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10 Methods of Data Presentation That Really Work in 2024

Leah Nguyen • 15 July, 2024 • 13 min read

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

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

How can you clear up those confusing numbers and make your presentation as clear as the day? Let's check out these best ways to present data. 💎

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Data Presentation - What Is It?

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

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

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

Good data presentation helps…

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

Methods of Data Presentation and Examples

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

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

#1 - Tabular 

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

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

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

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

(Source: CustomerThermometer )

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

#3 - Pie chart

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

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

#4 - Bar chart

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

They can be as simple as this:

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

#5 - Histogram

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

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

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

#6 - Line graph

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

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

#7 - Pictogram graph

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

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

#8 - Radar chart

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

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

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

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

#9 - Heat map

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

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

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

#10 - Scatter plot

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

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

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

5 Data Presentation Mistakes to Avoid

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

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

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

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

#2 - Use the wrong type of chart

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

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

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

#3 - Make it 3D

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

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

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

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

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

#5 - Bombard the audience with too much information

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

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should end your session with open-ended questions to see what your participants really think.

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

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

For example:

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

Frequently Asked Questions

What is a chart presentation.

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

When can I use charts for the presentation?

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

Why should you use charts for presentation?

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

What are the 4 graphical methods of presenting data?

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

Leah Nguyen

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

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Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

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

Table of Contents

What is a Data Presentation?

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

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

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

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

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

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

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

Real-Life Application of Bar Charts

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

Step 1: Selecting Data

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

The sales manager has highlighted these products for the presentation.

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

Step 2: Choosing Orientation

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

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

Step 3: Colorful Insights

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

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

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

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

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

Real-life Application of Line Graphs

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

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

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

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

Step 3: Connecting Trends

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

Step 4: Adding Clarity with Color

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

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

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

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

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

Real-Life Application of a Dashboard

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

Step 1: Defining Key Metrics

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

Step 2: Choosing Visualization Widgets

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

Step 3: Dashboard Layout

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

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

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

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

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

Real-Life Application of a Treemap Chart

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

Step 1: Define Your Data Hierarchy

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

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

Step 2: Choose a Suitable Tool

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

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

Step 3: Make a Treemap Chart with PowerPoint

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

Step 5: Input Your Data

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

Step 6: Customize the Treemap

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

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

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

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

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

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

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

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

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

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

Real-Life Application of Pie Charts

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

Step 1: Define Your Data Structure

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

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

Step 2: Insert a Pie Chart

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

For instance:

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

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

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

Step 03: Results Interpretation

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

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

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

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

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

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

Real-Life Application of a Histogram

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

Step 1: Gather Data

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

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

Step 2: Define Bins

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

Step 3: Count Frequency

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

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

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

Step 4: Create the Histogram

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

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

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

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

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

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

Real-Life Application of Scatter Plot

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

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

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

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

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

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

1. Fact Sheet Dashboard for Data Presentation

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

Use This Template

2. 3D Column Chart Infographic PPT Template

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

3. Data Circles Infographic PowerPoint Template

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

4. Colorful Metrics Dashboard for Data Presentation

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

5. Animated Data Presentation Tools for PowerPoint & Google Slides

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

6. Statistics Waffle Charts PPT Template for Data Presentations

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

7. Data Presentation Dashboard Template for Google Slides

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

8. Weather Dashboard for Data Presentation

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

9. Social Media Marketing Dashboard Data Presentation Template

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

10. Project Management Summary Dashboard Template

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

11. Profit & Loss Dashboard for PowerPoint and Google Slides

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

Overwhelming visuals

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

Inappropriate chart types

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

Lack of context

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

Inconsistency in design

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

Failure to provide details

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

Lack of focus

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

Visual accessibility issues

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Data presentation: A comprehensive guide

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

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

Whether you're an experienced presenter or just starting, this guide will help you present your data like a pro. We'll dive deep into what data presentation is, why it's crucial, and how to master it. So, let's embark on this data-driven journey together.

What is data presentation?

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

Why are data presentations important?

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

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

Types of data presentation:

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

1. Textual presentation

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

2. Tabular presentation

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

3. Graphical presentation

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

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

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

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

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

What to include in data presentation?

When creating your data presentation, remember these key components:

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

How to structure an effective data presentation?

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

1. Know your audience

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

2. Have a clear message

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

3. Tell a compelling story

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

4. Leverage visuals

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

5. Be clear and concise

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

6. Practice your delivery

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

A basic structure for an effective data presentation

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

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

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

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

Additional tips for enhancing your data presentation

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

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

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

Do’s and don'ts on a data presentation

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

Don'ts:

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

Summarizing key takeaways

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

FAQ's on a data presentation

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

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

2. What are some common methods of data presentation?

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

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

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

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

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

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

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

Create your data presentation with prezent

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

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

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

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

Thank you for joining us on this data-driven journey. Stay tuned for more insights, and remember, data presentation is your ticket to making numbers come alive! Sign up for our free trial or book a demo ! ‍

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

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

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

Click to jump ahead:

10 Essential data presentation examples + methods you should know

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

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

1. Bar graph

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

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

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

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

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

2. Line graph

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

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

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

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

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

3. Pie chart

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

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

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

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

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

4. Scatter plot

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

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

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

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

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

5. Histogram

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

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

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

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

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

6. Stacked bar chart

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

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

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

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

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

7. Area chart

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

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

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

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

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

8. Tabular presentation

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

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

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

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

9. Textual data

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

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

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

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

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

10. Pictogram

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

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

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

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

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

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

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

1. Title and objective

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

2. Key data points

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

3. Context and significance

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

4. Key takeaways

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

5. Visuals and charts

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

6. Implications or actions

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

7. Q&A and discussion

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

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

Overloading with data

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

Assuming everyone’s on the same page

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

Misleading visuals

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

Not providing context

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

Not citing sources properly

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

Not telling a story

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

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

Ignoring data quality

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

Simplify your visuals

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

Missing the emotional connection

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

Skipping the actionable insights

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

Can you provide some data presentation examples for business reports?

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

What are some creative data presentation examples for academic presentations?

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

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

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

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

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

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

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

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

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

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

What is the difference between data visualization and data presentation?

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

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

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

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

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

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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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A Guide to Effective Data Presentation

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

Tools for effective data presentation

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

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

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

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

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

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

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

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

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

Examples of Good Charts

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

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

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

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

Example of Poorly Crafted Charts

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

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

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

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

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

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

Internal Audience

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

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

External Audience

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

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

Audiences for Live and Emailed Presentation

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

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

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

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

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

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

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

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

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

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

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

• Investment Banking Pitch Books
• Excel Dashboards
• Financial Modeling Guide
• Startup Pitch Book
• See all business intelligence resources
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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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Mastering the Art of Presenting Data in PowerPoint

Presenting data in PowerPoint is easy. However, making it visually appealing and effective takes more time and effort. It’s not hard to bore your audience with the same old data presentation formats. So, there is one simple golden rule: Make it not boring.

When used correctly, data can add weight, authority, and punch to your message. It should support and highlight your ideas, making a concept come to life. But this begs the question: How to present data in PowerPoint?

After talking to our 200+ expert presentation designers, I compiled information about their best-kept secrets to presenting data in PowerPoint. 

Below, I’ll show our designers ' favorite ways to add data visualization for global customers and their expert tips for making your data shine. Read ahead and master the art of data visualization in PowerPoint!

Feel free to explore sections to find what's most useful!

How to present data in PowePoint: a step-by-step guide

Creative ways to present data in powerpoint.

• Tips for data visualization

Seeking to optimize your presentations? – 24Slides designers have got you covered!

How you present your data can make or break your presentation. It can make it stand out and stick with your audience, or make it fall flat from the go.

It’s not enough to just copy and paste your data into a presentation slide. Luckily, PowerPoint has many smart data visualization tools! You only need to put in your numbers, and PowerPoint will work it up for you.

Follow these steps, and I guarantee your presentations will level up!

1. Collect your data

First things first, and that is to have all your information ready. Especially for long business presentations, there can be a lot of information to consider when working on your slides. Having it all organized and ready to use will make the whole process much easier to go through.

Consider where your data comes from, whether from research, surveys, or databases. Make sure your data is accurate, up-to-date, and relevant to your presentation topic.

Your goal will be to create clear conclusions based on your data and highlight trends.

2. Know your audience

Knowing who your audience is and the one thing you want them to get from your data is vital. If you don’t have any idea where to start, you can begin with these key questions:

• What impact do you want your data to make on them?
• Is the subject of your presentation familiar to them?
• Are they fellow sales professionals?
• Are they interested in the relationships in the data you’re presenting?

By answering these, you'll be able to clearly understand the purpose of your data. As a storyteller, you want to capture your audience’s attention.

3. Choose a data visualization option

One key to data visualization in PowerPoint is being aware of your choices and picking the best one for your needs. This depends on the type of data you’re trying to showcase and your story.

When showcasing growth over time, you won’t use a spider chart but a line chart. If you show percentages, a circle graph will probably work better than a timeline. As you can see, knowing how to work with charts, graphs, and tables can level up your presentation.

Later, we’ll review some of the most common tools for data visualization in PowerPoint. This will include what these graphs and charts are best for and how to make the most of each. So read ahead for more information about how to present data in PowerPoint!

4. Be creative!

PowerPoint can assist with creating graphs and charts, but it's up to you to perfect them. Take into account that PowerPoint has many options. So, don't be afraid to think outside the box when presenting your data.

To enhance your presentation design, try out different color schemes, fonts, and layouts. Add images, icons, and visual elements to highlight your ideas.

If this sounds complicated to you, there's no need to worry. At the end of this article, you’ll find some easy tips for upgrading your data visualization design!

At this point, you might wonder: what is the best way to present data in PowerPoint? Well, let me tell you: it's all about charts. To accomplish a polished presentation, you must use charts instead of words. When visualizing quantitative data, a picture is worth a thousand words.

Based on +10 years of expertise, we've identified key chart types and creative ways to work with them. Let's delve into each one!

Line Charts

Line charts are a classic, which can make them boring. However, if done correctly, they can be striking and effective. But where does their popularity come from? Here's the answer: Line charts work great to show changes over time.

Another critical difference is that line charts are accumulative. For example, you can join them to a column chart to show different data at a glance. They allow data visualization effectively, making it easier to figure out.

To make the most of them, mastering how to work with line charts is essential. But there is good news: you will have a lot of freedom to customize them!

Download our Free Line Chart Template here .

Bar and column charts

Bar and column charts are another classic choice. Again, they are simple and great for comparing different categories. They organize them around two axes: one shows numbers, and the other shows what we want to compare.

But when should you use a bar chart or a column chart? A bar chart is better when comparing different categories and having long labels. A column chart, on the other hand, is better if you have a few categories and want to show changes over time.

You also have the waterfall option, which is perfect for highlighting the difference between gains and losses. It also adds a dynamic touch to your presentation!

Unsure how to implement these charts? Here's how to add a bar or a column chart in PowerPoint.

Download our Bar and Column Chart Template here .

Venn diagram

Venn diagrams are definitely something to consider when discussing data visualization—even if its focus is not quantitative data! Venn diagrams are best for showcasing similarities and differences between two (or more) categories or products. 

By using overlapping circles, you can quickly and easily see common features between separate ideas. The shared space of the circles shows what is the same between the groups. However, items in the outer parts of each circle show what isn’t a common trait.

They make complex relationships easy to understand. Now, you only need to know how to create a Venn diagram in PowerPoint —quite simple!

Download our Free Venn Diagram Template here .

Pie charts are a great way to show different percentages of a whole. They immediately identify the largest and smallest values. This means that they are great options for drawing attention to differences between one group and another.

However, many people misuse pie charts by overpacking them. As a rule, keep the chart to six or fewer sections. That way, the data is striking, not confusing. Then, make the pie chart your own with small, individual details and designs.

Once again, the powerful presentation of data is in simplicity.

Are you considering incorporating it into your presentation? Here’s how to easily add a pie chart in PowerPoint.

Download our Free Pie Chart Template here .

Bubble Charts

Bubble charts playfully present data in an incredibly visual way. But, what makes them so unique? It's easy: they show different values through varying circle sizes.

Squeezed together, the circles also show a holistic viewpoint. Bigger bubbles catch the eye, while small bubbles illustrate how the data breaks down into smaller values. ¿The result? A presentation of data in a visual form.

It can be one of the most graphic ways to represent the spending distribution. For example, you can instantly see your biggest costs or notice how important finances are getting lost in a sea of bubbles. This quick analysis can be incredibly handy.

Download our Free Bubble Chart Template here .

Maps are the go-to solution for presenting geographic information . They help put data in a real-world context. You usually take a blank map and use color for the important areas.

Blocks, circles, or shading represent value. Knowing where certain data is can be crucial. A consistent color scheme makes it easy to show how valuable each section is.

They also work great when paired with other forms of data visualization. For example, you can use pie charts to provide information about offices in different cities around the world or bar charts to compare revenue in different locations.

Download our Free World Map Template here .

If you want to display chronological data, you must use a timeline. It’s the most effective and space-efficient way to show time passage.

They make it easy for your audience to understand the sequence of events with clear and concise visuals.

You can use timelines to show your company’s history or significant events that impacted your business. Like maps, you can easily mix them with other types of data visuals. This characteristic allows you to create engaging presentations that tell a comprehensive story.

At this point, it's a matter of understanding how to add a timeline correctly in PowerPoint . Spoiler: it's incredibly easy.

Download our Free Timeline Chart Template here .

Flowcharts, like timelines, represent a succession of events. The main difference is that timelines have determined start and finish points and specific dates. Flowcharts, on the other hand, show the passing from one step to the next.

They are great for showing processes and info that need to be in a specific order. They can also help you communicate cause-and-effect information in a visually engaging way.

Their best feature is that (unlike timelines) they can also be circular, meaning this is a recurrent process. All you need now is to become familiar with creating a flowchart in PowerPoint .

Download our Free Flowchart Template here .

5 Tips for data visualization in PowerPoint

Knowing how to present data in PowerPoint presentations is not hard, but it takes time to master it. After all, practice makes perfect!

I've gathered insights from our 200+ expert designers , and here are the top five tips they suggest for enhancing your data presentations!

1. Keep it simple

Don’t overload your audience with information. Let the data speak for itself. If you write text below a chart, keep it minimalist and highlight the key figures. The important thing in a presentation is displaying data in a clear and digestible way.

Put all the heavy facts and figures in a report, but never on a PowerPoint slide.

You can even avoid charts altogether to keep it as simple as possible. And don't get me wrong. We've already covered that charts are the way to go for presenting data in PowerPoint, but there are a few exceptions.

This begs the question: when shouldn't you use charts in PowerPoint? The answer is quite short. If your data is simple or doesn't add much value to your presentation, you might want to skip using charts.

2. Be original

One of the best ways to make your data impactful is originality. Take time to think about how you could present information uniquely. Think of a whole new concept and play around with it. Even if it’s not yet perfect, people will appreciate the effort to be original.

Experiment with creative ways to present your data, adding storytelling techniques , unique design elements, or interactive features. This approach can make the data more appealing and captivating for your audience.

You can even mix up how to present data in PowerPoint. Instead of just one format, consider using two different types of data presentation on a single slide. For instance, try placing a bar chart on the left and a pie chart showcasing different data on the right.

3. Focus on your brand

Keeping your presentation on-brand can genuinely make you stand out from the crowd! Even if you just focus on your brand’s color scheme, it will make your presentation look more polished and professional. 

Have fun experimenting with data visualization tools to ensure they match your company’s products and services. What makes you different from others?

Add your brand's style into your visualization to ensure brand consistency and recognition. Use colors, fonts, and logos aligned with your company's image.

You can even make a presentation that more subtly reflects your brand. Think of what values you want to associate with your company and how you can display these in your presentation design.

4. Highlight key information

Not distracting your audience nicely brings us to our third point: Highlight key information. Being detailed and informative is important, but grabbing and keeping the audience's attention is crucial.

Presenting numbers in PowerPoint can be difficult, but it doesn’t must be. Make your audience listen to the bigger message of your words, not just the exact details. All the smaller particulars can be confirmed later.

Your listeners don’t want to know the facts and figures to the nearest decimal. They want the whole number, which is easy to spot and understand.

The meaning of the number is more important than its numerical value. Is it high or low? Positive or negative? Good or bad for business? These are the questions to which you want the answers to be clear.

Using colors is an excellent way to work with this. Colors are also a great visual tool to showcase contrast. For example, when you're working on a graph to display your revenue, you can showcase expenses in red and earnings in green. This kind of color-coding will make your data visualization clear from first sight!

5. Use Templates!

Presentation templates can be your best friend when you want to present data effectively in PowerPoint.

They offer pre-designed layouts and styles that can ensure consistency throughout your presentation. Templates allow you to adjust colors, fonts, and layouts to match your branding or personal preferences.

Microsoft Office has its own library of templates, but you can also find some pretty amazing ones online. Take some extra time to search and pick one that truly fits your needs and brand. 

¿The good news? Our Templates by 24Slides platform has hundreds of PowerPoint chart templates, all completely free for you to use . You can even download different templates and mix and match slides to make the perfect deck. All are entirely editable, so you can add your own data and forget about design.

If you liked the look of some examples in this article, you might be in luck! Most are part of these, and you can also find them on our Templates platform.

In this article, I've shown why knowing how to present data efficiently in PowerPoint is crucial. Data visualization tools are a must to ensure your message is clear and that it sticks with your audience.

However, achieving results that really stand out could be a huge challenge for beginners.  So, If you want to save time and effort on the learning curve of presenting data in PowerPoint, you can always trust professionals!

With 10+ years of experience and more than 200 designers worldwide, we are the world’s largest presentation design company across the globe.

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Top 5 Easy-to-Follow Data Presentation Examples

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

And this is where data presentation examples come in.

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

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

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

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

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

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

Table of Content:

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

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

• Relationship
• Trend and patterns

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

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

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

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

The presentation is the key to success.

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

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

1. Waterfall Chart

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

2. Double Bar Graph

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

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

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

3. Slope Chart

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

Besides, they’re also called Slope Graphs .

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

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

A TreeMap is a data structure that stores key-value pairs in a sorted order using a Red-Black tree, ensuring efficient search, insertion, and deletion operations.

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

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

• Macy’s Store

5. Radar Chart

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

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

6. Sankey Chart

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

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

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

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

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

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

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

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

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

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

What is ChartExpo?

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

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

ChartExpo comes with a 7-day free trial period.

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

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

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

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

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

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

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

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

How to Edit the Sankey Chart?

• Click the Edit Chart button, as shown above.

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

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

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

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

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

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

Visualizing supports this and lightens the decision-making process.

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

Check out more benefits of data presentation examples below:

1. Easy to understand

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

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

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

2. Spot patterns

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

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

3. Data Narratives

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

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

4. Speed up the decision-making process

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

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

Take a look at the table below.

Can you give reliable insights into the table above?

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

1. Textual Ways of Presenting Data

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

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

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

2. Statistical data presentation

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

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

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

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

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

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

3. Measure of Dispersion

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

There are two types of measures of dispersion, namely:

• Absolute Measure of Dispersion
• Relative Measure of Dispersion

4. Tabular Ways of Data Presentation and Analysis

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

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

5. Graphical Data Representation

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

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

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

What is the main objective of data presentation?

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

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

What is the importance of graphs and charts in business?

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

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

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

This is where data presentation examples come into play.

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

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

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

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

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Why presentation of data is important?

With the digitalization era, data went from scarce, expensive, and challenging to find to abundant, cheap, and complicated to process. That’s when the need for statistics presentation of data has emerged. Reliable and reasonable amounts of information were so vast that they were challenging to seize, store, understand, and analyze with traditional methods.

What Is Data Presentation?

Terabytes of unused data in a data center is a burden. If correctly processed, it can become digital gold. Similarly, your company or startup has valuable data, and data analysis presentation is the most convenient and attractive way to demonstrate your growth projections, monthly expenditures, revenue achievements, etc.

To present data effectively, you need to:

• Know how to illustrate the different methods of presentation of data;
• Determine the different types of graphs and diagrams and their uses;
• Represent a set of data using various data presentation methods.

If you feel or exactly realize that you lack knowledge and expertise in these points, we advise contacting a presentation design agency to have all numbers formatted and drawn in attractive pie charts, bar graphs, and all kinds of diagrams.

How to Present Data in a PowerPoint Presentation?

Methods of data presentation.

There are 3 main methods of data representation in PowerPoint:

We are here for a data PowerPoint presentation, so let’s focus on the last method. Graphical representation of data enables your audience to study the cause and effect relationship between two variables. It helps in easy and quick understanding of data for listeners of different preparation and knowledge levels.

Kinds of Graphs/Diagrams

Numbers have an important story to tell, and using a correct graph or diagram will nail this story:

• A bar graph is used to show relationships/comparisons between groups;
• A pie or circle graph shows the percentage effectively;
• A line graph is most useful in displaying data that changes continuously over time;
• Pictograph uses small figures of objects called isotopes in making comparisons (each picture represents a definite quantity).

This variety keeps your hands open to choice and improvisation. However, if this factor, on the contrary, restrains you from presentation design, you should address presentation services that make both PowerPoint and Google slides design .

Data Presentation Tips

Presenting data on slides should follow specific principles to remain informative while visually attractive:

• Only show the data you’re talking about;
• Don’t just copy and paste a big Excel table;
• Never present a single number;
• Highlight 1 focal point per slide;
• Charts and graphs are pictures and should tell stories;
• Use colors;
• Use consistent formatting;
• Use appropriate chart types;
• Use stickers to protect yourself.

Nobody likes too many boring numbers, and data by itself is useless. Use these tips to make it more friendly to the audience, and your audience will appreciate your effort.

Let’s Sum up

Presenting data seems like a complex task, but mastering it will show your diligence and expertise. Remember, your job as a presenter is to help your audience cut through all the noise. You must help them interpret the data in a meaningful way. Use today’s information when it comes to visualizing data by incorporating charts and graphs into a presentation everybody understands and story persuading anyone.

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When and how should you use data in a presentation?

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

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

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

Remember that You Are Telling Your Audience a Story

All presentations are basically story-telling opportunities.

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

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

Use Data to Tell the Story

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

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

‘ What is the story in this data? ’,

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

A Picture Tells a Thousand Words

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

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

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

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

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

Consider this set of data:

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

Now consider the same data in a pie chart:

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

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

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

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

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

KISS: Keep It Simple, Stupid!

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

But does it really help to make your point?

Then don’t present it.

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

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

Leave It Out...

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

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

Remember KISS: Keep It Simple, Stupid.

Highlight the Main Features to Draw Out the Insights

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

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

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

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

Take-home message

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

In other words:

‘What happened there?’

‘What are we going to do about it?’

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

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

Continue to: Writing Your Presentation Working with Visual Aids

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

Presentation of Data

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

What is Meant by Presentation of Data?

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

Presentation of Data Examples

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

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

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

Find the range for the given data.

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

The data given is called the raw data.

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

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

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

Therefore, Range = 95-25 = 70.

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

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

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

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

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

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

Therefore, the presentation of data is given as below:

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

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

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

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

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

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

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

Practice Problems

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

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Professor Sapna Sarupria receives 2024 CoMSEF Impact Award

MINNEAPOLIS / ST. PAUL (8/16/2024) – Department of Chemistry Professor Sapna Sarupria was recently awarded the 2024  CoMSEF Impact Award from the Computational Molecular Science and Engineering Forum (CoMSEF ) of the American Institute of Chemical Engineers (AIChE). The award recognizes one person within 15 years of their highest degree for their outstanding research in computational molecular science and engineering each year. This highly competitive award represents the top computational chemists in the field. 

Sarupria is recognized for her numerous contributions to the advancement of computational methods for studying rare events. She is also recognized for her service to the broader research community and her dedicated advocacy for diversity and inclusivity in STEM and higher education.  She will deliver a presentation titled “Seeing the Invisible: In Nucleation (and in society)” during the CoMSEF Plenary Session at the 2024 AIChE Annual Meeting. CoMSEF Impact Award winners receive a plaque commemorating their accomplishment and an honorarium.

The Sarupria lab is called the SAMPEL lab (SAMPEL = Simulations and Advanced Methods for Probing Energy Landscapes). SAMPEL uses molecular simulations and statistical mechanics to study condensed phase phenomena. They also develop and apply rare event path sampling techniques. These techniques enable accessing processes that involve high free energy barriers and are typically inaccessible in straightforward molecular simulations. Current projects in the SAMPEL lab include ice nucleation, enzyme engineering, polyamide desalination membranes, enzymatic breakdown of polymers, and stabilization of vaccines. These projects are motivated by applications in energy, biology and sustainable technologies. In addition to leading the research efforts of SAMPEL lab, Sarupria is engaged in several education and equity efforts. She co-founded the NSF funded  Institute for Computational Molecular Science Education (i-CoMSE) and has led the organization of two workshops focused on Machine Learning in Molecular Science which were held at UMN – Twin Cities. She also co-organizes a virtual seminar series “ Statistical Thermodynamics and Molecular Simulations (STMS) ” that has been successfully running since 2020 and attracts over 80+ participants at every event! So far STMS has hosted 82 seminars with 164 speakers. Additionally, Sarupria is the Chair of the ACS PHYS Theory sub-division, elected trustee of the not-for-profit Computer Aids for Chemical Engineering ( CACHE ), and member-at-large of the Executive Board of the Program Committee (EBPC) of AIChE. She is also the co-Director of the recently established NSF-funded National Research Traineeship program (NRT) Data-Driven Discovery and Engineering from Atoms to Processes (3DEAP) housed in the Department of Chemistry and the Department of Chemical Engineering and Materials Science at UMN.

SAMPEL Group website

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• Open access
• Published: 12 August 2024

Micropapillary breast carcinoma in comparison with invasive duct carcinoma. Does it have an aggressive clinical presentation and an unfavorable prognosis?

• Yasmine Hany Abdel Moamen Elzohery 1 , 5 ,
• Amira H. Radwan 2 , 5 ,
• Sherihan W. Y. Gareer 2 , 5 ,
• Mona M. Mamdouh 3 , 5 ,
• Inas Moaz 4 , 5 ,
• Abdelrahman Mohammad Khalifa 5 ,
• Osama Abdel Mohen 5 ,
• Mohamed Fathy Abdelfattah Abdelrahman Elithy 5   nAff6 &
• Mahmoud Hassaan 5   nAff7  

BMC Cancer volume  24 , Article number:  992 ( 2024 ) Cite this article

253 Accesses

Metrics details

Invasive micropapillary carcinoma (IMPC) was first proposed as an entity by Fisher et al. In the 2003 World Health Organization (WHO) guidelines for histologic classification of the breast tumors. IMPC was recognized as a distinct, rare histological subtype of breast cancer.

IMPC is emerging as a surgical and oncological challenge due to its tendency to manifest as a palpable mass, larger in size and higher in grade than IDC with more rate of lymphovascular invasion (LVI) and lymph node (LN) involvement, which changes the surgical and adjuvant management plans to more aggressive, with comparative prognosis still being a point of ongoing debate.

Aim of the study

In this study, we compared the clinicopathological characteristics, survival and surgical management of breast cancer patients having invasive micropapillary carcinoma pathological subtype in comparison to those having invasive duct carcinoma.

This is a comparative study on female patients presented to Baheya center for early detection and treatment of breast cancer, in the period from 2015 to 2022 diagnosed with breast cancer of IMPC subtype in one group compared with another group of invasive duct carcinoma. we analyzed 138 cases of IMPC and 500 cases of IDC.

The incidence of LVI in the IMPC group was 88.3% in comparison to 47.0% in the IDC group (p < 0.001). IMPC had a higher incidence of lymph node involvement than the IDC group (68.8% and 56% respectively). IMPC had a lower rate of breast conserving surgery (26% vs.37.8%) compared with IDC.

The survival analysis indicated that IMPC patients had no significant difference in overall survival compared with IDC patients and no differences were noted in locoregional recurrence rate and distant metastasis rate comparing IMPCs with IDCs.

The results from our PSM analysis suggested that there was no statistically significant difference in prognosis between IMPC and IDC patients after matching them with similar clinical characteristics. However, IMPC was found to be more aggressive, had larger tumor size, greater lymph node metastasis rate and an advanced tumor stage.

Peer Review reports

Introduction

Breast cancer is the most common cancer in women. In the 2012 World Health Organization (WHO) classification of breast cancer. Breast Cancer is classified into up to 21 different histological types depending on cell growth, morphology and architecture patterns [ 1 ]. The invasive carcinoma of no special type (IBC-NST), which is known as invasive ductal carcinoma (IDC), is the most frequently occurring histological type, which constitutes around 75% of invasive breast carcinoma [ 2 ].

Invasive micropapillary carcinoma (IMPC) was first proposed as an entity by Fisher et al. in 1980 [ 3 ] and first described as the term “invasive micropapillary carcinoma” by Siriaunkgul et al. [ 4 ] in 1993.

In the 2003 World Health Organization (WHO) guidelines for histologic classification of the breast tumors [ 5 ]. IMPC was recognized as a distinct, rare histological subtype of breast cancer. While micropapillary histological architecture is present in 2–8% of breast carcinomas, pure micropapillary carcinoma is uncommon and accounts for 0.9–2% of all breast cancers [ 6 ].

IMPC exhibits more distinct morphologic architecture than the IDC, characterized by pseudopapillary and tubuloalveolar arrangements of tumor cell clusters in clear empty sponge-like spaces that resemble extensive lymphatic invasion [ 7 ]. The neoplastic cell exhibits an “inside-out” pattern, known as the reverse polarity pattern [ 2 ].

Most studies demonstrate that the radiological findings of IMPC are irregular-shaped masses with an angular or spiculated margin on ultrasound, mammography and MRI with heterogeneous enhancement and washout kinetics on MRI [ 8 ].

IMPC had tendency to manifest as a palpable mass, larger in size and higher in grade than IDC with more rate of lymphovascular invasion (LVI) and lymph node (LN) involvement, which changes the surgical and adjuvant management plans to more aggressive, with comparative prognosis still being a point of ongoing debate [ 9 ].

In this study, we compared the clinicopathological characteristics, survival and surgical management of breast cancer patients having invasive micropapillary carcinoma pathological subtype in comparison to those having invasive ductal carcinoma.

Patient and method

This is a comparative study on female patients presented to Baheya center for early detection and treatment of breast cancer, in the period from 2015 to 2022 diagnosed with breast cancer of IMPC subtype in one group compared with another group of invasive duct carcinoma.

This retrospective study analyzed 138 cases of IMPC and 500 cases of IDC. Informed consent was obtained from all patients. Ethical approval is obtained from Baheya center for early detection and treatment of breast cancer and National research center ethics committee. Baheya IRB protocol number:202305150022.

The following clinical-pathological features were analyzed for each case: patient age at diagnosis, clinical presentation, laterality, imaging findings, histopathological examination, treatment plan with either primary surgical intervention or other treatment protocol according to tumor stage and biological subtypes.

A breast pathologist evaluated the tumor size, type, grade, lymphovascular invasion, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) receptor and the axillary lymph node involvement.

According to the ASCO/CAP guideline update, 2019: Samples with 1% to 100% of tumor nuclei positive for ER or progesterone receptor (PgR) are interpreted as positive. If ER (not PgR), 1% to 10% of tumor cell nuclei are immunoreactive, the sample are reported as ER Low Positive. There are limited data on the overall benefit of endocrine therapies for patients with low level (1%-10%) ER expression, but they currently suggest possible benefit, so patients are considered eligible for endocrine treatment. A sample is considered negative for ER or PgR if < 1% or 0% of tumor cell nuclei are immunoreactive [ 10 ]. An Allred score between 0 and 8. This scoring system looks at what percentage of cells test positive for hormone receptors, along with how well the receptors show up after staining, called intensity: proportion of cells staining (0, no staining; 1, < 1%; 2, between 1 and 10%; 3, between 11 and 33%; 4, between 34 and 66% and 5, between 67%–100% of the cells staining). Intensity of positive tumor cells (0, none; 1, weak, 2, intermediate; and 3, strong) [ 11 ].

HER2 Test Guideline IHC Recommendations, 2018. IHC 0: as defined by no staining observed or membrane staining that is incomplete and is faint/barely perceptible and within <  = 10% of the invasive tumor cells. IHC 1 + : as defined by incomplete membrane staining that is faint/barely perceptible and within > 10% of the invasive tumor cells. IHC 2 + : The revised definition of IHC 2 + (equivocal) is weak to moderate complete membrane staining observed in > 10% of tumor cells. IHC 3 + : based on circumferential membrane staining that is complete, intense in > 10% of tumor cells. [ 12 ].

ASCO–CAP HER2 SISH Test Guideline Recommendations,2018 Twenty nuclei (each containing red (Chr17) and black (HER2) signals) should be enumerated. The final results for the HER2 status are reported based on the ratio formed by dividing the sum of HER2 signals for all 20 nuclei divided by the sum of Chromosome 17 signals for all 20 nuclei. The amplification status is defined as Amplified if the HER2/Chromosome 17 ratio > / = 2.0 and the average Her2 gene copy number is > / = 4.0. It is non-Amplified if the HER2/Chromosome 17 ratio < 2.0 with the Her2 gene copy number is < 4.0. If the HER2/Chr17 ratio is < 2 and the Her2 gene copy number is between 4.0 and 6.0, or, HER2/Chr17 ratio is > / = 2 and the Her2 gene copy number is < 4, or HER2/Chr17 ratio is < 2 and the Her2 gene copy number is > / = 6.0, an additional work should be done. [ 12 ].

Follow-up duration was calculated from the date of diagnosis to the date of the last follow-up. Patients still alive at the last follow-up censored or to the date of occurrence of any event or death.

Disease-free survival was defined as the duration (months) from the initial diagnosis of breast cancer to first any type of recurrence (invasive ipsilateral breast tumor recurrence, local invasive recurrence, regional invasive recurrence, invasive contra lateral breast cancer, distant metastasis.

Overall survival (OS) is defined as the time from diagnosis of breast cancer to death from any cause.

Data were statistically analyzed using an IBM-compatible personal computer with Statistical Package for the Social Sciences (SPSS) version 23. Quantitative data were expressed as mean, standard deviation (SD) and range (minimum–maximum). Qualitative data were expressed as Number (N) and percentage (%), while A P value of < 0.05 was statistically significant. For comparison of unmatched data, chi-square tests were used for categorical variables and t-tests or Mann–Whitney tests for continuous variables.

In this study, we analyzed 138 cases of IMPC which presented to our center in the period from 2015 to 2022.We included a total number of 500 cases of IDC as controls with a ratio of controls to cases 4:1.

Propensity score matching (PSM) is a method for filtrating experimental and control cases of similar characteristics, which are called the matching variables, from existing data to make them comparable in a retrospective analysis. PSM reduce the effect of selection bias. So, the comparison of outcomes between two groups can be fair.

The variables for propensity score matching were selected as follows: age (years), tumour size (cm), nodal status, HR status and HER2 status.

To diminish the effects of baseline differences and potential confounds in clinical characteristics and patients across histology subtypes for outcome differences (disease-free survival and overall survival), PSM method was applied with each micropapillary patient matched to one IDC patient who showed similar baseline characteristics in terms of: menopausal status, comorbidities, multiplicity, histologic grade, tumor size, stage, nodal status, ER /PR status. Differences in prognosis were assessed by Kaplan–Meier analysis.

Most of the patients were postmenopausal, the mean age of patients in IMPC group was 57.36 ± 11.321 years while the mean age of the IDC group was 56.63 ± 9.719 years ( p  = 0.45) (Table 1 ).

The most common presentation of IMPC on breast mammography was an irregular shaped mass with a non-circumscribed spiculated margin. while, the most common sonographic finding of IMPC was hypoechoic mass with irregular shapes and spiculated margins. Associated microcalcifications were found in 49 patients (35.5%) of IMPC group. Figs. ( 1 , 2 ): Radiological characteristics of IMPC.

A , B 37-years-old female patient presented with Left breast UOQ extensive fine pleomorphic and amorphous calcifications of segmental distribution, with UOQ multiple indistinct irregular masses. C ultrasound showed left breast UOQ multiple irregular hypoechoic masses with calcific echogenic foci, the largest is seen at 1 o’clock measuring 13 × 15mm. Intraductal echogenic lesions are noted

A , B , C 40-years-old female patient presented with left UOQ extensive pleomorphic microcalcifications of segmental distribution reaching the areola, with multiple well-circumscribed small obscured masses. D , E complementary Ultrasound showed left 2 o’clock multiple ill-defined and well-defined hypoechoic masses (BIRADS 5)

All patients underwent axillary sonography where 77 patients (55.8%) of the IMPC group exhibited pathological lymph nodes and 18 patients (13%) had indeterminate lymph nodes demonstrating preserved hila and associated with either a symmetrical increase of their cortical thickness reaching 3mm or with a focal increase in the cortical thickness.

Multiple lesions were detected in 30% of IMPC patients in comparison to 7% of IDC patients. Intra-ductal extension with nipple involvement was found in 44 patients (31.9%) of the IMPC group (Table 2 ).

MRI was done for 5 cases (3.6%), while CESM was performed for 18 cases (13%) of the IMPC group, the commonest presentation of IMPC in contrast study was irregular shaped enhanced mass in 21 patients and non-mass enhancement was found in 5 patients. Figs. ( 3 , 4 ).

Further imaging modalities. A , B , C 60-years-old female patient had right breast irregular hypoechoic solid mass by ultrasound (BIRADS 5). D , E CESM showed a right breast irregular heterogeneously enhancing solid mass

Role of CESM in diagnosis of IMPC patients. A , B 42-years-old patient presented with a left LIQ irregular spiculated mass with suspicious microcalcifications, other similar lesions were seen anterior and posterior at the same line. C Ultrasound showed a heterogeneously hypoechoic irregular mass with a spiculated outline with multiple similar satellite lesions were seen anterior and posterior to the main lesions

The average tumor size in the IMPC and IDC groups was 3.37 ± 2.04 cm and 2.72 ± 1.39 cm, respectively ( P  < 0.001).

The percentage of tumors larger than 5cm, was reported 9.5% in IMPC and 7.4% in IDC.

The pure form of IMPC was the most common type and found in 90 cases (65%) and 47 cases (34%) were mixed type where IDC was the commonest associated type.

There are 6 cases in the IMPC group diagnosed as invasive mucinous carcinoma on biopsy, then in the specimen was mixed invasive micropapillary, IBC-NST and invasive mucinous carcinoma.

On core biopsy, 28 cases were diagnosed as IMPC with focal IDC component, but in corresponding specimens 10 cases were only approved to be mixed invasive micropapillary and invasive duct carcinoma, while others diagnosed as pure invasive micropapillary carcinoma without IDC component.

On the other hand, 48 of our cases were diagnosed as IDC on core biopsy, but in the final specimen examination, 17 of these cases were diagnosed as pure invasive micropapillary carcinoma without invasive ductal component.

The explanation of controversy in proper histologic subtyping of carcinoma on core biopsy and the definite subtype on the corresponding specimen was that the ductal component which only represented in the biopsy is a very minor component of the tumor or the limited sampling, tissue fragmentation and architecture distortion in core biopsy may cause diagnostic pitfalls as regard precise subtyping of the tumor.

The incidence of LVI in the IMPC group was 88.3% in comparison to 47.0% in the IDC group ( p  < 0.001).

IMPC had a higher incidence of lymph node involvement than the IDC group (68.8% and 56% respectively) with N3 stage reported in 12.4% of IMPC patients.

IMPC had a higher nuclear grade than the IDC group (25.1% and 15.2% respectively).

The percentage of ER-positive patients was 97.8% in the IMPC group and 87.6% in the IDC group ( p  < 0.001), while PR-positive cases were 98.6% in the IMPC group and 88.8% in the IDC group ( p  < 0.001). HER2 status was positive in 4.3% of IMPCs and 8% of IDCs ( p  = 0.23) (Table 3 ) (Figs. 5 ,  6 ).

A case of invasive micropapillary carcinoma. A case of invasive micropapillary carcinoma, grade II. A Tissue core biopsy, × 100, B MRM specimen × 100 with Positive metastatic L. nodes 2/15, C ER is positive in > 90% of tumor cells, × 100, D PR is positive in > 90% of tumor cells, × 400, E HER2/neu is negative, × 400 and F) Ki-67 labelling index is high, × 200. This case was considered as luminal type pure invasive micropapillary carcinoma. (100 micron 20__ 50 micron 40)

A case of invasive duct carcinoma. A case of invasive duct carcinoma, grade II. A Tissue core biopsy, × 100, B MRM specimen, × 200 with negative L. nodes 0/16, C ER is positive in > 90% of tumor cells, × 200, D PR is positive in > 90% of tumor cells, × 100, E HER2/neu is negative, × 400. This case was considered as luminal type pure invasive duct carcinoma

Regarding definitive surgical management, IMPC had a lower rate of breast conserving surgery (26% vs.37.8%) compared with IDC. While, 49.3% of IMPC patients underwent modified radical mastectomy in comparison to 46% of the IDC patients. Such high incidence of mastectomy was due to the advanced stage at presentation, presence of multiple lesions and presence of intra-ductal extension with nipple involvement.

The incidence of re-surgery in the IMPC group was only in 3 cases, two of them underwent completion mastectomy after the initial conservative breast surgery and axillary clearance. While one patient underwent wider margin excision as positive margin for an invasive residual disease was found.

Two patients in the IMPC group had distant metastasis at the initial diagnosis, they had multiple metastatic lesions and received systemic treatment but one of them underwent palliative mastectomy.

Systemic chemotherapy was administered to 107 patients (77.5%) in the IMPC group and to 207 patients (41%) in the IDC group. Hormonal therapy was administered to all IMPC patients and 76% patients in the IDC group (Table 4 ).

The overall median follow-up duration was 21 months (range 6 – 88 months) with mean follow up duration = 29.8months.

Among the 138 IMPC patients, local recurrence developed in 3 cases, they developed a recurrence at 6,18 and 48 months postoperative. Distant metastasis developed in 5 patients in the form of bone, lung, hepatic and mediastinal lymph node metastasis.

The survival analysis indicated that IMPC patients had no significant difference in overall survival compared with IDC patients and no differences were noted in locoregional recurrence rate comparing IMPCs with IDCs (2.2% and 0.4% respectively). P value for local recurrence = 0.12 (yates corrected chi square).

Distant metastasis rate comparing IMPCs with IDCs was (3.7% and 5.4% respectively). P value for distant metastasis = 0.53 (Table 5 ).

Comparison of OS between IDC and micropapillary cases (Matched by propensity score matching -PSM).

Case Processing Summary

Overall Comparisons

• Test of equality of survival distributions for the different levels type

Disease free survival

• Test of equality of survival distributions for the different levels of type

IMPC is a highly invasive type of breast cancer. Hashmi A.A. et al. [ 13 ] found that the incidence of IMPC is very low accounting for 0.76–3.8% of breast carcinomas.

Shi WB et al.; [ 7 ] in a study comparing 188 IMPC cases and 1,289 invasive ductal carcinoma (IDC) cases from China showed that IMPC can occur either alone or mixed with other histological types, such as ductal carcinoma in situ, mucinous carcinoma and IDC. Furthermore, the majority of patients had mixed IMPC.

Fakhry et al. [ 14 ] reported that 64.7% of IMPC patients were pure type. In our study, we found that the pure form of IMPC was the commonest type and presented in 90 patients (65%) and 47 cases (34%) were mixed type which was similar to that reported by Nassar et al. [ 15 ], and Guo et al. [ 16 ] in their studies.

In our study, the commonest finding of IMPC on breast mammography was an irregular shaped mass with a non-circumscribed spiculated margin. While, the commonest sonographic finding of IMPC was hypoechoic mass with irregular shapes and spiculated margins.

These findings were similar to the results demonstrated by Jones et al., [ 17 ] which found that the commonest morphologic finding of IMPC was an irregular high-density lesion (50% of patients) with spiculated margin (42% of patients). However, Günhan-Bilgen et al. [ 18 ] reported that an ovoid or round lesion was found in 53.8% of patients.

Alsharif et al., [ 19 ] reported that the commonest sonographic finding of IMPC was hypoechoic masse (39/41, 95%) with irregular shape (30/41, 73.2%) and angular or spiculated margin (26/41, 63.4%).

In our study, MRI was done for 5 cases (3.6%), while CESM was performed for 18 cases (13%) of the IMPC group, the commonest presentation of IMPC in contrast study was irregular shaped enhanced lesion in 21 cases and non-mass enhancement was presented in 5 cases.

Nangogn et al. [ 20 ] and yoon et al. [ 8 ] recorded that the commonest finding of IMPCs in MRI was spiculated irregular mass with early rapid initial heterogenous enhancement, indicating that the MRI findings correlated with the invasiveness of IMPC.

Fakhry et al. [ 14 ] conducted a study on 68 cases, out of which 17 cases underwent CEM. In all of these cases, the masses showed pathological enhancement, which was either in the form of mass enhancement (12/17 patients, 70.6%) or non-mass enhancement (4/17 patients, 23.5%). The majority of the enhanced masses were irregular in shape (11/12 patients, 91.7%).

All patients underwent axillary sonography and 77 patients (55.8%) of the IMPC group exhibited pathological lymph nodes; this percentage was similar to that recorded by Nangong et al. [ 20 ] which was 54.8% and lower than that recorded by Jones et al. [ 17 ] but higher than that of Günhan et al. [ 18 ] which were 67% and 38% respectively.

Günhan et al. [ 18 ] reported microcalcification in about 66.7% of the cases. In our study, associated microcalcifications were found in 49 patients (35.5%) of the IMPC group. Yun et al. [ 21 ] and Adrada et al. [ 22 ] showed a fine pleomorphic appearance (66.7% and 68%).

Hao et al. [ 23 ] compared the rate of tumors larger than 5cm, reporting 3% in IDC and 4.3% in IMPC. In our study, the rate of tumors larger than 5cm, was reported 7.4% in the IDC patients and 9.5% in the IMPC patients.

Yu et al., et al. [ 24 ] documented in a study comparing 72 cases of IMPC and 144 cases of IDC of the breast that IMPC had a higher nuclear grade than IDC (52.8% vs. 37.5% respectively). In our study, IMPC had a higher nuclear grade than the IDC group (25.1% and 15.2% respectively).

Verras GI et al.; [ 9 ] demonstrated that IMPC was an aggressive breast cancer subtype with a great tendency to lymphovascular invasion and lymph node metastasis. In our study, the incidence of LVI in the IMPC patients was 88.3% in comparison to 47.0% in the IDC patients ( p  < 0.001). Tang et al., [ 25 ] also reported that lymphovascular involvement was more common among the IIMPC group than IDC group, with a percentage of 14.7% compared to only 0.1% in the IDC group.

Also, Shi et al. [ 7 ] reported that LVI was detected in 74.5% of cases. Furthermore, the frequency of LVI was found to be greater in IMPC cases when compared to IDC cases. Jones et al., [ 17 ] recorded angiolymphatic invasion in 69% of cases.

Hashmi et al. [ 13 ] reported in his comparative study that nodal involvement was present in 49.5% of IDC patients and N3 stage was only 15.6% in IDC patients compared to 33% in IMPC patients. In our study, the percentage of lymph node involvement of IMPC and IDC patients were 68.8% and 56% respectively with N3 stage reported in 12.4% of IMPC patients.

Guan et al. [ 26 ], Lewis et al., [ 27 ], Pettinato et al., [ 28 ] and De La Cruz et al., [ 29 ] recorded a higher percentage of lymph node metastasis in IMPC patients, reaching 90%, 92.9%,55.2% and 60.9% respectively.

The management of IMPC remains controversial, particularly among breast surgeons. Modified radical mastectomy was the preferred surgical procedure for the majority of IMPC case reports, as found in a study conducted by Yu et al., [ 24 ] where 99% of IMPC cases underwent modified radical mastectomy. Fakhry et al. [ 14 ] reported that 76.5% of the patients underwent modified radical mastectomy. In our study, 49.3% of IMPC patients received modified radical mastectomy.

IMPC patients were also prone to accept BCS rather than mastectomy in the previous series conducted by Lewis GD,et al. [ 27 ] and Vingiani, A. et al. [ 30 ]. However, the precise prognosis value of BCS for patients with IMPC remained unknowable. In our study, IMPC had a lower rate of breast conserving surgery (26% vs.37.8%) compared with IDC.

IMPC was characterized by a high incidence of ER and PR positivity. Our study recorded a high percentage of ER (97.8%) and PR (98.6%) expression. Our findings are similar to those found by Walsh et al., [ 31 ] who reported ER and PR expression of 90% and 70%, respectively. Zekioglu et al. [ 32 ] demonstrated a rate of ER and PR expression of 68% and 61%respectively.

In this study, we reported a relatively lower percentage of HER-2 positivity (4.3%). Also, Nangong et al. [ 20 ] showed HER 2 overexpression in 26.4% of cases.

However, Cui et al. [ 33 ] reported a much higher incidence of HER 2 positivity and Perron et al., [ 34 ] reported that 65% of IMPCs were HER-2 positive.

Chen, A et al. [ 35 ] reported that that the percentage of radiation therapy for IMPC patients was similar to those seen in IDC patients and demonstrates a similar benefit of radiation treatment in both groups. In our study,77.5% patients received radiotherapy in IMPC group in compared to 59.4% patients in IDC group.

Shi et al. [ 7 ] found that patients with IMPC had worse recurrence-free survival (RFS) and overall survival (OS) rates as compared to those with IDC. However, because IMPC is relatively rare, most studies had reported on small sample sizes with limited follow-ups.

Yu et al., [ 24 ] conducted a comparison between IMPC and IDC patients, and the results showed that the IMPC group had a greater tendency for LRR compared to the IDC group ( P  = 0.03), but the distant metastasis rate ( P  = 0.52) and OS rate ( P  = 0.67) of the IMPC showed no statistical differences from the IDC group.

Nevertheless, several recent studies documented that IMPC had better or similar prognosis in comparison to IDC.

Hao et al. [ 23 ] and Vingiani et al. [ 30 ] documented that there was no statistically significant difference in OS and disease-free survival between IMPC patients and IDC patients which was similar to our results. locoregional recurrence rate comparing IMPCs with IDCs was (2.2% and 0.4% respectively). P value for local recurrence = 0.12 (yates corrected chi square). Distant metastasis rate comparing IMPCs with IDCs was (3.7% and 5.4% respectively). P value for distant metastasis = 0.53.

Chen H et al. [ 36 ], compared the overall survival in patient groups with similar nodal involvement and found that IMPC group had better breast cancer–specific survival and overall survival than IDC group.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Invasive micropapillary carcinoma

Invasive duct carcinoma

Modified radical mastectomy

Conserving breast surgery

Estrogen receptor

Progesterone receptor

Lymphovascular invasion

Contrast enhanced spectral mammography

Overall survival

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Mohamed Fathy Abdelfattah Abdelrahman Elithy

Present address: Department of Surgical Oncology, Faculty of Medicine, Al Azhar University, Cairo, Egypt

Mahmoud Hassaan

Present address: Departement of Surgical Oncology, National Cancer Institute, Cairo University, Giza, Egypt

Authors and Affiliations

Department of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt

Yasmine Hany Abdel Moamen Elzohery

Department of Radiodiagnosis, NCI, Cairo University, Giza, Egypt

Amira H. Radwan & Sherihan W. Y. Gareer

Department of Pathology, National Cancer Institute, Cairo University, Giza, Egypt

Mona M. Mamdouh

Department of Epidemiology and Preventive Medicine, National Liver Institute, Menoufia, Egypt

Baheya Center for Early Detection and Treatment of Breast Cancer, Giza, Egypt

Yasmine Hany Abdel Moamen Elzohery, Amira H. Radwan, Sherihan W. Y. Gareer, Mona M. Mamdouh, Inas Moaz, Abdelrahman Mohammad Khalifa, Osama Abdel Mohen, Mohamed Fathy Abdelfattah Abdelrahman Elithy & Mahmoud Hassaan

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Mohamed fathy participated in the sequence alignment and Yasmine hany drafted the manuscript. Mahmoud Hassan participated in the design of the study. Inas Moaz and Abdelrahman Mohammad performed the statistical analysis. Amira H. Radwan and Sherihan WY Gareer conceived the study. Mona M Mamdouh and Osama abdel Mohen participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yasmine Hany Abdel Moamen Elzohery .

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Elzohery, Y.H.A.M., Radwan, A.H., Gareer, S.W.Y. et al. Micropapillary breast carcinoma in comparison with invasive duct carcinoma. Does it have an aggressive clinical presentation and an unfavorable prognosis?. BMC Cancer 24 , 992 (2024). https://doi.org/10.1186/s12885-024-12673-0

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Received : 05 April 2024

Accepted : 23 July 2024

Published : 12 August 2024

DOI : https://doi.org/10.1186/s12885-024-12673-0

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