what are the methods of presentation of data

• Interactive Presentation

10 Methods of Data Presentation That Really Work in 2024

Leah Nguyen • 20 August, 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. 💎

More Tips with AhaSlides

• Marketing Presentation
• Survey Result Presentation
• Types of Presentation

Start in seconds.

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.

Tips to Engage with Polls & Trivia

More from AhaSlides

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.

To learn more Maths-related concepts, stay tuned with BYJU’S – The Learning App and download the app today!

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Request OTP on Voice Call

Post My Comment

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

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.

If you need a quick method to create a data presentation, check out our  AI presentation maker . A tool in which you add the topic, curate the outline, select a design, and let AI do the work for you.

[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

Like this article? Please share

Data Analysis, Data Science, Data Visualization Filed under Design

Related Articles

Filed under Google Slides Tutorials • June 3rd, 2024

How To Make a Graph on Google Slides

Creating quality graphics is an essential aspect of designing data presentations. Learn how to make a graph in Google Slides with this guide.

Filed under Design • March 27th, 2024

How to Make a Presentation Graph

Detailed step-by-step instructions to master the art of how to make a presentation graph in PowerPoint and Google Slides. Check it out!

Filed under Presentation Ideas • February 12th, 2024

Turning Your Data into Eye-opening Stories

What is Data Storytelling is a question that people are constantly asking now. If you seek to understand how to create a data storytelling ppt that will complete the information for your audience, you should read this blog post.

Leave a Reply

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 ! ‍

More zenpedia articles

How many slides are needed for a 30-minute presentation?

The fusion of technology and traditional methods: Multimedia presentation trends in 2024

How remote teams can achieve their communication goals with effective strategies

Get the latest from Prezent community

Join thousands of subscribers who receive our best practices on communication, storytelling, presentation design, and more. New tips weekly. (No spam, we promise!)

Call Us Today! +91 99907 48956 | [email protected]

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.

Recommended Courses

Data Visualization

Using powerbi &tableau.

Tableau for Data Analysis

MySQL Certification Program

The PowerBI Masterclass

Need help call our support team 7:00 am to 10:00 pm (ist) at (+91 999-074-8956 | 9650-308-956), keep in touch, email: [email protected].

WhatsApp us

An official website of the United States government

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

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

• Publications
• Account settings

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

• Advanced Search
• Journal List
• Korean J Anesthesiol
• v.70(3); 2017 Jun

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.

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

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

Partner Center

9 Data Presentation Tools for Business Success

• By Judhajit Sen
• May 29, 2024

A data presentation is a slide deck that shares quantitative information with an audience using visuals and effective presentation techniques . The goal is to make complex data easily understandable and actionable using data presentation examples like graphs and charts, tables, dashboards, and clear text explanations. 

Data presentations help highlight trends, patterns, and insights, allowing the audience to grasp complicated concepts or trends quickly. This makes it easier for them to make informed decisions or conduct deeper analysis.

Data visualization in presentations is used in every field, from academia to business and industry. Raw data is often too complex to understand directly, so data analysis breaks it down into charts and graphs. These tools help turn raw data into useful information.

Once the information is extracted, it’s presented graphically. A good presentation can significantly enhance understanding and response.

Think of data presentation as storytelling in business presentations with charts. A common mistake is assuming the audience understands the data as well as the presenter. Always consider your audience’s knowledge level and what information they need when you present your data.

To present the data effectively:

1. Provide context to help the audience understand the numbers.

2. Compare data groups using visual aids.

3. Step back and view the data from the audience’s perspective.

Data presentations are crucial in nearly every industry, helping professionals share their findings clearly after analyzing data.

Key Takeaways

• Simplifying Complex Data: Data presentations turn complex data into easy-to-understand visuals and narratives, helping audiences quickly grasp trends and insights for informed decision-making.
• Versatile Tools: Various tools like bar charts, dashboards, pie charts, histograms, scatter plots, pictograms, textual presentations, and tables each serve unique purposes, enhancing the clarity and impact of the data.
• Audience Consideration: Tailor your presentation to the audience’s knowledge level, providing context and using simple visuals to make the information accessible and actionable.
• Effective Data Storytelling: Combining clear context, organized visuals, and thoughtful presentation ensures that the data’s story is conveyed effectively, supporting better business decisions and success.

Following are 9 data presentation tools for business success.

Bar charts are a simple yet powerful method of presentation of the data using rectangular bars to show quantities or frequencies. They make it easy to spot patterns or trends at a glance. Bar charts can be vertical (column charts) or horizontal, depending on how you want to display your data.

In a bar graph, categories are displayed on one axis, usually the x-axis for vertical charts and the y-axis for horizontal ones. The bars’ lengths represent the values or frequencies of these categories, with the scale marked on the opposite axis.

These charts are ideal for comparing data across different categories or showing trends over time. Each bar’s height (or length in a horizontal chart) is directly proportional to the value it represents. This visual representation helps illustrate differences or changes in data.

Bar charts are versatile tools in business reports, academic presentations, and more. To make your bar charts effective:

• Ensure they are concise and have easy-to-read labels.
• Avoid clutter by not including too many categories, making the chart hard to read.
• Keep it simple to maintain clarity and impact, whether your bars go up or sideways.

Line Graphs

Line graphs show how data changes over time or with continuous variables. They connect points of data with straight lines, making it easy to see trends and fluctuations. These graphs are handy when comparing multiple datasets over the same timeline.

Using line graphs, you can track things like stock prices, sales projections, or experimental results. The x-axis represents time or another continuous variable, while the y-axis shows the data values. This setup allows you to understand the ups and downs in the data quickly.

To make your graphs effective, keep them simple. Avoid overcrowding with too many lines, highlight significant changes, use labels, and give your graph a clear, catchy title. This will help your audience grasp the information quickly and easily.

A data dashboard is a data analysis presentation example for analyzing information. It combines different graphs, charts, and tables in one layout to show the information needed to meet one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs) by displaying visuals you’ve already made in worksheets.

It’s best to keep the number of visuals on a dashboard to three or four. Adding too many can make it hard to see the main points. Dashboards are helpful for business analytics, like analyzing sales, revenue, and marketing metrics. In manufacturing, they help users understand the production scenario and track critical KPIs for each production line.

Dashboards represent vital points of data or metrics in an easy-to-understand way. They are often an  interactive presentation idea , allowing users to drill down into the data or view different aspects of it.

Pie charts are circular graphs divided into parts to show numerical proportions. Each portion represents a part of the whole, making it easy to see each component’s contribution to the total.

The size of each slice is determined by its value relative to the total. A pie chart with more significant points of data will have larger slices, and the whole chart will be more important. However, you can make all pies the same size if proportional representation isn’t necessary.

Pie charts are helpful in business to show percentage distributions, compare category sizes, or present simple data sets where visualizing ratios is essential. They work best with fewer variables. For more variables, it’s better to use a pie chart calculator that helps to create pie charts easily for various data sets with different color slices. 

Each “slice” represents a fraction of the total, and the size of each slice shows its share of the whole. Pie charts are excellent for showing how a whole is divided into parts, such as survey results or demographic data.

While pie charts are great for simple distributions, they can get confusing with too many categories or slight differences in proportions. To keep things clear, label each slice with percentages or values and use a legend if there are many categories. If more detail is needed, consider using a donut chart with a blank center for extra information and a less cluttered look.

A histogram is a graphical presentation of data  to help in understanding the distribution of numerical values. Unlike bar charts that show each response separately, histograms group numeric responses into bins and display the frequency of reactions within each bin. The x-axis denotes the range of values, while the y-axis shows the frequency of those values.

Histograms are useful for understanding your data’s distribution, identifying shared values, and spotting outliers. They highlight the story your data tells, whether it’s exam scores, sales figures, or any other numerical data.

Histograms are great for visualizing the distribution and frequency of a single variable. They divide the data into bins, and the height of each bar indicates how many points of data fall into that bin. This makes it easy to see trends like peaks, gaps, or skewness in your data.

To make your histogram effective, choose bin sizes that capture meaningful patterns. Clear axis labels and titles also help in explaining the data distribution.

Scatter Plot

Using individual data points, a scatter plot chart is a presentation of data in visual form to show the relationship between two variables. Each variable is plotted along the x-axis and y-axis, respectively. Each point on the scatter plot represents a single observation.

Scatter plots help visualize patterns, trends, and correlations between the two variables. They can also help identify outliers and understand the overall distribution of data points. The way the points are spread out or clustered together can indicate whether there is a positive, negative, or no clear relationship between the variables.

Scatter plots can be used in practical applications, such as in business, to show how variables like marketing cost and sales revenue are related. They help understand data correlations, which aids in decision-making.

To make scatter plots more effective, consider adding trendlines or regression analysis to highlight patterns. Labeling key data points or tooltips can provide additional information and make the chart easier to interpret.

A pictogram is the simplest form of data presentation and analysis, often used in schools and universities to help students grasp concepts more effectively through pictures.

This type of diagram uses images to represent data. For example, you could draw five books to show the number of books sold in the first week of release, with each image representing 1,000 books. If consumers bought 5,000 books, you would display five book images.

Using simple icons or images makes the information visually intuitive. Instead of relying on numbers or complex graphs, pictograms use straightforward symbols to depict data points. For example, a thumbs-up emoji can illustrate customer satisfaction levels, with each emoji representing a different level of satisfaction.

Pictograms are excellent for visual data presentation. Choose symbols that are easy to interpret and relevant to the data to ensure clarity. Consistent scaling and a legend explaining the symbols’ meanings are essential for an effective presentation.

Textual Presentation

Textual presentation uses words to describe the relationships between pieces of information. This method helps share details that can’t be shown in a graph or table. For example, researchers often present findings in a study textually to provide extra context or explanation. A textual presentation can make the information more transparent.

This type of presentation is common in research and for introducing new ideas. Unlike charts or graphs, it relies solely on paragraphs and words.

Textual presentation also involves using written content, such as annotations or explanatory text, to explain or complement data. While it doesn’t use visual presentation aids like charts, it is a widely used method for presenting qualitative data. Think of it as the narrative that guides your audience through the data.

Adequate textual data may make complex information more accessible. Breaking down complex details into bullet points or short paragraphs helps your audience understand the significance of numbers and visuals. Headings can guide the reader’s attention and tell a coherent story.

Tabular Presentation

Tabular presentation uses tables to share information by organizing data in rows and columns. This method is useful for comparing data and visualizing information. Researchers often use tables to analyze data in various classifications:

Qualitative classification: This includes qualities like nationality, age, social status, appearance, and personality traits, helping to compare sociological and psychological information.

Quantitative classification: This covers items you can count or number.

Spatial classification: This deals with data based on location, such as information about a city, state, or region.

Temporal classification: This involves time-based data measured in seconds, hours, days, or weeks.

Tables simplify data, making it easily consumable, allow for side-by-side comparisons, and save space in your presentation by condensing information.

Using rows and columns, tabular presentation focuses on clarity and precision. It’s about displaying numerical data in a structured grid, clearly showing individual data points. Tables are invaluable for showcasing detailed data, facilitating comparisons, and presenting exact numerical information. They are commonly used in reports, spreadsheets, and academic papers.

Organize tables neatly with clear headers and appropriate column widths to ensure readability. Highlight important data points or patterns using shading or font formatting. Tables are simple and effective, especially when the audience needs to know precise figures.

Elevate Business Decisions with Effective Data Presentations

Data presentations are essential for transforming complex data into understandable and actionable insights. Data presentations simplify the process of interpreting quantitative information by utilizing data presentation examples like charts, graphs, tables, infographics, dashboards, and clear narratives. This method of storytelling with visuals highlights trends, patterns, and insights, enabling audiences to make informed decisions quickly.

In business, data analysis presentations are invaluable. Different types of presentation tools like bar charts help compare categories and track changes over time, while dashboards consolidate various metrics into a comprehensive view. Pie charts and histograms offer clear views of distributions and proportions, aiding in grasping the bigger picture. Scatter plots reveal relationships between variables, and pictograms make data visually intuitive. Textual presentations and tables provide detailed context and precise figures, which are essential for thorough analysis and comparison.

Consider the audience’s knowledge level to tailor the best way to present data in PowerPoint. Clear context, simple visuals, and thoughtful organization ensure the data’s story is easily understood and impactful. Mastering these nine data presentation types can significantly enhance business success by making data-driven decisions more accessible and practical.

Frequently Asked Questions (FAQs)

1. What is a data presentation?

A data presentation is a slide deck that uses visuals and narrative techniques to make complex data easy to understand and actionable. It includes charts, graphs, tables, infographics, dashboards, and clear text explanations.

2. Why are data presentations important in business?

Data presentations are crucial because they help highlight trends, patterns, and insights, making it easier for the audience to understand complicated concepts. This enables better decision-making and deeper analysis.

3. What types of data presentation tools are commonly used?

Common tools include bar charts, line graphs, dashboards, pie charts, histograms, scatter plots, pictograms, textual presentations, and tables. Each tool has a unique way of representing data to aid understanding.

4. How can I ensure my data presentation is effective?

To ensure effectiveness, provide context, compare data sets using visual aids, consider your audience’s knowledge level, and keep visuals simple. Organizing information thoughtfully and avoiding clutter enhances clarity and impact.

Transform Your Data into Compelling Stories with Prezentium

Unlock the full potential of your business data with Prezentium ‘s expert presentation services. Our AI-powered solutions turn complex data into clear, actionable insights, helping you easily make informed decisions. 

Prezentium’s Overnight Presentations ensure you wake up to a stunning, ready-to-use presentation in your inbox by 9:30 am PST. Send your requirements by 5:30 pm PST, and let our team combine business acumen, visual design, and data science to craft a presentation that highlights trends and insights seamlessly.

Our Presentation Specialists transform raw ideas and meeting notes into captivating presentations. Whether you need new designs or bespoke templates, our experts bring your vision to life with precision and creativity.

Enhance your team’s skills with Zenith Learning, our interactive workshops that blend structured problem-solving with visual storytelling. Learn to present data effectively and make a lasting impact in your business communications.

Prezentium’s services are designed to help you make the most of your data, from bar charts to dashboards, ensuring your presentations are informative and visually engaging. Let us help you tell your data’s story in a way that resonates. Contact Prezentium today to elevate your business presentations.

Why wait? Avail a complimentary 1-on-1 session with our presentation expert. See how other enterprise leaders are creating impactful presentations with us.

Shark Tank Presentation Tips: Winning Shark Tank Pitch Elements

Visual communication: benefits, importance, and examples, 7 public speaking tips for enhancing your public speaking abilities.

Statistical data presentation

• Korean Journal of Anesthesiology 70(3):267

• Dongguk Unversity Ilsan Hospital, Goyang, Republic of Korea

• Inje University, Sanggye Paik Hospital

Abstract and Figures

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Heru Junanto
• Galih Zahrohtul
• Daffa Triyawan
• Farah Sukmawati Wahidah
• Istiningsih Istiningsih
• Thomas Unruh
• Sutrisno Sutrisno
• Urip Meilina Kurniawati

• Hulya Ertaş-Kılıç
• Melihan Unlu
• Betul Keray-Dinçel
• Joyce M. Kyung
• MaryDee Fisher
• David DeCarlucci

• Miriam D. Weisberg
• Rushabh M. Vakharia

• Saba Nashir
• Ahmad Seddiq Seddiqi
• Chan Woo Lee

• Yushi Adachi

• Helen Ki Shinn
• Youngyoen Hwang
• Byung-Gun Kim
• Hyun Kyoung Lim
• Tae Kyun Kim
• Sung-Hwan Cho
• Mi-Soon Lee
• Yong-Ik Kim
• Sung Kwan Choi
• Myung Ha Yoon
• Jung Il Choi

• Jung-Hee Ryu

• Woojun Jung
• Minho Hwang

• Jun Haeng Lee
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

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).

• PRESENTATION SKILLS

Presenting Data

Search SkillsYouNeed:

Presentation Skills:

• A - Z List of Presentation Skills
• Top Tips for Effective Presentations
• General Presentation Skills
• What is a Presentation?
• Preparing for a Presentation
• Organising the Material
• Writing Your Presentation
• Deciding the Presentation Method
• Managing your Presentation Notes
• Working with Visual Aids
• Managing the Event
• Coping with Presentation Nerves
• Dealing with Questions
• How to Build Presentations Like a Consultant
• 7 Qualities of Good Speakers That Can Help You Be More Successful
• Self-Presentation in Presentations
• Specific Presentation Events
• Remote Meetings and Presentations
• Giving a Speech
• Presentations in Interviews
• Presenting to Large Groups and Conferences
• Giving Lectures and Seminars
• Managing a Press Conference
• Attending Public Consultation Meetings
• Managing a Public Consultation Meeting
• Crisis Communications
• Elsewhere on Skills You Need:
• Communication Skills
• Facilitation Skills
• Teams, Groups and Meetings
• Effective Speaking
• Question Types

Subscribe to our FREE newsletter and start improving your life in just 5 minutes a day.

You'll get our 5 free 'One Minute Life Skills' and our weekly newsletter.

We'll never share your email address and you can unsubscribe at any time.

When and how should you use data in a presentation?

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

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

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

Remember that You Are Telling Your Audience a Story

All presentations are basically story-telling opportunities.

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

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

Use Data to Tell the Story

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

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

‘ What is the story in this data? ’,

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

A Picture Tells a Thousand Words

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

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

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

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

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

Consider this set of data:

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

Now consider the same data in a pie chart:

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

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

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

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

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

KISS: Keep It Simple, Stupid!

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

But does it really help to make your point?

Then don’t present it.

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

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

Leave It Out...

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

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

Remember KISS: Keep It Simple, Stupid.

Highlight the Main Features to Draw Out the Insights

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

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

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

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

Take-home message

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

In other words:

‘What happened there?’

‘What are we going to do about it?’

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

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

Continue to: Writing Your Presentation Working with Visual Aids

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

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
• Share this article

Create a free account to unlock this Template

Access and download collection of free Templates to help power your productivity and performance.

Already have an account? Log in

Supercharge your skills with Premium Templates

Take your learning and productivity to the next level with our Premium Templates.

Upgrading to a paid membership gives you access to our extensive collection of plug-and-play Templates designed to power your performance—as well as CFI's full course catalog and accredited Certification Programs.

Already have a Self-Study or Full-Immersion membership? Log in

Access Exclusive Templates

Gain unlimited access to more than 250 productivity Templates, CFI's full course catalog and accredited Certification Programs, hundreds of resources, expert reviews and support, the chance to work with real-world finance and research tools, and more.

Already have a Full-Immersion membership? Log in

We use essential cookies to make Venngage work. By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts.

Manage Cookies

Cookies and similar technologies collect certain information about how you’re using our website. Some of them are essential, and without them you wouldn’t be able to use Venngage. But others are optional, and you get to choose whether we use them or not.

Strictly Necessary Cookies

These cookies are always on, as they’re essential for making Venngage work, and making it safe. Without these cookies, services you’ve asked for can’t be provided.

Show cookie providers

• Google Login

Functionality Cookies

These cookies help us provide enhanced functionality and personalisation, and remember your settings. They may be set by us or by third party providers.

Performance Cookies

These cookies help us analyze how many people are using Venngage, where they come from and how they're using it. If you opt out of these cookies, we can’t get feedback to make Venngage better for you and all our users.

• Google Analytics

Targeting Cookies

These cookies are set by our advertising partners to track your activity and show you relevant Venngage ads on other sites as you browse the internet.

• Google Tag Manager
• Infographics
• Daily Infographics
• Popular Templates
• Accessibility
• Graphic Design
• Graphs and Charts
• Data Visualization
• Human Resources
• Beginner Guides

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.

Discover popular designs

Infographic maker

Brochure maker

White paper online

Newsletter creator

Flyer maker

Timeline maker

Letterhead maker

Mind map maker

Ebook maker

• Accountancy
• Business Studies
• Organisational Behaviour
• Human Resource Management
• Entrepreneurship

Textual Presentation of Data: Meaning, Suitability, and Drawbacks

Presentation of Data refers to the exhibition of data in such a clear and attractive way that it is easily understood and analysed. Data can be presented in different forms, including Textual or Descriptive Presentation, Tabular Presentation, and Diagrammatic Presentation.

Textual Presentation

Textual or Descriptive Presentation of Data is one of the most common forms of data presentation. In this, data is a part of the text of the study or a part of the description of the subject matter of the study. It is usually preferred when the quantity of data is not very large. For example, there are 50 students in a class, among them 30 are boys and 20 are girls. This is the data that can be understood with the help of a simple text and no table or pie diagram is required for the same. 

Suitability

Textual Presentation of Data is suitable when the quantity of data is not large. It means that a small portion of data that is presented as a part of the subject matter of study can become useful supportive evidence to the given text. Therefore, instead of saying that the price of petrol is skyrocketing, it can be said that the price of petrol has increased by 20% in the last 2 years, and this statement will be more meaningful and precise. Under textual presentation of data, an individual does not have to support the text with the help of a diagram or table as the text in itself is very small and has few observations. 

Advantages of Textual Presentation of Data

Textual Presentation of Data has the following benefits:

1. It allows the researcher to make an elaborate interpretation of data during the presentation. 

2. A researcher can easily present qualitative data that cannot be presented in tabular or graphical form using the textual presentation of data. 

3. If the data is present in small sets, a textual presentation can be easily used. For example, there are 50 students in a class, among them, 30 are boys and 20 are girls. This is the data that can be understood with the help of a simple text and no table or pie diagram is required for the same. 

Disadvantages of Textual Presentation of Data

Textual Presentation of Data has the following drawbacks:

1. One of the major drawbacks of the textual presentation of data is that it provides extensive data in the form of text and paragraphs which makes it difficult for the user of data to draw a proper conclusion at a glance. This facility is provided in tabular or diagrammatic presentation of data.

2. This method of presenting data is not suitable for large sets of data as these sets contain too many details. 

3. Besides, one has to read through the whole text in order to understand and comprehend the main point of the data.

Please Login to comment...

Similar reads.

• Statistics for Economics
• Commerce - 11th
• 105 Funny Things to Do to Make Someone Laugh
• Best PS5 SSDs in 2024: Top Picks for Expanding Your Storage
• Best Nintendo Switch Controllers in 2024
• Xbox Game Pass Ultimate: Features, Benefits, and Pricing in 2024
• #geekstreak2024 – 21 Days POTD Challenge Powered By Deutsche Bank

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 13 September 2024

Text mining method to unravel long COVID’s clinical condition in hospitalized patients

• Pilar Tavares Veras Florentino 1 , 2   na1 ,
• Vinícius de Oliveira Araújo 2 , 3   na1 ,
• Henrique Zatti 2   na1 ,
• Caio Vinícius Luis 4 ,
• Célia Regina Santos Cavalcanti 4 ,
• Matheus Henrique Citibaldi de Oliveira 4 ,
• Anderson Henrique França Figueredo Leão   ORCID: orcid.org/0000-0002-0721-0866 4 ,
• Juracy Bertoldo Junior 2 ,
• George G. Caique Barbosa 2 ,
• Ernesto Ravera 4 ,
• Alberto Cebukin 4 ,
• Renata Bernardes David 4 ,
• Danilo Batista Vieira de Melo 5 ,
• Tales Mota Machado 2 , 6 ,
• Nancy C. J. Bellei 7 ,
• Viviane Boaventura 1 , 3 ,
• Manoel Barral-Netto   ORCID: orcid.org/0000-0002-5823-7903 1 , 3   na2 &
• Soraya S. Smaili   ORCID: orcid.org/0000-0001-5844-1368 4   na2  

Cell Death & Disease volume  15 , Article number:  671 ( 2024 ) Cite this article

Metrics details

• Epidemiology
• Viral infection

Long COVID is characterized by persistent that extends symptoms beyond established timeframes. Its varied presentation across different populations and healthcare systems poses significant challenges in understanding its clinical manifestations and implications. In this study, we present a novel application of text mining technique to automatically extract unstructured data from a long COVID survey conducted at a prominent university hospital in São Paulo, Brazil. Our phonetic text clustering (PTC) method enables the exploration of unstructured Electronic Healthcare Records (EHR) data to unify different written forms of similar terms into a single phonemic representation. We used n-gram text analysis to detect compound words and negated terms in Portuguese-BR, focusing on medical conditions and symptoms related to long COVID. By leveraging text mining, we aim to contribute to a deeper understanding of this chronic condition and its implications for healthcare systems globally. The model developed in this study has the potential for scalability and applicability in other healthcare settings, thereby supporting broader research efforts and informing clinical decision-making for long COVID patients.

Introduction

Advances in emerging technologies such as artificial intelligence (AI) and machine learning (ML) hold promise for the development of healthcare transformation in prediction, contact tracing, screening, diagnosis and treatment, significantly improving medical practice [ 1 , 2 , 3 , 4 , 5 , 6 ]. One potential utility of AI is to assist in the extraction of information from electronic medical records. Natural language processing (NLP) is a subfield of AI that enables computers to learn from unstructured medical records and adapt to new language patterns over time, which can be useful for administrative and research purposes. Unlike NLP, which seeks to comprehend the overall meaning of text, text mining focus on addressing a particular problem in a specific domain determined in advance, potentially employing some NLP techniques in the process [ 7 ]. Although efforts have been made to use text mining to extract information from medical records in the English language, studies in languages other than English are still emerging [ 8 , 9 ] and are urgently needed for specific systems.

Historically, clinically relevant information from electronic health records (EHRs) has been extracted via manual review by clinical experts, resulting in scalability and cost [ 10 ]. This is particularly evident for chronic diseases, where clinical notes are more common than structured medical records data [ 11 ]. These unstructured data provide a great opportunity to test the performance of text mining in automatically extracting clinically meaningful information, which may be useful for research and administrative purpose [ 10 ].

Long COVID is a chronic disease characterized by persistence of symptoms for more than 1 month (according to Central of Disease Control—CDC) or more than 3 months (according to World Health Organization—WHO) that still lacks a definitive clinical characterization. New tools that help perform a meticulous analysis of vast amounts of unstructured data from EHR can uncover patterns, symptoms, and outcomes that might otherwise elude traditional research methods enabling a deeper comprehension of long COVID. Recent descriptions of long COVID are based on studies conducted and compiled in 2021 and more detailed studies conducted in 2022 with the aim of reaching a consensus [ 12 , 13 , 14 ]. Based on these studies, many works have sought to highlight what actually occurs after SARS-CoV-2 infection. It remains unclear why the virus causes so many different symptoms affecting a variety of systems and what defines their frequency and prevalence. Thus, understanding what happens after COVID-19 and identifying which sequelae correspond to the postacute disease are increasingly important. Several symptoms can persist for many months or years, in addition to elevated risks of complications and death [ 15 , 16 , 17 ]. However, there are still may doubts and inconsistencies related to long COVID, especially regarding patients who were hospitalized, as well as the correlation between the length of hospitalization and the severity of the disease. Therefore, it is necessary to investigate and follow patients who were hospitalized with COVID-19 in different hospitals and to clearly identify the risk factors that require attention after COVID-19 and how these factors affect patients’ lives after the disease.

In this study, we sought to classify and automatically extract data from a long Covid survey from a large hospital in the city of São Paulo, one of the Brazilian cities most affected by the pandemic. We analyzed the EHR and created a model that can be applied in other hospitals.

Materials and methods

Study design and data source.

This is a cross sectional study that uses a national database for training a model of text mining and EHR from a referral hospital for testing the model. The training dataset provided the tokens for text mining. We then performed text mining on the testing dataset and compared the results with those of manual human classification.

The training dataset was built from the information system for severe acute respiratory illness (SIVEP-Gripe), in which all COVID-19 hospital admissions and deaths in Brazil were registered by federal law. The SIVEP-Gripe is the national registration database for severe acute respiratory syndrome (SARS) in Brazil including COVID-19 data, and all COVID-19 hospitalizations and deaths. SARS is defined as an individual who presents with dyspnea/respiratory discomfort, persistent pressure or pain in the chest, oxygen saturation less than 95% without oxygen, or cyanosis of the lips or face ( https://www.gov.br/saude/pt-br/assuntos/coronavirus/artigos/definicao-e-casos-suspeitos ). This database has been widely used as a source for other epidemiological studies [ 18 , 19 , 20 , 21 ]. In the present work, the SIVEP-Gripe was used to create a token dictionary for unstructured text from clinical questionnaires. The dataset is publicly available at https://opendatasus.saude.gov.br/group/dados-sobre-srag .

The testing dataset was built from the EHR of patients who were hospitalized for COVID-19 at the Hospital São Paulo, the University Hospital (UH) of the Federal University of São Paulo (Unifesp), from March 2020 to June 2022 and were followed after discharge at a Post-COVID-19 Disease Unit (PCDU). The PCDU is a multidisciplinary unit where health professionals assist patients and administer a questionnaire to gather information on any prolonged signs or symptoms after the acute phase of COVID-19. This questionnaire includes information on acute COVID-19, evolution of the infection, medical history, and post-acute phase signs and symptoms. These data were also linked to demographic information and SARS-CoV-2 PCR results from patients.

Hospital São Paulo uses multiple information systems, resulting in the dispersion of relevant information across different databases. As an initial search strategy to extract data, terminologies related to the infectious disease COVID-19 were applied to the Clinical Notes Database (MongoDB) for the period from March 1, 2020 to September 30, 2022. A total of eight clinical encounter forms were preselected from this data structure, and the clinical records of 16,017 patients were collected. The form data were extracted and grouped into eight JSON (JavaScript object notation) files that were converted to the Tidy Data format and saved as CSV (comma separated values) text files. Following the analysis of the files by the technical-scientific team, the clinical encounter form “Post-COVID Care (Pneumo)”, containing records of 440 patients, was chosen for the analysis of demographic and hospital historical data. The second database of interest was the general patient record, a relational database (Oracle) that was queried using the Standard Query Language (SQL). We extracted data on emergency room visits, outpatient consultations, appointments, exam results, hospitalizations, and surgeries. The extracted data from this database were stored in *.XLSX (Microsoft Excel 2007) file format. The extracted medical conditions and patient symptoms were validated with the assistance of ambulatory pneumology and infectiology at the UH.

Clinical data collection

The SIVEP-Gripe dataset (training dataset), which contains ≥2.6 million entries and information on medical conditions and symptoms in the form of unstructured text, was used for the training dataset. Comorbidity and symptom information was extracted from this system via a phoneme approach using the metaphonept-br library ( https://github.com/carlosjordao/metaphone-ptbr ). All data processing was run in Python (Version: 3.10) using Jupyter Notebooks.

Before beginning the text analysis, it was necessary to normalize and clean the text strings. To this end, we utilized regular expressions to clean special characters and to make a diverse set of separators between words uniform to a whitespace. The phonetic text clustering (PTC) method groups terms together according to their phonetics, effectively consolidating variations of similar terms into a single phonemic representation and using n-gram text analysis to detect compound words [ 22 ]. This method not only captured and grouped terms but also allowed the accommodation of synonyms, abbreviations, typographical errors, and the different conjunctions found in Brazilian Portuguese.

To ensure the accuracy of these results, a dictionary of similar terms was carefully curated by five specialists in internal medicine, infectology, pharmacology, pathology, otorhinolaryngology and public health and three medical students. This step prevented the grouping of different terms into the same phoneme. This curation process was crucial in enabling the use of the dictionary to identify medical conditions from unstructured text from different and more complex contexts. Scripts from methodology employed are found in https://github.com/CampusVirtualFiocruz/Text-Mining-Clinical-Data-UNIFESP .

PTC validation with long COVID questionnaires

To validate the PTC method, the clinical information of patients collected from the long COVID questionnaire was organized into structured and unstructured data (Testing dataset). The structured data consisted of yes–no and multiple-choice answers, as well as numerical variables. The unstructured data comprised textual patient reports, including records of symptoms, clinical signs, laboratory tests, previous medical conditions, and lifestyle habits, such as smoking.

Subsequently, an automated approach to process the unstructured variables from the long COVID questionnaires was applied. We searched for all previously defined terms in the curated dictionary and focused on the most frequent comorbidities associated with COVID-19, which included obesity, hypertension, diabetes mellitus, chronic obstructive pulmonary disease (COPD), asthma, hypothyroidism, and hyperthyroidism. Information on patients’ smoking history was also collected to classify patients as smokers or former smokers. Medical records that did not include information on comorbidities or smoking history were classified as having no comorbidities or having a non-smoking history.

In addition, information on long COVID symptoms (cough, fatigue, headache and myalgia) ( https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html ) was collected. For symptoms, the focus was on terms in the questionnaires that described patient information in the present, excluding the text referring to symptoms reported in the past (acute phase).

Importantly, the medical condition terms detected in the unstructured text of the long COVID dataset could be in the context of a negative report, i.e., the patient confirming or denying the medical condition. To address this issue, we assessed negative operators such as “deny” ( nega , in Brazilian Portuguese) that appeared before the comorbidity of interest and until the following sentence with regular expressions ( https://docs.python.org/3/library/re.html ). This allowed us to capture instances where patients denied having the specified condition. Figure 1 shows a diagram detailing the method developed in this study using the extracted dataset. Scripts from methodology employed can be found in https://github.com/CampusVirtualFiocruz/Text-Mining-Clinical-Data-UNIFESP .

The figure shows the process of extracting and processing clinical data using phonetic text clustering (PTC) from the SARS database (2.6 million entries) and UNIFESP unstructured medical data. n-grams (1–4) are extracted using the NLTK library, grouped by phonemes with the metaphone-ptbr library, and used to create a dictionary. A medical specialist validates the dictionary by excluding incorrect terms. UNIFESP unstructured medical data are automatically read, with negated terms recognized using regular expressions (re lib), and integrated into the UNIFESP structured medical data. This process combines automated text processing with manual validation to improve data accuracy and completeness for analysis.

Long COVID study population

The study population included adults (≥18 years) who were hospitalized due to acute COVID-19 at the Hospital São Paulo and discharged. The data were collected between March 2020 and June 2022. We excluded individuals who (1) had no SARS-CoV-2 PCR result or had a negative result; (2) did not have a recorded date of first symptoms; (3) completed questionnaires less than 30 days after the first occurrence of symptoms; (4) were hospitalized for more than 120 days; (5) did not have records of COVID-19 evolution during the acute phase; (6) had date inconsistencies; (7) had encounters after the first questionnaire; or (8) had no severity classification in the acute phase.

Demographic variables from the long COVID dataset, such as sex, age (stratified into 18–39, 40–59, 60–79, and ≥80 years), and race (divided into white, black, mixed-brown, Asian, and Indigenous), were evaluated. Due to the small sample size, Asian and Indigenous individuals were combined for the analysis. Additionally, other variables, such as medical history, length of hospital stay (stratified into 0–14, 15–30, 31–60, and ≥60 days), and severity of the acute phase of COVID-19, were included. The severity categories were defined as moderate (non-ICU ward), severe (intensive care unit; ICU), or critical (ICU with mechanical ventilation).

Statistical analysis

To validate the accuracy of the automated approach, we compared the automated results with manually searched and labeled clinical data. The manual labeling was performed by six of the authors with clinical training, and each record was individually labeled three times. We conducted Pearson’s chi-square test to compare the automated and manual term counts to assess the accuracy of the text mining. Then, we performed a descriptive analysis of the long COVID findings to validate our findings against those of previous studies on the topic. Also, we performed a generalized linear model (GLM) to assess the relationship between the demographic characteristics and the presence of post-COVID-19 symptoms.

Ethical approval

The Brazilian National Commission in Research Ethics approved the research protocol (CONEP approval number 4.921.308 and CAAE registration no. 58619822.6.1001.5505).

Automated labeling of the training dataset

First, we investigated the records from the SIVEP-Gripe. A total of 2,490,196 SARS records of patients admitted to hospitals between December 31, 2019, and March 27, 2023 were collected. All records were then analyzed as input for the PTC tokenization of medical conditions and symptoms, which were used to create the dictionary that was used to structure the data and to create the database for long COVID. Overall, 635,921 (25.5%) records reported one or more medical conditions, and 849,976 (34.1%) reported one or more SARS-related symptoms in the unstructured text field (Fig. S1 and Table 1 ). From the unstructured clinical data, a dictionary collecting synonyms, misspelled and derivative words into a unique term (Table S1 ) was produced. Based on this dictionary, 20 of the most frequent medical conditions and 10 of the most frequent symptoms (Table 1 ) were captured for further analyses.

SARS patient records were stratified by medical conditions and symptoms in a “yes/no” format, such as diabetes mellitus, obesity, cardiopathy, loss of smell, loss of taste, fatigue and cough. The results showed that 22,458 of the terms containing medical conditions captured from the unstructured text overlapped with at least one of the binary comorbidities with a “yes” response in the questionnaire. In addition, 1418 terms overlapped with at least one of the binary symptom variables with a “yes” response. Thus, to evaluate the gain of information, records with overlapping medical conditions or symptoms were excluded. The terms that were not included in the binary variables from the questionnaire appeared more frequently in the unstructured text annotation (Fig. 2 ).

The figure shows the most frequent terms captured using PTC. A displays the most frequent terms related to medical conditions, highlighting conditions such as hypertension, smoker status, and hypothyroidism, among others. B shows the most frequent terms associated with symptoms, including headache, myalgia, and asthenia. The bar colors indicate whether the terms appeared in the structured data before the application of PTC (red for “Yes”, green for “No”).

Among medical conditions, the most frequent term captured by the automated reading was “hypertension”, present in 303,109 entries, representing 11.3% of the total database (Fig. 2A ), followed by “smoker” in 61,110 entries, representing 2.3%; “hypothyroidism” in 39,550 entries, representing 1.5%; and “COPD” in 30,387 entries, representing 1.1%. Additionally, “smoking” was found in 61,110 entries (2.3%). Among symptoms, the most frequent terms were “headache”, present in 215,225 entries, representing 8.0% of the total database; “myalgia”, present in 213,035 entries, representing 7.9%; “asthenia”, present in 124,086 entries, representing 4.6%; and “runny nose”, present in 122,766 entries, representing 4.5% (Fig. 2B ).

Validating text mining on EHRs

Data from patients who were admitted with COVID-19 at the Hospital São Paulo, stayed in the hospital for more than 30 days, and were followed at the PCDU after discharge were evaluated.

To validate the PTC method on these data obtained from Hospital São Paulo, 398 post-COVID patient questionnaires collected from the PCDU (Fig. S2 ) were cross-checked. The dictionary derived from records of SARS-hospitalized patients was applied. Medical conditions and symptoms from these post-COVID-19 patients were extracted and studied by using an automated method. The results obtained were compared with those obtained through manual searches conducted by specialists, which showed a high degree of similarity in present, absent and negated terms. According to this method, the similarity ranged from 93% to 99% for medical condition terms and from 87% to 95% for symptom terms (Table S2 ). The statistical significance of these findings is reflected in the p values for all terms, which were less than 0.01 (Fig. 3 and Table S2 ).

This figure compares the performance of manual versus automated methods in identifying the most frequently reported terms in unstructured text for A symptoms such as cough, myalgia, fatigue, and headache, and B medical conditions including hypertension, diabetes, former smoker status, and obesity. The bar plots indicate the counts of terms identified as present, absent, or negated, with significant differences between methods ( p  < 0.001). The upper bars represent the manual method, and the lower bars represent the automated method.

The study population was divided into individuals who reported no symptoms (29.1%) and those with at least one symptom 30 days after COVID-19 onset (70.9%) (Table 2 ). Data revealed that 24% of patients with three or more medical conditions showed post-COVID-19 symptoms after 30 days of discharge from the hospital (24% with symptoms against 17% without symptoms).

The demographic data revealed that both groups had a similar gender distribution, with slightly more males (58% without symptoms, 56% with symptoms). Ethnicity distribution indicated that self-declared white individuals were the majority in both groups (55% without symptoms, 60% with symptoms), followed by mixed brown (34% without symptoms, 24% with symptoms). Furthermore, the symptomatic group had a higher percentage of older individuals (43% aged 60–79) compared to the asymptomatic group (32% aged 60–79). Although a slight difference was observed between both groups. The results from generalized linear model (GLM) to assess the relationship between the demographic characteristics and the presence of post-COVID-19 symptoms showed no statistically significant differences (Table S3 ).

For patients who presented with at least one symptom, the most prevalent symptom was dyspnea (77.7%), followed by cough (21.3%) and fatigue (13.5%). Low oxygen saturation (below 27.3%) was the most common continuous variable reported. In terms of lifestyle, 25.9% were former smokers. A total of 48.6% of the population with symptoms after 30 days had hypertension, 26.9% had diabetes, and 15.2% had obesity (Fig. 4 ).

The most common symptoms and medical conditions reported by patients in the study are represented in the figure. A shows the percentage of patients with symptoms and B displays the percentage of patients with medical conditions. The data were collected through questionnaires and reveal the prevalence of these conditions in the study population.

Three different developments resulted from this study. First, we built a text mining workflow that was able to extract structured medical information from clinical notes in Brazilian Portuguese. Second, this method, in conjunction with the validated text tokens, could be used as a platform for future analyses of long COVID in hospitals that use different systems. Finally, the method was applied back to the training dataset (SIVEP-Gripe), enriching the national database and resulting in more detailed clinical characterizations of SARS in Brazil in the last decade.

The method developed for text mining of clinical data was based on grouping synonyms by phoneme. Our method was able to extract clinical information that was not available previously as variables, with a total informational gain of 32.30% for the 30 categories of comorbidities and symptoms from the records of hospitalized SARS patients. Furthermore, we validated our method against human labeling using electronic records from patients who returned to the post-COVID-19 unit after being discharged for 30 days, which allowed us to describe the clinical findings related to long COVID in those patients.

The initial difficulty was structuring a database from a set of unstructured data that would allow subsequent analysis of a disease such as COVID-19 and post acute symptoms, characterized as long COVID. The benchmarks used were previous studies on COVID-19 and vaccine effectiveness using national health system datasets, from which cohorts for studies on the effectiveness of different vaccines administered in Brazil were formed, and gathered at the national databases [ 20 , 23 ]. Thus, it was possible to enrich the same dataset and cross-check the informational gain using data from patients who were admitted to the UH and who, after discharge, were followed up at the PCDU due to various symptoms.

The method developed in this study exhibited robust performance and was subsequently used to investigate the effects of long COVID in patients who were admitted to the UH and were followed for several months after being discharged. Phonemic representation has been used previously to cluster variations in writing and represent these clusters as an n-gram [ 24 , 25 ], but this is, for the best of our knowledge, the first time that it has been used for clinical notes in Brazilian Portuguese [ 22 ]. This plot captured groups of variations in terms, such as close synonyms, abbreviations, and typographical errors typical of the language, which confirmed the validation and interpretability of the PTC method.

Importantly, the construction of this method allowed for a more accurate analysis of symptoms in patients followed by the PCDU of Hospital São Paulo, which showed that the majority of individuals presented dyspnea as a prevalent symptom, often accompanied by low oxygen saturation. These data are in accordance with other studies that used different methods, including the studies that reported low oxygen saturation during physical exercise [ 26 , 27 ]. Since dyspnea is one of the most frequent and well-documented symptoms of long COVID [ 28 ], it is notable that, besides its detection, our study and method provided further information concerning low oxygen saturation. In addition, other symptoms, such as fatigue and muscle pain, were detected and had been described by other authors [ 29 ], corroborating the quality of the new method to extract symptoms from non-structured data.

Importantly, the curation and constant maintenance of the dictionary will be continued, and we will update it with new information and terms used by other health services. Thus, new qualifiers of clinical conditions, such as different degrees of dyspnea and the evolution of these clinical conditions over time, which may encompass periods of improvement and worsening, will be included in the dictionary. In addition, creating specific platforms to characterize and identify a little-known and difficult-to-diagnose condition, such as long COVID, represents an important advance for data modeling and decision-making after the occurrence of COVID-19. The tool created from the methods used in this study has characteristics that indicate the possibility of analyzing data in the language in which medical records are written, in addition to machine and human checking, which can overcome the lack of homogeneity in different records and allow more accurate results. These results are important, although it is necessary to emphasize that the risks of death and hospitalization remained statistically high in different phases of the pandemic, particularly in those who were hospitalized during the acute phase of SARS-CoV-2 infection and in countries such as Brazil [ 30 ], in which a high number of cases were reported. Therefore, these countries must also consider the substantial number of individuals with COVID-19 sequelae and provide health care to the population. Since there is also evidence of COVID-19 sequelae in individuals who were not hospitalized, it is crucial to emphasize the importance of treating those who were infected and prevent reinfections. Therefore, reducing the risk of long-term sequelae remains a need in terms of public health and health policies.

Finally, there are still many gaps and regional disparities in long COVID research. In particular, there are significant geographic gaps in the available research data, with an abundance of studies originating from Northern Hemisphere populations and a paucity of information regarding long COVID in low- and middle-income countries. There is a critical need for more focused research in these regions. Therefore, the use of text mining to evaluate non-structured EHRs provides a great opportunity to improve the knowledge of long COVID in areas with resource-limited settings.

The method and modeling presented in this work and the use of cohorts of data to predict and treat long COVID patients will be crucial, and more studies should be performed to not only increase knowledge but also develop the necessary care and rehabilitation methods in addition to the planning of the primary health care system. In this context, studies such as the present one should be expanded to help understand long COVID and predict its effects. These studies will allow the development of prevention and treatment that will lead to higher quality standards in population health even in the face of the a pandemic.

Data availability

Due to ethical and legal reasons, supporting data are not available.

Elpeltagy M, Sallam H. Automatic prediction of COVID–19 from chest images using modified ResNet50. Multimed Tools Appl. 2021;80:26451–63.

Article   PubMed   PubMed Central   Google Scholar  

Abbar S, Mokbel M. The role of AI in digital contact tracing. In: Leveraging artificial intelligence in global epidemics. Gruenwald L, Jain S, Groppe S, editors. Academic Presss, Elsevier; 2021. pp. 203–21.

Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, et al. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access. 2020;8:132665–76.

Article   Google Scholar  

Cau R, Faa G, Nardi V, Balestrieri A, Puig J, Suri JS, et al. Long-COVID diagnosis: from diagnostic to advanced AI-driven models. Eur J Radiol. 2022;148:110164.

Ke Y-Y, Peng T-T, Yeh T-K, Huang W-Z, Chang S-E, Wu S-H, et al. Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomed J. 2020;43:355–62.

Chang Z, Zhan Z, Zhao Z, You Z, Liu Y, Yan Z, et al. Application of artificial intelligence in COVID-19 medical area: a systematic review. J Thorac Dis. 2021;13:7034–53.

Cohen AM. A survey of current work in biomedical text mining. Brief Bioinforma. 2005;6:57–71.

Article   CAS   Google Scholar  

Névéol A, Dalianis H, Velupillai S, Savova G, Zweigenbaum P. Clinical natural language processing in languages other than English: opportunities and challenges. J Biomed Semant. 2018;9:12.

Rocha HAL, Solha EZM, Furtado V, Justino FL, Barreto LAL, Da Silva RG, et al. COVID-19 outbreaks surveillance through text mining applied to electronic health records. BMC Infect Dis. 2024;24:359.

Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med Inf. 2019;7:e12239.

Wei W-Q, Teixeira PL, Mo H, Cronin RM, Warner JL, Denny JC. Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. J Am Med Inform Assoc. 2016;23:e20–7.

Article   PubMed   Google Scholar  

Nurek M, Rayner C, Freyer A, Taylor S, Järte L, MacDermott N, et al. Recommendations for the recognition, diagnosis, and management of long COVID: a Delphi study. Br J Gen Pr. 2021;71:e815–25.

Soriano JB, Murthy S, Marshall JC, Relan P, Diaz JV. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022;22:e102–7.

Article   CAS   PubMed   Google Scholar  

McGrath LJ, Scott AM, Surinach A, Chambers R, Benigno M, Malhotra D. Use of the postacute sequelae of COVID-19 diagnosis code in routine clinical practice in the US. JAMA Netw Open. 2022;5:e2235089.

Kingery JR, Safford MM, Martin P, Lau JD, Rajan M, Wehmeyer GT, et al. Health status, persistent symptoms, and effort intolerance one year after acute COVID-19 infection. J Gen Intern Med. 2022;37:1218–25.

Bowe B, Xie Y, Al-Aly Z. Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med. 2022;28:2398–405.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bowe B, Xie Y, Al-Aly Z. Postacute sequelae of COVID-19 at 2 years. Nat Med. 2023;29:2347–57.

Ranzani OT, Bastos LSL, Gelli JGM, Marchesi JF, Baião F, Hamacher S, et al. Characterisation of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. Lancet Respir Med. 2021;9:407–18.

Oliveira EA, Colosimo EA, Silva ACSE, Mak RH, Martelli DB, Silva LR, et al. Risk factors for COVID-19 mortality in hospitalised children and adolescents in Brazil—āuthors’ reply. Lancet Child Adolesc Health. 2021;5:e40–2.

Cerqueira-Silva T, Andrews JR, Boaventura VS, Ranzani OT, De Araújo Oliveira V, Paixão ES, et al. Effectiveness of CoronaVac, ChAdOx1 nCoV-19, BNT162b2, and Ad26.COV2.S among individuals with previous SARS-CoV-2 infection in Brazil: a test-negative, case-control study. Lancet Infect Dis. 2022;22:791–801.

Florentino PTV, Alves FJO, Cerqueira-Silva T, de Araújo Oliveira V, Júnior JBS, Penna GO, et al. Effectiveness of BNT162b2 booster after CoronaVac primary regimen in pregnant people during omicron period in Brazil. Lancet Infect Dis. 2022;22:1669–70.

Bird S, Klein E, Loper E. Natural language processing with Python. 1st ed. Beijing; Cambridge [Mass]: O’Reilly; 2009.

Cerqueira-Silva T, Katikireddi SV, De Araujo Oliveira V, Flores-Ortiz R, Júnior JB, Paixão ES, et al. Vaccine effectiveness of heterologous CoronaVac plus BNT162b2 in Brazil. Nat Med. 2022;28:838–43.

Rahimian M, Warner JL, Jain SK, Davis RB, Zerillo JA, Joyce RM. Significant and distinctive n -Grams in oncology notes: a text-mining method to analyze the effect of opennotes on clinical documentation. JCO Clin Cancer Inform. 2019;3:1–9.

Golz C, Richter D, Sprecher N, Gurtner C. Mental health-related communication in a virtual community: text mining analysis of a digital exchange platform during the Covid-19 pandemic. BMC Psychiatry. 2022;22:430.

Schäfer H, Teschler M, Mooren FC, Schmitz B. Altered tissue oxygenation in patients with post COVID-19 syndrome. Microvasc Res. 2023;148:104551.

Guarnieri G, Lococo S, Bertagna De Marchi L, Cecchetto A, Molena B, Arcaro G, et al. Persistent oxygen desaturation during exercise in patients with long COVID. Eur Respir J. 2022;60:3725.

Domènech-Montoliu S, Puig-Barberà J, Pac-Sa MR, Vidal-Utrillas P, Latorre-Poveda M, Del Rio-González A, et al. Complications post-COVID-19 and risk factors among patients after six months of a SARS-CoV-2 infection: a population-based prospective cohort study. Epidemiologia. 2022;3:49–67.

Global Burden of Disease Long COVID Collaborators, Wulf Hanson S, Abbafati C, Aerts JG, Al-Aly Z, Ashbaugh C, et al. Estimated global proportions of individuals with persistent fatigue, cognitive, and respiratory symptom clusters following symptomatic COVID-19 in 2020 and 2021. JAMA. 2022;328:1604.

Katikireddi SV, Cerqueira-Silva T, Vasileiou E, Robertson C, Amele S, Pan J, et al. Two-dose ChAdOx1 nCoV-19 vaccine protection against COVID-19 hospital admissions and deaths over time: a retrospective, population-based cohort study in Scotland and Brazil. Lancet. 2022;399:25–35.

Download references

Acknowledgements

The authors acknowledge Dr. Lucia Pellanda, Dr. Ethel Maciel, Dr. Adhemar Arthur Chioro, and Dr. Nisia Trindade for their support and discussion on this work. We also acknowledge the support of Fiotec-Fiocruz, FAP-Unifesp, CNPq 400504/2023-5 and FAPESP 2019/02821-8. This study is part of the Alert-Early System of Outbreaks with Pandemic Potential with financial support from the Rockefeller Foundation's Health Initiative (grant 2023-PPI-007 awarded to M-BN).

Author information

These authors contributed equally: Pilar Tavares Veras Florentino, Vinícius de Oliveira Araújo, Henrique Zatti.

These authors jointly supervised this work: Manoel Barral-Netto, Soraya S. Smaili.

Authors and Affiliations

Laboratório de Medicina e Saúde Pública de Precisão (MeSP2), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil

Pilar Tavares Veras Florentino, Viviane Boaventura & Manoel Barral-Netto

Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil

Pilar Tavares Veras Florentino, Vinícius de Oliveira Araújo, Henrique Zatti, Juracy Bertoldo Junior, George G. Caique Barbosa & Tales Mota Machado

Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil

Vinícius de Oliveira Araújo, Viviane Boaventura & Manoel Barral-Netto

Departamento de Farmacologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil

Caio Vinícius Luis, Célia Regina Santos Cavalcanti, Matheus Henrique Citibaldi de Oliveira, Anderson Henrique França Figueredo Leão, Ernesto Ravera, Alberto Cebukin, Renata Bernardes David & Soraya S. Smaili

Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil

Danilo Batista Vieira de Melo

Diretoria de Tecnologia da Informação, Universidade Federal de Ouro Preto, Ouro Preto, Brazil

Tales Mota Machado

Disciplina de Moléstias Infecciosas, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil

Nancy C. J. Bellei

You can also search for this author in PubMed   Google Scholar

Contributions

PTVF and VdOA conducted the study; NBVB, MB-N and SS conceived the idea; NB and VB idealized the plan of clinical analyzes, PTVF, HZ and AHFFL conducted the data analysis and JB and GGCB verified the scripts. All authors contributed to the study design. ER and AC were responsible for data obtaining, VdOA, ER, AC, PTVF, TMM, JB and GGCB were responsible for data curating and processing; RBD, VB, CVL, CRSC, MHC and DBVdM were responsible for the manual verification of the text mining automated extraction. PTVF and SS wrote the first draft, and further revised the manuscript; all authors critically revised the manuscript and approved the final version for submission. MB-N and SS jointly supervised this work.

Corresponding authors

Correspondence to Manoel Barral-Netto or Soraya S. Smaili .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Edited by Mauro Piacentini

Supplementary information

Supplementary material, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Veras Florentino, P.T., Araújo, V.d.O., Zatti, H. et al. Text mining method to unravel long COVID’s clinical condition in hospitalized patients. Cell Death Dis 15 , 671 (2024). https://doi.org/10.1038/s41419-024-07043-4

Download citation

Received : 13 April 2024

Revised : 28 August 2024

Accepted : 29 August 2024

Published : 13 September 2024

DOI : https://doi.org/10.1038/s41419-024-07043-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies