpose of the presentation and should be addressed clearly. Its significance should be highlighted and should cause the targeted audience to take some action on the matter.

An example of a serious and profound idea is given below.

To communicate this big idea, we have to come up with appropriate and effective visual displays to show both the good and bad things surrounding the idea. It should put emphasis and attention on the most important part, which is the critical cash balance and capital investment situation for next year. This is an important component of data presentation.

The storyboarding below is how an analyst would build the presentation based on the big idea. Once the issue or the main idea has been introduced, it will be followed by a demonstration of the positive aspects of the company’s performance, as well as the negative aspects, which are more important and will likely require more attention.

Various ideas will then be suggested to solve the negative issues. However, before choosing the best option, a comparison of the different outcomes of the suggested ideas will be performed. Finally, a recommendation will be made that centers around the optimal choice to address the imminent problem highlighted in the big idea.

This storyboard is taken from our course on how to present data .

To get to the final point (recommendation), a great deal of analysis has been performed, which includes the charts and graphs discussed earlier, to make the whole presentation easy to follow, convincing, and compelling for your audience.

CFI offers the Business Intelligence & Data Analyst (BIDA)® certification program for those looking to take their careers to the next level. To keep learning and developing your knowledge base, please explore the additional relevant resources below:

• Investment Banking Pitch Books
• Excel Dashboards
• Financial Modeling Guide
• Startup Pitch Book
• See all business intelligence resources
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Data Presentation

Josée Dupuis, PhD, Professor of Biostatistics, Boston University School of Public Health

Wayne LaMorte, MD, PhD, MPH, Professor of Epidemiology, Boston University School of Public Health

Introduction

While graphical summaries of data can certainly be powerful ways of communicating results clearly and unambiguously in a way that facilitates our ability to think about the information, poorly designed graphical displays can be ambiguous, confusing, and downright misleading. The keys to excellence in graphical design and communication are much like the keys to good writing. Adhere to fundamental principles of style and communicate as logically, accurately, and clearly as possible. Excellence in writing is generally achieved by avoiding unnecessary words and paragraphs; it is efficient. In a similar fashion, excellence in graphical presentation is generally achieved by efficient designs that avoid unnecessary ink.

Excellence in graphical presentation depends on:

• Choosing the best medium for presenting the information
• Designing the components of the graph in a way that communicates the information as clearly and accurately as possible.

Table or Graph?

• Tables are generally best if you want to be able to look up specific information or if the values must be reported precisely.
• Graphics are best for illustrating trends and making comparisons

The side by side illustrations below show the same information, first in table form and then in graphical form. While the information in the table is precise, the real goal is to compare a series of clinical outcomes in subjects taking either a drug or a placebo. The graphical presentation on the right makes it possible to quickly see that for each of the outcomes evaluated, the drug produced relief in a great proportion of subjects. Moreover, the viewer gets a clear sense of the magnitude of improvement, and the error bars provided a sense of the uncertainty in the data.

Principles for Table Display

• Sort table rows in a meaningful way
• Avoid alphabetical listing!
• Use rates, proportions or ratios in addition (or instead of) totals
• Show more than two time points if available
• Multiple time points may be better presented in a Figure
• Similar data should go down columns
• Highlight important comparisons
• Show the source of the data

Consider the data in the table below from http://www.cancer.gov/cancertopics/types/commoncancers

Our ability to quickly understand the relative frequency of these cancers is hampered by presenting them in alphabetical order. It is much easier for the reader to grasp the relative frequency by listing them from most frequent to least frequent as in the next table.

However, the same information might be presented more effectively with a dot plot, as shown below.

Data from http://www.cancer.gov/cancertopics/types/commoncancers

Principles of Graphical Excellence from E.R. Tufte

Pattern Perception

Pattern perception is done by

• Detection: recognition of geometry encoding physical values
• Assembly: grouping of detected symbol elements; discerning overall patterns in data
• Estimation: assessment of relative magnitudes of two physical values

Geographic Variation in Cancer

As an example, Tufte offers a series of maps that summarize the age-adjusted mortality rates for various types of cancer in the 3,056 counties in the United States. The maps showing the geographic variation in stomach cancer are shown below.

These maps summarize an enormous amount of information and present it efficiently, coherently, and effectively.in a way that invites the viewer to make comparisons and to think about the substance of the findings. Consider, for example, that the region to the west of the Great Lakes was settled largely by immigrants from Germany and Scand anavia, where traditional methods of preserving food included pickling and curing of fish by smoking. Could these methods be associated with an increased risk of stomach cancer?

John Snow's Spot Map of Cholera Cases

Consider also the spot map that John Snow presented after the cholera outbreak in the Broad Street section of London in September 1854. Snow ascertained the place of residence or work of the victims and represented them on a map of the area using a small black disk to represent each victim and stacking them when more than one occurred at a particular location. Snow reasoned that cholera was probably caused by something that was ingested, because of the intense diarrhea and vomiting of the victims, and he noted that the vast majority of cholera deaths occurred in people who lived or worked in the immediate vicinity of the broad street pump (shown with a red dot that we added for clarity). He further ascertained that most of the victims drank water from the Broad Street pump, and it was this evidence that persuaded the authorities to remove the handle from the pump in order to prevent more deaths.

Humans can readily perceive differences like this when presented effectively as in the two previous examples. However, humans are not good at estimating differences without directly seeing them (especially for steep curves), and we are particularly bad at perceiving relative angles (the principal perception task used in a pie chart).

The use of pie charts is generally discouraged. Consider the pie chart on the left below. It is difficult to accurately assess the relative size of the components in the pie chart, because the human eye has difficulty judging angles. The dot plot on the right shows the same data, but it is much easier to quickly assess the relative size of the components and how they changed from Fiscal Year 2000 to Fiscal Year 2007.

Consider the information in the two pie charts below (showing the same information).The 3-dimensional pie chart on the left distorts the relative proportions. In contrast the 2-dimensional pie chart on the right makes it much easier to compare the relative size of the varies components..

More Principles of Graphical Excellence

Exclude Unneeded Dimensions

These 3-dimensional techniques distort the data and actually interfere with our ability to make accurate comparisons. The distortion caused by 3-dimensional elements can be particularly severe when the graphic is slanted at an angle or when the viewer tends to compare ends up unwittingly comparing the areas of the ink rather than the heights of the bars.

It is much easier to make comparisons with a chart like the one below.

Source: Huang, C, Guo C, Nichols C, Chen S, Martorell R. Elevated levels of protein in urine in adulthood after exposure to

the Chinese famine of 1959–61 during gestation and the early postnatal period. Int. J. Epidemiol. (2014) 43 (6): 1806-1814 .

Omit "Chart Junk"

Consider these two examples.

Here is a simple enumeration of the number of pets in a neighborhood. There is absolutely no reason to connect these counts with lines. This is, in fact, confusing and inappropriate and nothing more than "chart junk."

Source: http://www.go-education.com/free-graph-maker.html

Moiré Vibration

Moiré effects are sometimes used in modern art to produce the appearance of vibration and movement. However, when these effects are applied to statistical presentations, they are distracting and add clutter because the visual noise interferes with the interpretation of the data.

Tufte presents the example shown below from Instituto de Expansao Commercial, Brasil, Graphicos Estatisticas (Rio de Janeiro, 1929, p. 15).

 While the intention is to present quantitative information about the textile industry, the moiré effects do not add anything, and they are distracting, if not visually annoying.

Present Data to Facilitate Comparisons

Here is an attempt to compare catches of cod fish and crab across regions and to relate the variation to changes in water temperature. The problem here is that the Y-axes are vastly different, making it hard to sort out what's really going on. Even the Y-axes for temperature are vastly different.

http://seananderson.ca/courses/11-multipanel/multipanel.pdf1

The ability to make comparisons is greatly facilitated by using the same scales for axes, as illustrated below.

Data source: Dawber TR, Meadors GF, Moore FE Jr. Epidemiological approaches to heart disease:

the Framingham Study. Am J Public Health Nations Health. 1951;41(3):279-81. PMID: 14819398

It is also important to avoid distorting the X-axis. Note in the example below that the space between 0.05 to 0.1 is the same as space between 0.1 and 0.2.

Source: Park JH, Gail MH, Weinberg CR, et al. Distribution of allele frequencies and effect sizes and

their interrelationships for common genetic susceptibility variants. Proc Natl Acad Sci U S A. 2011; 108:18026-31.

Consider the range of the Y-axis. In the examples below there is no relevant information below $40,000, so it is not necessary to begin the Y-axis at 0. The graph on the right makes more sense.

Also, consider using a log scale. this can be particularly useful when presenting ratios as in the example below.

Source: Broman KW, Murray JC, Sheffield VC, White RL, Weber JL (1998) Comprehensive human genetic maps:

Individual and sex-specific variation in recombination. American Journal of Human Genetics 63:861-869, Figure 1

We noted earlier that pie charts make it difficult to see differences within a single pie chart, but this is particularly difficult when data is presented with multiple pie charts, as in the example below.

Source: Bell ML, et al. (2007) Spatial and temporal variation in PM2.5 chemical composition in the United States

for health effects studies. Environmental Health Perspectives 115:989-995, Figure 3

When multiple comparisons are being made, it is essential to use colors and symbols in a consistent way, as in this example.

Source: Manning AK, LaValley M, Liu CT, et al.  Meta-Analysis of Gene-Environment Interaction:

Joint Estimation of SNP and SNP x Environment Regression Coefficients.  Genet Epidemiol 2011, 35(1):11-8.

Avoid putting too many lines on the same chart. In the example below, the only thing that is readily apparent is that 1980 was a very hot summer.

Data from National Weather Service Weather Forecast Office at

http://www.srh.noaa.gov/tsa/?n=climo_tulyeartemp

Make Efficient Use of Space

Reduce the Ratio of Ink to Information

This isn't efficient, because this graphic is totally uninformative.

Source: Mykland P, Tierney L, Yu B (1995) Regeneration in Markov chain samplers.  Journal of the American Statistical Association 90:233-241, Figure 1

Bar graphs add ink without conveying any additional information, and they are distracting. The graph below on the left inappropriately uses bars which clutter the graph without adding anything. The graph on the right displays the same data, by does so more clearly and with less clutter.

Multiple Types of Information on the Same Figure

Choosing the Best Graph Type

Bar Charts, Error Bars and Dot Plots

As noted previously, bar charts can be problematic. Here is another one presenting means and error bars, but the error bars are misleading because they only extend in one direction. A better alternative would have been to to use full error bars with a scatter plot, as illustrated previously (right).

Consider the four graphs below presenting the incidence of cancer by type. The upper left graph unnecessary uses bars, which take up a lot of ink. This layout also ends up making the fonts for the types of cancer too small. Small font is also a problem for the dot plot at the upper right, and this one also has unnecessary grid lines across the entire width.

The graph at the lower left has more readable labels and uses a simple dot plot, but the rank order is difficult to figure out.

The graph at the lower right is clearly the best, since the labels are readable, the magnitude of incidence is shown clearly by the dot plots, and the cancers are sorted by frequency.

Single Continuous Numeric Variable

In this situation a cumulative distribution function conveys the most information and requires no grouping of the variable. A box plot will show selected quantiles effectively, and box plots are especially useful when stratifying by multiple categories of another variable.

Histograms are also possible. Consider the examples below.

Two Variables

 The two graphs below summarize BMI (Body Mass Index) measurements in four categories, i.e., younger and older men and women. The graph on the left shows the means and 95% confidence interval for the mean in each of the four groups. This is easy to interpret, but the viewer cannot see that the data is actually quite skewed. The graph on the right shows the same information presented as a box plot. With this presentation method one gets a better understanding of the skewed distribution and how the groups compare.

The next example is a scatter plot with a superimposed smoothed line of prediction. The shaded region embracing the blue line is a representation of the 95% confidence limits for the estimated prediction. This was created using "ggplot" in the R programming language.

Source: Frank E. Harrell Jr. on graphics:  http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf (page 121)

Multivariate Data

The example below shows the use of multiple panels.

Source: Cleveland S. The Elements of Graphing Data. Hobart Press, Summit, NJ, 1994.

Displaying Uncertainty

• Error bars showing confidence limits
• Confidence bands drawn using two lines
• Shaded confidence bands
• Bayesian credible intervals
• Bayesian posterior densities

Confidence Limits

Shaded Confidence Bands

Source: Frank E. Harrell Jr. on graphics:  http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf

Source: Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Forest Plot

This is a Forest plot summarizing 26 studies of cigarette smoke exposure on risk of lung cancer. The sizes of the black boxes indicating the estimated odds ratio are proportional to the sample size in each study.

Data from Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Summary Recommendations

• In general, avoid bar plots
• Avoid chart junk and the use of too much ink relative to the information you are displaying. Keep it simple and clear.
• Avoid pie charts, because humans have difficulty perceiving relative angles.
• Pay attention to scale, and make scales consistent.
• Explore several ways to display the data!

12 Tips on How to Display Data Badly

Adapted from Wainer H.  How to Display Data Badly.  The American Statistician 1984; 38: 137-147. 

• Show as few data as possible
• Hide what data you do show; minimize the data-ink ratio
• Ignore the visual metaphor altogether
• Only order matters
• Graph data out of context
• Change scales in mid-axis
• Emphasize the trivial;  ignore the important
• Jiggle the baseline
• Alphabetize everything.
• Make your labels illegible, incomplete, incorrect, and ambiguous.
• More is murkier: use a lot of decimal places and make your graphs three dimensional whenever possible.
• If it has been done well in the past, think of another way to do it

Additional Resources

• Stephen Few: Designing Effective Tables and Graphs. http://www.perceptualedge.com/images/Effective_Chart_Design.pdf
• Gary Klaas: Presenting Data: Tabular and graphic display of social indicators. Illinois State University, 2002. http://lilt.ilstu.edu/gmklass/pos138/datadisplay/sections/goodcharts.htm (Note: The web site will be discontinued to be replaced by the Just Plain Data Analysis site).

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Presentation of quantitative data

• A graphical format is usually preferable to tabular presentation of data, but the authors may determine the most appropriate format to display their data.  The goal of data presentation should be clarity and transparency.  Poor figure preparation can hide important trends in data and reduce trust in the results and conclusions. Poor choices in data presentation may also serve to confuse or mislead the reader. While the author is given some freedom in how to exhibit their data, papers lacking transparency in data presentation, or ones where the data is presented in a way to obfuscate data variation or present data in a misleading fashion maybe rejected for publication.  
• So called dynamite/plunger plots/bar charts, without the superposition of individual data points, should be avoided as these as they can disguise outliers or trends and conceal data distribution. Scatter (dot) plots showing summary measures (i.e., mean, variation) and data points or bar plots with superimposed data points are preferred for low sample size data. For data sets with larger sample sizes, boxplots or violin plots with arithmetic mean and standard deviation are preferred.  
• Individual points must be shown for small group size experiments. Typically, individual points can be clearly resolved for experiments with less than 100 samples. For larger sample sizes, violin plots or box and whisker plots without a data overlay may be more appropriate. It is preferred that these larger data sets be made available as supplements for transparency purposes. As appropriate, the sample size (n) should be stated in the figure legend. 
• Measures of variation: Standard deviation quantifies the variation of values in a dataset. Standard error indicates the uncertainty around the estimate of the mean measurement, for given a sample size, and is useful to calculate confidence intervals ( Altman BMJ 2005 ). To compare the uncertainty of mean estimation between groups, accounting for statistical power provided by the sample size, the standard error of the mean can be used. To visualize the variation of the samples in a given group, the standard deviation is preferred. 
• The y-axis of bar graphs should originate at 0 or should show a clear scale break in cases where this would be difficult. Dot/scatter plots, box plots, and violin plots need show the y-axis at zero, but y-values must be clearly labeled. 
• Scatterplots, overlaid with a trendline with confidence intervals, are suggested for plots attempting to show a relationship. The goodness of the trendline fit should be stated on the plot or in the figure legend. Other chart formats such as line charts, Kaplan-Mayer plots and histograms may be more appropriate for certain types of data. In these cases, individual datum points and standard error should still be shown when appropriate. For time series datasets with many groups or large sample sizes, the mean and variation may be shown, with the individual data values included as supplementary tables. 
• When describing results of statistical tests, exact p-values should be shown unless this interferes with the clarity of the plot. The meaning of any shortcuts (e.g., asterisks, etc.), if used, must be explicitly stated in the figure legend or table.  
• The judicious use of color is encouraged. The choice of colors must take into account vision accessibility challenges such as various forms of color blindness. Authors are encouraged to avoid red/green color combinations as these are the most commonly problematic. Various colorblind simulators are available for free on the internet or within popular figure preparation software such as Adobe Photoshop. 
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Presentation of Data

Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.

What is Meant by Presentation of Data?

As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.

Presentation of Data Examples

Now, let us discuss how to present the data in a meaningful way with the help of examples.

Consider the marks given below, which are obtained by 10 students in Mathematics:

36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

Find the range for the given data.

Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

The data given is called the raw data.

First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.

Therefore, the lowest mark is 25 and the highest mark is 95.

We know that the range of the data is the difference between the highest and the lowest value in the dataset.

Therefore, Range = 95-25 = 70.

Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.

Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.

Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)

10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.

In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.

For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.

Therefore, the presentation of data is given as below:

The following example shows the presentation of data for the larger number of observations in an experiment.

Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)

95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.

Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.

In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.

Hence, the presentation of data in the grouped frequency table is given below:

Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.

Practice Problems

• The heights of 50 students (in cms) are given below. Present the data using the grouped frequency table by taking the class intervals as 160 -165, 165 -170, and so on.  Data: 161, 150, 154, 165, 168, 161, 154, 162, 150, 151, 162, 164, 171, 165, 158, 154, 156, 172, 160, 170, 153, 159, 161, 170, 162, 165, 166, 168, 165, 164, 154, 152, 153, 156, 158, 162, 160, 161, 173, 166, 161, 159, 162, 167, 168, 159, 158, 153, 154, 159.
• Three coins are tossed simultaneously and each time the number of heads occurring is noted and it is given below. Present the data using the frequency distribution table. Data: 0, 1, 2, 2, 1, 2, 3, 1, 3, 0, 1, 3, 1, 1, 2, 2, 0, 1, 2, 1, 3, 0, 0, 1, 1, 2, 3, 2, 2, 0.

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ChartExpo Survey

Top 5 Easy-to-Follow Data Presentation Examples

You’ll agree when we say that poring through numbers is tedious at best and mentally exhausting at worst.

And this is where data presentation examples come in.

Charts come in and distill data into meaningful insights. And this saves tons of hours, which you can use to relax or execute other tasks. Besides, when creating data stories, you need charts that communicate insights with clarity.

There are 5 solid and reliable data presentation methods: textual, statistical data presentation, measures of dispersion, tabular, and graphical data representation.

Besides, some of the tested and proven charts for data presentation include:

• Waterfall Chart
• Double Bar Graph
• Slope Chart
• Treemap Charts
• Radar Chart
• Sankey Chart

There are visualization tools that produce simple, insightful, and ready-made data presentation charts. Yes, you read that right. These tools create charts that complement data stories seamlessly.

Remember, without visualizing data to extract insights, the chances of creating a compelling narrative will go down.

Table of Content:

What is data presentation, top 5 data presentation examples:, how to generate sankey chart in excel for data presentation, importance of data presentation in business, benefits of data presentation, what are the top 5 methods of data presentation.

Data presentation is the process of using charts and graphs formats to display insights into data. The insights could be:

• Relationship
• Trend and patterns

Data Analysis  and  Data Presentation  have a practical implementation in every possible field. It can range from academic studies, and commercial, industrial , and marketing activities to professional practices .

In its raw form, data can be extremely complicated to decipher. Examples of data presentation, such as chord diagrams , are an important step toward breaking down data into understandable charts or graphs.

You can use tools (which we’ll talk about later) to analyze raw data.

Once the required information is obtained from the data, the next logical step is to present it in a graphical presentation, such as a Box and Whisker presentation .

The presentation is the key to success.

Once you’ve extracted actionable insights, you can craft a compelling data story. Keep reading because we’ll address the following in the coming section: the importance of data presentation in business, including how tools like a Sunburst Chart can enhance your analysis.

Let’s take a look at the five data presentation examples below:

1. Waterfall Chart

A Waterfall Chart is a graphical representation used to depict the cumulative impact of sequential positive or negative values on a starting point over a designated time frame. It typically consists of a series of horizontal bars, with each bar representing a stage or category in a process.

2. Double Bar Graph

A Double Bar Chart displays more than one data series in clustered horizontal columns.

Each data series shares the same axis labels, so horizontal bars are grouped by category.

Bars directly compare multiple series in a given category. The chart is amazingly easy to read and interpret, even for a non-technical audience.

3. Slope Chart

Slope Charts are simple graphs that quickly and directly show  transitions, changes over time, absolute values, and even rankings .

Besides, they’re also called Slope Graphs .

This is one of the data presentation examples you can use to show the before and after story of variables in your data.

Slope Graphs can be useful when you have two time periods or points of comparison and want to show relative increases and decreases quickly across various categories between two data points.

Take a look at the table below. Can you provide coherent and actionable insights into the table below?

Notice the difference after visualizing the table. You can easily tell the performance of individual segments in:

• Macy’s Store

5. Radar Chart

Radar Chart is also known as Spider Chart or Spider Web Chart. A radar chart is very helpful to visualize the comparison between multiple categories and variables.

A radar Chart is one of the data presentation examples you can use to compare data of two different time ranges e.g. Current vs Previous. Radar Chart with different scales makes it easy for you to identify trends, patterns, and outliers in your data. You can also use Radar Chart to visualize the data of Polar graph equations.

6. Sankey Chart

You can use the Sankey Chart to visualize data with flow-like attributes, such as material, energy, cost, etc.

This chart draws the reader’s attention to the enormous flows, the largest consumer, the major losses , and other insights.

The aforementioned visualization design, including the Mosaic plot presentation , is one of the data presentation examples that use links and nodes to uncover hidden insights into relationships between critical metrics.

The size of a node is directly proportionate to the quantity of the data point under review.

So how can you access the data presentation examples (highlighted above)?

Excel is one of the most used tools for visualizing data because it’s easy to use. 

However, you cannot access ready-made and visually appealing data presentation charts, such as a funnel chart , for storytelling. But this does not mean you should ditch this freemium data visualization tool.

Did you know you can supercharge your Excel with add-ins to access visually stunning and ready-to-go data presentation charts?

Yes, you can increase the functionality of your Excel and access ready-made data presentation examples for your data stories.

The add-on we recommend you to use is ChartExpo.

What is ChartExpo?

We recommend this tool (ChartExpo) because it’s super easy to use.

You don’t need to take programming night classes to extract insights from your data. ChartExpo is more of a ‘drag-and-drop tool,’ which means you’ll only need to scroll your mouse and fill in respective metrics and dimensions in your data.

ChartExpo comes with a 7-day free trial period.

The tool produces charts that are incredibly easy to read and interpret . And it allows you to save charts in the world’s most recognized formats, namely PNG and JPG.

In the coming section, we’ll show you how to use ChartExpo to visualize your data with one of the data presentation examples (Sankey).

  To install ChartExpo add-in into your Excel, click this link .

• Open your Excel and paste the table above.
• Click the My Apps button.

• Then select ChartExpo and click on  INSERT, as shown below.

• Click the Search Box and type “Sankey Chart” .

• Once the chart pops up, click on its icon to get started.

• Select the sheet holding your data and click the Create Chart from Selection button.

How to Edit the Sankey Chart?

• Click the Edit Chart button, as shown above.

• Once the Chart Header Properties window shows, click the Line 1 box and fill in your title.

• To change the color of the nodes, click the pen-like icons on the nodes.
• Once the color window shows, select the Node Color and then the Apply button.

• Save your changes by clicking the Apply button.
• Check out the final chart below.

Data presentation examples are vital, especially when crafting data stories for the top management. Top management can use data presentation charts, such as Sankey, as a backdrop for their decision.

Presentation charts, maps, and graphs are powerful because they simplify data by making it understandable & readable at the same time. Besides, they make data stories compelling and irresistible to target audiences.

Big files with numbers are usually hard to read and make it difficult to spot patterns easily. However, many businesses believe that developing visual reports focused on creating stories around data is unnecessary; they think that the data alone should be sufficient for decision-making.

Visualizing supports this and lightens the decision-making process.

Luckily, there are innovative applications you can use to visualize all the data your company has into dashboards, graphs, and reports. Data visualization helps transform your numbers into an engaging story with details and patterns.

Check out more benefits of data presentation examples below:

1. Easy to understand

You can interpret vast quantities of data clearly and cohesively to draw insights, thanks to graphic representations.

Using data presentation examples, such as charts, managers and decision-makers can easily create and rapidly consume key metrics.

If any of the aforementioned metrics have anomalies — ie. sales are significantly down in one region — decision-makers will easily dig into the data to diagnose the problem.

2. Spot patterns

Data visualization can help you to do trend analysis and respond rapidly on the grounds of what you see.

Such patterns make more sense when graphically represented; because charts make it easier to identify correlated parameters.

3. Data Narratives

You can use data presentation charts, such as Sankey or Area Charts , to build dashboards and turn them into stories.

Data storytelling can help you connect with potential readers and audiences on an emotional level.

4. Speed up the decision-making process

We naturally process visual images 60,000 times faster than text. A graph, chart, or other visual representation of data is more comfortable for our brain to process.

Thanks to our ability to easily interpret visual content, data presentation examples can dramatically improve the speed of decision-making processes.

Take a look at the table below.

Can you give reliable insights into the table above?

Keep reading because we’ll explore easy-to-follow data presentation examples in the coming section. Also, we’ll address the following question: what are the top 5 methods of data presentation?

1. Textual Ways of Presenting Data

Out of the five data presentation examples, this is the simplest one.

Just write your findings coherently and your job is done. The demerit of this method is that one has to read the whole text to get a clear picture.  Yes, you read that right.

The introduction, summary, and conclusion can help condense the information.

2. Statistical data presentation

Data on its own is less valuable. However, for it to be valuable to your business, it has to be:

No matter how well manipulated, the insights into raw data should be presented in an easy-to-follow sequence to keep the audience waiting for more.

Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and qualitative information.

On the other hand, a graph is a very effective visual tool because:

• It displays data at a glance
• Facilitates comparison
• Reveals trends, relationships, frequency distribution, and correlation

Text, tables, and graphs are incredibly effective data presentation examples you can leverage to curate persuasive data narratives.

3. Measure of Dispersion

Statistical dispersion is how a key metric is likely to deviate from the average value. In other words, dispersion can help you to understand the distribution of key data points.

There are two types of measures of dispersion, namely:

• Absolute Measure of Dispersion
• Relative Measure of Dispersion

4. Tabular Ways of Data Presentation and Analysis

To avoid the complexities associated with qualitative data, use tables and charts to display insights.

This is one of the data presentation examples where values are displayed in rows and columns. All rows and columns have an attribute (name, year, gender, and age).

5. Graphical Data Representation

Graphical representation uses charts and graphs to visually display, analyze, clarify, and interpret numerical data, functions, and other qualitative structures.

Data is ingested into charts and graphs, such as Sankey, and then represented by a variety of symbols, such as lines and bars.

Data presentation examples, such as Bar Charts , can help you illustrate trends, relationships, comparisons, and outliers between data points.

What is the main objective of data presentation?

Discovery and communication are the two key objectives of data presentation.

In the discovery phase, we recommend you try various charts and graphs to understand the insights into the raw data. The communication phase is focused on presenting the insights in a summarized form.

What is the importance of graphs and charts in business?

Big files with numbers are usually hard to read and make it difficult to spot patterns easily.

Presentation charts, maps, and graphs are vital because they simplify data by making it understandable & readable at the same time. Besides, they make data stories compelling and irresistible to target audiences.

Poring through numbers is tedious at best and mentally exhausting at worst.

This is where data presentation examples come into play.

Charts come in and distill data into meaningful insights. And this saves tons of hours, which you can use to handle other tasks. Besides, when creating data stories, it would be best if you had charts that communicate insights with clarity.

Excel, one of the popular tools for visualizing data, comes with very basic data presentation charts, which require a lot of editing.

We recommend you try ChartExpo because it’s one of the most trusted add-ins. Besides, it has a super-friendly user interface for everyone, irrespective of their computer skills.

Create simple, ready-made, and easy-to-interpret Bar Charts today without breaking a sweat.

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TEXTUAL, TABULAR & DIAGRAMMATIC PRESENTATION OF DATA

STATISTICS : PRESENTATION OF DATA

Data can be presented in three ways:

• Textual presentation
• Tabular presentation
• Diagrammatic presentation

1. Textual Mode of presentation is  layman’s method of presentation of data.  Anyone can prepare, anyone can understand. No specific skill(s) is/are required.

2. Tabular Mode of presentation is  the most accurate mode of presentation of data.  It requires a lot of skill to prepare, and some skill(s) to understand. Table facilitates comparison.

But, Table should be good enough as per some points of view:

• 1. Appealing
• 2. Well-balanced
• 3. Compulsory Title and Table Number
• 4. Title should be  self-explanatory
• 5. Units must be properly mentioned
• 6. Comparison should be easy
• 7. Sources and footnotes (if any) must be mentioned at the bottom

Below is a sample of how a table should look like:

Table No. 1: Format of a table

* Sources: 1. Kailasha Foundation – Fun & Learn Portal LMS Directory *Footnotes: The entire upper part of the table is called BOX HEAD.

3. Diagrammatic Mode of Presentation:

A. Non-Frequency Diagrams: Non-frequency diagrams correspond to the data  which are NOT frequency data.  (a) Bar Diagrams (b) Line Diagrams (Historiagram) (c) Pie Diagram or Pie Chart

B. Frequency Diagrams: Frequency Data are presented. Mostly class-intervals are presented via this mode. Three most common frequency diagrams are: (a) Histogram (b) Frequency Polygon (c) Ogives: (i) Less than type Ogives (ii) More than type Ogives

• 1. Bar Diagram and Line Diagram are inter-convertible
• 2. Bar Diagram and Line Diagram can both be of simple and multiple types
• 3. Multiple bar diagram or Multiple Line diagram is used when two related series (in same unit) are to be compared
• 4. Multiple axis bar diagram or Multiple axis Line diagram is used when units in the two series are different

ILLUSTRATIONS OF PRESENTATION OF DATA:

Bar Diagrams:

Line Diagram:

Multiple  Bar Diagram:

Frequency Polygon:

FREQUENCY CURVE:

A smooth join of all vertices of a frequency polygon. This is broadly divided into four shapes:

(i) Bell Shaped (Most Common Shape) (ii) U-Shaped (iii) J – Shaped: Simple J – shaped & Inverted J – Shaped (iv) Mixed Curve (Second Most Common Shape)

• 1. CENSUS: The collection of data from every element in a population or universe or arena of statistical enquiry.
• 2. SAMPLE: The collection of data from subgroup or subset of the population.
• 3. FREQUENCY: The number of times a certain value or class of values occurs.
• 4. CUMULATIVE FREQUENCY: The running total of the frequencies at each class interval level.
• 5. FREQUENCY DISTRIBUTION: The organization of raw data in table form with classes and frequencies.
• 6. CLASS LIMITS: The  originally assigned extreme values  of classes are called class limits, viz. Lower class limit and upper class limit.
• 7. CLASS WIDTH: The difference between the upper and lower boundaries  (NOT limits) of any class.
• 8. CLASS BOUNDARY: After making the distribution continuous, the upper class boundary of a class becomes equal to the lower class boundary of the next class.
• 9. CLASS MARK: The mid-point of any class is called the class mark.

VIDEO DESCRIPTIONS:

Hindi explanation:.

ENGLISH EXPLANATION:

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24 Presentation Statistics You Should Know in 2022

Written by: Mahnoor Sheikh

Looking for relevant and up-to-date presentation statistics to guide your next presentation?

A great presentation not only looks beautiful, but also manages to engage the audience and helps them remember important information after it’s finished.

In this article, we’ve curated a list of interesting and useful presentation statistics that will help you design and deliver stunning presentations that leave a strong impact.

Before we dive in, here’s a short selection of 8 easy-to-edit presentation templates you can edit, share and download with Visme. View more templates below:

Presentation Statistics [Infographic]

Check out the infographic below to view a visual summary of all the presentation statistics. If you want to read the full post with all the details, keep scrolling.

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Presentation Statistics on Fear of Public Speaking

Does the thought of giving a presentation make you feel nervous?

You’re not alone. Millions of people all over the world are affected by glossophobia, or a fear of public speaking.

In fact, studies have estimated that 75% of adults are affected by a fear of public speaking.

Glossophobia can affect a person in multiple ways.

They may experience anxiety, feel uncomfortable in large gatherings or even feel embarrassed when speaking in public—all of which can lead to lower self-esteem.

Speaking of anxiety, did you know that 90% of anxiety that people feel right before giving a presentation is due to lack of preparation?

This shows us how important it is to rehearse well before a presentation. Not doing so can lead to difficulty in speaking, sweaty palms and overall a presentation that could have been better.

Presentation Statistics on Design

A beautiful presentation can not only help you create a great first impression in front of your audience, but can also make you feel more confident while you’re presenting.

In fact, studies show that 91% of presenters feel more confident when presenting with a well-designed slide deck.

But designing a presentation that’s stunning and effective isn't as easy as you might think. If you’re a non-designer, you might find it tricky to put together a nice-looking slide deck using basic presentation software.

You’re not the only one. Research shows that 45% of presenters find it difficult to design creative layouts for their presentations.

If you’re using a drag-and-drop presentation maker like Visme , you don’t need to worry about finding beautiful layouts. You can access a large library of fully designed presentation templates and themes that you can use to create your own slide deck in minutes.

Creating beautiful presentations requires the use of high-quality visuals that add value to your content and make your slides look more engaging.

But the type of visuals you use largely affects the aesthetic appeal and effectiveness of your presentation, and finding the right ones can be challenging.

According to studies, 41% of presenters find it challenging to find and use great visuals in their presentations.

You also need to choose the right fonts for your presentation. The fonts you choose should be clear and attractive, as well as consistent with your brand.

Research shows that 7% of presenters find it challenging to look for attractive fonts to use in their presentations.

This means that the ideal presentation software should have a large library of fonts available for presenters to choose from.

Now, when it comes to designing, you’re probably wondering whether you should design your presentation yourself or hire a professional to do it for you.

Interestingly, a study showed that 65.7% of presenters prefer to design presentations on their own, with no help from a professional designer.

This shows that presenters like having total control over what their presentation looks like. It helps them better prepare and know their way around the slides.

But designing a presentation can take time, especially if you want it to look beautiful.

According to research, 47% of presenters put in more than 8 hours into designing their presentations.

This statistic makes sense, considering that a well-designed presentation requires you to look for external resources, such as images and data. You might even need to spend hours creating graphs and charts out of spreadsheets.

If you’re creating a business presentation, you probably want to use your company’s brand colors. Surprisingly, there are plenty of presenters who don’t agree with that.

According to studies, 35.3% of presenters actually prefer to use bright and vibrant colors to make their presentations look more engaging, instead of using their actual brand colors.

Considering that engagement is highly important during a presentation, this makes a lot of sense.

However, you should always try not to use colors that clash too much with your brand, as they may negatively impact your brand image.

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Presentation Statistics on the Art of Presenting

A memorable presentation involves more than just a beautiful slide deck and well-researched content. It’s just as important, if not more, to have great presenting skills.

According to SOAP presentations , the elements that contribute most to effective presentations include voice (38%) and non-verbal communication (55%).

The actual content of your presentation only makes up about 7%.

This shows how important it is to have good presentation skills, as it can largely determine whether your presentation is a success or not.

Plus, research shows that it can take as little as 5 seconds for the audience to determine whether a presenter is charismatic or not.

If they find you uncharismatic, they might lose interest or even stop listening to you.

Body language.

A key part of presentation skills is your body language. The more comfortable you are in your skin, the more likely you’ll be to deliver a powerful presentation.

For starters, you need to make enough eye contact with your audience to engage them.

According to research, the ideal amount of eye contact to make an emotional connection with your listeners is between 60% and 70% .

Another important thing to consider is how you carry yourself in front of your audience. The way you pose or walk around on the stage can determine how well you present.

Studies show that power posing , such as open arms, keeping your hands on your hips and a straight back, can increase confidence and reduce stress during a presentation.

So, the next time you’re presenting, make sure you don’t stay in a corner huddled up behind the podium. You need to look confident if you want to feel confident.

Storytelling.

The way you present information can have a huge impact on how your audience processes that information.

Some people like to simply state facts and figures. But is that really effective?

Not if you want your audience to remember your message.

Research shows that people are 22 times more likely to remember a fact when it’s been told in the form of a story.

Humans love stories. A strong narrative can enable people to make sense of information faster because it helps them see how that information relates to them.

Another study showed that after a presentation, 63% of attendees were able to remember stories, while only 5% could remember statistics.

The next time you want to present a statistic or fact, think about how you can relate it to people and their lives. Wrap it in a story so they’re able to process it more effectively and remember it after your presentation.

Presentation Statistics on Audience Engagement

If your presentation fails to engage your audience, you might not get your point across or make a strong impact.

You need to grab their focus so they stay hooked to your presentation till the very end. This task, though, is not as easy as it sounds.

According to Prezi, 4 in 5 business professionals said they shifted their focus away from the speaker in the last presentation they attended.

And let’s face it. Presentations can often get boring, especially when the speaker is droning on and on about a topic.

In fact, a research showed that 79% of people agree that “most presentations are boring.”

So, how do you make a boring presentation interesting? How do you get your audience to sit up in their seats and focus?

Studies show that the key to engaging your audience is to make them feel involved in your presentation. Of course, storytelling is one of the best ways to do that.

The study by Prezi showed that 55% of people find that a great story is what mainly helps them focus during a presentation.

Another way to get your audience involved with your presentation is to interact with them during the presentation.

In fact, studies show that if a presenter does all the talking without letting the audience participate, then audience engagement drops by 14% .

This is why you should make sure your presentation is interactive. For example, you could ask your audience questions throughout your presentation to keep them feeling involved.

Presentation Statistics on PowerPoint

Microsoft PowerPoint is the most widely used presentation software in the world.

In fact, more than 35 million PowerPoint presentations are given each day to over 500 million audiences. But does that make PowerPoint the best presentation software out there?

According to studies, most people stop listening to a PowerPoint presentation within 10 minutes .

While not the most effective presentation software, PowerPoint is still immensely popular.

Despite having dozens of newer and better options for creating presentations, 89% of people still use PowerPoint to put together their slideshow.

But their reason for doing so isn’t always the tool’s effectiveness.

In fact, the top three reasons why people still use PowerPoint to create presentations is because they’re familiar with the tool (73%), they find it easy to use (59%) or they simply don’t have a choice (43%).

Learn From These Presentation Statistics

Did you enjoy the presentation statistics above? Keep these facts in mind before your next presentation to make sure your message hits home.

If you’re looking for a powerful and easy-to-use alternative to PowerPoint, check out Visme’s online presentation maker . It’s free!

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About the Author

Mahnoor Sheikh is the content marketing manager at Visme. She has years of experience in content strategy and execution, SEO copywriting and graphic design. She is also the founder of MASH Content and is passionate about tea, kittens and traveling with her husband. Get in touch with her on LinkedIn .

What is included in a statistical slide deck service?

July 8, 2024 /

A statistical slide deck service typically includes several key components designed to present data in a clear, engaging, and impactful manner. Firstly, it involves data analysis and interpretation, where raw data is transformed into meaningful insights. This is followed by the creation of visually appealing charts, graphs, and infographics that make complex information easily digestible. The service also includes the development of a coherent narrative that ties the data together, ensuring that each slide logically flows into the next. Additionally, professional design elements such as consistent color schemes, typography, and layout are incorporated to enhance readability and aesthetic appeal. Finally, the service often provides revisions and feedback sessions to ensure the final product meets your specific needs and objectives. By leveraging these elements, a statistical slide deck service helps you effectively communicate your data-driven story to your audience.

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Transforming the understanding and treatment of mental illnesses.

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• Health Topics
• Brochures and Fact Sheets
• Help for Mental Illnesses
• Clinical Trials

Bipolar Disorder

Bipolar disorder , sometimes referred to as manic-depressive disorder, is characterized by dramatic shifts in mood, energy, and activity levels that affect a person’s ability to carry out day-to-day tasks. These shifts in mood and energy levels are more severe than the normal ups and downs that are experienced by everyone.

Additional information about bipolar disorder can be found on the NIMH Health Topics page on Bipolar Disorder .

Prevalence of Bipolar Disorder Among Adults

• An estimated 2.8% of U.S. adults had bipolar disorder in the past year.
• Past year prevalence of bipolar disorder among adults was similar for males (2.9%) and females (2.8%).
• An estimated 4.4% of U.S. adults experience bipolar disorder at some time in their lives. 2

Bipolar Disorder with Impairment Among Adults

• An estimated 82.9% of people with bipolar disorder had serious impairment, the highest percent serious impairment among mood disorders. 3
• An estimated 17.1% had moderate impairment.

Prevalence of Bipolar Disorder Among Adolescents

• An estimated 2.9% of adolescents had bipolar disorder, and 2.6% had severe impairment. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria were used to determine impairment.
• The prevalence of bipolar disorder among adolescents was higher for females (3.3%) than for males (2.6%).

Data Sources

• Harvard Medical School, 2007. National Comorbidity Survey (NSC). (2017, August 21). Retrieved from https://www.hcp.med.harvard.edu/ncs/index.php   . Data Table 2: 12-month prevalence DSM-IV/WMH-CIDI disorders by sex and cohort   .
• Harvard Medical School, 2007. National Comorbidity Survey (NSC). (2017, August 21). Retrieved from https://www.hcp.med.harvard.edu/ncs/index.php   . Data Table 1: Lifetime prevalence DSM-IV/WMH-CIDI disorders by sex and cohort   .
• Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005 Jun;62(6):617-27. PMID: 15939839 
• Merikangas KR, He JP, Burstein M, Swanson SA, Avenevoli S, Cui L, Benjet C, Georgiades K, Swendsen J. Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication--Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry. 2010 Oct;49(10):980-9. PMID: 20855043 

Statistical Methods and Measurement Caveats

National comorbidity survey replication (ncs-r).

Diagnostic Assessment and Population:

• The NCS-R is a nationally representative, face-to-face, household survey conducted between February 2001 and April 2003 with a response rate of 70.9%. DSM-IV mental disorders were assessed using a modified version of the fully structured World Health Organization Composite International Diagnostic Interview (WMH-CIDI), a fully structured lay-administered diagnostic interview that generates both International Classification of Diseases, 10 th Revision, and DSM-IV diagnoses. The DSM-IV criteria were used here. The Sheehan Disability Scale (SDS) assessed disability in work role performance, household maintenance, social life, and intimate relationships on a 0–10 scale. Participants for the main interview totaled 9,282 English-speaking, non-institutionalized, civilian respondents. Bipolar disorder was assessed in a subsample of 5,692 adults. The NCS-R was led by Harvard University

Survey Non-response:

• In 2001-2002, non-response was 29.1% of primary respondents and 19.6% of secondary respondents.
• Reasons for non-response to interviewing include: refusal to participate (7.3% of primary, 6.3% of secondary); respondent was reluctant- too busy but did not refuse (17.7% of primary, 11.6% of secondary); circumstantial, such as intellectual developmental disability or overseas work assignment (2.0% of primary, 1.7% of secondary); and household units that were never contacted (2.0).
• For more information, see PMID: 15297905  .

National Comorbidity Survey Adolescent Supplement (NCS-A)

• The NCS-A was carried out under a cooperative agreement sponsored by NIMH to meet a request from Congress to provide national data on the prevalence and correlates of mental disorders among U.S. youth. The NCS-A was a nationally representative, face-to-face survey of 10,123 adolescents aged 13 to 18 years in the continental United States. The survey was based on a dual-frame design that included 904 adolescent residents of the households that participated in the adult U.S. National Comorbidity Survey Replication and 9,244 adolescent students selected from a nationally representative sample of 320 schools. The survey was fielded between February 2001 and January 2004. DSM-IV mental disorders were assessed using a modified version of the fully structured World Health Organization Composite International Diagnostic Interview.
• The overall adolescent non-response rate was 24.4%. This is made up of non-response rates of 14.1% in the household sample, 18.2% in the un-blinded school sample, and 77.7% in the blinded school sample. Non-response was largely due to refusal (21.3%), which in the household and un-blinded school samples came largely from parents rather than adolescents (72.3% and 81.0%, respectively). The refusals in the blinded school sample, in comparison, came almost entirely (98.1%) from parents failing to return the signed consent postcard.
• For more information, see PMID: 19507169  .

Cosmological Constraints from Galaxy Cluster Statistics in KiDS

• Lesci, Giorgio Francesco

Breaking the degeneracies between the galaxy cluster mass-observable relation and cosmological parameters is one of the crucial quests in the current cosmological studies. This can be achieved through joint analyses of mass measurements and cosmological probes such as cluster abundance and clustering, as a function of redshift and mass proxy. Novel methododologies must be developed to get the most out of the potentialities of current and upcoming galaxy cluster surveys, with a particular focus on enhancing cluster mass calibrations. With this presentation, I will provide an overview on the cosmological analyses carried out by our group on the Kilo Degree Survey (KiDS) photometric data, based on the detections of galaxy clusters performed through the use of the cluster finder code AMICO (Adaptive Matched Identifier of Clustered Objects). Firstly, I will focus on the cosmological constraints derived from cluster count and clustering measurements in the third data release of KiDS (KiDS-DR3). The analysis included about 3700 galaxy clusters over the redshift range z ∈ [0.1, 0.6] and in an effective area of 377 square degrees. Through a joint analysis of mass-richness relation and cluster statistics, we derived robust constraints on the fundamental cosmological parameters Ωm and σ8, also showing the impact of the cosmological probes on the constraints on the mass-richness relation. In addition, I will detail the preliminary results on mass calibration and cosmological parameters based on the AMICO cluster sample derived from the fourth KiDS data release (KiDS-1000). This cluster catalogue covers an effective area of 840 square degrees and the mass calibration extends up to redshift z = 0.8, with this leading to a cosmological sample of about 8000 detections. I will discuss the blinding strategy adopted for our cosmological analysis, along with a thorough description of the AMICO selection function. Moreover, I will detail how self-organising maps, commonly used to calibrate galaxy photometric redshifts, can be employed to assess the robustness of galaxy cluster weak-lensing measurements.

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Gdp up by 0.3% in both the euro area and the eu, announcement.

Following recommendations for a harmonised European revision policy for national accounts and balance of payments , EU countries will carry out a benchmark revision of their national accounts estimates in 2024. The purpose of this benchmark revision is to implement changes introduced by the amended ESA 2010 regulation , and to incorporate new data sources and other methodological improvements. Most of the revised quarterly and annual country data are expected to be released by Eurostat between June and October 2024, and will be progressively integrated in European estimates. The impact of these revisions is expected to be limited, but still noticeable for some European aggregates and more pronounced for certain Member States. For further details, please consult the available documentation on Eurostat’s website .

In the second quarter of 2024, seasonally adjusted GDP increased by 0.3% in both the euro area and the EU , compared with the previous quarter, according to a preliminary flash estimate published by Eurostat, the statistical office of the European Union . In the first quarter of 2024, GDP had also grown by 0.3% in both zones.

These preliminary GDP flash estimates are based on data sources that are incomplete and subject to further revisions.

Compared with the same quarter of the previous year, seasonally adjusted GDP increased by 0.6% in the euro area and by 0.7% in the EU in the second quarter of 2024, after +0.5% in the euro area and +0.6% in the EU in the previous quarter.

Among the Member States for which data are available for the second quarter of 2024, Ireland (+1.2%) recorded the highest increase compared to the previous quarter, followed by Lithuania (+0.9%) and Spain (+0.8%). The highest declines were recorded in Latvia (-1.1%), Sweden (-0.8%) and Hungary (-0.2%). The year on year growth rates were positive for eight countries and negative for three.

The next estimates for the second quarter of 2024 will be released on 14 August 2024.

Notes for users

The reliability of GDP flash estimates was tested by dedicated working groups and revisions of subsequent estimates are continuously monitored . Further information can be found on Eurostat website .

With this preliminary flash estimate, euro area and EU GDP figures for earlier quarters are not revised.

All figures presented in this release may be revised with the GDP t+45 flash estimate scheduled for 14 August 2024 and subsequently by Eurostat’s regular estimates of GDP and main aggregates (including employment) scheduled for 6 September 2024 and 18 October 2024, which will reflect the impact of countries’ benchmark revisions as available.

The preliminary flash estimate of GDP growth for the second quarter of 2024 presented in this release is based on the data of 18 Member States, covering 96% of euro area GDP and 94% of EU GDP.

Release schedule

Comprehensive estimates of European main aggregates (including GDP and employment) are based on countries regular transmissions and published around 65 and 110 days after the end of each quarter. To improve the timeliness of key indicators, Eurostat also publishes flash estimates for GDP (after around 30 and 45 days) and employment (after around 45 days). Their compilation is based on estimates provided by EU Member States on a voluntary basis.

This news release presents preliminary flash estimates for euro area and EU after around 30 days.

Methods and definitions

European quarterly national accounts are compiled in accordance with the European System of Accounts 2010 (ESA 2010).

Gross domestic product (GDP) at market prices measures the production activity of resident production units. Growth rates are based on chain-linked volumes.

Two statistical working papers present the preliminary GDP flash methodology for the European estimates and Member States estimates .

The method used for compilation of European GDP is the same as for previous releases.

Geographical information

Euro area (EA20): Belgium, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Austria, Portugal, Slovenia, Slovakia and Finland.

European Union (EU27): Belgium, Bulgaria, Czechia, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, the Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland and Sweden.

For more information

Website section on national accounts , and specifically the page on quarterly national accounts

Database section on national accounts and metadata on quarterly national accounts

Statistics Explained articles on measuring quarterly GDP and presentation of updated quarterly estimates

Country specific metadata

Country specific metadata on the recording of Ukrainian refugees in main aggregates of national accounts

European System of Accounts 2010

Euro indicators dashboard

Release calendar for Euro indicators

European Statistics Code of Practice

Get in touch

Media requests

Eurostat Media Support

Phone: (+352) 4301 33 408

E-mail: [email protected]

Further information on data

Thierry COURTEL

Johannes BUCK

E-mail: [email protected]

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• Open access
• Published: 31 July 2024

High prevalence of late presentation with advanced HIV disease and its predictors among newly diagnosed patients in Kumasi, Ghana

• Samuel Asamoah Sakyi 1   na1 ,
• Samuel Kwarteng 1 , 2   na1 ,
• Ebenezer Senu 1   na1 ,
• Alfred Effah 1 ,
• Stephen Opoku 1 ,
• Success Acheampomaa Oppong 1 , 2 ,
• Kingsley Takyi Yeboah 2 ,
• Solomon Abutiate 2 ,
• Augustina Lamptey 2 ,
• Mohammed Arafat 2 ,
• Festus Nana Afari-Gyan 2 ,
• Samuel Kekeli Agordzo 1 ,
• Oscar Simon Olympio Mensah 1 ,
• Emmauel Owusu 3 ,
• Tonnies Abeku Buckman 1 , 4 ,
• Benjamin Amoani 5 &
• Anthony Kwame Enimil 6  

BMC Infectious Diseases volume  24 , Article number:  764 ( 2024 ) Cite this article

221 Accesses

Metrics details

Late presentation with advanced HIV disease (LP-AHD) remains a significant challenge to Human Immunodeficiency Virus (HIV) care, contributing to increased morbidity, mortality, and healthcare costs. Despite global efforts to enhance early diagnosis, a considerable proportion of individuals with HIV infection are unaware of being infected and therefore present late for HIV care. For the first time in Ghana, this study assessed the prevalence of LP-AHD and associated factors among people diagnosed with HIV (PDWH).

This bi-center retrospective cross-sectional study included 315 PDWH at the Aniniwah Medical Centre and Komfo Anokye Teaching Hospital, both in Kumasi, Ghana. A well-structured questionnaire was used to collect data on sociodemographic, clinical, lifestyle and psychosocial factors from the study participants. Statistical analyses were done in SPSS version 26.0 and GraphPad Prism version 8.0 at significant p -value of < 0.05 and 95% confidence interval. Predictors of LP-AHD were assessed using binary logistic regression models.

This study observed that, 90 out of the 315 study PDWH (28.6%) reported late with advanced HIV disease (AHD). Participants within the age group of 36–45 years (adjusted Odds Ratio [aOR]: 0.32, 95% CI: 0.14–0.69; p  = 0.004) showed a significantly decreased likelihood of LP-AHD. However, participants who perceived cost of HIV care to be high (aOR: 7.04, 95% CI: 1.31–37.91; p  = 0.023), who were diagnosed based on clinical suspicion (aOR: 13.86, 95 CI: 1.83–104.80; p  = 0.011), and missed opportunities for early diagnosis by clinicians (aOR: 2.47, 95% CI: 1.30–4.74; p  = 0.006) were significantly associated with increased likelihood of LP-AHD.

The prevalence of LP-AHD among PDWH in Ghana is high. Efforts to improve early initiation of HIV/AIDS care should focus on factors such as the high perceived costs of HIV care, diagnosis based on clinical suspicion, and missed opportunities for early diagnosis by physicians.

Peer Review reports

Introduction

Acquired Immune Deficiency Syndrome (AIDS) caused by Human Immunodeficiency Virus (HIV), remains a significant global health challenge despite efforts by international and local initiatives to address the epidemic [ 1 ]. At the end of 2022, an estimated 39 million people worldwide were living with HIV [ 2 ]. Africa remains the most affected, with nearly 1 in every 25 adults (3.4%) living with HIV and accounting for more than two-thirds of the people living with HIV worldwide [ 3 ]. Ghana has HIV prevalence of 1.7%, affecting 334,713 people and accounting for over fourteen thousand annual deaths [ 4 ]. HIV testing has been a gateway to HIV care, however coverage with HIV testing services is not adequate [ 5 ]. Despite the availability and accessibility of HIV testing, people continue to test late in the course of HIV infection [ 6 ].

In 2015, WHO recommended that all people living with HIV start ART (Antiretroviral therapy) irrespective of clinical or immune status [ 7 ]. However, people living with HIV continue to present to care late and with advanced disease [ 7 ]. Late presentation with Advanced HIV disease (LP-AHD) is defined as an individual presenting with a CD4 + count lower than 200 cells/µL or at WHO clinical stage-3 or stage-4 [ 8 ]. LP-AHD can lead to serious outcomes, such as increased mortality [ 9 , 10 ], development of opportunistic infections [ 11 ], increased risk of drug resistance to antiretroviral therapies (ART) [ 12 ], high healthcare costs [ 12 ], and increased transmission due to ignorance of infection status [ 13 ].

Multiple sociodemographic, psychosocial, and structural risk factors at the patient and provider levels have been found to be associated with LP-AHD. Fear of HIV-related stigma [ 14 ] and discrimination [ 15 ], poor social support [ 14 ], and low risk perception [ 13 ] are among the common patient-related factors that discourage people from seeking timely testing. Providers have described inadequate time and resources [ 16 ], the burdensome counseling and consent process [ 17 ], and the provider’s perceived low risk of transmission [ 18 ], as barriers to providing HIV testing.

Despite the challenges LP-AHD poses to HIV care, there is a paucity of data on the prevalence and factors associated with LP-AHD. There is no study that has been done to assess the scope of LP-AHD in the Ghanaian context. For the first time, we determined the prevalence and predictors of LP-AHD among newly diagnosed HIV/AIDS subjects in Ghana.

Materials and methods

Study design and study site.

A retrospective cross-sectional study was conducted at HIV clinics of Komfo Anokye Teaching Hospital (KATH) and Aniniwah Medical Centre (AMC) in the Ashanti Region of Ghana. KATH and AMC are located in Kumasi, the capital of the Ashanti region of Ghana and have a well-resourced HIV care clinics making it suitable for the successful implementation of the study.

Study population

This study recruited newly diagnosed individuals confirmed of HIV/AIDS by clinicians using the standard diagnoses protocols.

Inclusion criteria

Participants eligible for inclusion in this study were adults aged 18 years and older with a newly confirmed diagnosis of HIV infection based on standardized diagnostic criteria. In addition, these participants were willing to share relevant information for the research study. Also, these individuals had complete and valid data for the study variables, which encompass sociodemographic, clinical, and psychosocial factors considered in the study.

Exclusion criteria

Individuals below the age of 18 are excluded, as well as those with incomplete or missing data related to crucial study variables encompassing sociodemographic, clinical, and psychosocial factors. Additionally, individuals who decline informed consent or express unwillingness to share pertinent information for research purposes were not considered.

Sample size calculation and sampling technique

The sample size was calculated using the Cochrane formula: \(\:n=\:\frac{{z}^{2}pq}{{e}^{2}}\) ; where: n is the minimum sample size, p is the prevalence of HIV in Ghana = 1.7% [ 6 ], q = 1-p, (98.3), z = z value at 95% confidence (1.96), and e is the margin of error (0.05). Hence a minimum of twenty-six (26) participants were required for the study. To increase statistical power, 315 people diagnosed of HIV/AIDS were included for the study. The participants included 252 from the Komfo Anokye Teaching Hospital (KATH) and 63 from the Aniniwah Medical Centre, both within the Ashanti Region of Ghana.

Ethical consideration

Ethical approval was sought from the Committee on Human Research, Publication, and Ethics at the School of Medicine and Dentistry of the Kwame Nkrumah University of Science and Technology (KNUST) (CHRPE/AP/385/23). Written informed consent was sought from each participant before the data collection of the study. Participation was voluntary and participants were allowed to opt out any time on their personal reasons.

Data collection

Data for this study were collected using a structured questionnaire comprising four main sections: Sociodemographic, Lifestyle, Clinical Factors, and Psychosocial Factors. The questionnaire was designed to gather comprehensive information pertaining to the study’s objectives. Data were collected through face-to-face interviews conducted by trained research assistants. Participants’ responses were recorded as per their answers to the structured questions in the questionnaire. Participants were stratified into late presenters and non-late presenter based on the WHO HIV staging system.

Definition of dependent variables

Participant with LP-AHD – People confirmed with HIV/AIDS at WHO stage 3 or 4 at the time of diagnosis.

Participant without LP-AHD – People confirmed with HIV/AIDS at WHO stage 1 or 2 at the time of diagnosis.

Data management and statistical analyses

Data analysis was performed using Statistical Package for the Social Sciences 26.0 software (SPSS, Inc.; Chicago, IL, USA) and GraphPad Prism version 8.0. Categorical variables were presented as frequencies and percentages. A simple bar chart was used to illustrate the prevalence of LP-AHD among study participants. Pearson chi-square test or Fischer exact test was conducted to ascertain the relationship between LP-AHD and the study variables. Variables were also tested in the Univariate logistic regression prediction model and significant variables from the univariate and potential cofounders were tested in the multivariate logistic regression prediction model to assess the independent predictors of LP-AHD. p -value of less than 0.05 and 95% confidence interval were considered statistically significant.

Sociodemographic characteristics of study participants

Of the 315 participants enrolled in the study, one-third (33.3%) were within the ages of 46-55years, one-quarter (25.4%) were within the age range of 36–45 years with the least percentage of age range being 18–35 years (13.0%). Most of the participant in the study were females (85.7%) with males accounting for 14.3% of the population. Considering marital status, 37.5% were married with 17.5% being single. Also, a higher percentage of the participants had 2–3 children (44.3%) and Junior High School education (39.0%). Majority of the enrolled participants lived in urban areas (85.7%), were Christians (87.6%) and Akans (81.6%). With respect to employment status, 7.3% were employees whilst 63.5% were self-employed. Moreover, 47.5% of the participants earned < \(\not C\) 500 and 45.0% took 31–60 min to reach the clinic on visit days ( Table  1 ).

Prevalence of LP-AHD

The study found that the prevalence of LP-AHD among the study participants was 28.6% which accounted for 90 of the participants. 225 of the study participants did not present late (71.4%) (Fig.  1 ).

Prevalence of late presentation with advanced HIV disease (LP-AHD)

Sociodemographic factors associated with late presentation with advanced HIV disease (LP-AHD)

The age of the study participants showed a significant association with LP-AHD ( p  = 0.002). On the contrary, gender ( p  = 0.140), educational level ( p  = 0.480), marital status ( p  = 0.257), number of children ( p  = 0.290), educational level ( p  = 0.480), residence ( p  = 0.067), employment status ( p  = 0.183), ethnicity ( p  = 0.933), religion ( p  = 0.678), monthly income ( p  = 0.784), and time taken to reach the clinic ( p  = 0.928) did not exhibit statistically significant associations with LP-AHD ( Table  2 ).

Lifestyle factors associated with late presentation with advanced HIV disease (LP-AHD)

Alcohol intake ( p value = 0.714), alcohol Intake Frequency ( p value = 0.152), knowledge of partner’s HIV status ( p  = 0.164), history of smoking ( p =  0.798), action taken by participants when feeling sick ( p  = 0.167), history of Needle Sharing for Drug Use ( p  = 0.352), history of blood donation ( p  = 0.129) and number of sexual partners ( p  = 0.479) all showed no statistical significance with LP-AHD (Table  3 ).

Clinical factors associated with LP-AHD

Opportunistic infections ( p  = 0.017) showed a significant association with LP-AHD. However, type of HIV infection ( p  = 0.388), history of other STDs ( p  = 0.400), opportunistic infection ( p  = 0.692), viral suppression ( p  = 0.295) and viral rebound ( p  = 0.487) did not exhibit a significant association with LP-AHD ( Table  4 ).

Psychosocial factors associated with late presentation with advanced HIV disease

Various reasons for delayed testing ( p  < 0.001) showed a significant association with LP-AHD. Additionally, participants with a fear of perceived side effects of ART ( p  = 0.002), participants who expressed concerns about confidentiality ( p  = 0.002), had difficulty accessing healthcare ( p  = 0.029), fear of stigma associated with HIV/AIDS ( p  < 0.001), participants who disclosed their HIV Status to partner/close relative ( p  = 0.011), participants who sought medical care that eventually led to their HIV diagnosis ( p  < 0.0001) were all significantly associated with LP-AHD ( Table  5 ).

Predictors of LP-AHD

In a univariate logistic regression model, participants aged 36–45 years (cOR: 0.30, 95% CI: (0.14–0.65), p  = 0.002) showed a 70% reduced likelihood of LP-AHD compared to the reference age group (56–90 years). Moreover, participants who were diagnosed based on clinical suspicion (cOR: 17.36, 95% CI: (2.33–129.33), p  = 0.005), were not tested after reporting to the hospital with symptoms (cOR: 3.06, 95% CI: (1.74–5.36), p  < 0.0001), thought HIV testing was expensive (cOR: 19.17, 95% CI: (3.83–95.96), p  < 0.001), had perceived severe side effects of ART (cOR: 2.39, 95% CI: (1.29–4.44), p  = 0.006), had concerns about confidentiality (cOR: 2.25, 95% CI: (1.35–3.74), p  = 0.002), had difficulty accessing healthcare (cOR: 1.75, 95% CI: (1.06–2.90), p  = 0.029) or had a fear of stigma associated with HIV/AIDS (cOR: 2.51, 95% CI: (1.52–4.14), p  < 0.001) were associated with decreased chances of presenting late with advanced HIV disease.

In a multivariate logistic regression model, participants lower aged of 36–45 years were associated with approximately 70% decreased chances of LP-AHD (aOR: 0.32, 95% CI: (0.14–0.60), p  = 0.004). Participants who weren’t told to get tested after reporting to the hospital (aOR: 2.47, 95% CI: (1.30–4.74), p  = 0.006), were diagnosed based on clinical suspicion hospital (aOR: 13.86, 95% CI: (1.83–104.80), p  = 0.011) or thought HIV testing was expensive hospital (aOR: 7.04, 95% CI (1.31–37.91), p  = 0.023) ( Table  5 ).

Although there has been remarkable progress in HIV prevention and treatment, LP-AHD is still a major public health problem globally. A significant proportion of people living with HIV do not know they are infected and therefore seek medical care late. This study revealed that approximately 29% (28.6%) of the study participants presented late for care at WHO HIV stages 3 and 4. Moreover, the age group of 36–45 years, perceived high cost of HIV care, diagnosis based on clinical suspicion, and missed opportunities for early diagnosis by were associated with LP-AHD.

The prevalence of LP-AHD in this study, 28.6% was lower compared to previous studies conducted by Gesesew et al., in Ethiopia (65.5%) [ 5 ] and Jeong et al., in Asia (72-83.3%) [ 19 ]. The disparity could be due to the utilization of just WHO staging for defining LP-AHD in this study whilst the other studies employed both CD4 count and WHO staging in their classification. Although the prevalence of LP-AHD in this study is lower compared to the overall prevalence of late presentation in Africa estimated to be between 35% and 65%, it still undermines the 95-95-95 targets set by the UNAIDS to end HIV/AIDS by 2030.

The current study did not find any significant association between gender and LP-AHD. However, there was a larger proportion of males (37.8%) who presented with LP-AHD compared to females (27.0%); even though majority of study population included in the study were females (85.7%). This could be due to the fact that men may not be seeking care consistently and are accessing treatment at a later stage of their disease than women, which has also been reported in previous study [ 20 ]. Previous studies have cited being male as a strong correlate for LP-AHD [ 14 , 21 , 22 ]. However this trend conflicts with other studies which showed that being female is a risk factor for LP-AHD [ 5 , 23 ]. This study findings call for enhance public health education especially among males on the clinical important of continuous health seeking behaviors.

Moreover, participants in lower age group between 36 and 45 years bracket were less likely to present late than their older counterparts and this is supported by other studies which showed that old age was a predictor of LP-AHD [ 13 , 24 ]. This may be due to the misconception that older people are perceived to be at lower risk of HIV infection, which may lead to lower awareness and a lower sense of urgency for regular HIV testing in older populations.

Also, seeking herbal medication was a significant cause of two-fold increased likelihood of LP-AHD among study participants. Reliance on unorthodox medication has also been reported by Agaba et al. as a cause of LP-AHD [ 25 ]. Over dependence on herbal medicine and faith healers coupled with unaware of HIV status could account for HIV progression and eventual presentation with advanced HIV disease.

Investigating the type of HIV infection did not reveal a conclusive statistically significant relationship with LP-AHD. However, a previous study conducted in Guinea-Bissau showed that HIV-2 and HIV-1/2 dually infected patients had lower risk of late presentation compared with HIV-1 infected patients [ 26 ]. Differences in study populations and methods may account for this disparity. While history of other STDs and opportunistic infection were not statistically significant findings in this current study, results from a prospective study of 115 PDWH in Turkey [ 27 ] showed that opportunistic infections were likely implications of late diagnosis as the immune system is weakened and more susceptible to other infections. People with such infections fall into the WHO HIV staging criteria for late presentation and are therefore diagnosed as such.

Although there is routine HIV testing in the Ghanaian healthcare setting, individuals may not be screened for HIV unless they specifically request a test or present with symptoms that prompt clinicians to consider HIV testing. This finding ties in with the missed chances of diagnosis observed in this study as patients were not tested initially when they reported to the hospital with symptoms.

Furthermore, those who perceived HIV testing as expensive showed a nineteen times increased risk of LP-AHD. This calls for awareness creation as HIV testing is covered by the Ghana national health insurance [ 28 ]. Furthermore, concerns about confidentiality were associated with more than a two-fold increased risk of LP-AHD although it didn’t reach significance after adjusting for cofounders. Other studies have also identified confidentiality issues as a barrier to HIV self-testing [ 13 , 29 ] showing the importance of privacy protection and adherence to ethical standards in the healthcare setting.

The findings from this study could inform policymakers, public health officials, and HIV physicians in their fight against HIV. By recognizing the challenges to a timely HIV diagnosis, physicians can prioritize HIV testing, especially for people who show evidence of symptoms or risk factors. Additionally, awareness of the association between the perceived cost of HIV treatment and delayed presentation may prompt physicians and public health officials to address financial misinformation regarding HIV care since WHO has been HIV care entirely free as well increase access to testing and treatment options.

This study presents a novel data on the prevalence and predictors of LP-AHD in Ghana. However, using WHO HIV grading alone to distinguish between late and non-late presenters limited the scope of this study. Additionally, our study used participant self-reported responses and available hospital records, which presented issues with subject bias and data completeness. The retrospective nature of this study also shows the need for a prospective investigation that would allow for real-time monitoring of late presenters and, also to reduce the possibility of bias. Future studies are needed to investigate the immunological correlates of late presenters.

The prevalence of LP-AHD among people diagnosed with HIV is high. Missed opportunities for early diagnosis by clinicians, diagnosis based on clinical suspicion, old age and perceived cost of HIV care were associated with LP-AHD. These factors call for targeted campaigns among the general populace and clinicians alike to promote routine HIV testing and enhanced education on the awareness of early signs of HIV.

Data availability

All data generated or analyzed during this study are included in this article and can be requested from the corresponding author.

Abbreviations

Human Immunodeficiency Virus/ Acquired Immune Deficiency Syndrome (AIDS)

People diagnosed with HIV

Late presentation with advanced HIV/AIDS disease

Committee on Human Research, Publication, and Ethics

Kwame Nkrumah University of Science and Technology

World health organization

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Acknowledgements

The authors are grateful to study participants, as well as research assistants who contributed in diverse ways to the successful implementation of the study.

This study did not receive funding from private, government or non-for-profit organization.

Author information

Samuel Asamoah Sakyi, Samuel Kwarteng and Ebenezer Senu contributed equally and are all first authors.

Authors and Affiliations

Department of Molecular Medicine, School of Medicine and Dentistry, Kwame Nkrumah University of Science and Technology, Kumasi, Ashanti region, Ghana

Samuel Asamoah Sakyi, Samuel Kwarteng, Ebenezer Senu, Alfred Effah, Stephen Opoku, Success Acheampomaa Oppong, Samuel Kekeli Agordzo, Oscar Simon Olympio Mensah & Tonnies Abeku Buckman

Department of Medical Diagnostics, Faculty of Allied Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ashanti region, Ghana

Samuel Kwarteng, Success Acheampomaa Oppong, Kingsley Takyi Yeboah, Solomon Abutiate, Augustina Lamptey, Mohammed Arafat & Festus Nana Afari-Gyan

Department of Medical Microbiology, College of Health Sciences, University of Ghana Medical School, Accra, Greater Accra region, Ghana

Emmauel Owusu

Department of Medical Laboratory Sciences, KAAF University College, Buduburam, Accra, Greater Accra region, Ghana

Tonnies Abeku Buckman

Department of Biomedical Science, School of Allied Health Sciences, University of Cape Coast, Cape Coast, Central region, Ghana

Benjamin Amoani

Pediatric Infectious Disease Unit, Child Health Directorate, Komfo Anokye Teaching Hospital, Kumasi, Ashanti region, Ghana

Anthony Kwame Enimil

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SAS and AKE supervised the project, SK, ES, AE and SO were involved in the statistical analyses, SAS, SK, ES, AE, SO, SAO, KTY, SA, AL, MA, FNAG, SKA, OSOM, EO, TAB, BA, and AKE were involved in study design, data curation, methodology, manuscript drafting. All authors reviewed the manuscript.

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Correspondence to Ebenezer Senu .

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Ethical approval was sought from the Committee on Human Research, Publication, and Ethics at the School of Medicine and Dentistry of the Kwame Nkrumah University of Science and Technology (KNUST) (CHRPE/AP/385/23). Written informed consent was sought from each participant before the data collection of the study. Participation was voluntarily and participants were allowed to opt out any time on their personal reasons.

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Sakyi, S.A., Kwarteng, S., Senu, E. et al. High prevalence of late presentation with advanced HIV disease and its predictors among newly diagnosed patients in Kumasi, Ghana. BMC Infect Dis 24 , 764 (2024). https://doi.org/10.1186/s12879-024-09682-6

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DOI : https://doi.org/10.1186/s12879-024-09682-6

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