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Understanding Data Presentations (Guide + Examples)

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

Table of Contents

What is a Data Presentation?

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

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

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

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

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

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

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

Real-Life Application of Bar Charts

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

Step 1: Selecting Data

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

The sales manager has highlighted these products for the presentation.

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

Step 2: Choosing Orientation

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

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

Step 3: Colorful Insights

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

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

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

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

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

Real-life Application of Line Graphs

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

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

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

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

Step 3: Connecting Trends

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

Step 4: Adding Clarity with Color

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

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

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

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

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

Real-Life Application of a Dashboard

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

Step 1: Defining Key Metrics

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

Step 2: Choosing Visualization Widgets

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

Step 3: Dashboard Layout

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

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

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

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

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

Real-Life Application of a Treemap Chart

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

Step 1: Define Your Data Hierarchy

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

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

Step 2: Choose a Suitable Tool

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

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

Step 3: Make a Treemap Chart with PowerPoint

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

Step 5: Input Your Data

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

Step 6: Customize the Treemap

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

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

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

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

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

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

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

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

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

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

Real-Life Application of Pie Charts

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

Step 1: Define Your Data Structure

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

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

Step 2: Insert a Pie Chart

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

For instance:

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

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

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

Step 03: Results Interpretation

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

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

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

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

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

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

Real-Life Application of a Histogram

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

Step 1: Gather Data

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

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

Step 2: Define Bins

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

Step 3: Count Frequency

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

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

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

Step 4: Create the Histogram

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

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

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

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

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

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

Real-Life Application of Scatter Plot

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

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

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

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

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

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

1. Fact Sheet Dashboard for Data Presentation

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

Use This Template

2. 3D Column Chart Infographic PPT Template

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

3. Data Circles Infographic PowerPoint Template

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

4. Colorful Metrics Dashboard for Data Presentation

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

5. Animated Data Presentation Tools for PowerPoint & Google Slides

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

6. Statistics Waffle Charts PPT Template for Data Presentations

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

7. Data Presentation Dashboard Template for Google Slides

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

8. Weather Dashboard for Data Presentation

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

9. Social Media Marketing Dashboard Data Presentation Template

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

10. Project Management Summary Dashboard Template

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

11. Profit & Loss Dashboard for PowerPoint and Google Slides

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

Overwhelming visuals

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

Inappropriate chart types

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

Lack of context

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

Inconsistency in design

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

Failure to provide details

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

Lack of focus

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

Visual accessibility issues

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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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How to Present Data Effectively

You’re sitting in front of your computer and ready to put together a presentation involving data.   The numbers stare at you from your screen, jumbled and raw.   How do you start?   Numbers on their own can be difficult to digest. Without any context, they’re just that—numbers.   But organize them well and they tell a story.   In this blog post, we’ll go into the importance of structuring data in a presentation and provide tips on how to do it well. These tips are practical and applicable for all sorts of presentations—from marketing plans and medical breakthroughs to project proposals and portfolios. 

What is data presentation?

3 essential tips on data presentation, use the right chart, keep it simple, use text wisely and sparingly.

In many ways, data presentation is like storytelling—only you do them with a series of graphs and charts.  One of the most common mistakes presenters make is being so submerged in the data that they fail to view it from an outsider’s point of view.   Always keep this in mind: What makes sense to you may not make sense to your audience. To portray figures and statistics in a way that’s comprehensible to your viewers, step back, put yourself in their shoes, and consider the following: 

• How much do they know about the topic?
• How much information will they need?
• What data will impress them?

Providing a context helps your audience visualize and understand the numbers. To help you achieve that, here are three tips on how to represent data effectively.  

Whether you’re using Google Slides or PowerPoint, both come equipped with a range of design tools that help you help your viewers make sense of your qualitative data.  The key here is to know how to use them and how to use them well. In these tips, we’ll cover the basics of data presentation that are often overlooked but also go beyond basics for more professional advice. 

The downside of having too many tools at your disposal is that it makes selecting an uphill task.   Pie and bar charts are by far the most commonly used methods as they are versatile and easy to understand. 

If you’re looking to kick things up a notch, think outside the box. When the numbers allow for it, opt for something different. For example, donut charts can sometimes be used to execute the same effect as pie charts. 

But these conventional graphs and charts aren’t applicable to all types of data. For example, if you’re comparing numerous variables and factors, a bar chart would do no good. A table, on the other hand, offers a much cleaner look.

Pro tip : If you want to go beyond basics, create your own shapes and use their sizes to reflect proportion, as seen in this next image.

Their sizes don’t have to be an exact reflection of their proportions. What’s important here is that they’re discernible and are of the same shape so that your viewers can grasp its concept at first glance.  Note that this should only be used for comparisons with large enough contrasts. For instance, it’d be difficult to use this to compare two market sizes of 25 percent and 26 percent. 

When it comes to making qualitative data digestible, simplicity does the trick.  Limit the number of elements on the slide as much as possible and provide only the bare essentials. 

See how simple this slide is? In one glance, your eye immediately goes to the percentages of the donut because there are no text boxes, illustrations, graphics, etc. to distract you.  Sometimes, more context is needed for your numbers to make sense. In the spirit of keeping your slides neat, you may be tempted to spread the data across two slides. But that makes it complicated, so putting it all on one slide is your only option.  In such cases, our mantra of “keep it simple” still applies. The trick lies in neat positioning and clever formatting.  

In the above slides, we’ve used boxes to highlight supporting figures while giving enough attention to the main chart. This separates them visually and helps the audience focus better.  With the slide already pretty full, it’s crucial to use a plain background or risk overwhelming your viewers.  

Last but certainly not least, our final tip involves the use of text.  Just because you’re telling a story with numbers doesn’t mean text cannot be used. In fact, the contrary proves true: Text plays a vital role in data presentation and should be used strategically.  To highlight a particular statistic, do not hesitate to go all out and have that be the focal point of your slide for emphasis. Keep text to a minimum and as a supporting element. 

Make sure your numbers are formatted clearly. Large figures should have thousands separated with commas. For example, 4,498,300,000 makes for a much easier read than “4498300000”. Any corresponding units should also be clear.  With data presentation, don’t forget that numbers are still your protagonist, so they must be highlighted with a larger or bolder font.  Where there are numbers and graphics, space is scarce so every single word must be chosen wisely.   The key here is to ensure your viewers understand what your data represents in one glance but to leave it sufficiently vague, like a teaser, so that they pay attention to your speech for more information.  → Slidesgo’s free presentation templates come included with specially designed and created charts and graphs that you can easily personalize according to your data. Give them a try now! 

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

Leah Nguyen • 05 April, 2024 • 17 min read

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

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

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

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

Table of Contents

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

#3 – Pie chart

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

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

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What are Methods of Data Presentation?

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

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

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

Good data presentation helps…

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

Methods of Data Presentation and Examples

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

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

#1 – Tabular 

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

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

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

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

(Source: CustomerThermometer )

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

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

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

Bonus example: A literal ‘pie’ chart! 🥧

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

They can be as simple as this:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5 Data Presentation Mistakes to Avoid

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

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

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

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

#2 – Use the wrong type of chart

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

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

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

#3 – Make it 3D

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

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

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

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

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

#5 – Bombard the audience with too much information

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

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

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

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

For example:

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

What is chart presentation?

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

When can I use charts for presentation?

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

Why should use charts for presentation?

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

What are the 4 graphical methods of presenting data?

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

Leah Nguyen

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

Tips to Engage with Polls & Trivia

More from AhaSlides

Data presentation: A comprehensive guide

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

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

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

What is data presentation?

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

Why are data presentations important?

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

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

Types of data presentation:

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

1. Textual presentation

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

2. Tabular presentation

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

3. Graphical presentation

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

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

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

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

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

What to include in data presentation?

When creating your data presentation, remember these key components:

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

How to structure an effective data presentation?

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

1. Know your audience

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

2. Have a clear message

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

3. Tell a compelling story

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

4. Leverage visuals

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

5. Be clear and concise

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

6. Practice your delivery

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

A basic structure for an effective data presentation

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

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

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

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

Additional tips for enhancing your data presentation

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

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

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

Do’s and don'ts on a data presentation

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

Don'ts:

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

Summarizing key takeaways

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

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

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

2. What are some common methods of data presentation?

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

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

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

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

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

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

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

Create your data presentation with prezent

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

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

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

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

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

Sign up for our free trial or book a demo ! ‍

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When and how should you use data in a presentation?

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

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

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

Remember that You Are Telling Your Audience a Story

All presentations are basically story-telling opportunities.

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

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

Use Data to Tell the Story

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

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

‘ What is the story in this data? ’,

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

A Picture Tells a Thousand Words

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

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

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

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

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

Consider this set of data:

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

Now consider the same data in a pie chart:

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

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

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

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

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

KISS: Keep It Simple, Stupid!

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

But does it really help to make your point?

Then don’t present it.

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

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

Leave It Out...

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

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

Remember KISS: Keep It Simple, Stupid.

Highlight the Main Features to Draw Out the Insights

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

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

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

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

Take-home message

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

In other words:

‘What happened there?’

‘What are we going to do about it?’

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

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

Continue to: Writing Your Presentation Working with Visual Aids

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

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10 Tips for Presenting Data

Big data. Analytics. Data science. Businesses are clamoring to use data to get a competitive edge, but all the data in the world won’t help if your stakeholders can’t understand, or if their eyes glaze over as you present your incredibly insightful analysis . This post outlines my top ten tips for presenting data.

It’s worth noting that these tips are tool agnostic—whether you use Data Studio, Domo, Tableau or another data viz tool, the principles are the same. However, don’t assume your vendors are in lock-step with data visualization best practices! Vendor defaults frequently violate key principles of data visualization, so it’s up to the analyst to put these principles in practice.

Here are my 10 tips for presenting data:

• Recognize that presentation matters
• Don’t scare people with numbers
• Maximize the data pixel ratio
• Save 3D for the movies
• Friends don’t let friends use pie charts
• Choose the appropriate chart
• Don’t mix chart types for no reason
• Don’t use axes to mislead
• Never rely solely on color
• Use color with intention

1) Recognize That Presentation Matters

The first step to presenting data is to understand that how you present data matters . It’s common for analysts to feel they’re not being heard by stakeholders, or that their analysis or recommendations never generate action. The problem is, if you’re not communicating data clearly for business users, it’s really easy for them to tune out.

Analysts may ask, “But I’m so busy with the actual work of putting together these reports. Why should I take the time to ‘make it pretty’?”

Because it’s not about “making things pretty.” It’s about making your data understandable.

My very first boss in Analytics told me, “As an analyst, you are an information architect.” It’s so true. Our job is to take a mass of information and architect it in such a way that people can easily comprehend it.

Take these two visuals. The infographic style shows Top 10 Salaries at Google. The first one is certainly “prettier.” However, the visual is pretty meaningless, and you have to actually read the information to understand any of it. (That defeats the purpose of a data viz!)

Pretty, but not helpful

On the flip side, the simpler (but far less pretty) visualization makes it very easy to see:

• Which job category pays the most
• Which pays the least
• Which has the greatest range of salaries
• Which roles have similar ranges

It’s not about pretty. When it comes to presenting data clearly, “informative” is more important than “beautiful.”

Just as we optimize our digital experiences, our analyses must be optimized to how people perceive and process information. You can think of this as a three-step process:

• Information passes through the Visual Sensory Register . This is pre-attentive processing—it’s what we process before we’re even aware we’re doing so. Certain things will stand out to us, objects may get unconsciously grouped together.
• From there, information passes to Short Term Memory. This is a limited capacity system, and information not considered “useful” will be discarded. We will only retain 3-9 “chunks” of visual information. However, a “chunk” can be defined differently based on how information is grouped. For example, we might be able to remember 3-9 letters. But, we could also remember 3-9 words, or 3-9 song lyrics! Your goal, therefore, is to present information in such a way that people can easily “chunk” information, to allow greater retention through short-term memory. (For example, a table of data ensures the numbers themselves can’t possibly all be retained, but a chart that shows our conversion rate trending down may be retained as one chunk of information—“trending down.”)
• From short-term memory, information is passed to Long-Term Memory. The goal here is to retain meaningful information—but not the precise details.

2) Don’t Scare People with Numbers

Analysts like numbers. Not everybody does! Many of your stakeholders may feel overwhelmed by numbers, data, charts. But when presenting data, there are little things you can do to make numbers immediately more “friendly.”

Simple formatting

Don’t make people count zeros in numbers! (e.g. 1000000 vs. 100,000,000).

Skip unnecessary decimals

How many decimals are “necessary” depends on the range of your values. If your values range from 2 to 90 percent, you don’t need two decimals places.

But on the flip side, if you have numbers that are really close (for example, all values are within a few percent of each other) it’s important to include decimal places.

Too often, this comes from confusing “precision” with “accuracy.” Just because you are more precise (in including more decimal places) doesn’t make your data more accurate. It just gives the illusion of it.

Right align numbers

Always right-align columns of numbers. This is the default in many solutions, but not always. What it allows for is your data to form a “quasi bar chart” where people can easily scan for the biggest number, by the number of characters. This can be harder to do if you center-align.

3) Maximize the Data-Pixel Ratio

The Data-Pixel Ratio originally stems from Edward Tufte’s “Data-Ink Ratio”, later renamed the “Data-Pixel Ratio” by Stephen Few. The more complicated explanation (with an equation, GAH!) is:

A simpler way of thinking of it: Your pixels (or ink) should be used for data display, and not for fluff or decoration. (I like to explain that I’m just really stingy with printer ink—so, I don’t want to print a ton of wasted decorations.)

Here are some quick transformations to maximize the data-pixel ratio:

Avoid repeating information

For example, if you include the word “Region” in the column header, there’s no need to repeat the word in each cell within the column. You don’t even need to repeat the dollar sign. Once we know the column is in dollars, we know all the values are too.

Avoid repeating information when presenting data

For bar and column charts:

• Remove borders (that Excel loves to put in by default, and Google Sheets still doesn’t let you remove them, grumble grumble.)
• Display information horizontally. Choosing a bar over a column chart can make the axis easier to read.
• Condense axes, to show values “in Millions” or “in K”, rather than unnecessarily repeating zeros (“,000”)

For line charts:

• Remove unnecessary legends. If you only have one series in a line chart, the title will explain what the chart is—a legend is duplicated information.
• Grey (or even remove) grid lines. While sometimes grid lines can be useful to help users track across to see the value on the y-axis, the lines don’t need to be heavy to guide the eyes (and certainly not as visually important as the data).

4) Save 3D for the Movies

These two charts have the same information. In the top left one, you can see at a glance that the bar is slightly above $150,000. In the bottom one, you can “kind of sort of tell” that it’s at $150,000, but you have to work much harder to figure that out. With a 3D chart you’re adding an extra cognitive step, where someone has to think about what they’re looking at.

And don't even get me started on this one:

However, I’ll concede: there is an exception to every rule. When is 3D okay? When it does a better job telling the story , and isn’t just there to make it “snazzy.” For example, take this recent chart from the 2016 election: 3D adds a critical element of information, that a 2D version would miss.

5) Friends Don’t Let Friends Use Pie Charts

It’s easy to hate on pie charts (and yet, every vendor is excited to announce that they have ZOMG EXPLODING DONUT CHARTS! just added in their recent release).

However, there are some justified reasons for the backlash against the use (and especially, the overuse) of pie charts when presenting data:

• We aren’t as good at judging the relative differences in area or circles, versus lines . For example, if we look at a line, we’re more easily able to say “that line is about a third bigger.”We are not adept at doing this same thing with area or circles, so often a bar or column chart is simply easier for us to process.
• They’re used incorrectly . Pie charts are intended to show “parts of a whole”, so a pie chart that adds up to more than 100% is a misuse of the visualization.
• They have too many pieces . Perhaps they do add up to 100%, but there’s little a pie chart like this will do to help you understand the data.

With that understood, if you feel you must use pie charts, the following stipulations apply:

• The pie chart shouldn’t represent more than three items.
• The data has to represent parts of a whole (aka, the pieces must add to 100%).
• You can only use one. As soon as you need to compare data (for example, three series across multiple years) then pie charts are a no-go. Instead, go for a stacked bar chart.

Like 3D, pie charts are acceptable when they are the best possible way for presenting data and getting your message across. This is an example of where, hands-down, a pie chart is the right visualization:

6) Choose the Appropriate Chart for Presenting Data

A chart should be carefully chosen, to convey the message you want someone to take from your data presentation. For example, are you trying to show that the United States and India’s average order value are similar? Or that India’s revenue is trending up more quickly? Or that Asia is twice the rest of the world?

For a more comprehensive guide, check out Extreme Presentation’s Chart Chooser. But in the meantime, here is a quick version for some commonly used charts:

Line charts

Use line charts to demonstrate trends. If there are important things that happened, you can also highlight specific point

Bar or column charts

Bar or column charts should be used to emphasize the differences between things.

If you don’t have much space, you might consider using sparklines for presenting data trends. Sparklines are a small chart contained within a single cell of a table. (You can also choose to use bar charts within your data table.)

Here are some resources on how to build sparklines into the different data viz platforms:

Google Sheets

7) Don’t Mix Chart Types for No Reason

I repeat. Don’t mix chart types for no reason . Presenting data sets together should tell a story or reveal insights together, that isn’t possible if left apart. Unfortunately, far too many charts involving cramming multiple data series on them is purely to conserve the space of adding another chart. The problem is, as soon as you put those two series of data together, your end users are going to assume there’s a connection between them (and waste valuable brain power trying to figure out what it is).

Below are good and bad examples of mixing chart types when presenting data. On the first, we have a column and line chart together, because we’re trying to demonstrate that the two metrics trend similarly. Together they are telling a story, that they wouldn’t tell on two separate charts.

The second, however, is an example of “just trying to fit two series onto a chart.”

For the second chart, a better option for presenting the data might be to have two side-by-side bar or column charts.

8) Don’t Use Axes to Mislead

“If you torture the data long enough, it will confess to anything” – Ronald Coase

One easy way to mislead readers is to change the axes of your data. Doing so quickly magnifies what might be small differences, and can distort the story your data is telling you. For example, starting the axis at 155,000 makes the differences between the highs and lows look more dramatic.

In the next example, the line chart doesn’t actually correspond to the axis! (Did you know 8.6 is more than 8.8?!)

The most truthful option is to always start your axes at zero. But sometimes, we need to show differences in metrics that don’t shift much over time. (For example, our conversion rate might range between 1.0% and 1.3% from month to month.) In that case, my recommendation would be to show the more truthful axis starting at zero, but provide a second view of the chart (a “zoomed in view”, so to speak) that shows a smaller range on the axis, so you can see the month-to-month change.

9) Never Rely Solely on Color When Presenting Data

Color is commonly used as a way to differentiate “good” vs. “bad” results, or “above” or “below” target. The problem is, about ten percent of the population is colorblind! And it’s not just red/green colorblind (though that’s the most common). There are many other kinds of colorblindness. As a result, ten percent of your stakeholders may actually not be comprehending your color scheme. (Not to mention, all black and white printers are “colorblind.”)

That doesn’t mean you can’t use any red or green (it can be an easily understood color scheme) when presenting data. But you do have to check that your data visualization is understandable by those with colorblindness, or if someone prints your document in black and white.

Additionally, there are also differences in how colors are perceived in different cultures. (For example, red means “death” in some cultures.) If you are distributing your data presentation globally, this is an additional factor to be conscious of.

10) Use Color with Intention

In the below chart, the colors are completely meaningless. (Or, as I like to call it, “rainbow barf.”)

Being careful with color also means using it consistently. If you are using multiple charts with the same values, you have to keep the colors consistent. Consider the tax on someone’s interpretation of your visualization if they constantly have to think “Okay, Facebook is blue on this chart, but it’s green on this other one.” Not only are you making them think really hard to do those comparisons, but more likely, they’re going to draw an incorrect conclusion.

So be thoughtful with how you use color! A good option can be to use brand colors. These are typically well-understood uses of color (for example, Facebook is blue, YouTube is red.) This may help readers understand the chart more intuitively.

(Data Studio only recently added a feature where you can keep the colors of data consistent across charts!)

Another user-friendly method of using color intentionally is to match your series color to your axis (where you have a dual-axis chart). This makes it very easy for a user to understand which series relates to which axis, without much thought.

Bonus Tip 11. Dashboards Should Follow The Above Data Visualization Rules

So, what about dashboards? Dashboards should follow all the same basic rules of presenting data, plus one important rule:

“A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.” -Stephen Few (Emphasis added.)

Key phrase: “on a single screen.” If you are expecting someone to look at your dashboard, and make connections between different data points, you are relying on their short-term memory. (Which, as discussed before, is a limited-capacity system.) So, dashboards must follow all the same data viz rules, but additionally, to be called a “dashboard”, it must be one page/screen/view. (So, that 8 page report is not a “dashboard”! You can have longer “reports”, but to truly be considered a “dashboard”, they must fit into one view.)

I hope these tips for presenting data have been useful! If you’re interested in learning more, these are some books I’d recommend checking out:

The Wall Street Journal Guide to Information Graphics

Information Dashboard Design

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10 Superb Data Presentation Examples To Learn From

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

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

On this page:

How to present data effectively? Clever tips.

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

Download the above infographic in PDF

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

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

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

1. Keep it simple and clear

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

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

2. Pick up the right visuals for the job

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

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

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

3. Break the complex concepts into multiple graphics

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

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

4. Carefully choose the colors

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

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

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

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

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

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

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

6. Use good templates and software tools

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

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

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

10 Superb data presentation examples 

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

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

1. Sales Stage History  Funnel Chart 

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

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

2. Facebook Ads Data Presentation Examples

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

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

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

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

3. Sales Opportunity Data Presentation

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

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

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

4. Power 100 Data Visualization 

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

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

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

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

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

6. Digital Health Research Data Visualization Example

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

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

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

7. Disease Research Data Visualization Examples

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

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

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

8. Textual and Qualitative Data Presentation Example

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

9. Product Metrics Graph Example

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

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

10. Supply Chain Data Visualization Example 

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

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

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

About The Author

Silvia Valcheva

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

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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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Using powerbi &tableau.

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• Korean J Anesthesiol
• v.70(3); 2017 Jun

Statistical data presentation

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

Sangseok Lee

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

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

Introduction

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

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

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

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

Data Presentation

Data can be presented in one of the three ways:

–as text;

–in tabular form; or

–in graphical form.

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

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

Text presentation

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

Table presentation

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

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

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

Heat maps for better visualization of information than tables

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

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

Graph presentation

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

Scatter plot

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

Bar graph and histogram

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

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

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

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

Line plot with whiskers

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

Box and whisker chart

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

Three-dimensional effects

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

Drawing a graph: example

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

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

Conclusions

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

<Appendix>

Output for presentation.

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

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

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

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

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

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

Bitmap and Vector

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

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

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

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

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

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

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

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

6) Joint Photographic Experts Group.

7) Portable Network Graphics.

8) Graphics Interchange Format

9) Tagged Image File Format; TIFF

10) Encapsulated PostScript.

11) Computer Graphics Metafile.

12) Scalable Vector Graphics.

13) Portable Document Format.

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Takeda’s TAK-861 Phase 2b Late-Breaking Data Presentations at SLEEP 2024 Demonstrate Clinically Meaningful Impact of Oral Orexin Agonist in Narcolepsy Type 1 Compared to Placebo

Phase 2b Trial Demonstrated Statistically Significant and Clinically Meaningful Improvements Across Primary and all Secondary Endpoints up to 8 Weeks

TAK-861 is the First Oral Orexin Receptor 2 Agonist to Potentially Address the Underlying Pathophysiology of NT1

Safety Results Indicated TAK-861 is Generally Safe and Well Tolerated

Phase 3 Trials of TAK-861 to be Initiated in 1H FY2024

OSAKA, Japan and CAMBRIDGE, Massachusetts, June 3, 2024 – Takeda ( TSE: 4502/NYSE:TAK ) will present today positive results from its Phase 2b trial of TAK-861 in narcolepsy type 1 (NT1) as late-breaking data presentations at SLEEP 2024, the 38th annual meeting of the American Academy of Sleep Medicine and the Sleep Research Society. TAK-861 is an investigational oral orexin receptor 2 (OX2R) agonist and, based on the results, has the potential to provide transformative efficacy in addressing the overall disease burden in people with NT1. The randomized, double-blind, placebo-controlled, multiple dose trial, TAK-861-2001 ( NCT05687903 Go to https://classic.clinicaltrials.gov/ct2/show/NCT05687903?term=TAK-861&draw=2&rank=3 ), in 112 patients with NT1 demonstrated statistically significant and clinically meaningful improvements across primary and secondary endpoints, with efficacy sustained over 8 weeks of treatment.*

NT1 is a chronic, rare neurological central disorder of hypersomnolence caused by a significant loss of orexin neurons, resulting in low levels of orexin neuropeptides in the brain and cerebrospinal fluid. No currently approved treatments target the underlying pathophysiology of NT1. People with NT1 suffer from excessive daytime sleepiness (EDS), cataplexy (sudden loss of muscle tone), disrupted nighttime sleep, hypnagogic and hypnopompic hallucinations and sleep paralysis. These debilitating symptoms lead to a markedly reduced quality of life and can severely impact job performance, academic achievement and personal relationships. TAK-861 is designed to address the orexin deficiency in NT1 by selectively stimulating the orexin receptor 2.

The presentation highlights results from the Phase 2b trial including:

The primary endpoint demonstrated statistically significant and clinically meaningful increased sleep latency on the Maintenance of Wakefulness Test (MWT) versus placebo across all doses (LS mean difference versus placebo all p ≤0.001). Improvements were sustained over 8 weeks.

Consistent results were achieved in the key secondary endpoints including the Epworth Sleepiness Scale (ESS) and Weekly Cataplexy Rate (WCR), demonstrating significantly improved subjective measures of sleepiness and cataplexy (sudden loss of muscle tone) frequency versus placebo that were also sustained over 8 weeks.

The majority of NT1 patients in the trial were found to be within normative ranges for MWT and ESS by the end of the 8-week treatment period as a result of these sustained improvements.

The majority of the participants who completed the trial enrolled in the long-term extension (LTE) study with some patients reaching one year of treatment.

The trial also included additional exploratory endpoints that showed meaningful improvements in narcolepsy symptoms and functioning according to most participants. These data will also be presented in poster presentations at SLEEP and at future scientific congresses.

The dataset showed that TAK-861 was generally safe and well tolerated during the study, with no treatment-related serious treatment-emergent adverse events (TEAEs) or discontinuations due to TEAEs.

No cases of hepatotoxicity or visual disturbances were reported in the Phase 2b trial or in the ongoing LTE study. The most common TEAEs were insomnia, urinary urgency and frequency, and salivary hypersecretion. Most TEAEs were mild to moderate in severity, and most started within 1-2 days of treatment and were transient.

“In this trial, TAK-861's profile balanced efficacy and safety with the potential to establish a new standard of care for people with NT1,” said Sarah Sheikh, M.D., M.Sc., B.M., B.Ch., MRCP, Head, Neuroscience Therapeutic Area Unit and Head, Global Development at Takeda. “We are dedicated to investigating the full potential of orexin biology and advancing TAK-861 to late-stage clinical trials, with the ultimate goal of delivering a potential first-in-class treatment that can make a meaningful difference for patients.”

Based on these results, and in consultation with global health authorities, Takeda plans to initiate global Phase 3 trials of TAK-861 in NT1 in the first half of its fiscal year 2024. The Phase 2b data also supported the recent Breakthrough Therapy designation for TAK-861 for the treatment of EDS in NT1 from the U.S. Food and Drug Administration (FDA). Breakthrough Therapy designation is a process designed to expedite the development and review of a drug that is intended to treat a serious or life-threatening condition, for which preliminary clinical evidence indicates that the drug may demonstrate substantial improvement over available therapies on at least one clinically significant endpoint.

Takeda will be hosting a call to discuss these data this evening, June 3, at 7:30 p.m. CT for investors and analysts. Presentation slides and a virtual meeting link will be available here .

Additional presentations on TAK-861 will be shared during the SLEEP 2024 poster presentation session on Tuesday, June 4, from 10:00 to 11:45 a.m. CT, assessing function and health-related quality of life in individuals with NT1, as well as patient satisfaction with TAK-861 treatment. There is no change in Takeda’s full year consolidated forecast for the fiscal year ending March 31, 2025 (FY2024), announced on May 9, 2024.

About Takeda’s Orexin Franchise

Takeda is advancing the field of orexin therapeutics with a multi-asset franchise offering tailored treatments to unlock the full potential of orexin. Orexin is a key regulator of the sleep-wake cycle and is involved in other essential functions, including respiration and metabolism. TAK-861 is the leading program in this franchise. The company is also progressing multiple orexin agonists in patient populations with normal levels of orexin neuropeptides and other indications where orexin biology is implicated. This includes TAK-360, an oral OX2R agonist being investigated for narcolepsy type 2 and idiopathic hypersomnia, which recently initiated a Phase 1 trial and received Fast Track designation from the U.S. FDA, and danavorexton (TAK-925), an intravenously administered OX2R agonist being investigated in a Phase 2 trial in patients with moderate to severe obstructive sleep apnea undergoing general anesthesia.

About Takeda

Takeda is focused on creating better health for people and a brighter future for the world. We aim to discover and deliver life-transforming treatments in our core therapeutic and business areas, including gastrointestinal and inflammation, rare diseases, plasma-derived therapies, oncology, neuroscience and vaccines. Together with our partners, we aim to improve the patient experience and advance a new frontier of treatment options through our dynamic and diverse pipeline. As a leading values-based, R&D-driven biopharmaceutical company headquartered in Japan, we are guided by our commitment to patients, our people and the planet. Our employees in approximately 80 countries and regions are driven by our purpose and are grounded in the values that have defined us for more than two centuries. For more information, visit www.takeda.com .

* The topline results were announced on February 8, 2024, via a press release, “Takeda Intends to Rapidly Initiate the First Global Phase 3 Trials of TAK-861, an Oral Orexin Agonist, in Narcolepsy Type 1 in First Half of Fiscal Year 2024."

Media Contacts:

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Yuko Yoneyama

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+81 70-2610-6609

U.S. and International Media

Rachel Wallace

Important Notice

For the purposes of this notice, “press release” means this document, any oral presentation, any question-and-answer session and any written or oral material discussed or distributed by Takeda Pharmaceutical Company Limited (“Takeda”) regarding this release. This press release (including any oral briefing and any question-and-answer in connection with it) is not intended to, and does not constitute, represent or form part of any offer, invitation or solicitation of any offer to purchase, otherwise acquire, subscribe for, exchange, sell or otherwise dispose of, any securities or the solicitation of any vote or approval in any jurisdiction. No shares or other securities are being offered to the public by means of this press release. No offering of securities shall be made in the United States except pursuant to registration under the U.S. Securities Act of 1933, as amended, or an exemption therefrom. This press release is being given (together with any further information which may be provided to the recipient) on the condition that it is for use by the recipient for information purposes only (and not for the evaluation of any investment, acquisition, disposal or any other transaction). Any failure to comply with these restrictions may constitute a violation of applicable securities laws. The companies in which Takeda directly and indirectly owns investments are separate entities. In this press release, “Takeda” is sometimes used for convenience where references are made to Takeda and its subsidiaries in general. Likewise, the words “we”, “us” and “our” are also used to refer to subsidiaries in general or to those who work for them. These expressions are also used where no useful purpose is served by identifying the particular company or companies.

Forward-Looking Statements

This press release and any materials distributed in connection with this press release may contain forward-looking statements, beliefs or opinions regarding Takeda’s future business, future position and results of operations, including estimates, forecasts, targets and plans for Takeda. Without limitation, forward-looking statements often include words such as “targets”, “plans”, “believes”, “hopes”, “continues”, “expects”, “aims”, “intends”, “ensures”, “will”, “may”, “should”, “would”, “could”, “anticipates”, “estimates”, “projects” or similar expressions or the negative thereof. These forward-looking statements are based on assumptions about many important factors, including the following, which could cause actual results to differ materially from those expressed or implied by the forward-looking statements: the economic circumstances surrounding Takeda’s global business, including general economic conditions in Japan and the United States; competitive pressures and developments; changes to applicable laws and regulations, including global health care reforms; challenges inherent in new product development, including uncertainty of clinical success and decisions of regulatory authorities and the timing thereof; uncertainty of commercial success for new and existing products; manufacturing difficulties or delays; fluctuations in interest and currency exchange rates; claims or concerns regarding the safety or efficacy of marketed products or product candidates; the impact of health crises, like the novel coronavirus pandemic, on Takeda and its customers and suppliers, including foreign governments in countries in which Takeda operates, or on other facets of its business; the timing and impact of post-merger integration efforts with acquired companies; the ability to divest assets that are not core to Takeda’s operations and the timing of any such divestment(s); and other factors identified in Takeda’s most recent Annual Report on Form 20-F and Takeda’s other reports filed with the U.S. Securities and Exchange Commission, available on Takeda’s website at: https://www.takeda.com/investors/sec-filings-and-security-reports/ or at www.sec.gov Go to https://www.sec.gov . Takeda does not undertake to update any of the forward-looking statements contained in this press release or any other forward-looking statements it may make, except as required by law or stock exchange rule. Past performance is not an indicator of future results and the results or statements of Takeda in this press release may not be indicative of, and are not an estimate, forecast, guarantee or projection of Takeda’s future results.

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Press Releases

Intel accelerates ai everywhere at computex 2024; redefines compute power, performance and affordability with new xeon 6, gaudi accelerators and lunar lake architecture to grow ai pc leadership.

AI runs best on Intel across the compute continuum from the data center, cloud and network to the edge and PC.

NEWS HIGHLIGHTS

• Launches Intel® Xeon® 6 processors with Efficient-cores (E-cores), delivering performance and power efficiency for high-density, scale-out workloads in the data center. Enables 3:1 rack consolidation, rack-level performance gains of up to 4.2x and performance per watt gains of up to 2.6x 1 .
• Announces pricing for Intel® Gaudi® 2 and Intel® Gaudi® 3 AI accelerator kits, delivering high performance with up to one-third lower cost compared to competitive platforms 2 . The combination of Xeon processors with Gaudi AI accelerators in a system offers a powerful solution for making AI faster, cheaper and more accessible.
• Unveils Lunar Lake client processor architecture to continue to grow the AI PC category. The next generation of AI PCs – with breakthrough x86 power efficiency and no-compromise application compatibility – will deliver up to 40% lower system-on-chip (SoC) power when compared with the previous generation 3 .

TAIPEI, Taiwan--(BUSINESS WIRE)-- Today at Computex, Intel unveiled cutting-edge technologies and architectures poised to dramatically accelerate the AI ecosystem – from the data center, cloud and network to the edge and PC. With more processing power, leading-edge power efficiency and low total cost of ownership (TCO), customers can now capture the complete AI system opportunity.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20240603799554/en/

At Computex in Taipei, Taiwan, on June 4, 2024, Intel launched the Intel Xeon 6 processors with Efficient-cores (E-cores). For companies looking to refresh aging infrastructure to help reduce costs and free up space, Intel Xeon 6 with E-cores offers significant rack density advantages, enabling a 3-to-1 rack-level consolidation. (Credit: Intel Corporation)

“AI is driving one of the most consequential eras of innovation the industry has ever seen,” said Intel CEO Pat Gelsinger. “The magic of silicon is once again enabling exponential advancements in computing that will push the boundaries of human potential and power the global economy for years to come.”

More: Intel at Computex 2024 (Press Kit)

Gelsinger continued, “Intel is one of the only companies in the world innovating across the full spectrum of the AI market opportunity – from semiconductor manufacturing to PC, network, edge and data center systems. Our latest Xeon, Gaudi and Core Ultra platforms, combined with the power of our hardware and software ecosystem, are delivering the flexible, secure, sustainable and cost-effective solutions our customers need to maximize the immense opportunities ahead.”

Intel Enables AI Everywhere

During his Computex keynote, Gelsinger highlighted the benefits of open standards and Intel’s powerful ecosystem helping to accelerate the AI opportunity. He was joined by luminaries and industry-leading companies voicing support, including Acer Chairman and CEO Jason Chen, ASUS Chairman Jonney Shih, Microsoft Chairman and CEO Satya Nadella, and Inventec’s President Jack Tsai, among others.

Gelsinger and others made it clear that Intel is revolutionizing AI innovation and delivering next-generation technologies ahead of schedule. In just six months, the company went from launching 5th Gen Intel® Xeon® processors to introducing the inaugural member of the Xeon 6 family; from previewing Gaudi AI accelerators to offering enterprise customers a cost-effective, high-performance generative AI (GenAI) training and inference system; and from ushering in the AI PC era with Intel® Core™ Ultra processors in more than 8 million devices to unveiling the forthcoming client architecture slated for release later this year.

With these developments, Intel is accelerating execution while pushing the boundaries of innovation and production speed to democratize AI and catalyze industries.

Modernizing the Data Center for AI: Intel Xeon 6 Processors Improve Performance and Power Efficiency for High-Density, Scale-Out Workloads

As digital transformations accelerate, companies face mounting pressures to refresh their aging data center systems to capture cost savings, achieve sustainability goals, maximize physical floor and rack space, and create brand-new digital capabilities across the enterprise.

The entire Xeon 6 platform and family of processors is purpose-built for addressing these challenges with both E-core (Efficient-core) and P-core (Performance-core) SKUs to address the broad array of use cases and workloads, from AI and other high-performance compute needs to scalable cloud-native applications. Both E-cores and P-cores are built on a compatible architecture with a shared software stack and an open ecosystem of hardware and software vendors.

The first of the Xeon 6 processors to debut is the Intel Xeon 6 E-core (code-named Sierra Forest), which is available beginning today. Xeon 6 P-cores (code-named Granite Rapids) are expected to launch next quarter.

With high core density and exceptional performance per watt, Intel Xeon 6 E-core delivers efficient compute with significantly lower energy costs. The improved performance with increased power efficiency is perfect for the most demanding high-density, scale-out workloads, including cloud-native applications and content delivery networks, network microservices and consumer digital services.

Additionally, Xeon 6 E-core has tremendous density advantages, enabling rack-level consolidation of 3-to-1, providing customers with a rack-level performance gain of up to 4.2x and performance per watt gain of up to 2.6x when compared with 2nd Gen Intel® Xeon® processors on media transcode workloads 1 . Using less power and rack space, Xeon 6 processors free up compute capacity and infrastructure for innovative new AI projects.

Fact Sheet: Intel Xeon 6 Processors

Providing High Performance GenAI at Significantly Lower Total Cost with Intel Gaudi AI Accelerators

Today, harnessing the power of generative AI becomes faster and less expensive. As the dominant infrastructure choice, x86 operates at scale in nearly all data center environments, serving as the foundation for integrating the power of AI while ensuring cost-effective interoperability and the tremendous benefits of an open ecosystem of developers and customers.

Intel Xeon processors are the ideal CPU head node for AI workloads and operate in a system with Intel Gaudi AI accelerators, which are purposely designed for AI workloads. Together, these two offer a powerful solution that seamlessly integrates into existing infrastructure.

As the only MLPerf-benchmarked alternative to Nvidia H100 for training and inference of large language models (LLM), the Gaudi architecture gives customers the GenAI performance they seek with a price-performance advantage that provides choice and fast deployment time at lower total cost of operating.

A standard AI kit including eight Intel Gaudi 2 accelerators with a universal baseboard (UBB) offered to system providers at $65,000 is estimated to be one-third the cost of comparable competitive platforms. A kit including eight Intel Gaudi 3 accelerators with a UBB will list at $125,000, estimated to be two-thirds the cost of comparable competitive platforms 4 .

Intel Gaudi 3 accelerators will deliver significant performance improvements for training and inference tasks on leading GenAI models, helping enterprises unlock the value in their proprietary data. Intel Gaudi 3 in an 8,192-accelerator cluster is projected to offer up to 40% faster time-to-train 5 versus the equivalent size Nvidia H100 GPU cluster and up to 15% faster training 6 throughput for a 64-accelerator cluster versus Nvidia H100 on the Llama2-70B model. In addition, Intel Gaudi 3 is projected to offer an average of up to 2x faster inferencing 7 versus Nvidia H100, running popular LLMs such as Llama-70B and Mistral-7B.

To make these AI systems broadly available, Intel is collaborating with at least 10 top global system providers, including six new providers who announced they’re bringing Intel Gaudi 3 to market. Today’s new collaborators include Asus, Foxconn, Gigabyte, Inventec, Quanta and Wistron, expanding the production offerings from leading system providers Dell, Hewlett Packard Enterprise, Lenovo and Supermicro.

Accelerating On-Device AI for laptop PCs; New Architecture Delivers 3x AI Compute and Incredible Power-Efficiency

Beyond the data center, Intel is scaling its AI footprint at the edge and in the PC. With more than 90,000 edge deployments and 200 million CPUs delivered to the ecosystem, Intel has enabled enterprise choice for decades.

Today the AI PC category is transforming every aspect of the compute experience, and Intel is at the forefront of this category-creating moment. It’s no longer just about faster processing speeds or sleeker designs, but rather creating edge devices that learn and evolve in real time – anticipating user needs, adapting to their preferences, and heralding an entirely new era of productivity, efficiency and creativity.

AI PCs are projected to make up 80% of the PC market by 2028, according to Boston Consulting Group. In response, Intel has moved quickly to create the best hardware and software platform for the AI PC, enabling more than 100 independent software vendors (ISVs), 300 features and support of 500 AI models across its Core Ultra platform.

Quickly building on these unmatched advantages, the company today revealed the architectural details of Lunar Lake – the flagship processor for the next generation of AI PCs. With a massive leap in graphics and AI processing power, and a focus on power-efficient compute performance for the thin-and-light segment, Lunar Lake will deliver up to 40% lower SoC power 3 and more than 3 times the AI compute 8 . It’s expected to ship in the third quarter of 2024, in time for the holiday buying season.

Lunar Lake’s all-new architecture will enable:

• New Performance-cores (P-cores) and Efficient-cores (E-cores) deliver significant performance and energy efficiency improvements.
• A fourth-generation Intel neural processing unit (NPU) with up to 48 tera-operations per second (TOPS) of AI performance. This powerful NPU delivers up to 4x AI compute over the previous generation, enabling corresponding improvements in generative AI.
• An all-new GPU design, code-named Battlemage, combines two new innovations: X e 2 GPU cores for graphics and X e Matrix Extension (XMX) arrays for AI. The X e 2 GPU cores improve gaming and graphics performance by 1.5x over the previous generation, while the new XMX arrays enable a second AI accelerator with up to 67 TOPS of performance for extraordinary throughput in AI content creation.
• Advanced low-power island, a novel compute cluster and Intel innovation that handles background and productivity tasks with extreme efficiency, enabling amazing laptop battery life.

As others prepare to enter the AI PC market, Intel is already shipping at scale, delivering more AI PC processors through 2024’s first quarter than all competitors together. Lunar Lake is set to power more than 80 different AI PC designs from 20 original equipment manufacturers (OEMs). Intel expects to deploy more than 40 million Core Ultra processors in market this year.

Fact Sheet: Intel Unveils Lunar Lake Architecture

As Gordon Moore famously said, “Whatever has been done, can be outdone,” and Intel stands as the vanguard of this relentless pursuit of progress. With global scale spanning client, edge, data center and cloud, a robust ecosystem grounded in open standards, and powerful, cost-effective solutions, Intel is not just powering AI everywhere; it is shaping its future. Today’s announcements are not just a technological leap, but an invitation to customers and partners to seize unprecedented possibilities and pioneer the next era of their own innovations.

Forward-Looking Statements

This release contains forward-looking statements that involve a number of risks and uncertainties, including with respect to Intel’s product roadmap and anticipated product sales and competitiveness and projected growth and trends in markets relevant to Intel’s business. Such statements involve many risks and uncertainties that could cause our actual results to differ materially from those expressed or implied, including those associated with:

• the high level of competition and rapid technological change in our industry;
• the significant long-term and inherently risky investments we are making in R&D and manufacturing facilities that may not realize a favorable return;
• the complexities and uncertainties in developing and implementing new semiconductor products and manufacturing process technologies;
• our ability to time and scale our capital investments appropriately and successfully secure favorable alternative financing arrangements and government grants;
• implementing new business strategies and investing in new businesses and technologies;
• changes in demand for our products;
• macroeconomic conditions and geopolitical tensions and conflicts, including geopolitical and trade tensions between the US and China, the impacts of Russia's war on Ukraine, tensions and conflict affecting Israel and the Middle East, and rising tensions between mainland China and Taiwan;
• the evolving market for products with AI capabilities;
• our complex global supply chain, including from disruptions, delays, trade tensions and conflicts, or shortages;
• product defects, errata and other product issues, particularly as we develop next-generation products and implement next-generation manufacturing process technologies;
• potential security vulnerabilities in our products;
• increasing and evolving cybersecurity threats and privacy risks;
• IP risks including related litigation and regulatory proceedings;
• the need to attract, retain, and motivate key talent;
• strategic transactions and investments;
• sales-related risks, including customer concentration and the use of distributors and other third parties;
• our significantly reduced return of capital in recent years;
• our debt obligations and our ability to access sources of capital;
• complex and evolving laws and regulations across many jurisdictions;
• fluctuations in currency exchange rates;
• changes in our effective tax rate;
• catastrophic events;
• environmental, health, safety, and product regulations;
• our initiatives and new legal requirements with respect to corporate responsibility matters; and
• other risks and uncertainties described in this release, our 2023 Form 10-K, and our other filings with the SEC.

Given these risks and uncertainties, readers are cautioned not to place undue reliance on such forward-looking statements. Readers are urged to carefully review and consider the various disclosures made in this release and in other documents we file from time to time with the SEC that disclose risks and uncertainties that may affect our business.

Unless specifically indicated otherwise, the forward-looking statements in this release are based on management's expectations as of the date of this release, unless an earlier date is specified, including expectations based on third-party information and projections that management believes to be reputable. We do not undertake, and expressly disclaim any duty, to update such statements, whether as a result of new information, new developments, or otherwise, except to the extent that disclosure may be required by law.

About Intel

Intel (Nasdaq: INTC) is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better. To learn more about Intel’s innovations, go to newsroom.intel.com and intel.com .

AI runs best on Intel across the compute continuum from the data center, cloud and network to the edge and PC as of May 2024, based on broad compatibility, extensive software options, unique architecture, and impressive performance of Intel offerings, which combine to deliver the best overall AI experience, including in comparison to competitive offerings. See intel.com/performanceindex for details. Results may vary.

1 See [7T1] at intel.com/processorclaims: Intel® Xeon® 6. Results may vary.

2 Pricing estimates based on publicly available information and Intel internal analysis.

3 Disclaimer for footnote: Power measurements are based on Lunar Lake reference platform using YouTube 4K 30fps AV1. See backup for details. Results may vary.

4 Pricing guidance for cards and systems is for modeling purposes only. Please consult your original equipment manufacturer (OEM) of choice for final pricing. Results may vary based upon volumes and lead times.

5 Source for Nvidia H100 GPT 3 performance: https://mlcommons.org/benchmarks/training/ , v3.1, closed division round. Accessed April 30, 2024​. Intel Gaudi 3 measurements and projections by Habana Labs, April 2024; Results may vary​ Intel Gaudi 3 performance projections are not verified by MLCommons Association. The MLPerf name and logo are registered and unregistered trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use strictly prohibited. See http://www.mlcommons.org/ for more information.​

6 Source for Nvidia H100 LLAMA2-70B performance https://developer.nvidia.com/deep-learning-performance-training-inference/training , April 29, 2024, a “Large Language Model” tab.​ Intel Gaudi 3 measurements and projections by Habana Labs, April 2024; Results may vary​

7 Source for Nvidia performance: Overview — tensorrt_llm documentation (nvidia.github.io) , May, 2024. Reported numbers are per GPU.​ Intel Gaudi 3 projections by Habana Labs, April 2024; Results may vary​

8 Based on total number of platform Tops on Lunar Lake vs. prior generation.

© Intel Corporation. Intel, the Intel logo and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

View source version on businesswire.com: https://www.businesswire.com/news/home/20240603799554/en/

Cory Pforzheimer 1-805-895-2281 [email protected]

Source: Intel Corporation

Released Jun 3, 2024 • 11:00 PM EDT

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Data Mapping to Identify High Lead Exposure Risk Locations in the U.S.

There is no level of lead exposure that is known to be without risk. While average blood lead levels in the United States have declined, millions of children and adults are still exposed to various sources of lead, especially in underserved communities. Significant disparities in lead exposure remain along racial, ethnic, and socioeconomic lines. We understand many sources of lead exposure, for example paint in older homes, but limited data on sources and blood lead levels across the country make it difficult to identify communities that may have disproportionate exposures. (For more information, see the Federal Action Plan to Reduce Lead Exposure .)

EPA’s Lead Strategy , or the Strategy to Reduce Lead Exposures and Disparities in U.S. Communities, focuses on eliminating these disparities through identification and reduction of lead exposures with science-based coordinated approaches and stakeholder engagement. The Agency’s commitments under the Lead Strategy include developing and applying a “blueprint” for identifying locations with high potential lead exposure risk. This blueprint is to be shared with internal and external public health partners for broader applicability and capacity building in the United States.

EPA’s Lead Strategy aligns with, and supports the 2018 Federal Action Plan to Reduce Childhood Lead Exposures and Associated Health Impacts (Action Plan) , a product of the President’s Task Force on Environmental Health Risks and Safety Risks to Children . The research goal (Goal 4) of the Action Plan includes, “Generate data, maps and mapping tools to identify high exposure communities or locations and disparities for prioritization efforts to reduce children’s blood lead levels.” The President’s Task Force priority activities for 2024-2028 include specific commitments on the lead mapping blueprint and pilot studies. To address the Agency’s commitments, EPA scientists are working collaboratively with federal and state partners to identify and address locations with high lead exposures.

Publications

• This paper presents an innovative science-based approach EPA developed and applied to identify areas with high prevalence of children’s elevated blood lead levels. Identifying locations with the highest percentage of elevated blood lead levels, and the contributing sources and exposures of those EBLLs, can assist with prioritizing actions to reduce, prevent, and mitigate lead exposure risk as called for in the Federal Lead Action Plan. EPA and Michigan Department of Health and Human Services are using the results of this paper to inform lead actions.
• In this publication, EPA identifies Ohio census tracts with high prevalence of children’s elevated blood lead levels and further evaluates lead exposure indices used as proxies for identifying hotspots. The study further tests and demonstrates the science-based approach that EPA developed with Michigan data. The published results were consistent with Ohio Department of Health targeted testing plans for lead screening and confirmed previous findings, that old housing and demographics are reasonable predictors of lead exposure.
• Led by EPA’s Office of Research and Development with collaborating partners from Department of Housing and Urban Development, Centers for Disease Control and Prevention, and Agency for Toxic Substances and Disease Registry, this paper identifies states and counties with highest potential lead exposure risk from old housing sources of lead, expanding the MI and OH papers methodology to national scale.
• Since data on blood lead levels and lead sources are not available for all communities, these methodologies can help states and others use available data to identify areas for further investigation and action.
• While lead data mapping and analyses continue to evolve, the results of this study support using available lead indices to identify potential high lead exposure risk locations in the absence of blood lead data and can inform further analyses for prioritizing actions.
• This paper summarizes and presents the state-of-the-science of publicly available methods, data (i.e., blood lead data, lead exposure indices, and environmental data), and maps for identifying U.S. lead exposure risk hotspots.
• It discusses Federal collaborations and remaining challenges and presents the concept for a systematic approach (i.e., blueprint) utilizing data across agencies.
• This paper lays out how EPA, in collaboration with other federal agencies such as the Centers for Disease Control and Prevention, Food and Drug Administration, and Department of Housing and Urban Development, is committed to preventing lead exposure and its health effects.
• It includes a section on "targeting lead exposure hotspots” and discusses how Federal scientists are working to improve publicly available maps of lead exposure risk, using geospatial modeling and analyses to support the agencies’ related goals.

Select Presentations:

• 2022 Tools and Resources Webinar: Data Mapping and Analyses to Identify U.S. Locations with High Lead Exposures
• Learn About Lead
• Protect Your Family
• Renovation, Repair and Painting Program
• Evaluating & Eliminating Lead-Based Paint Hazards
• Real Estate Disclosure
• Lead Laws and Regulations
• En español: Plomo

Snowflake Data Cloud Summit 2024 live: All the news and updates as they happened

The 2024 snowflake data cloud summit is a wrap – here's everything that happened in san francisco.

After a roaring week at the 2024 Snowflake Data Cloud Summit, the conference has come to an end. 

Snowflake unveiled a raft of new product and service announcements across the week, including an exciting partnership extension with Nvidia. 

As with many events over the last few months, generative AI featured heavily, with Snowflake banging the drum on its latest AI-supported products and platform features.

There's been a real buzz in San Francisco today with the Data Cloud Summit kicking off. We've still got a few hours until the opening keynote begins, but you can check out all of our predictions for the week ahead and potential talking points. 

• Four things to look out for at the Snowflake Data Cloud Summit 2024

We're just a few hours away from the opening keynote and the expo hall is starting to get busy - you can hear the chatter of people networking and talking about opportunities from the press lounge. All very exciting! 

Before the keynote starts, we're in an exclusive press roundtable with Snowflake CEO Sridhar Ramaswamy and Benoit Dageville, co-founder and president of product. Some of it is under embargo but we'll be sure to share all the details as soon as we can...

Some very interesting questions being asked during this Q&A. One thing we can tell you is that AI has already been mentioned a few times! 

Partners and effective partnerships are also a hot topic and one we expect to dominate during the keynotes and sessions this week. 

ITPro took a little wander out to the expo earlier today. We didn't have much time but hope to get back out there and meet some partners over the next few days. 

Just over an hour to go until the opening keynote so let's rewind back a few months and remind ourselves of when Ramaswamy got the call (or, rather, when the news of his appointment was made public). 

It was February 28 this year when Snowflake confirmed that former CEO Frank Slootman was retiring and that Ramaswamy (who joined the business with the Neeva acquisition back in May 2023) would be taking the helm, moving up from his role of SVP of AI. 

“In the last 12 years, Frank and the entire team have established Snowflake as the leading cloud data platform that is providing enterprises with the secure, scalable and cost effective data foundation and cutting-edge AI building blocks they need to build for the future,” Ramaswamy said at the time. 

“I am honored to have been chosen to lead the company into this next chapter of growth. We have an enormous opportunity ahead to help all customers leverage AI to deliver massive business value. My focus will be on accelerating our ability to bring innovation to our customers and partners.” That was the promise so we're very much looking forward to the delivery and the future vision, which we hope to hear more about later today. 

Nearly time folks! The hall for the opening keynote session is filling up nicely... 

"Making AI accessible to everyone in your organization" claims the opening video. 

"Welcome to the largest gathering of AI and data professionals in the world," is r Ramaswamy's opening statement. He added we have entry level analysts to CEOs in attendance and said they are the epicenter of all things happening here and in the data world. "The Snowflake Data Cloud platform has exploded over the last 10 years..." Ramaswamy' added. "We have thousands of customers sharing data every day. We're serving five billion queries every single day. That is almost the scale of Google searches that happen on the planet."

Ramaswamy gave thanks to customers "who  trust us with their data and their business" and also ensured the gratitude extended to partners and Snowflakes employees, too. 

"I’ve been in this role three months. In some respects it feels like three years. I’ve been a big fan of Snowflake for many years before that," he said. 

He then took a few steps back to talk about his entry into the industry and Snowflake. Ramaswamy was very passionate about drumming but his parents told him “no” so he decided to get a PhD in databases, as disgruntled drummers often do. 

More than 200 partners are here today and we have a feeling there will be a big partnership announcement/talk incoming soon. 

"The AI data cloud is lighting up every corner of the enterprise and we're just getting started," Ramaswamy stressed. "You want a simple and efficient way to deploy the right engine for the right job... And know [your data is] not owned by anyone but you. That is why I am thrlled to announce the Polaris Catalog," he added to a round of applause from the audience. 

Ai is a big talking point and one that has come up with every customer Ramaswamy has met in his first three months in the lead role. 

"For the first time, everyone in the organization can talk to their data in fluid, natural language," he said. "But, here’s the issue. The bar for AI use in enterprise is much higher than in consumer AI. Consumer AI is not ready for business use." 

Ramaswamy said: "Every rational business lead should ask this question [about the cost of AI]. Before stressing how businesses simply can't afford to be writing blank cheques when it comes to AI. 

"We are pioneering the era of enteprise AI. It is easy. It is efficient. It is trusted."

Ramaswamy talked up the world-class AI team working at Snowflake. 

"When things go from the chosen few to the many, [that is when things take off]," he said. 

"The next 10 years are going to drive so much innovation it will dwarf what has been donw in the last 20 or 30 years," adding that Snowflake cannot do this alone, however. Indeed, he said " It takes a village and that is why we have partnerships."

One of those key partnerships is with Nvidia and Ramaswamy is joined on stage (via video conference rather than IRL) by the firm's CEO Jensen Huang.

"No company is more synonymous with AI than Nvidia," Ramaswamy said, adding that the two companies together were moving AI from aspirational theory to reality. 

"The AI infrastructure of the world is really built here," Jensen said, alluding to the reason he couldn't be at the Summit in person. "I’m here to unite the ecosystem so we can focus on creating the infrastructure for the new industrial revolution.”

The partnership announcement between the two firms will enable "customers and partners can harness to build customized AI data applications in Snowflake, powered by NVIDIA AI," according to the official press release. 

When it comes to AI you can either be first to market, or you can be second and have something that really makes an impact, Jensen said, likely alluding to those who are jumping on the bandwagon by simply rebranding existing solutions or rushing. 

Talking about the enhanced Nvidia Snowflake collaboration, in response to a question from Ramaswamy asking what excites him most about the announcement, Jensen added: “This is a profound announcement in a couple of different ways. NeMo Retriever is a semantic query library. It does the embedding of your data. The most important asset of a company is its priority data. And that sits on Snowflake."

He added: “We can connect extremely large and propriety data with your ability to chat with it and connect it to bunch of other microservices." 

"For the very first time we are bring HPC and accelerated GenAI computing to the data," he said, before saying that this news was a really big deal for all concerned. 

So much potential, possibilities and opportunities on the horizon, according both leaders. 

Jensen concluded "The announcement today is really game changing. I am so happy we did it and that Snowflake drove this innovation."

While Ramaswamy said: "It is incredible to have partners like Nvidia and Jensen [as we move forward on this AI journey."

Before leaving the stage Ramaswamy reiterated that we are in "the enterprise era of AI."

Four chief data officers are now on the stage for a real-world discussion moderated by Denise Persson, chief marketing officer at Snowflake. 

The panelists are:

• Anu Jain, head of data and technology, JP Morgan Chase.
• Caitlin Halferty, chief global data officer, Ericsson
• Shahran Haider, deputy CDO, NYC Health + Hospitals
• Thomas Davey, CDO, Booking.com

“This is an exciting time for us all in the data space,” Jain said, before saying the key traits CDOs should have are being curious and embracing continuous learning. 

Haider added: "You can never keep up with all the data demands of the organization, so in some respects don’t even try!" But he stressed that it was really about staying true to business priorities, which for NYC Health + Hospitals is all about delivering the best patient care, regardless of who the patient is."

Booking.com has always been at the leading edge when it comes to using data, according to Davey. But he did warn that "every data decision comes with opportunities but also risks." 

Ericsson's Halferty said: “We are very fortunate to operate in a very data-rich environment… every day we are thinking about how can we design data systems that will enable us to accelerate and derisk.. How can we provide the uninterrupted , unlimited connectivity we have all become accustomed to." She added it was also important to attract - and retain - the best talent for the firm to achieve its mission.  

The leaders are now talking about where they are on their AI journey. 

"We heavily utilize ML, AI and now GenAI to make sure we put the right products and services in front of the right customer at the right time," Booking.com's Davey said. "We've really built a platform where we lower the barrier to entry in a very safe and compliant way. We think the new GenAI capablities are just another tool in that tool box." 

As an example, he said it has been almost a year since the company released its AI trip planner in the US. 

"Our strategy is very foundational to what we do," Davey added. "And we think this is what is really going to drive that connected and personalized experience."

JP Morgan Chase's Jain said that her firm's previous use of AI had been largely focused on cost reduction but that is shifting to revenue generation. 

She added that, at some point, the firm can imagine using AI to rethink certain workflows. 

"Your AI journey can not be complete without thinking about the talent," Jain said. "We have over 2,000 professional who are experts in data science as well as AI. We have a centre of excellence focused on building world-class AI solutions."

The discussion is coming to a close and the common themes seem to be:

• Being curious and open to exploration but not completely ignorant of risk
• Staying in your lane/true to the business you are in
• Thinking about security and compliance
• Prioritizing and ensuring the work being done is mapped to business priorities

And that's a wrap for the opening keynote of day one of Snowflake Summit 2024. We hope you found it insightful. Come back tomorrow for all the info from the day two keynote session.

As for me, I'm off to explore what's inside this large Snowflake truck parked outside the Moscone Center!

The opening keynote yesterday was action-packed, to say the least. Here’s a brief recap while we’re waiting for the morning session to begin. 

• Nvidia CEO and founder Jensen Huang tuned in via video call to discuss all the latest partnership news between the two firms. Nvidia and Snowflake have long-standing ties, and the companies are collaborating extensively on enabling AI innovation.

• Four chief data officers from partner organizations took to the stage for a panel discussion exploring practical examples of AI innovation in the real world - Snowflake is guiding them throughout the process, from early development to deployment.

AI, naturally, featured heavily yesterday. It's been a common theme at a host of major industry events so far in 2024, and it's clear this will continue for the rest of the year. 

Snowflake has been making moves in this regard, and has been highly vocal about positioning itself as the go-to provider to enterprises seeking to adopt generative AI and maximize the use of their data. 

This has been very successful so far for the company. Snowflake recorded impressive growth in its recent earnings call, which Ramaswamy attributed to surging enterprise interest in AI. 

While we're waiting for the day-two keynote to begin, why not catch up on all our recent Snowflake coverage?

• Snowflake’s AI strategy is cooking up a storm with enterprise customers

• Snowflake unveils Arctic, an enterprise-grade LLM

Just moments to go now until day two's keynote starts. 

This is how Snowflake is billing the session: "Industry-leading companies leverage the Snowflake AI Data Cloud to transform their businesses through AI innovation. Join Snowflake CEO Sridhar Ramaswamy, Co-Founder and President of Product Benoit Dageville, and EVP of Product Christian Kleinerman as they unveil the latest innovations in Snowflake’s unified platform that make it easy to break down silos, develop and distribute modern apps, and securely empower everyone with AI. You’ll see live demos from Snowflake’s engineering and product teams, and hear from some of the best-known global organizations on how they are shaping industries with the Snowflake AI Data Cloud."

Looks like a packed agenda with a great line up of speakers. 

Video playing showcasing how different customers are making use of Snowflake to help drive their businesses forward. 

Snowflake CEO Sridhar Ramaswamy now on stage, saying he left the keynote yesterday feeling super excited. 

"Welcome to day two of Snowflake Summit," he said. "We have an action-packed day ahead of us. Yesterday we talked about the enterprise era of AI and our vision. Today we are going to hear all about the platform." And he promised there would be many announcements... 

Ramaswamy is talking about the first time he met the founders and how the meeting was meant to be an hour long but ended up being three hours long. 

Before Ramaswamy leaves the stage he reiterates that there is a reason Snowflake is one product and one engine rather than many. It's all about simplicity, he said. 

Ramaswamy is now joined on stage by Snowflake co-founder and president of product Benoit Dageville. Really sweet moment where the two of them hug - you can tell there is genuine respect and admiration here. 

Dageville stared by saying that whether the attendees were long-standing customers [and partners] or just starting on their journey with Snowflake: “I am so so grateful that you are here today.“

“Today I would like to get geeky with you and share why I believe Snowflake architecture is really the best one for all your AI applications," he added. 

Dageville talks about the core tenets the Snowflake plaftorm is built on, particularly as we move through this new era of AI. "Enterprise AI is only as good as the data foundation it is built on," he said. "We are talking about AI for the enterprise not for planning your next vacation." 

Getting a sense we will be playing word bingo and 'simple' and 'simplicity' will come up quite a great deal today and rest of the week. But, in an increasingly complex world, this is what business and IT decision makers absolutely need. 

“As with everything with Snowflake we want to combine maximum efficiency with extreme simplicity," Dageville said. 

“What is the fun of AI without the ability to share it? Snowflake is really the only platform where you can share data, models and AI apps across clouds and regions,” he added. And the power of AI and data can be made available to any person or department, he said. 

Dageville concluded his part of the keynote with a rallying call to customers and partners by saying: "I am excited about what we are building. I am even more excited about what you will build. And with Snowflake the technology is really no longer the limit. Your imagination is. So please go unleash the power of the Data Cloud to build amazing apps. Thank you all and enjoy the rest of the week with us." 

Snowflake executive vice president of product Christian Kleinerman is now on stage. 

"We love the opportunity to be together and we all can learn from one another," he starts by saying. 

He says he's going to focus on three key areas in terms of talking points:

• Strengthening your data foundation
• Applications 

It's about data on your terms, according to Kleinerman.

Some cool stats now. 

Kleinerman announces Serverless Flex Tasks.

[At Summit five years ago, we said] with Snowflake we give you flexibility. Things have evolved [since then. There is the merging of lake houses, data meshing. We want to give you choice and fit the architecture you have in mind. This is where open file and open table formats fit in," Kleinerman said, before adding that Icerberg tables are generally available in all clouds today. 

Kleinerman now joined on stage by Arun Ulagaratchagan, corporate vice president of Azure Data at Microsoft. The pair talk about the fact they recently shared the stage at the Microsoft Build conference to talk about how they are taking their partnerhsip to the next level. 

So many announcements incoming it’s hard to keep up. Four key ones are:

• Universal search
• Internal marketplace
• Uniform listing locators 
• AI object descriptions

"Everywhere we see friction we just go and take it out as much as we can," Kleinerman says. 

Kleinerman now talking about Snowflake Trust Center…

“Please go look at Trust Center and at least understand where you are and take action,” he said. “We are very excited that Trust Center is also entering the process to be generally available soon!”

Snowflake Performance Index is now the topic, which Kleinerman says, helps applications get faster and, in turn, can generate cost savings or efficiencies. 

“All in all, we help you have a strong data foundation,” Kleinerman says. 

Dietmar Mauersberger, vice president of data,  Siemens AG, Siemens is now the lead speaker. 

He says there is a powerful saying in the business that translates to: “If Siemens only knew what Siemens knows.”

“It’s about making the implicit knowledge really explicit and accessible to everyone. We have accumulated a lot of knowledge,” Mauersberger added. 

“2024 is a turning point as the physical and digital worlds are merging.”

Mauersberger says the next revolution is digitalisation. 

“We are transforming the everyday for everyone,” Mauersberger said, before talking about Siemens’ journey and how it looked to big tech companies to help forge a path forward. 

“Working with Snowflake actually gave us the confidence [to do this],” he said. 

“We stopped this analysis paralysis and just did it. We stuck our neck out, convinced that our team and the team of Snowflak would actually pull this through.” 

Some very interesting stats from the Siemens and Snowflake Data Cloud collaboration. 

"We completed the migration without any additional budget... That's just the beginning our data appetite is actually growing," Mauersberger said. 

"Snowflake is really at the heart of our transformation."

Mauersberger added that at the beginning of Siemens' Gen AI journey it looked at more than 450 use cases. But that it needed to ensure viability and usefulness beyond poem creation. 

"Today we are closer than ever to truly know what Siemens knows," Mauersberger concluded. 

We’re now in demo mode! Jeff Hollan, director of product, Snowpark and Developer Platform at Snowflake has taken to the stage to show things in action. “If you can dream it you can build it on Snowflake,” he concluded. 

Kleinerman is now talking about simplifying DevOps. 

Snowflake Notebooks are now in public preview in all cloud, according to Kleinerman. In addition to the Container runtimes and Snowflake co-pilot, which Kleinerman says they will continue to evolve and make even better.  

"The features store helps you with both training and inference. All in all, we want you to be able to do the entire lifecycle of ML in Snowflake without copying data out," Kleinerman added. 

Kleinerman is giving a nod to the many partners using Cortex AI. 

He says Cortex AI has three distinct layers:

Demo time again with Cortex AI running tasks super quickly, as shown by Amanda Kelly, director of product at Snowflake. 

She got the proof of concept up and running with just a few lines of code. 

Kelly says this was achieved at one tenth of the cost. "Who doesn't love saving money," she said. 

Kleinerman now talking about how chat is the thing everyone is talking about/wants. 

"[Chat is] one of the killer applications of AI for us in the data world. How do I expand access to all my business users? How do I build apps?" he said. "I am extremely delighted to introduce to you today Cortex Analyst. It will make it trivial for you to create a chatbot."

Kleinerman then announced Cortex Search.

"Cortex search is exactly that… It very soon in public preview," he said.  

"Our goal is to simplify the process of building a chat bot. In true Snowflake fashion we have extracted... and we have simplified until we can simplify no more. We have made it so easy we think anyone can build a chatbot." Kleinerman then repeated the fact Snowflake has made it so easy that anyone can create a chatbot before announcing someone random from the audience would now be coming onto the stage to create a chatbot in front of us all! 

"The possibilities are enormous... Creating applications and experiences for all sorts of roles who have not been thinking about data as the north star," Kleinerman added. 

"We're incredibly excited to help you lead your organizations in this era of enterprise AI and continue to find ways to get moe value and isight out of your datas," Kleinerman said. 

"We will continue our relentless pace of innovation. I hope all of you walk out of here inspired but also excited about what you can do."

That's a wrap on day two's keynote. We'll be back tomorrow to live blog the day three keynote. 

Leading today's session we have Christian Kleinerman, EVP for product at Snowflake.

Christian, along with a host of Snowflake leaders, will be guiding us through all the latest updates from the company's extensive partner network, which has grown rapidly in recent years.

The session will explore how partners can harness some of the latest Snowflake platform features to accelerate AI and app development, and supercharge growth for the year ahead.

It's been a busy few days in San Francisco. Outside of the keynote coverage, IT Pro has been spending time interviewing Snowflake executives, customers and partners. We've taken in and digested a lot of information and can't wait to publish articles covering a range of topics and discussion areas. 

For those wondering what was in the Snowflake data truck parked outside the Moscone Center (you may recall I mentioned it at the end of the day one keynote live blog) ... Turns out it was a mobile meeting room! Alas, I didn't get to go in it as people were having meetings in it each time I attempted to visit. Oh well.

Next to the truck, though, was this very interesting sign. 

For everyone who checks in (and has their badge scanned) at the Snowflake Snowmobile (the official name for the truck), the firm will donate $5 to the World Wildlife Foundation - up to a maximum of $75,000. What a great idea and a worthy cause. I do hope it hits that ceiling donation to help save the bears. Polar bears could be completely extinct by 2100 if action - which requires time and money - isn't taken. 

More than 200 partners are believed to be in attendance at this year's Summit and the overall attendee count is expected to be around 20,000. Although important to note the latter number may be a mix of in person and online attendees, so we're hoping to get a final number post-event. 

In the run up to this week's event, Snowflake went big on partnering marketing as to why they should attend. Here's the sell by the numbers: 

We hope you have enjoyed our live coverage and we look forward to you joining us at our next live blog. Thanks for reading! 

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Press Release Details

Computer industry joins nvidia to build ai factories and data centers for the next industrial revolution.

• Top Computer Manufacturers Unveil Array of Blackwell-Powered Systems Featuring Grace CPUs, NVIDIA Networking and Infrastructure
• Broad Portfolios Encompass Cloud, On-Premises, Embedded and Edge AI Systems
• Offerings Range From Single to Multi-GPUs, x86 to Grace, Air to Liquid Cooling

TAIPEI, Taiwan, June 02, 2024 (GLOBE NEWSWIRE) -- COMPUTEX -- NVIDIA and the world’s top computer manufacturers today unveiled an array of NVIDIA Blackwell architecture-powered systems featuring Grace CPUs, NVIDIA networking and infrastructure for enterprises to build AI factories and data centers to drive the next wave of generative AI breakthroughs.

During his COMPUTEX keynote, NVIDIA founder and CEO Jensen Huang announced that ASRock Rack , ASUS , GIGABYTE , Ingrasys , Inventec , Pegatron , QCT, Supermicro, Wistron and Wiwynn will deliver cloud, on-premises, embedded and edge AI systems using NVIDIA GPUs and networking.

“The next industrial revolution has begun. Companies and countries are partnering with NVIDIA to shift the trillion-dollar traditional data centers to accelerated computing and build a new type of data center — AI factories — to produce a new commodity: artificial intelligence,” said Huang. “From server, networking and infrastructure manufacturers to software developers, the whole industry is gearing up for Blackwell to accelerate AI-powered innovation for every field.”

To address applications of all types, the offerings will range from single to multi-GPUs, x86- to Grace-based processors, and air- to liquid-cooling technology.

Additionally, to speed up the development of systems of different sizes and configurations, the NVIDIA MGX ™ modular reference design platform now supports NVIDIA Blackwell products. This includes the new NVIDIA GB200 NVL2 platform, built to deliver unparalleled performance for mainstream large language model inference, retrieval-augmented generation and data processing.

GB200 NVL2 is ideally suited for emerging market opportunities such as data analytics, on which companies spend tens of billions of dollars annually. Taking advantage of high-bandwidth memory performance provided by NVLink ® -C2C interconnects and dedicated decompression engines in the Blackwell architecture speeds up data processing by up to 18x, with 8x better energy efficiency compared to using x86 CPUs.

Modular Reference Architecture for Accelerated Computing To meet the diverse accelerated computing needs of the world’s data centers, NVIDIA MGX provides computer manufacturers with a reference architecture to quickly and cost-effectively build more than 100 system design configurations.

Manufacturers start with a basic system architecture for their server chassis, and then select their GPU, DPU and CPU to address different workloads. To date, more than 90 systems from over 25 partners have been released or are in development that leverage the MGX reference architecture, up from 14 systems from six partners last year. Using MGX can help slash development costs by up to three-quarters and reduce development time by two-thirds, to just six months.

AMD and Intel are supporting the MGX architecture with plans to deliver, for the first time, their own CPU host processor module designs. This includes the next-generation AMD Turin platform and the Intel ® Xeon ® 6 processor with P-cores (formerly codenamed Granite Rapids). Any server system builder can use these reference designs to save development time while ensuring consistency in design and performance.

NVIDIA’s latest platform, the GB200 NVL2, also leverages MGX and Blackwell. Its scale-out, single-node design enables a wide variety of system configurations and networking options to seamlessly integrate accelerated computing into existing data center infrastructure.

The GB200 NVL2 joins the Blackwell product lineup, which also includes NVIDIA Blackwell Tensor Core GPUs, GB200 Grace Blackwell Superchips and the GB200 NVL72.

An Ecosystem Unites NVIDIA’s comprehensive partner ecosystem includes TSMC, the world’s leading semiconductor manufacturer and an NVIDIA foundry partner, as well as global electronics makers, which provide key components to create AI factories. These include manufacturing innovations such as server racks, power delivery, cooling solutions and more from companies such as Amphenol, Asia Vital Components (AVC), Cooler Master, Colder Products Company (CPC), Danfoss, Delta Electronics and LITEON.

As a result, new data center infrastructure can quickly be developed and deployed to meet the needs of the world’s enterprises — and further accelerated by Blackwell technology, NVIDIA Quantum-2 or Quantum-X800 InfiniBand networking , NVIDIA Spectrum™-X Ethernet networking and NVIDIA BlueField ® -3 DPUs — in servers from leading systems makers Dell Technologies, Hewlett Packard Enterprise and Lenovo.

Enterprises can also access the NVIDIA AI Enterprise software platform , which includes NVIDIA NIM™ inference microservices , to create and run production-grade generative AI applications.

Taiwan Embraces Blackwell Huang also announced during his keynote that Taiwan's leading companies are rapidly adopting Blackwell to bring the power of AI to their own businesses.

Taiwan’s leading medical center, Chang Gung Memorial Hospital, plans to use the NVIDIA Blackwell computing platform to advance biomedical research and accelerate imaging and language applications to improve clinical workflows, ultimately enhancing patient care.

Foxconn, one of the world’s largest makers of electronics, is planning to use NVIDIA Grace Blackwell to develop smart solution platforms for AI-powered electric vehicle and robotics platforms, as well as a growing number of language-based generative AI services to provide more personalized experiences to its customers.

Additional Supporting Quotes

• R. Adam Norwitt, president and CEO at Amphenol: “NVIDIA’s groundbreaking AI systems require advanced interconnect solutions, and Amphenol is proud to be supplying critical components. As an important partner in NVIDIA’s rich ecosystem, we are able to provide highly complex and efficient interconnect products for Blackwell accelerators to help deliver cutting-edge performance.”
• Spencer Shen, chairman and CEO at AVC: “AVC plays a key role in NVIDIA products, providing efficient cooling for its AI hardware, including the latest Grace Blackwell Superchip. As AI models and workloads continue to grow, reliable thermal management is important to handle intensive AI computing — and we’re with NVIDIA every step of the way.”
• Jonney Shih, chairman at ASUS: “ASUS is working with NVIDIA to take enterprise AI to new heights with our powerful server lineup, which we’ll be showcasing at COMPUTEX. Using NVIDIA’s MGX and Blackwell platforms, we’re able to craft tailored data center solutions built to handle customer workloads across training, inference, data analytics and HPC.”
• Janel Wittmayer, president of Dover Corporation’s CPC : “CPC’s innovative, purpose-built connector technology enables the easy and reliable connection of liquid-cooled NVIDIA GPUs in AI systems. With a shared vision of performance and quality, CPC has the capacity and expertise to supply critical technological components to support NVIDIA’s incredible growth and progress. Our connectors are central to maintaining the integrity of temperature-sensitive products, which is important when AI systems are running compute-intensive tasks. We are excited to be part of the NVIDIA ecosystem and bring our technology to new applications.”
• Andy Lin, CEO at Cooler Master : “As the demand for accelerated computing continues to soar, so does demand for solutions that effectively meet energy standards for enterprises leveraging cutting-edge accelerators. As a pioneer in thermal management solutions, Cooler Master is helping unlock the full potential of the NVIDIA Blackwell platform, which will deliver incredible performance to customers.”
• Kim Fausing, CEO at Danfoss: “Danfoss’ focus on innovative, high-performance quick disconnect and fluid power designs makes our couplings valuable for enabling efficient, reliable and safe operation in data centers. As a vital part of NVIDIA’s AI ecosystem, our work together enables data centers to meet surging AI demands while minimizing environmental impact.”
• Ping Cheng, chairman and CEO at Delta Electronics: “The ubiquitous demand for computing power has ignited a new era of accelerated performance capabilities. Through our advanced cooling and power systems, Delta has developed innovative solutions capable of enabling NVIDIA’s Blackwell platform to operate at peak performance levels, while maintaining energy and thermal efficiency.”
• Etay Lee, vice president and general manager at GIGABYTE: “With our collaboration spanning nearly three decades, GIGABYTE has a deep commitment to supporting NVIDIA technologies across GPUs, CPUs, DPUs and high-speed networking. For enterprises to achieve even greater performance and energy efficiency for the compute-intensive workloads, we’re bringing to market a broad range of Blackwell-based systems.”
• Young Liu, chairman and CEO at Hon Hai Technology Group: “As generative AI transforms industries, Foxconn stands ready with cutting-edge solutions to meet the most diverse and demanding computing needs. Not only do we use the latest Blackwell platform in our own servers, but we also help provide the key components to NVIDIA, giving our customers faster time-to-market.”
• Jack Tsai, president at Inventec: “For nearly half a century, Inventec has been designing and manufacturing electronic products and components — the lifeblood of our business. Through our NVIDIA MGX rack-based solution powered by the NVIDIA Grace Blackwell Superchip, we’re helping customers enter a new realm of AI capability and performance.”
• Anson Chiu, president at LITEON Technology: “In pursuit of greener and more sustainable data centers, power management and cooling solutions are taking center stage. With the launch of the NVIDIA Blackwell platform, LITEON is releasing multiple liquid-cooling solutions that enable NVIDIA partners to unlock the future of highly efficient, environmentally friendly data centers.”
• Barry Lam, chairman at Quanta Computer: “We stand at the center of an AI-driven world, where innovation is accelerating like never before. NVIDIA Blackwell is not just an engine; it is the spark igniting this industrial revolution. When defining the next era of generative AI, Quanta proudly joins NVIDIA on this amazing journey. Together, we will shape and define a new chapter of AI.”
• Charles Liang, president and CEO at Supermicro: “Our building-block architecture and rack-scale, liquid-cooling solutions, combined with our in-house engineering and global production capacity of 5,000 racks per month, enable us to quickly deliver a wide range of game-changing NVIDIA AI platform-based products to AI factories worldwide. Our liquid-cooled or air-cooled high-performance systems with rack-scale design, optimized for all products based on the NVIDIA Blackwell architecture, will give customers an incredible choice of platforms to meet their needs for next-level computing, as well as a major leap into the future of AI.”
• C.C. Wei, CEO at TSMC:  “TSMC works closely with NVIDIA to push the limits of semiconductor innovation that enables them to realize their visions for AI. Our industry-leading semiconductor manufacturing technologies helped shape NVIDIA’s groundbreaking GPUs, including those based on the Blackwell architecture.”
• Jeff Lin, CEO at Wistron: “As a key manufacturing partner, Wistron has been on an incredible journey alongside NVIDIA delivering GPU computing technologies and AI cloud solutions to customers. Now we’re working with NVIDIA's latest GPU architectures and reference designs, such as Blackwell and MGX, to quickly bring tremendous new AI computing products to market.”
• William Lin, president at Wiwynn: “Wiwynn is focused on helping customers address the rising demand for massive computing power and advanced cooling solutions in the era of generative AI. With our latest lineup based on the NVIDIA Grace Blackwell and MGX platforms, we’re building optimized, rack-level, liquid-cooled AI servers tailored specifically for the demanding workloads of hyperscale cloud providers and enterprises.”

To learn more about the NVIDIA Blackwell and MGX platforms, watch Huang’s COMPUTEX keynote .

About NVIDIA NVIDIA (NASDAQ: NVDA) is the world leader in accelerated computing.

For further information, contact: Kristin Uchiyama NVIDIA Corporation +1-408-313-0448 [email protected]

Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, performance, and availability of our products, services, and technologies, including NVIDIA Blackwell architecture-powered systems, NVIDIA networking and infrastructure for enterprises, NVIDIA MGX modular reference design platform, NVIDIA GB200 NVL2 platform, NVLink-C2C, NVIDIA Blackwell Tensor Core GPUs, GB200 Grace Blackwell Superchips, GB200 NVL72, NVIDIA Quantum-2 and Quantum-X800 InfiniBand networking, NVIDIA Spectrum-X Ethernet networking, and NVIDIA BlueField-3 DPUs; third parties using and adopting our technologies and products, our collaboration and partnership with third parties and the benefits and impact thereof, and the features, performance and availability of their offerings; generative AI being the defining technology of our time, and Blackwell being the engine that will drive this new industrial revolution; and the whole industry — from server, networking and infrastructure manufacturers to software developers — gearing up for Blackwell to accelerate AI-powered innovation for every field are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2024 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, BlueField, NVIDIA MGX, NVIDIA NIM, NVIDIA Spectrum, and NVLink are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

A photo accompanying this announcement is available at: https://www.globenewswire.com/NewsRoom/AttachmentNg/6319a9a5-0d6b-43a2-b728-902bc9922315

AI Factories

NVIDIA and top computer manufacturers unveiled an array of NVIDIA Blackwell products and more technologies to build a new type of data center — AI factories.

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Cisco Live 2024: Cisco Announces New AI-powered Innovations and Investments to Help Customers Unlock a More Connected and Secure Future

News Summary:

• AI-powered innovations build customers’ digital resilience by uniquely combining the power of the network with industry-leading security, observability, and data, simplifying adoption, and offering visibility and insight across the entire digital footprint.
• Cisco Investments has launched a $1 Billion Global AI Investment Fund to advance industry innovation and customer readiness, and support Cisco’s strategy to connect and protect organizations in an AI-powered future.
• This year’s show headlined by keynote speakers, including Cisco Chair and CEO Chuck Robbins; 7x Super Bowl Champion, Entrepreneur & Philanthropist Tom Brady; and from McLaren Racing, CEO Zak Brown and F1 Team Driver Oscar Piastri.

CISCO LIVE, LAS VEGAS, June 4, 2024:  Cisco (NASDAQ: CSCO) kicks off Cisco LIVE 2024 with new AI-powered innovations and investments that deliver a clear message: AI isn’t just the latest turning point in technology. It’s an efficient, intelligent source of digital resilience that can connect and protect entire organizations and power growth, scale, and an inclusive future for all.  

At its premier networking and security event, Cisco is launching AI-enriched networking, security, and observability solutions across its entire portfolio. These are designed to give customers the visibility and insights they need to connect and protect their entire digital footprint and build digital resilience.

“We're thrilled to share incredible innovation and new AI-powered capabilities for our customers this week at Cisco Live,” said Chuck Robbins, Chair and CEO of Cisco. “Cisco is uniquely positioned to revolutionize the way infrastructure and data connect and protect organizations of all sizes, and we are confident we are the right strategic partner for our customers in this era of AI."

Cisco Investments also announced a $1B global investment fund to expand and develop secure and reliable AI solutions. Cisco is making strategic investments in Cohere, Mistral AI, and Sale AI that will advance several critical areas including customer readiness, compute infrastructure, foundational models, model development, and training.

Cisco’s Product Innovations Tackle Customers’ Core Challenges

Cisco customers are in the spotlight, showcasing the pivotal role Cisco plays as a crucial strategic ally in business and technology. Customers featured at Cisco Live include Steve Madden, Room & Board, Marriott, AT&T, Equinix, WWE, SoFi Stadium and Hollywood Park and McLaren F1 Racing.  

Making AI easier with AI & Networking

Cisco Nexus   HyperFabric AI clusters : This breakthrough AI cluster solution with NVIDIA features a single place to design, deploy, monitor and assure AI pods and data center workloads. It guides users from design, to validated deployment, to monitoring and assurance for enterprise-ready AI infrastructure. 

• Cisco Networking Cloud: New AI-native Digital Experience Assurance innovations from Cisco ThousandEyes unlock cross-domain assurance for Cisco Networking Cloud and beyond. Customers are now empowered to see and manage every enterprise, cloud, SaaS, and Internet network for exceptional digital experiences to every user, everywhere, every time.

Making AI safer with AI & Security

• In an increasingly complex and hyper-distributed world, Cisco tips the balance of power in favor of the defenders with new capabilities across the Cisco Security Cloud . Customers gain an AI-fortified security posture backed by market-leading firewall infrastructure, new sources of telemetry, superior network visibility, and AI-native management architecture.
• A little over one month after the launch of Cisco Hypershield, Cisco is announcing Cisco Hypershield support for AMD Pensando DPUs and Intel IPUs. It enables enterprises to realize an AI-driven, distributed security architecture that seamlessly goes from the cloud to the data centers to the edge while still being highly performing and energy efficient.

Making AI work with AI & Observability

• Customers benefit from Cisco’s recent landmark acquisition of Splunk with flawless, secure digital experiences powered by unparalleled visibility and real-time insights that help standardize the observability practice – getting customers ahead of future demands and set up for new opportunities.  
• At the event, Cisco will share that it has combined the power of the Splunk platform with Cisco AppDynamics Application Performance Monitoring (APM) with the introduction of Splunk Log Observer for Cisco AppDynamics . This integration will enable users to drive faster troubleshooting across on-prem and hybrid environments. Cisco is announcing its AI assistant for Cisco AppDynamics to empower users to unlock meaningful guidance and insights to make informed decisions faster and more accurately than ever. 

Making AI work with the Future of Work

• AI-Powered Contact Center and Hybrid Work Innovations: New capabilities in Webex Contact Center will help organizations design and manage conversational self-service experiences, provide an AI Assistant for contact center agents, and integrate third-party virtual agent solutions.  
• Additionally, AI Assistant for Webex Suite will soon be generally available to customers. IT admins will benefit from remote device access management in Webex Control Hub, and allnew Desk Reservation with Cisco Spaces makes it easy to find and book desk spaces and Cisco Collaboration Devices.

Enabling AI Skills

• New AI Fundamentals for Partners: Cisco will announce the first stage of its AI partner specialization training that will equip partners with the knowledge needed to optimize infrastructure for AI workloads using Cisco technology. It will explore AI basics, including key concepts and terms, a taxonomy of AI solutions and an overview of Cisco’s AI strategy, governance and use cases to master AI solution implementation in, on and through Cisco platforms.  
• New Cisco Certification in AI: Organizations can build an AI-ready workforce with the announcement of Cisco’s new certification in designing modern AI architecture . The vendoragnostic certification will enable employers, employees and jobseekers to gain and validate the skills needed to design modern AI/ML compute and networks now and into the future.

About Cisco Live

Cisco LIVE 2024, the premier networking and security event that brings together the global Cisco community to showcase the latest innovations and inspire attendees to strive for a more inclusive future for all. Welcoming more than 20,000 attendees in-person, with nearly one million more tuning in from around the world via a live-streamed digital event, Cisco LIVE 2024 is set to be Cisco’s most impactful conference to date.

To experience the live keynotes, learn more about Cisco Live news announcements and view additional digital content, visit The Newsroom .

Additional Resources:

        •      Flickr album with on-site photos uploaded daily from Cisco LIVE 2024.

About Cisco

Cisco (NASDAQ: CSCO) is the worldwide technology leader that securely connects everything to make anything possible. Our purpose is to power an inclusive future for all by helping our customers reimagine their applications, power hybrid work, secure their enterprise, transform their infrastructure, and meet their sustainability goals. Discover more on The Newsroom and follow us on X (formerly Twitter) at @Cisco .

Cisco and the Cisco logo are trademarks or registered trademarks of Cisco and/or its affiliates in the U.S. and other countries. A listing of Cisco’s trademarks can be found at http://www.cisco.com/go/trademarks . Third-party trademarks mentioned are the property of their respective owners. The use of the word ‘partner’ does not imply a partnership relationship between Cisco and any other company.

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