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Understanding Data Presentations (Guide + Examples)

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

Table of Contents

What is a Data Presentation?

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

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

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

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

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

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

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

Real-Life Application of Bar Charts

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

Step 1: Selecting Data

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

The sales manager has highlighted these products for the presentation.

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

Step 2: Choosing Orientation

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

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

Step 3: Colorful Insights

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

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

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

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

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

Real-life Application of Line Graphs

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

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

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

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

Step 3: Connecting Trends

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

Step 4: Adding Clarity with Color

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

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

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

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

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

Real-Life Application of a Dashboard

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

Step 1: Defining Key Metrics

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

Step 2: Choosing Visualization Widgets

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

Step 3: Dashboard Layout

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

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

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

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

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

Real-Life Application of a Treemap Chart

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

Step 1: Define Your Data Hierarchy

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

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

Step 2: Choose a Suitable Tool

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

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

Step 3: Make a Treemap Chart with PowerPoint

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

Step 5: Input Your Data

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

Step 6: Customize the Treemap

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

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

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

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

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

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

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

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

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

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

Real-Life Application of Pie Charts

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

Step 1: Define Your Data Structure

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

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

Step 2: Insert a Pie Chart

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

For instance:

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

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

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

Step 03: Results Interpretation

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

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

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

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

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

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

Real-Life Application of a Histogram

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

Step 1: Gather Data

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

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

Step 2: Define Bins

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

Step 3: Count Frequency

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

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

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

Step 4: Create the Histogram

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

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

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

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

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

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

Real-Life Application of Scatter Plot

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

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

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

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

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

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

1. Fact Sheet Dashboard for Data Presentation

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

Use This Template

2. 3D Column Chart Infographic PPT Template

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

3. Data Circles Infographic PowerPoint Template

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

4. Colorful Metrics Dashboard for Data Presentation

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

5. Animated Data Presentation Tools for PowerPoint & Google Slides

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

6. Statistics Waffle Charts PPT Template for Data Presentations

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

7. Data Presentation Dashboard Template for Google Slides

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

8. Weather Dashboard for Data Presentation

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

9. Social Media Marketing Dashboard Data Presentation Template

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

10. Project Management Summary Dashboard Template

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

11. Profit & Loss Dashboard for PowerPoint and Google Slides

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

Overwhelming visuals

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

Inappropriate chart types

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

Lack of context

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

Inconsistency in design

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

Failure to provide details

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

Lack of focus

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

Visual accessibility issues

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Data presentation: A comprehensive guide

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

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

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

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

What is data presentation?

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

Why are data presentations important?

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

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

Types of data presentation

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

1. Textual presentation

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

2. Tabular presentation

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

3. Graphical presentation

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

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

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

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

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

What to include in data presentation

When creating your data presentation, remember these key components:

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

How to structure an effective data presentation

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

1. Know your audience

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

2. Have a clear message

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

3. Tell a compelling story

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

4. Leverage visuals

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

5. Be clear and concise

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

6. Practice your delivery

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

A basic structure for an effective data presentation

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

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

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

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

Additional tips for enhancing your data presentation

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

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

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

Do’s and don'ts on a data presentation

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

Don'ts:

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

Summarizing key takeaways

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

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

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

2. What are some common methods of data presentation?

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

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

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

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

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

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

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

Create your data presentation with prezent

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

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

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

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

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

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

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

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

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

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Blog Data Visualization

10 Data Presentation Examples For Strategic Communication

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.

Blog Data in government

https://dataingovernment.blog.gov.uk/2021/03/02/presenting-data-5-tips-for-making-your-data-understandable/

Presenting data: 5 tips for making your data understandable

I’ve been passionate about presenting data clearly for over 10 years now, and recently took the lead of a cross-government group of “Presentation Champions”. We share best practice for designing charts and tables, and we’re learning about writing for the web to help everyone understand the huge amount of data that government publishes.

In this role and in my day job as a statistician for DWP Digital, I tell stories with data.

Sometimes these stories help teams to make decisions about whether a change they made to a process has worked, or if they need to try something else. Or sometimes the stories make government data available to the public in an easy-to-understand way.

In all cases, the most important thing is to write for everyone, and to tell the story in a way that your readers understand. With that in mind, I’ve pulled together some tips on presenting data that I hope will help you to make it as open and accessible as possible.

You are not your audience

It’s really easy to write something or present data in a way that you understand. But you have a lot of baggage: your subject knowledge of the thing you’re presenting, its historical context, your technical knowledge of the way you did the analysis, and what conclusions can be made from the analysis. It would be very unusual for everyone seeing your data presentation to have exactly the same knowledge as you. Otherwise, what would be the benefit of sharing that knowledge with them?

It’s essential to think about who your audience is going to be, and to write for them. If you don’t know who your audience is going to be, like when I present government data for the public, you can’t make any assumptions about what they already know.

Keep it simple

The use of plain English will make your analysis easier to understand for everyone - even those as steeped in jargon as you are. Nobody ever read a simple sentence and thought “well, that was too easy to understand.”

Jargon and acronyms, which are often used in government, can alienate people. You might be presenting your work to a team you know really well and assume everyone knows what HRTs and JBCs are. (habitual residence test and the Joint Biosecurity Centre if you were wondering). However, if one member of the team is new to the role, you risk making them feel excluded because they can’t keep up.

Explain your acronyms, and give a bit of context about the subject.

This is the case with technical concepts and language too. When I analyse whether a change to a process has made a difference, I use statistical tests to see whether that change might have happened by chance, or whether it was a “significant” change. When I present the results to the team, I will always talk about what the test means and what conclusions we can or can’t make.

Use annotations to help tell the story

I’m steeped in the detail of the things I present analysis about, but my audience might not be. I try to tell the story and give enough context so that people can understand why the data I’m showing them is important.

Simple annotations can really help this. Below are two charts I made from data about the social security benefit Universal Credit (UC). It is a payment to help with your living costs. You may be able to get it if you’re on a low income, out of work or you cannot work.

The title of this chart tells you what the chart shows: the number of claims to UC. It shows a massive peak during the year, and that the number has gone back down again. But it doesn’t say what caused that peak.

Simply by adding an “active title” to the second chart, I’m explaining more: that claims to UC peaked when lockdown began. I’ve also added an annotation directly onto the chart to point out where 23 March is - I’ve not assumed that you know when the lockdown happened in the UK.

Doing this helps people to think “oh, I see” and it helps me to know that they’ve understood from the chart what I wanted them to. I’ve told my story.

Accessibility = Inclusivity

Accessibility isn’t (just) a legal box to tick, it helps us make sure that everyone can use government statistics and data, regardless of the technology they use. This is for everybody.

Making data easy to understand is only one of the ways of making it accessible: information published on the internet needs to meet all of the Web Content Accessibility (WCAG) Guidelines .

In the cross-government group of presentation champions that I lead, we’ve been focusing on how to make government statistics meet these guidelines. WCAG asks that content should be:

• perceivable: having captions on video/audio, and contrasting colours that can be seen by people with colour-blindness
• operable: can be navigated by people who use only a keyboard not a mouse, or use screen-readers
• understandable: using plain English, or identifying errors and helping people correct them if a website needs input from you, like applying for UC
• robust: being compatible with assistive technologies, and using mark-up correctly

The GOV.UK platform does a lot of the technical side of this for us, which is why we have focused on the way we present charts and use words to describe what’s happening.

The presentation champions carry out reviews of government publications and offer advice to the authors about meeting best practice in how we design charts and the words we use. While similar to user research, where researchers would talk to people who use the statistics, but it can help to get an “outsider’s” perspective - to take a step back from the detail.

Remember that data helps people to make decisions

Data has taken much more of the limelight this year than before. Charts were being shown on the television on a daily basis during the early phase of the COVID-19 pandemic.

People need access to information like this in an easy-to-understand way, because they are making decisions about how safe it is to leave the house. This may sound overly dramatic, but these could be life and death decisions for some people. It is our duty to make sure they can make well-informed decisions.

Data presentation training

If you’d like to learn more about presenting data, here are some resources that are free to use.

• DWP’s Data Visualisation Thinking
• Government Statistical Society’s Writing about Statistics
• Government Statistical Society’s Introduction to Data Visualisation

I hope these tips will be useful for anyone looking to present data – and if you have any further suggestions I’d love to hear them in the comments!

Sharing and comments

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Comment by Michael Holland posted on 11 March 2021

Couldn't agree more on the use of jargon and acronyms being alienating to the reader. Overly technical language only serves the subject specialists, it is a bit self indulgent, and excludes the majority of public users.

Comment by Peter Gilmartin posted on 10 March 2021

Thanks Catherine. Some helpful reminders. I always think that the point about your audience is the most important.

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

2. Column chart

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

3. Pie Charts

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

4. Bar charts

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

5. Histograms

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

6. Box plots

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

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

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

Importance of Data Presentation

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

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

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

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

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

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

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

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

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10 Superb Data Presentation Examples To Learn From

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

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

On this page:

How to present data effectively? Clever tips.

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

Download the above infographic in PDF

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

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

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

1. Keep it simple and clear

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

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

2. Pick up the right visuals for the job

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

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

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

3. Break the complex concepts into multiple graphics

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

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

4. Carefully choose the colors

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

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

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

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

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

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

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

6. Use good templates and software tools

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

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

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

10 Superb data presentation examples 

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

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

1. Sales Stage History  Funnel Chart 

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

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

2. Facebook Ads Data Presentation Examples

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

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

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

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

3. Sales Opportunity Data Presentation

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

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

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

4. Power 100 Data Visualization 

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

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

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

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

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

6. Digital Health Research Data Visualization Example

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

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

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

7. Disease Research Data Visualization Examples

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

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

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

8. Textual and Qualitative Data Presentation Example

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

9. Product Metrics Graph Example

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

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

10. Supply Chain Data Visualization Example 

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

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

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

About The Author

Silvia Valcheva

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

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Data Presentation – What is It?

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

Leah Nguyen • 05 Apr 2024 • 11 min read

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

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

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

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

Table of Contents

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

#2 – Text

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

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

More tips with ahaslides.

• Marketing Presentation
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• Types of Presentation

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What are Methods of Data Presentation?

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

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

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

Good data presentation helps…

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

Methods of Data Presentation and Examples

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

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

#1 – Tabular 

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

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

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

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

(Source: CustomerThermometer )

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

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

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

Bonus example: A literal ‘pie’ chart! 🥧

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

They can be as simple as this:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5 Data Presentation Mistakes to Avoid

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

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

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

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

#2 – Use the wrong type of chart

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

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

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

#3 – Make it 3D

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

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

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

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

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

#5 – Bombard the audience with too much information

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

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

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

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

For example:

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

What is chart presentation?

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

When can I use charts for presentation?

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

Why should use charts for presentation?

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

What are the 4 graphical methods of presenting data?

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

Leah Nguyen

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

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Data Presentation

• Reference work entry
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• Filomena Maggino 2 &
• Marco Trapani 3  

Many international institutions, like World Bank and UNESCO (Patel et al. 2003 ) and Eurostat ( 2000a , b ), have identified different attributes to be considered in evaluating quality of statistics, such as methodological soundness, integrity, serviceability, and accessibility.

At the same time, less attention is paid to presentation and communication of statistics, which represent important aspects of the statistical activities and should be considered an integral part of data production and dissemination.

The need to deal with this issue is significantly increasing especially in the perspective of the role the statistics have in ICT societies. Presentation and communication of quality of life data are not easy tasks to be carried on since they cannot be accomplished through improvising and approximating methods and instruments. They require a combined and joint knowledge and expertise of statistical methodology, cognitive science, and communication.

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Aristotele. (1996). Retorica (trad.it. a cura di Dorati M.). Milano: Oscar Mondadori.

Google Scholar  

Ellero, M. P. (1997). Introduzione alla retorica . Milano: Sansoni Editore.

Eurostat. (2000a, April 4–5). Definition of quality in Statistics Eurostat Working Group on Assessment of Quality in Statistics , Eurostat/A4/Quality/00/General/Definition, Luxembourg.

Eurostat. (2000b). Standard Quality Report, Eurostat Working Group on Assessment of Quality in Statistics , Eurostat/A4/Quality/00/General/Standard Report, Luxembourg, April 4–5.

Giovannini, E. (2008, May 26–27). The role of communication in transforming statistics into knowledge, OECD . Paper to be presented at conference innovative approaches to turning statistics into knowledge, Stockholm.

Kosslyn, S. M. (2006). Graph design for the eye and mind . New York: Oxford University Press.

Book   Google Scholar  

Kosslyn, S. M. (2007). Clear and to the point . Oxford: Oxford University Press.

Lakoff, G., & Johnson, M. (1980). Metaphors we live by . Chicago: University of Chicago Press.

Patel, S., Hiraga, M., Wang, L. (World Bank), Drew, D., & Lynd, D. (UNESCO). (2003). A framework for assessing the quality of education statistics . World Bank – Development Data Group and UNESCO – Institute for Statistics.

Perelman, C. (2005). Teoria e pratica dell’argomentazione (a cura di G. Fornari Luvarà) . Soveria Mannelli: Rubettino.

Statistics Canada. (2003). Statistics Canada quality guidelines (4th ed.). Statistics Canada, Ottawa, Catalogue No 12-539-XIE.

Vale, S. (2008, July 7–8). Accessibility and clarity: The most neglected dimensions of quality? Paper presented at Conference on Data Quality for International Organizations, Rome, Italy, nella Session 3: Dissemination platforms to make data more accessible and interpretable. UNECE.

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Maggino, F., Trapani, M. (2023). Data Presentation. In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-031-17299-1_666

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Types of Data Presentation

Data presentation refers to the process of organizing and displaying data in a clear and understandable manner. It focuses on effectively conveying information, insights, and key findings derived from the data in a concise and meaningful way. It may involve using text, visuals, and other elements to provide context, summarize data, and communicate the main points to the audience.

Data presentations can take various forms, including verbal and written formats. Here's a breakdown of the two –

Verbal Data Presentation

Verbal data presentations involve delivering information and insights orally, typically in the form of a speech, presentation, or discussion. This can be done in person, through video conferencing, or via recorded audio. Verbal presentations often involve accompanying visual aids, such as slides or multimedia elements, to support the delivery of the data. Verbal presentations allow for real-time interaction, the opportunity to address audience questions, and the flexibility to adapt the presentation based on audience feedback.

Written Data Presentation

Written data presentations involve conveying information and insights through written documents, reports, articles, or other written formats. These presentations are typically static and can be shared electronically or in print. Written presentations rely heavily on text, but they may also include visual elements, such as charts, graphs, tables, or infographics, to enhance data representation and understanding. Written presentations offer the advantage of being easily distributable and referenceable, allowing readers to review the information at their own pace.

Both verbal and written data presentations have their own advantages and are suitable for different contexts. Verbal presentations are effective for engaging the audience, facilitating discussions, and delivering real-time explanations. On the other hand, written presentations provide a more permanent record of the data, allow for careful review and analysis, and can be easily shared with a wider audience.

Here’s a breakdown of the advantages, disadvantages and use case of each data presentation format –

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Statistical data presentation

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

Sangseok Lee

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

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

Introduction

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

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

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

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

Data Presentation

Data can be presented in one of the three ways:

–as text;

–in tabular form; or

–in graphical form.

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

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

Text presentation

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

Table presentation

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

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

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

Heat maps for better visualization of information than tables

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

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

Graph presentation

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

Scatter plot

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

Bar graph and histogram

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

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

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

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

Line plot with whiskers

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

Box and whisker chart

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

Three-dimensional effects

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

Drawing a graph: example

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

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

Conclusions

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

<Appendix>

Output for presentation.

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

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

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

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

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

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

Bitmap and Vector

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

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

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

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

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

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

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

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

6) Joint Photographic Experts Group.

7) Portable Network Graphics.

8) Graphics Interchange Format

9) Tagged Image File Format; TIFF

10) Encapsulated PostScript.

11) Computer Graphics Metafile.

12) Scalable Vector Graphics.

13) Portable Document Format.

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Mobile phone ownership over time, who owns cellphones and smartphones, smartphone dependency over time, who is smartphone dependent, find out more, mobile fact sheet.

Large shares of Americans are connected to the world of digital information while “on the go” via smartphones and other mobile devices. Explore the patterns and trends that have shaped the mobile revolution below.

To better understand Americans’ smartphone and broadband adoption, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, please read our Q&A .

Here are the  questions used for this analysis , along with responses, and  its methodology ­­­.

The vast majority of Americans – 97% – now own a cellphone of some kind. Nine-in-ten own a smartphone, up from just 35% in Pew Research Center’s first survey of smartphone ownership conducted in 2011.

Note: The vertical line indicates a change in mode. Polls from 2002-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2002-2023.

Substantial majorities of Americans across a wide range of demographic groups are cellphone owners. The same is true for smartphone ownership – though some differences do emerge, particularly by age, household income and level of formal education.

% of U.S. adults who say they own a __, by …

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Today, 15% of U.S. adults are “smartphone-only” internet users – meaning they own a smartphone, but do not have home broadband service.

Source: Surveys of U.S. adults conducted 2013-2023. Data for each year is based on a pooled analysis of all surveys containing broadband and smartphone questions fielded during that year.

Reliance on smartphones for online access is especially common among Americans with lower household incomes and those with lower levels of formal education.

This fact sheet was compiled by Research Assistant  Olivia Sidoti , with help from Research Analyst  Risa Gelles-Watnick , Research Analyst  Michelle Faverio , Digital Producer  Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher Eugenie Park .

Follow these links for more in-depth analysis of the impact of mobile technology on American life.

• Americans’ Social Media Use  Jan. 31, 2024
• Americans’ Use of Mobile Technology and Home Broadband  Jan. 31 2024
• Q&A: How and why we’re changing the way we study tech adoption  Jan. 31, 2024

Find more reports and blog posts related to  internet and technology .

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What the New Overtime Rule Means for Workers

One of the basic principles of the American workplace is that a hard day’s work deserves a fair day’s pay. Simply put, every worker’s time has value. A cornerstone of that promise is the  Fair Labor Standards Act ’s (FLSA) requirement that when most workers work more than 40 hours in a week, they get paid more. The  Department of Labor ’s new overtime regulation is restoring and extending this promise for millions more lower-paid salaried workers in the U.S.

Overtime protections have been a critical part of the FLSA since 1938 and were established to protect workers from exploitation and to benefit workers, their families and our communities. Strong overtime protections help build America’s middle class and ensure that workers are not overworked and underpaid.

Some workers are specifically exempt from the FLSA’s minimum wage and overtime protections, including bona fide executive, administrative or professional employees. This exemption, typically referred to as the “EAP” exemption, applies when: 

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While the department increased the minimum salary required for the EAP exemption from overtime pay every 5 to 9 years between 1938 and 1975, long periods between increases to the salary requirement after 1975 have caused an erosion of the real value of the salary threshold, lessening its effectiveness in helping to identify exempt EAP employees.

The department’s new overtime rule was developed based on almost 30 listening sessions across the country and the final rule was issued after reviewing over 33,000 written comments. We heard from a wide variety of members of the public who shared valuable insights to help us develop this Administration’s overtime rule, including from workers who told us: “I would love the opportunity to...be compensated for time worked beyond 40 hours, or alternately be given a raise,” and “I make around $40,000 a year and most week[s] work well over 40 hours (likely in the 45-50 range). This rule change would benefit me greatly and ensure that my time is paid for!” and “Please, I would love to be paid for the extra hours I work!”

The department’s final rule, which will go into effect on July 1, 2024, will increase the standard salary level that helps define and delimit which salaried workers are entitled to overtime pay protections under the FLSA. 

Starting July 1, most salaried workers who earn less than $844 per week will become eligible for overtime pay under the final rule. And on Jan. 1, 2025, most salaried workers who make less than $1,128 per week will become eligible for overtime pay. As these changes occur, job duties will continue to determine overtime exemption status for most salaried employees.

The rule will also increase the total annual compensation requirement for highly compensated employees (who are not entitled to overtime pay under the FLSA if certain requirements are met) from $107,432 per year to $132,964 per year on July 1, 2024, and then set it equal to $151,164 per year on Jan. 1, 2025.

Starting July 1, 2027, these earnings thresholds will be updated every three years so they keep pace with changes in worker salaries, ensuring that employers can adapt more easily because they’ll know when salary updates will happen and how they’ll be calculated.

The final rule will restore and extend the right to overtime pay to many salaried workers, including workers who historically were entitled to overtime pay under the FLSA because of their lower pay or the type of work they performed. 

We urge workers and employers to visit  our website to learn more about the final rule.

Jessica Looman is the administrator for the U.S. Department of Labor’s Wage and Hour Division. Follow the Wage and Hour Division on Twitter at  @WHD_DOL  and  LinkedIn .  Editor's note: This blog was edited to correct a typo (changing "administrator" to "administrative.")

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Combining Cryptography and Other Techniques for Various Privacy-Preserving Applications

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Abstract. Secure multiparty computation (SMPC), fully homomorphic encryption (FHE) and differential privacy (DP) is a selection of Privacy-Enhancing Technologies (PETs) that protect input data confidentiality and enable the computation of a function without revealing the input data. Using PETs, one can keep sensitive data private and at the same time derive valuable insights from data analysis, optimizing the privacy-utility tradeoff. In this talk, we will introduce and compare different PETs in order to assist in selecting the most suitable options for a given application and show that they aren't a one-size-fits-all solution. We will present two applications, the first one is genomic data machine learning and second one is financial fraud detection.

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What you can learn from the security industry's evolution.

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Magnus Ahlqvist is president & CEO of Securitas , a security solutions partner with world-leading technology and expertise.

The security industry is in the midst of a major transformation—and is emerging as a dynamic source of technology innovation and business resilience. For leaders, there's much to glean from the industry's evolution.

Change is a constant, and so is innovation.

The safety and security sector joins many other industries that have experienced remarkable evolutions since the turn of the 21st century.

Consider, for example, how the retail industry has transformed since the dot-com era and the birth of e-commerce, or how the telecommunications industry has changed since the invention of the smartphone, apps and streaming services.

For security, its transformation lies in the shift from analog to digital, from reactive to proactive and from siloed projects to holistic programs. What was once considered a niche industry has become a global influence shaping technology innovation and business strategy.

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This transformation underscores the universal truth that innovation is a constant, and adaptation is a competitive advantage.

You can find opportunities in challenges.

Historically, security was primarily deployed as an essential function in safeguarding an organization’s people and assets. However, the scope and role of security have expanded greatly in recent years, particularly as global market trends evolve.

For instance, the industry’s health and safety innovations during the global pandemic helped leaders navigate evolving regulations and maintain business continuity despite increasing uncertainty.

From occupancy tracking and visitor management to environmental monitoring and remote security services, the industry rose to the challenge with new and enhanced solutions. Its evolution serves as a poignant reminder that reframing challenges as opportunities can lead to transformative outcomes.

Progress comes from embracing change.

Perhaps one of the biggest shifts in the security industry has been its digital transformation, which has paved the way for intelligence-led security.

The rise of cloud computing, advancements in Internet of Things (IoT) security devices and new use cases for artificial intelligence (AI) in security have contributed to the industry’s evolution in this area.

Predictive analytics and machine learning algorithms can now analyze vast datasets to identify patterns, anomalies and potential threats. Previously, security data—and its incredibly rich insights—remained locked within the security infrastructure.

This shift has marked a departure from reactive security measures and enabled organizations to proactively anticipate risks and vulnerabilities. Today, successful leaders recognize that a robust security program is not only essential for safeguarding their organizations but also enabling growth and innovation.

Just as the security industry is transforming by leveraging technological advancements, leaders can also harness change as a stepping stone toward a brighter future. After all, the key to progress lies in the willingness to embrace change.

Break down silos to see the bigger picture.

For years, the security function primarily existed in a silo within organizations. Too often without a seat at the decision-making table, security remained an untapped source of business intelligence until recently.

A significant shift occurred during the pandemic, elevating the purpose of security and emphasizing its importance as an integrated, cross-functional entity.

Security has become a key influence of business strategy, offering invaluable insights for marketing, human resources, environmental health and safety (EH&S), corporate and social responsibility and other important functions. In many cases, security is helping to drive operational efficiencies, increase business resilience and foster an environment conducive to innovation.

This transition in the industry presents a valuable lesson for leaders: Breaking down silos fosters collaboration and creates a more innovative and agile business environment.

As the industry continues to evolve, one thing is clear: Security remains a dynamic force capable of shaping the future of business strategy. Its transformation presents myriad learning opportunities for leaders and serves as a reminder of the industry’s resilience and potential.

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Unusual cause of muscle weakness, type II respiratory failure and pulmonary hypertension: a case report of ryanodine receptor type 1( RYR1) -related myopathy

• Yinong Chen 1 , 2 ,
• Shuai Zhang 2   na1 ,
• Wanmu Xie 2 ,
• Chen Wang 1 , 2 , 4 , 5 &
• Zhenguo Zhai 1 , 2 , 4 , 5   na1  

BMC Pulmonary Medicine volume  24 , Article number:  194 ( 2024 ) Cite this article

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Metrics details

Patients with congenital myopathies may experience respiratory involvement, resulting in restrictive ventilatory dysfunction and respiratory failure. Pulmonary hypertension (PH) associated with this condition has never been reported in congenital ryanodine receptor type 1( RYR1) -related myopathy.

Case presentation

A 47-year-old woman was admitted with progressively exacerbated chest tightness and difficulty in neck flexion. She was born prematurely at week 28. Her bilateral lower extremities were edematous and muscle strength was grade IV − . Arterial blood gas analysis revealed hypoventilation syndrome and type II respiratory failure, while lung function test showed restrictive ventilation dysfunction, which were both worse in the supine position. PH was confirmed by right heart catheterization (RHC), without evidence of left heart disease, congenital heart disease, or pulmonary artery obstruction. Polysomnography indicated nocturnal hypoventilation. The ultrasound revealed reduced mobility of bilateral diaphragm. The level of creatine kinase was mildly elevated. Magnetic resonance imaging showed myositis of bilateral thigh muscle. Muscle biopsy of the left biceps brachii suggested muscle malnutrition and congenital muscle disease. Gene testing revealed a missense mutation in the RYR1 gene (exon33 c.C4816T). Finally, she was diagnosed with RYR1 -related myopathy and received long-term non-invasive ventilation (NIV) treatment. Her symptoms and cardiopulmonary function have been greatly improved after 10 months.

Conclusions

We report a case of RYR1 -related myopathy exhibiting hypoventilation syndrome, type II respiratory failure and PH associated with restrictive ventilator dysfunction. Pulmonologists should keep congenital myopathies in mind in the differential diagnosis of type II respiratory failure, especially in patients with short stature and muscle weakness.

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Congenital myopathies, a group of rare inherited muscle diseases characterized by abnormalities in the structure of muscle fibers, vary in their clinical presentation, histopathology, and genetic causes [ 1 ]. Common clinical features of congenital myopathies include dysmorphic facial features secondary to muscle weakness, abnormal extrinsic eye movements, spinal malformation, malignant hyperthermia, and cardiac or respiratory involvement [ 1 , 2 ]. Respiratory failure with or without hypercapnia may occur when the diaphragm and other respiratory muscles are affected, resulting in serious complications or even death [ 2 , 3 , 4 ]. However, congenital myopathies can be easily misdiagnosed in clinical practice. The main histopathological types of those with respiratory insufficiency include core myopathy, myotubular myopathy, autosomal centronuclear myopathy, congenital fiber-type disproportion myopathy, and myosin storage myopathy [ 2 , 3 ]. Common genes associated with respiratory involvement include α-skeletal actin ( ACTA1 ), nebulin ( NEB ), selenoprotein 1 ( SEPN1 ), slow α-tropomyosin ( TPM3 ), and ryanodine receptor type 1 ( RYR1 ) [ 1 ].

Pulmonary hypertension (PH) is a pathophysiological disorder defined by a mean pulmonary arterial pressure (mPAP) > 20 mmHg at rest according to the latest guidelines. PH is divided into five groups based on etiology including chronic respiratory diseases and/or hypoxia [ 5 ]. Some patients may experience hypoventilation syndrome and exhibit hypoxemia with or without hypercapnia, which is defined as type II or type I respiratory failure, respectively. One of the causes of hypoventilation is restrictive lung diseases, a common manifestation of congenital myopathies affecting the respiratory muscles [ 3 ]. RYR1 -related myopathies are the most prevalent group of congenital myopathies and have multiple clinical phenotypes, such as symmetric proximal muscle weakness, significant respiratory involvement, King Denborough syndrome, arrhythmias, malignant hyperthermia and so on [ 6 , 7 , 8 ]. Up to now, PH has never been reported in cases of congenital RYR1 -related myopathy, which should be considered as a potential indirect cause in the differential diagnosis of respiratory dysfunction.

Here we report a female patient with RYR1 -related myopathy who exhibited type II respiratory failure and PH associated with restrictive ventilatory dysfunction.

A 47-year-old woman was admitted to our hospital with progressively exacerbated chest tightness after activity and echocardiographic suspicion of PH. Her body weight was 50 kg with the height of 150 cm. She was born prematurely at week 28. The patient had no similar family history, but her younger brother was also small and slight in stature. Her chest computed tomography (CT) five years ago showed no significant abnormalities. Four months before this admission, she experienced aggravating dyspnea. Lung function test indicated severe restrictive ventilation dysfunction and reduced diffusion capacity [forced expiratory volume in one second (FEV1) 36%pred, forced vital capacity (FVC) 34%pred, FEV1/FVC 111%, single-breath carbon monoxide diffusing capacity of the lungs (DLCO SB) 47%pred] (Table  1 ). Re-performed chest CT showed linear opacities, consolidation and nodules. Echocardiography was unavailable at that time. After antibiotics treatment, the symptom of dyspnea was improved while edema in both lower limbs gradually appeared. Meanwhile, she was diagnosed with hypertension (180/100 mmHg at highest) and received antihypertensive agents. Echocardiography two months ago showed mitral and tricuspid regurgitation, which may indicate underlying PH without obvious changes in the structure of the heart. Venous thromboembolism was excluded after CT pulmonary angiography and ultrasonography of the lower extremity veins. However, she had an enlarged main pulmonary artery of 34 mm in diameter. Symptomatic and supportive treatment did not bring significant improvement. One month ago, she was hospitalized again due to aggravated symptoms. The echocardiography indicated massive tricuspid regurgitation, mild right cardiac enlargement and suspicion of PH with an estimated pulmonary arterial systolic pressure (sPAP) of 76 mmHg, lack of signs of left heart disease. Arterial blood gas analysis (ABG) showed a partial pressure of carbon dioxide (PaCO 2 ) of 60 mmHg and a partial pressure of oxygen (PaO 2 ) of 57 mmHg. She received antibiotics, expectorants, bronchodilator and symptomatic treatment. The edema reduced but the chest tightness did not show significant improvement.

On admission, the patient presented with cyanosis and difficulty in neck flexion. She was afebrile, with a respiratory rate of 20 breaths per minute and a blood pressure of 122/75 mmHg. Heart rate was 109 beats per minute with a loud pulmonic component of the second heart sound. Her bilateral lower extremities were moderately edematous and muscle strength was mildly decreased in grade IV − , which defined as being able to engage in activities against light resistance. The admission electrocardiogram showed sinus tachycardia, right axis deviation, and negative T waves in leads V1-V3. The patient presented with hypoventilation, type II respiratory failure and a special phenomenon of hypoxia in the supine position. ABG while breathing room air revealed a PaO 2 of 38 mmHg in the supine position, but 52 mmHg in the sitting position (Table  1 ). Lung function test indicated that the residual volume of single breath (RV-SB) was 1.42 L and total lung capacity of single breath (TLC-SB) was 2.39 L. The DLCO/alveolar ventilation (VA) was 1.45 mmol/min/kPa/L, which was 82.4% of the predicted value. Supine FEV1, FVC, FEV1/FVC ratio and the percentage they occupied of the predicted value measurements were all lower than the sitting measurements (Table  1 ). Lower extremity ultrasound and echocardiography were repeated with similar results to previous examinations. D-dimer in plasma was 1.23 mg/L. Ventilation-perfusion scan was performed and excluded pulmonary embolism, while the simultaneous chest CT indicated scattered patchy and linear shadows in bilateral lungs, and an elevation of the right diaphragm (Fig.  1 ). RHC indicated that the mPAP was 38 mmHg, pulmonary arterial wedge pressure was 7 mmHg, cardiac output was 4.99 L/min, and pulmonary vascular resistance was 6.21 Wood’s Unit. It revealed severe precapillary pulmonary hypertension, and concurrent acute vasoreactivity testing was negative. Advanced right heart contrast echocardiography showed no shunts. It was less likely to be PH associated with left heart disease, congenital heart disease, or chronic pulmonary artery obstruction. The cause of PH was more likely chronic lung diseases and/or hypoxia. Combining the supine hypoxia phenomenon and restrictive ventilation dysfunction, diaphragmatic ultrasound examination was performed and then revealed reduced mobility of bilateral diaphragm. Specifically, the left diaphragm showed an excursion of 1.0 cm both during quiet breathing and at maximum inspiration, with a diaphragmatic thickening fraction of 26%. On the right side, the diaphragm had an excursion of 1.7 cm during quiet breathing and 1.8 cm at maximum inspiration, with a diaphragmatic thickening fraction of 14%. The red blood cell count, hemoglobin and hematocrit on admission were 5.71 × 10 12 /L, 109 g/L and 44.2%, respectively. In conjunction with anemia-related testing, iron-deficiency anemia was considered and treated. Polysomnography indicated that the patient experienced hypoventilation during nocturnal sleep, with a hypopnea index of 119.6 times per hour. The longest duration of hypopnea lasted 63 s, and the average blood oxygen saturation (SO 2 ) during sleep was only 73% (Table  1 ). Hypoventilation syndrome was definitively diagnosed.

Chest computed tomography of the patient. A - C lung window; D - I mediastinal window. Chest computed tomography on admission indicated scattered patchy and linear shadows in bilateral lungs. An elevation of the right diaphragm can be seen in Fig. C, F and G . Figure H and I showed the sagittal views of the right and left diaphragm at the same level, respectively

Except for weak positive for anti-TIF1γ antibody and antinuclear antibody (1:80), other immune-related antibody spectra were negative. The creatine kinase (CK) was mildly elevated with a value of 394 U/L. Magnetic resonance imaging (MRI) showed bilateral thigh muscle changed consistent with myositis (Fig.  2 ). According to the consultation of the rheumatologist, the muscle biopsy was performed on the left biceps brachii and revealed the pathological features of central core disease (Fig.  3 ).

Thigh magnetic resonance imaging of the patient. Magnetic resonance imaging showed bilateral thigh muscle changed consistent with myositis (multiple muscle atrophy and patchy slighted high signal in fat saturated sequence) mainly involved the adductor magnus and sartorius (arrows). A T1-weighted non-fat saturated coronal image; B T2-weighted non-fat saturated coronal image; C synthetic T2-weighted non-fat saturated axial image; D synthetic T2-weighted fat saturated axial image

The pathological features of patient with RYR1 gene mutation. A and B . H&E and MGT staining × 10: the central core of muscle fiber light stained with surrounded by purple circle; C and D PAS and ORO staining × 10: no accumulation of glycogen and lipid in muscle fibers; E and F . NADH-TR and COX staining × 10: the deficiency or light staining of NADH-TR and cytochrome C oxidase in centrally located of muscle fibers

Thus, we conducted genetic susceptibility gene whole exome sequencing, using the blood sample. The sequencing revealed a missense mutation in the RYR1 gene (exon33 c.C4816T), which was associated with congenital myopathies. Taking all factors into consideration, the patient was diagnosed with RYR1 -related myopathy. We initiated non-invasive ventilation (NIV) therapy (Spontaneous-Timed mode, inspiratory positive airway pressure was 14cmH 2 O, expiratory positive airway pressure was 4cmH 2 O, respiratory rate was 14 breaths per minute) to improve her ventilation, while closely monitoring her symptoms, breathing pattern and SO 2 . We used an in-laboratory overnight polysomnogram to titrate NIV in this patient. An oral-nasal mask was chosen for NIV. After regularly using non-invasive positive pressure ventilation at night, her chest tightness and cough significantly improved, so did her exercise tolerance and heart function. In addition to oral diuretics and long-term NIV therapy, we recommended home rehabilitation exercises upon discharge.

After 10 months, she no longer experienced chest tightness or shortness of breath and her muscle strength was better. ABG while breathing room air indicated a higher PaO 2 of 68 mmHg and a lower PaCO 2 of 52 mmHg in the supine position. The lung function test suggested an improvement in ventilation function compared to before (Table  1 ). Repeated echocardiography showed mild tricuspid regurgitation, with a sPAP of 32 mmHg. The red blood cell count, hemoglobin and hematocrit were 4.00 × 10 12 /L, 121 g/L and 36.6%, respectively. Repeated polysomnography indicated improved hypoventilation during nocturnal sleep, with a hypopnea index of only 1.2 times per hour. The longest duration of hypopnea decreased to 51 s, and the average SO 2 during sleep rose to 96%. We recommended the patient to continue home NIV treatment and rehabilitation, and have regular outpatient follow-up visits.

Discussion and conclusion

To our knowledge, this is the first report describing a case with RYR1 -related myopathy presented with hypoventilation syndrome, type II respiratory failure, and PH associated with restrictive ventilator dysfunction. Respiratory muscles, especially the diaphragm, were involved in the congenital myopathy caused by missense mutation in the RYR1 gene. It impaired the patient's ventilation function and caused hypoventilation syndrome, leading to chronic hypoxemia and hypercapnia, which processed to respiratory failure and resulted in the development of PH eventually.

As for the differential diagnosis process of PH, her medical history, echocardiography and RHC indicated low possibility of PH associated with left or congenital heart disease. The D-dimer test is widely recognized as a valuable biomarker that can rule out venous thromboembolism without the need for costly and time-consuming imaging tests. An approximately fourfold increase in the normal cut-off value of D-dimer in the plasma is associated with a significantly higher risk of pulmonary embolism [ 9 ]. This patient had a mild elevated D-dimer and further ventilation-perfusion scan ruled out pulmonary embolism. Meanwhile, her lung function test indicated restrictive ventilation dysfunction. The polysomnography indicated hypoventilation syndrome, while ABG revealed hypercapnia and hypoxemia. There was no evidence for connective tissue disease, HIV infection, portal hypertension or any other possible diseases leading to PH. Thus, we considered that PH was associated with hypoventilation in this patient, which was caused by respiratory muscle dysfunction resulting from myopathy.

PH due to hypoventilation is usually seen in sleep-related breathing disorders [ 10 , 11 ]. Patients with sleep-related hypoventilation syndromes are often accompanied by hypercapnia and hypoxemia, about half of whom also present with PH and their functional impairment can be improved by NIV [ 10 , 11 , 12 ]. Hypoxic and hypercapnic vasoconstriction may play an important pathological role in the reversibility of PH. Meanwhile, sympathetic activation and the elevated viscosity of the blood could also increase the pulmonary vascular resistance, leading to an increase in pulmonary artery pressure [ 10 , 11 ]. Experiments in rats also found that the PH and increased hematocrit linked to sleep-disordered breathing were a result of chronic intermittent hypercapnic hypoxia [ 13 ]. Similar mechanisms may be potentially involved in the development of PH in this patient. Her polysomnography indicated hypoventilation syndrome and ABG revealed hypercapnia and hypoxemia, secondary pulmonary vasoconstriction may explain her reversible PH after NIV. In addition, her follow-up blood routine test showed a decrease in red blood cell count and hematocrit, which may indicate the reduced blood viscosity indirectly. NIV improved her hypoventilation, ultimately improving her cardiopulmonary function.

Congenital myopathies describe a collection of diseases that vary in clinical, histological, and genetic characteristics, primarily impacting muscles. Clinical features, muscle imaging and biopsy, and genetic testing are necessary in the diagnosis [ 1 , 2 , 3 ]. The pathogenic genes mainly include dynamin 2 ( DNM2 ), myotubularin ( MTM1 ), ACTA1 , slow skeletal β-cardiac myosin ( MYH7 ), titin ( TTN ), NEB , SEPN1 , β-tropomyosin ( TPM2 ), TPM3 , amphiphysin 2 ( BIN1 ) and RYR1 [ 1 , 2 ]. Hypotonia and muscle weakness are the main clinical features [ 2 ]. Meanwhile, approximately 64.1% of the individuals exhibited varying degrees of respiratory impairment, with about half of them necessitating nocturnal NIV as a result of respiratory failure [ 4 ]. Since diaphragm weakness is one of the main features of myopathy, evaluating diaphragmatic dysfunction is of great importance in these patients. Ultrasonography is a quick and simple method that can be done at the bedside, which showed a strong relationship with the standard respiratory tests commonly used in clinical settings [ 14 ]. In the differential diagnosis of respiratory failure, the clinician should pay attention to the patient's medical history and clinical manifestations not only limit to respiratory system, without overlooking some rare but possible causes. Meanwhile, critical examinations such as diaphragmatic ultrasound should be considered in the diagnostic process. Reports of PH indirectly caused by congenital myopathies are relatively rare. PH impacts the quality of life for patients and can be fatal in some circumstances. Therefore, the etiological treatment is crucial for improving the prognosis [ 5 ].

Notably, in addition to congenital myopathies, certain diseases can also present with muscle weakness. The differential diagnosis includes neurologic, rheumatologic, endocrine, genetic, medication- or toxin-related, and infectious etiologies [ 15 , 16 ]. Different neurologic causes can manifest as signs of involvement of either upper or lower motor neurons, or both, with variable patterns of onset and involvement of muscles [ 15 , 16 , 17 , 18 ]. Creatine kinase levels are generally normal and do not necessitate a muscle biopsy. Weakness caused by inflammatory muscle diseases, with acute or subacute course, mainly affects the proximal muscles. Creatine kinase levels can be moderately or severely elevated, and muscle biopsies commonly show inflammatory infiltration. Patients often experience skin rashes, dysphagia, or other indications of immune system involvement [ 15 , 16 , 19 ]. Endocrine etiologies mainly include adrenal insufficiency, hyperthyroidism, hypothyroidism, Cushing's syndrome and secondary hyperparathyroidism. They also mainly involve the proximal muscles and are accompanied by manifestations of the primary disease [ 15 , 16 , 20 ]. When patients have a history of exposure to drugs (e.g. fluoroquinolones, glucocorticoids, or statins), toxins (e.g. heavy metals, alcohol, or certain recreational drugs), or infections (e.g. viruses or parasites), muscle weakness should be considered as possibly related to these factors after excluding other possible reasons [ 16 , 21 , 22 , 23 ]. As for genetic causes, most diseases primarily involve proximal muscles. Their patterns of onset often progress gradually. Patients usually have congenital abnormalities in other organs, like the brain, heart, ocular region, skeletal system and so on. Creatine kinase levels are variable, while muscle biopsies often indicate nonspecific myopathic changes, mainly including muscle fiber atrophy, degeneration, and regeneration [ 15 , 16 ]. When patients exhibit the aforementioned characteristics, it is necessary to consider the possibility of genetic etiologies and to perform genetic testing.

RYR1 -related myopathy is acknowledged as the most common core myopathy among the congenital myopathies and linked to a broad spectrum of diseases [ 6 , 24 ]. Although fatigue and weakness were key symptoms of interest among participants with RYR1 -related myopathy, 2.4% of patients also reported respiratory difficulties [ 25 ]. A retrospective cross-sectional study also revealed that 6.8% of patients with RYR1 -related myopathy had neonatal respiratory involvement and required nocturnal NIV therapy [ 4 ]. Muscle biopsy is crucial for diagnosis. Its primary histological subtypes encompass central core disease, multiminicore disease, core-rod myopathy, centronuclear myopathy, and congenital fiber-type disproportion. Regardless of the pathological type, there will be varying degrees of impact of respiratory function, resulting in pulmonary-related complications [ 6 , 26 ]. A recent study summarized the respiratory features of centronuclear myopathy included RYR1 -mutated individuals, and found that supine respiratory function measurements of patients were overall lower than sitting measurements [ 27 ]. The present case experienced aggravating dyspnea, hypoxia and worse respiratory function in supine position, and hypoventilation during sleep, which were consistent with the characteristics of the disease. Other suggestive clinical features include muscle weakness, hypotonia, myalgia, ophthalmoplegia, bulbar involvement, dysphagia, orthopedic deformities and malignant hyperthermia [ 6 , 24 ]. The levels of serum CK are often normal or only mildly elevated [ 2 , 7 ]. When patients exhibit the above characteristics, pulmonologists should consider the possibility of congenital myopathy.

In addition to clinical and histopathologic features, imaging examinations also have diagnostic significance. RYR1 -related myopathy mainly involves muscle atrophy and intramuscular fatty infiltration [ 6 , 28 ]. Muscle MRI or ultrasound can detect the patterns of muscle involvement or preservation, while the former is more commonly used and accurate in clinical practice [ 2 , 6 , 7 ]. The mainly involved muscles in the lower limbs include adductor magnus, sartorius, vastus lateralis, vastus intermedius, and vastus medialis, while the rectus femoris, adductor longus, and gracilis are not significantly affected [ 6 ]. An effective modified T1-weighted MRI-based algorithm has been proposed to identify and quantify fatty infiltration in patients with RYR1 -related myopathy [ 28 ]. Electromyography (EMG) is less useful in diagnosis given that it can either show normal results or indicate findings similar to myopathy or other neuromuscular disorders in most cases [ 2 ]. Thigh MRI of this patient indicated irregular morphology of the bilateral muscles, multiple muscle atrophy, multiple patchy slighted high signals in fat saturated sequence, and blurred fat gaps between muscles. The main muscles involved were the adductor magnus and sartorius, while the adductor longus and gracilis were relatively spared, which consistent with the pattern of muscle involvement and preservation in RYR1 -related myopathy. Gene testing contributes to the determination of diagnosis and the technology of next-generation sequencing (NGS) makes it more efficient and convenient to use in clinical settings [ 2 , 6 ].

Main therapies being developed for RYR1 -related myopathy include drugs that counteract the harmful effects of stress on the cellular environment and the modifications of RYR1 after protein synthesis, and drugs that directly target RYR1 or regulate the proteins that influence its functionality [ 6 , 29 , 30 , 31 ]. Further randomized controlled trials are needed to determine approved therapeutic medications. Moreover, patients with congenital myopathies who presented with nocturnal hypoventilation can benefited from NIV [ 1 , 2 , 3 ]. At the same time, we should pay attention to the fact that the presence of sialorrhea and neurobehavioral impairment, and absence of respiratory symptoms can affect negatively of NIV adaptation [ 32 ].

There are also some limitations of this report. The patient refused to undergo a follow-up RHC. Thus, we cannot obtain a complete comparison of her hemodynamic parameters after treatment. However, other non-invasive evaluations, such as echocardiography, indeed revealed an improvement in her cardiopulmonary function. Additionally, the underlying mechanisms of PH in this patient cannot be confirmed, further animal experiments will be needed to validate current hypotheses in the future.

In conclusion, we report the first case with RYR1 -related myopathy who exhibited hypoventilation syndrome, type II respiratory failure, and PH associated with restrictive ventilator dysfunction to the best of our knowledge. When patients exhibit characteristics of muscle weakness, pulmonologists should consider the possibility of myopathies including congenital myopathies. Imaging examinations, muscle biopsy and genetic testing are useful in the differential diagnosis. Meanwhile, attention should be paid to PH related to hypoventilation in clinical practice. NIV is effective and well tolerated in these patients.

Availability of data and materials

The data that support this case report are available from the corresponding author on reasonable request.

Abbreviations

Ryanodine receptor type 1

• Pulmonary hypertension

Right heart catheterization

Non-invasive ventilation

α-skeletal actin

Selenoprotein 1

Slow α-tropomyosin

Mean pulmonary arterial pressure

Computed tomography

Forced expiratory volume in one second

Forced vital capacity

Residual volume of single breath

Total lung capacity of single breath

Single-breath carbon monoxide diffusing capacity of the lungs

Alveolar ventilation

Pulmonary arterial systolic pressure

Partial pressure of carbon dioxide in arterial blood

Partial pressure of oxygen in arterial blood

Blood oxygen saturation

Fraction of inspiration oxygen

PaO 2 /FiO 2 ratio

Arterial blood gas analysis

Creatine kinase

Magnetic resonance imaging

Myotubularin

Slow skeletal β-cardiac myosin

β-Tropomyosin

Amphiphysin 2

Electromyography

Next-generation sequencing

Claeys KG. Congenital myopathies: an update. Dev Med Child Neurol. 2019;62(3):297–302. https://doi.org/10.1111/dmcn.14365 .

Article   PubMed   Google Scholar  

Cassandrini D, Trovato R, Rubegni A, Lenzi S, Fiorillo C, Baldacci J, Minetti C, Astrea G, Bruno C, Santorelli FM. Congenital myopathies: clinical phenotypes and new diagnostic tools. Ital J Pediatr. 2017;43(1):101. https://doi.org/10.1186/s13052-017-0419-z .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Gilbreath HR, Castro D, Iannaccone ST. Congenital Myopathies and Muscular Dystrophies. Neurol Clin. 2014;32(3):689–703. https://doi.org/10.1016/j.ncl.2014.04.006 .

Colombo I, Scoto M, Manzur AY, Robb SA, Maggi L, Gowda V, Cullup T, Yau M, Phadke R, Sewry C, et al. Congenital myopathies: Natural history of a large pediatric cohort. Neurology. 2015;84(1):28–35. https://doi.org/10.1212/WNL.0000000000001110 .

Article   PubMed   PubMed Central   Google Scholar  

Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, Carlsen J, Coats AJS, Escribano-Subias P, Ferrari P, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J. 2022;43(38):3618–731. https://doi.org/10.1093/eurheartj/ehac237 .

Article   CAS   PubMed   Google Scholar  

Lawal TA, Todd JJ, Meilleur KG. Ryanodine Receptor 1-Related Myopathies: Diagnostic and Therapeutic Approaches. Neurotherapeutics. 2018;15(4):885–99. https://doi.org/10.1007/s13311-018-00677-1 .

Zhou H, Jungbluth H, Sewry CA, Feng L, Bertini E, Bushby K, Straub V, Roper H, Rose MR, Brockington M, et al. Molecular mechanisms and phenotypic variation in RYR1-related congenital myopathies. Brain. 2007;130(8):2024–36. https://doi.org/10.1093/brain/awm096 .

Hayakawa I, Abe Y, Ono H, Kubota M. Severe congenital RYR1-associated myopathy complicated with atrial tachycardia and sinus node dysfunction: a case report. Ital J Pediatr. 2019;45(1):165. https://doi.org/10.1186/s13052-019-0756-1 .

Sikora-Skrabaka M, Skrabaka D, Ruggeri P, Caramori G, Skoczyński S, Barczyk A. D-dimer value in the diagnosis of pulmonary embolism—may it exclude only? J Thorac Dis. 2019;11(3):664–72. https://doi.org/10.21037/jtd.2019.02.88 .

Naeije R. Pulmonary hypertension in hypoventilation syndromes. Eur Respir J. 2013;43(1):12–5. https://doi.org/10.1183/09031936.00185213 .

Article   Google Scholar  

Adir Y, Humbert M, Chaouat A. Sleep-related breathing disorders and pulmonary hypertension. Eur Respir J. 2021;57(1):2002258. https://doi.org/10.1183/13993003.02258-2020 .

Held M, Walthelm J, Baron S, Roth C, Jany B. Functional impact of pulmonary hypertension due to hypoventilation and changes under noninvasive ventilation. Eur Respir J. 2013;43(1):156–65. https://doi.org/10.1183/09031936.00147712 .

McGuire M, Bradford A. Chronic intermittent hypercapnic hypoxia increases pulmonary arterial pressure and haematocrit in rats. Eur Respir J. 2001;18(2):279–85.

Ruggeri P, Lo Monaco L, Musumeci O, Tavilla G, Gaeta M, Caramori G, Toscano A. Ultrasound assessment of diaphragm function in patients with late-onset Pompe disease. Neurol Sci. 2020;41(8):2175–84. https://doi.org/10.1007/s10072-020-04316-6 .

Saguil A. Evaluation of the patient with muscle weakness. Am Fam Physician. 2005;71(7):1327–36.

PubMed   Google Scholar  

Larson ST, Wilbur J. Muscle Weakness in Adults: Evaluation and Differential Diagnosis. Am Fam Physician. 2020;101(2):95–108.

Hülsbrink R, Hashemolhosseini S. Lambert-Eaton myasthenic syndrome – Diagnosis, pathogenesis and therapy. Clin Neurophysiol. 2014;125(12):2328–36. https://doi.org/10.1016/j.clinph.2014.06.031 .

Juel VC, Massey JM. Myasthenia gravis. Orphanet Journal of Rare Diseases. 2007; 2(1).  https://doi.org/10.1186/1750-1172-2-44 .

Dalakas MC, Longo DL. Inflammatory Muscle Diseases. N Engl J Med. 2015;372(18):1734–47. https://doi.org/10.1056/NEJMra1402225 .

Singer PA, Cooper DS, Levy EG, Ladenson PW, Braverman LE, Daniels G, Greenspan FS, McDougall IR, Nikolai TF. Treatment guidelines for patients with hyperthyroidism and hypothyroidism. Standards of Care Committee, American Thyroid Association. JAMA. 1995;273(10):808–12.

Klopstock T. Drug-induced myopathies. Curr Opin Neurol. 2008;21(5):590–5. https://doi.org/10.1097/WCO.0b013e32830e2774 .

Thompson PD, Panza G, Zaleski A, Taylor B. Statin-Associated Side Effects. J Am Coll Cardiol. 2016;67(20):2395–410. https://doi.org/10.1016/j.jacc.2016.02.071 .

Simon L, Jolley SE, Molina PE. Alcoholic Myopathy: Pathophysiologic Mechanisms and Clinical Implications. Alcohol Res. 2017;38(2):207–17.

PubMed   PubMed Central   Google Scholar  

Ogasawara M, Nishino I. A review of core myopathy: central core disease, multiminicore disease, dusty core disease, and core-rod myopathy. Neuromuscul Disord. 2021;31(10):968–77. https://doi.org/10.1016/j.nmd.2021.08.015 .

Capella-Peris C, Cosgrove MM, Chrismer IC, Razaqyar MS, Elliott JS, Kuo A, Emile-Backer M, Meilleur KG. Understanding Symptoms in RYR1-Related Myopathies: A Mixed-Methods Analysis Based on Participants’ Experience. The Patient - Patient-Centered Outcomes Research. 2020;13(4):423–34. https://doi.org/10.1007/s40271-020-00418-7 .

Lawal TA, Todd JJ, Witherspoon JW, Bönnemann CG, Dowling JJ, Hamilton SL, Meilleur KG, Dirksen RT. Ryanodine receptor 1-related disorders: an historical perspective and proposal for a unified nomenclature. Skelet Muscle. 2020;10(1):32. https://doi.org/10.1186/s13395-020-00243-4 .

Bouma S, Cobben N, Bouman K, Gaytant M, van de Biggelaar R, van Doorn J, Reumers SFI, Voet NBM, Doorduin J, Erasmus CE, et al. Respiratory features of centronuclear myopathy in the Netherlands. Neuromuscul Disord. 2023;33(7):580–8. https://doi.org/10.1016/j.nmd.2023.06.003 .

Lawal TA, Patankar A, Todd JJ, Razaqyar MS, Chrismer IC, Zhang X, Waite MR, Jain MS, Emile-Backer M, Witherspoon JW, et al. Ryanodine Receptor 1-Related Myopathies: Quantification of Intramuscular Fatty Infiltration from T1-Weighted MRI. Journal of Neuromuscular Diseases. 2021;8(4):657–68. https://doi.org/10.3233/jnd-200549 .

Todd JJ, Lawal TA, Witherspoon JW, Chrismer IC, Razaqyar MS, Punjabi M, Elliott JS, Tounkara F, Kuo A, Shelton MO, et al. Randomized controlled trial of N-acetylcysteine therapy for RYR1-related myopathies. Neurology. 2020;94(13):e1434–44. https://doi.org/10.1212/wnl.0000000000008872 .

Lee CS, Hanna AD, Wang H, Dagnino-Acosta A, Joshi AD, Knoblauch M, Xia Y, Georgiou DK, Xu J, Long C, et al. A chemical chaperone improves muscle function in mice with a RyR1 mutation. Nat Commun. 2017;8(1):14659. https://doi.org/10.1038/ncomms14659 .

Dowling JJ, Lawlor MW, Dirksen RT. Triadopathies: An Emerging Class of Skeletal Muscle Diseases. Neurotherapeutics. 2014;11(4):773–85. https://doi.org/10.1007/s13311-014-0300-3 .

Russo M, Bonanno C, Profazio C, La Foresta S, Faraone C, Lizio A, Vita GL, Sframeli M, Aricò I, Ruggeri P, et al. Which are the factors influencing NIV adaptation and tolerance in ALS patients? Neurol Sci. 2020;42(3):1023–9. https://doi.org/10.1007/s10072-020-04624-x .

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Acknowledgements

The authors would like to thank the patient’s daughter for her cooperation with the follow-up.

Funding was provided by National High Level Hospital Clinical Research Funding and Elite Medical Professionals Project of China-Japan Friendship Hospital (No. ZRJY2021-QM11).

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Shuai Zhang and Zhenguo Zhai contributed equally to this work as corresponding authors.

Authors and Affiliations

Peking University China-Japan Friendship School of Clinical Medicine, Beijing, P.R. China

Yinong Chen, Chen Wang & Zhenguo Zhai

National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, P.R. China

Yinong Chen, Shuai Zhang, Wanmu Xie, Chen Wang & Zhenguo Zhai

Department of Rheumatology, China-Japan Friendship Hospital, Beijing, P.R. China

Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, P. R. China

Chen Wang & Zhenguo Zhai

Department of Respiratory Medicine, Capital Medical University, Beijing, P.R. China

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S.Z and Z.Z had the conception and design of the study. Y.C, S.Z and X.L prepared all the data from the patient and wrote the original draft. W.X, C.W and Z.Z critically revised the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Shuai Zhang or Zhenguo Zhai .

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Chen, Y., Zhang, S., Lu, X. et al. Unusual cause of muscle weakness, type II respiratory failure and pulmonary hypertension: a case report of ryanodine receptor type 1( RYR1) -related myopathy. BMC Pulm Med 24 , 194 (2024). https://doi.org/10.1186/s12890-024-03016-7

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Received : 25 December 2023

Accepted : 15 April 2024

Published : 22 April 2024

DOI : https://doi.org/10.1186/s12890-024-03016-7

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