presentation on data analytics

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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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Data Analytics Powerpoint Presentation Slides

Analyze raw data in order to make a conclusion by utilizing this Data Analytics PowerPoint Presentation Slides. Take the assistance of this data mining PPT visuals to mention the importance of social media and interactive platforms like Google, Facebook, Twitter, Youtube, Instagram. Showcase how cloud computing provides real-time information and on-demand insights with the help of data source PPT graphics. Take the aid of this big data management PPT templates to showcase the web services which provide free and quick information insights to everyone. You can also, discuss how big data is generated from the internet of things with the help of data transformation PPT graphics. You can also highlight the popular databases such as MS Access, DB2, Oracle, SQL, which can provide for the interaction of insights that are used to drive business profits. Display various data warehouse applications that help in the analysis of transactional data. Discuss the sources of big data such as legacy documents, media, cloud, social influencers, etc. Help your business operate more effectively by downloading this data integration PowerPoint Presentation

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PowerPoint presentation slides

This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyze, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. The presentation is readily available in both 4:3 and 16:9 aspect ratio. Alter the colors, fonts, font size, and font types of the template as per the requirements. It can be changed into formats like PDF, JPG, and PNG. It is usable for marking important decisions and covering critical issues. This presentation deck can be used by all professionals, managers, individuals, internal-external teams involved in any company organization.

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Content of this Powerpoint Presentation

Data may come across as a technical term to us but the truth is we analyze and process data in our everyday lives. From calculating the right amount of ingredients for a cup of coffee to giving ETAs of your assigned tasks, data analytics is part and parcel of our lives. Organizations employ data analytics tools to anticipate and achieve success.Identifying the right sources of data is a primary requirement for delivering accurate results and should be conveyed to teams handling these channels. For this, you need Data Analytics PowerPoint Presentation Slides to highlight the key sources of data procurement so that the relevant team will know whom to approach. 

Our complete deck on Data Analytics PowerPoint Presentation Slides offers a visually appealing way to guide your organization in identifying the correct sources of data. This data will then be sent to processing and analysis to generate valuable key insights. The data analysis thus obtained will be a fair, all-encompassing, and a reliable source of information for the organization to refer to and draw conclusions from. On this note, let’s explore the best presentation slides of this PPT Template to give you an idea of the investment you will make upon downloading it.

Template 1: Media

This slide of our data analytics PowerPoint Presentation will highlight the importance of media as a hub of data to draw insights on customer preferences and changing trends. It will emphasize on the importance of social media channels and interactive platforms in being a rich source of qualitative and quantitative data. By highlighting media as a reliable source of data, this PPT Template will guide teams in employing this important channel for data analytics.

Template 2: Cloud

With cloud-based products and services gaining significance,, it would be a missed opportunity not to leverage them for sourcing data. Highlight the significance of cloud computing, emphasizing its ability to accommodate large data files and its accessibility, making it a vast reservoir of data, on this PowerPoint slide. Highlight the fact that using cloud files to fetch data will widen the scope of information collected from sources thus validating your analysis more.

Template 3: Web

Utilize the world wide web as a data resource to guide your business strategies and assessments and point this important reservoir of data with this PPT Template. Your team can explore the plethora of researches, statistics, and news shared by verified portals to back up your data analytics report. The visuals and icons will add to the effect of conveying its importance. 

Template 4: Internet of Things

The contribution of IoT in data analytics will always be top-tier and you can convey the same with content-ready visuals of this PPT Design. Sensors, software, and other devices that gather first-hand data add credibility to subsequent analysis. performed on it thereof can be pointed out during the discussion and elaboration. During discussion, highlight the IoT devices utilized in your organization, showcasing their role in data analytics and organizational benefits. This PPT Layout facilitates easy awareness building. 

Template 5: Databases

Data is an asset and your organization can rely on previously collected, stored, and processed data that will guide future analysis. Emphasize the importance of your organizational database in guiding future analytics work. Use this slide to encourage data governance of the database and direct teams to rely on it for future data analytics. 

Template 6: Social Network Profiles

In this PPT Slide, you can focus on social media profiles being contributors to the data sent for analysis and drawing important conclusions. By examining profiles on platforms such as Twitter, Facebook, LinkedIn etc, garther a list of like-minded prospective clients to study their interests and to devise your business strategies. Using API integration, you can analyze relevant B2B marketers and tailor pitches accordingly. 

Template 7: Social influencers

Social influencers can serve as another source of data collection allowing you to tap into the potential of influencer marketing and use their profiles to collect important data, customer preferences, and inclinations. Blog posts, user forums, review sites, are some of the ways you can get the most out of influencer marketing contributing to your companies data analytics.

Template 8: Activity-Generated Data

Businesses can acquire additional data for processing and analysis by tracking usage, generating feedback forms, and enquiring about customer preferences. IoT embedded in applications, products, or as a part of service contract will help companies study the interest and usage of their services and products by clients. This will also be the basis of a reliable data analytics report for your company. 

Template 9: Big Data Sources

In this slide, you can summarize all the previously discussed big data sources and add to this list. Icons will support the easy visualization of the sources being discussed and you can edit the list as all of our slides are 100% editable and customizable.

Template 10: Network and In-Stream Monitoring Technologies

This PPT Slide will help you highlight the importance of network and in-stream monitoring technologies in data analytics. In this presentation design you can talk about how monitoring the incoming and outgoing traffic on a computer network will help users fetch data that will be helpful in data analytics. You can point to the need for specialized hardware and/or software in collecting this important data. So, download it now!

Know Your Tools

As you help your audience know the tools for data analysis, you can assign respective teams to be vigilant about collecting the big data. Discuss the process of collecting data and how to preserve it for long without depleting its value or tampering it. Use this carefully collected data to power your analytic reports and this journey will begin effectively upon downloading this comprehensive training material titled Data Analytics PowerPoint Presentation.

Data Analytics Powerpoint Presentation Slides with all 20 slides:

Use our Data Analytics Powerpoint Presentation Slides to effectively help you save your valuable time. They are readymade to fit into any presentation structure.

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Data Analytics Presentations (PPTs)

Welcome! This page showcases a collection of data analytics slides that I have been creating in recent times. As data becomes increasingly important in today’s world, it is essential to have a strong understanding of how to extract insights from it. My slides / PPTs will cover a wide range of data analytics topics, including data visualization, machine learning , statistical analysis, and data-driven decision-making. Whether you’re a data scientist , a business analyst, or simply interested in exploring the world of data analytics, I invite you to explore my collection of slides and learn more about this exciting and rapidly growing field.

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Data Analysis PowerPoint Templates & Presentation Slides

Download 100% editable data analysis PowerPoint templates and backgrounds for presentations in Microsoft PowerPoint.

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Data Analysis PowerPoint presentation templates are pre-designed slides that can be used for presenting results, insights, and conclusions derived from the analysis of various kinds of data. They often contain a variety of slide layouts, diagrams, charts, and other graphic elements that can effectively communicate complex data in a visually engaging and digestible manner.

Our editable data analysis presentation slides can help to prepare impeccable business reports and data analysis presentations with the help of editable & high-quality data analysis slide templates compatible with PowerPoint & Google Slides presentations.

Possible use cases, applications and presentation ideas for data analysis slide templates:

• Business Intelligence: A company might use data analysis templates to present results from its business intelligence efforts. This could include data about sales trends, customer demographics, and operational efficiency.
• Academic Research: Researchers can use data analysis presentation templates to present their research findings in conferences or seminars. They can showcase data about a variety of subjects, from social sciences to natural sciences.
• Marketing Campaign Analysis: Marketing professionals might use data analysis PowerPoint templates to present the results of a marketing campaign, analyzing data like audience engagement, conversion rates, and return on investment.
• SEO Strategy: A data analysis can also be used in a SEO-oriented presentation. This can help digital marketing teams, businesses, and SEO agencies to plan, implement, and report their SEO strategies effectively. The use of tools such as Google’s BigQuery can also demonstrate the ability to handle and analyze big data, which is increasingly important in today’s data-driven marketing landscape.
• Financial Analysis: Financial analysts could use slide templates on data analysis to present financial data such as revenue trends, cost analysis, budgeting, and forecasting.
• Healthcare Data Analysis: In the healthcare sector, data analysis templates can be used to present data on patient demographics, treatment effectiveness, and disease prevalence, for example.
• Consulting: Consultants and consulting firms often need to present data-driven insights to their clients. A data analysis PowerPoint template or presentation template for Google Slides would be suitable for this.
• Government & Public Policy: Government officials or policy analysts may use data analysis presentation templates to present data on social issues, economic trends, or the impact of certain policies.

These data analysis infographics and charts can help to prepare compelling data analysis presentation designs with charts and visually appealing graphics.

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Tutorial 1 : An Introduction to Data Analytics

Welcome to tutorial one of your Data Analytics for Beginners Course! It’s great to have you on board 😃

Over the course of five tutorials (starting now), you’ll take on the role of a data analyst and work with a real dataset to solve a business challenge. By the end of the course, you will:

• Be familiar with all the key steps in the data analysis process
• Understand, and be able to apply, some fundamental analysis techniques
• Have a first-hand glimpse of what it’s like to work as a data analyst
• Learn how AI is impacting the data industry and explore some AI tools and technologies that can help data analysts

So how does it work, and what’s in store?

This free short course consists of five tutorials (starting with this one). Each tutorial will introduce a key data analytics topic before taking you through a practical exercise. This is a very hands-on course, so be prepared to get stuck in!

In this tutorial, we’ll focus on setting the scene, introducing the field of data analytics and exploring what a data analyst actually does. We'll then take a look at whether data analysts are in demand, as well as the average data analyst salary💰 From there, we’ll lay out the business challenge you’ll seek to solve throughout the course and have you open up your dataset—ready for some real hands-on exercises in tutorial two.

Sound good? Here’s the breakdown of our tutorial one agenda:

• What is data analytics?
• What does a data analyst do?
• Are data analysts in demand, and what’s the average data analyst salary?
• Getting ready: What tools do you need for this course?
• Practical exercise: Opening your dataset
• Key takeaways and further reading

If you have questions or feedback along the way, simply reply to any of the course emails you receive. We'll be happy to hear from you 😊

Are you ready to begin your excursion into the fascinating world of data analytics? Let’s go! 🚀

1. What is data analytics?

First things first: What exactly is data analytics?

If we had to sum it up in a single sentence, we’d say it’s the process of analyzing raw data in order to draw out meaningful, actionable insights . It’s a form of business intelligence, enabling companies and organizations to make smart decisions based on what the data is telling them.

Data analytics encompasses the extraction (or collection) of raw data, the preparation and subsequent analysis of that data, and storytelling—sharing key insights from the data, using them to explain or predict certain scenarios and outcomes, and to inform decisions, strategies, and next steps.

Does that all sound a bit abstract? Let’s bring it to life with an example .

Imagine you’re a data analyst working for a public transport network—think MTA in New York City, or TFL in London. There’s a major sporting event coming up in the city, and you know that people will be flying in from all over to attend.

In order to avoid absolute chaos, you need to adapt the usual public transport schedule to accommodate for this influx of people and increase in travel throughout the city. How do you plan ahead with accuracy? 🤔

You guessed it…data analytics! You analyze data from similar events that have happened in the past and use it to predict the number, frequency, and types of journeys that are likely to occur around this event. With these insights, you’re able to ensure that public transportation continues to run smoothly 🚌

As you can see, data analytics replaces guesswork with data-driven insights . It helps you make sense of the past and predict future trends and behaviors, leaving you much better equipped to make smart decisions.

If you'd like to learn in video form, allow our colleague will to talk you through what it is"

You’ll find a more comprehensive explanation of what data analytics is in this guide , and it’s also worth reading up on the four different types of data analysis . For now, though, let’s explore the role of the data analyst.

2. What does a data analyst do?

As a data analyst, it’s your job to turn raw data into meaningful insights.

Any kind of data analysis usually starts with a specific problem you want to solve, or a question you need to answer—for example, “Why did we lose so many customers in the last quarter?” or “Why are patients dropping out of their therapy programs at the halfway mark?”

Define your question or problem statement

Collect the necessary raw data, clean the data so that it’s ready for analysis, analyze the data, create visualizations, share your findings.

At each stage, data analysts use a range of different tools —such as Microsoft Excel for data wrangling and Tableau for data visualizations. We explain each step in more detail in our guide to the data analysis process , and you’ll apply it first-hand throughout this course 😊

But the role isn’t just limited to a single process. As a data analyst, you occupy a very critical position; you’re the bridge between incomprehensible raw data and useful insights , empowering people in all areas of the organization to make smarter decisions and ultimately reach their goals. As such, you’ll work closely with managers, product owners, and department leads to identify goals, prioritize needs, and shape strategies.

And, in addition to actually analyzing data, you may also be responsible for building databases and dashboards, ensuring data quality and best practices, and maintaining relevant documentation.

Of course, the exact tasks and responsibilities will vary depending on where you work. To learn more about what a data analyst does, we recommend browsing data analyst jobs on sites like Indeed , LinkedIn , and Glassdoor , as well as checking out this interview with Radi , a data analyst who works at a rare disease research and diagnosis company.

3. Are data analysts in demand, and what is the average data analyst salary?

So far, we’ve looked at what data analytics is and how this translates into the day-to-day tasks of a data analyst.

Now let’s zoom out again and consider data analytics as a career path. If you do pursue a career as a data analyst, what kinds of industries and companies might you work for? Is this a secure career choice with high demand? And what kind of salary can you expect? Let’s take a look 👀

Where can data analysts work?

When you think of data analysts and the companies that hire them, names like Meta (Facebook), Netflix, and Spotify might spring to mind. It’s the big tech companies that rely most heavily on data, right?

Not necessarily. The truth is, data analysts work anywhere and everywhere. They are critical to almost any kind of organization and industry you can think of—from large corporations to fledgling startups, from financial institutions to government, healthcare, and non-profit organizations. Wherever data is being collected (and that’s pretty much everywhere these days!), there’s a need for data analysts.

If you’re considering a career in the field, you’ll find that your skills are needed everywhere. That’s one of the great things about the job: you’re not limited to a specific sector or type of company. Once you’re a qualified data analyst, the world really is your oyster 🤩

For a closer look at where a career in data might take you, check out our round-up of the top industries hiring data analysts right now , or this  guide to some of the most common data analytics job titles .

Are data analysts in demand?

So data analysts can work pretty much anywhere, but are they in demand? In a word, yes. The big data market is growing rapidly and exponentially—it’s estimated that, by 2025, it will be worth a whopping $229.4 billion USD . The more data we generate, the more we rely on data analysts to make sense of that data. Unsurprisingly, this general growth in data is mirrored in the job market.

The Jobs of Tomorrow report published by the World Economic Forum in 2020 identifies data and artificial intelligence (AI) as one of seven high-growth emerging professions, showing the highest growth rate at 41% per year .

In 2023 the rolling out of generative AI products like the large language models ChatGPT and Google's Bard took a lot of industries by surprise with their possibilities, but data is the one poised to take the most advantage of it. If you want to learn more about the possibilities of AI on the industry, check out these:

• CareerFoundry's Senior Data Scientist Tom talks you through How AI Will Change Your Life
• Our Guide to the Top AI Data Analysis Tools Currently Out There
• Interview: Beyond the Buzzword – Understanding the Ethical Implications of AI

And, if you research the most in-demand tech skills for 2024  and beyond, you’ll find that data analytics crops up time and time again. Still not convinced? Take a look at this recent report  about how the lack of these skills is such a global issue it's got its own title— the Data Skills Gap .

We won’t bombard you with any more statistics 🙊 Suffice it to say that data isn’t going anywhere, and nor is the need for data analysts. For a more detailed look at the demand for data analysts in your area, we recommend searching for data analyst jobs and filtering by location.

It’s also important to consider the remote job market, a topic we’ve covered extensively in this guide: Can data analysts work remotely? A closer look at the remote data analytics job market .

What is the average data analyst salary?

If you’re considering a career in data, you’ll want to know how much you could earn.

Of course, your earning potential will vary depending on where you work, your level of experience, and your exact job title. But, to give you a ballpark figure, the average data analyst salary in the United States comes in at $76,763 per year (correct at the time of writing—check the Indeed salary portal for the most up-to-date figures).

Once you’ve gained 3-5 years’ experience, this average climbs to $81,298 per year . Type in “senior data analyst” and you'll see an average yearly salary of $96,544  💸

As you can see, the earning potential for data analysts is relatively high. So, in addition to variety, impactful work, and job security, you can also count on a reasonable salary, too. For more salary insights, check out the following:

• The ultimate entry-level data analyst salary guide
• The industries currently paying the highest data analyst salaries
• The complete business analyst salary guide

We hope the first half of this tutorial has got you excited about the journey ahead, and about your potential new career as a data analyst 😃 We did promise you’d be getting hands-on with data, so let's get you set up with the tools you’ll need from here on out.

4. What tools do you need for this course?

In real life, data analysts work with a whole host of tools. But, for the purpose of this course, you’ll be working with Google Sheets and, a little bit later on, Google Slides.

You’ll access the dataset and carry out all your analysis in Google Sheets. You’ll then use Google Slides to create a presentation deck in order to summarize and present the findings of your analysis.

You can access both Google Sheets and Slides for free with a Google account (which you have if you use Gmail!), and you don’t need to download anything, so it shouldn’t take you long to get set up. We’ll show you how to create a gmail account in the next section.

5. Practical exercise: Defining the challenge and opening your dataset

First things first: Let’s get you acquainted with your dataset, which you’ll access in Google Sheets.

If you already have a Google account, you’re good to go! If you don’t yet have one, you can quickly and  easily create one . Simply follow the instructions on the sign-up screen.

​​Are you all set up with your Google account? Great! Now it’s time to get into character. For the duration of this course, you’ll take on the role of a data analyst working at Citi Bike—New York City’s official bike-sharing system. Ready? Let’s set the scene 🎬

Citi Bike is the largest bike-share program in the United States, with 20,000 bikes and over 1,300 pick-up stations across Manhattan, Brooklyn, Queens, the Bronx, and Jersey City. As stated on their website, the service was designed for quick trips with convenience in mind, offering a fun and affordable way to get around town. Users can sign up for annual membership, or buy a short-term pass through the Citi Bike app. Once they’ve joined, they simply locate a nearby bike, ride around as they please, and return it to a nearby station once they’re done 🚴

Like most organizations, Citi Bike is constantly looking for ways to improve their business model and provide an even better experience for their customers. Through the Citi Bike app, they are able to gather loads of useful data which, when analyzed, reveals great insights into things like user demographics and behavior—for example, when and where people pick up and drop off their bikes and how long the average journey lasts.

Such data is extremely valuable as it helps the good people at Citi Bike to understand how the service is being used, and to plan and make decisions accordingly. For example, at what rate is the customer base growing and how many more bikes should they install across the city to accommodate this growth? Where should they install the most bikes? Who should they tailor their marketing and advertising to? Essentially, data helps them to determine where and how their money and efforts can be invested for maximum impact.

So, throughout this course, your mission is to analyze data collected by Citi Bike (for the purposes of this short course, we will call them NY Citi Bike to make things consistent) and help key stakeholders to make smart, data-driven decisions based on the insights you uncover. Here’s what you’ll seek to investigate:

• What are the most popular pick-up locations across the city for NY Citi Bike rental?
• How does the average trip duration vary across different age groups?
• Which age group rents the most bikes?
• How does bike rental vary across the two user groups (one-time users vs long-term subscribers) on different days of the week?
• Does user age impact the average bike trip duration?

Each tutorial in this course will see you finding answers to these questions and, eventually, turning them into a meaningful story. You’ll start with a raw dataset and end up with professional visualizations and clear, actionable insights to present to key stakeholders 🤩 Exciting times ahead!

For today’s task, you’re going to access the dataset you’ll be working with throughout the course. We’ve shared a view-only file, so you’ll need to make your own copy to work on. To do so:

• Make sure you’re logged into your Google account, and then access our view-only dataset . We’ve called this version of the dataset “New York Citi Bikes_Raw Data.” * Note: We cannot give editing permissions as this would edit the sheet for all users- you will need to create a copy, please see below. * Google sheets is not responsive at this time on mobile devices, we highly recommend using a computer for the duration of this course.

Figure 3. 4. Click “Ok.” That’s it! You’ve now got your own Google Sheets file to work with throughout the course. Copy the link into your “Notes” app or into a Google Doc for easy access.

That’s all you need to do for now—we won’t start working with the data until the next tutorial.

Note: Citi Bike is a real company, and you’ll be working with real data. However, the project is purely hypothetical, created by us for the purpose of this short course 😊 We’ve sourced the data from a free data site: Kaggle.com, and customized it to fit the scope of our project, adding in extra variables as well as excluding other variables. Please ONLY use the data in the link below, or other links provided for the short course and do not download the data from the Kaggle.com site. Thanks!

6. Key takeaways and further reading

That marks the end of your first data analytics tutorial! We focused on setting the scene, exploring what data analytics is and what you might expect from a career in the field.

We’ve also introduced the project you’ll be working on, set out the key questions we’ll seek to answer throughout the course, and opened our Citi Bike dataset. For the rest of the course, we’ll delve into the data analysis process itself, getting hands-on with data and unlocking those valuable insights 🔑

In the next tutorial, we’ll introduce a crucial step in the data analysis process: Data cleaning . We’ll show you how to clean your dataset and prep it for analysis, so be sure to come wearing your data analyst hat 🎩

Keen to learn more? Continue your exploration of data analytics with these articles:

• What is the typical data analyst career path?
• What are some real-world examples of big data?
• Data bootcamp vs. a degree: Which is right for you?

See you soon! 👋

Take the quiz below to make sure you have learned all the important information— and that it really sticks!

Senior Program

Intrigued by a career in data analytics? Arrange a call with your program advisor today to find out if data analytics is a good fit for you—and how you can become a data analyst from scratch with the full CareerFoundry Data Analytics Program .

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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! 

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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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Data Science vs. Data Analytics: What’s the Difference?

Author: University of North Dakota May 22, 2024

The origins of data science are deeply rooted in the convergence of computer science and statistics.

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In the early days of computing, statisticians quickly recognized the profound potential of computers to enhance data analysis. Then, as data science continued to evolve, a complementary discipline began to take shape—data analytics.

While both fields deal with data, they exhibit distinctive characteristics and cater to different objectives, ultimately leading to divergent career paths. Continue reading to explore the key distinctions between data science vs. data analytics and determine which path aligns with your career aspirations.

What is Data Science?

Data science is an interdisciplinary field that focuses on extracting knowledge and insights from structured and unstructured data. Using various techniques, from statistics, machine learning and data mining, data scientists collect, clean and analyze data to make predictions and inform strategic decision-making. The process typically includes data collection, rigorous data cleaning, deep analysis and the interpretation of the results. 

This field applies to virtually every industry, including finance, healthcare and technology, showcasing its wide-ranging utility and importance in solving complex problems.

What is Data Analytics?

Data analytics is the systematic examination of datasets to uncover valuable insights and informed conclusions, crucial for strategic decision-making in organizations.

The discipline includes several distinct approaches. Descriptive analytics summarizes past data to clarify what has occurred, setting the stage for diagnostic analytics, which probes into the causes behind these events. Building on this understanding, predictive analytics uses historical data to anticipate future outcomes. Finally, prescriptive analytics offers targeted strategies based on these forecasts, helping organizations achieve optimal results.

This progression from understanding what has happened to shaping future outcomes is vital for enhancing performance and sparking innovation across various industries.

Education Requirements

Pursuing a data science or analytics career requires a solid educational foundation in relevant fields. Each area has its specific educational paths that help aspirants acquire the necessary skills and knowledge.

Data Science Education

The journey into data science typically begins with an undergraduate degree in Data Science , Computer Science , statistics, Mathematics or a related field. These programs lay the groundwork by equipping students with essential mathematical and computational skills crucial for handling and analyzing complex data sets.

To deepen their knowledge and specialize further, many individuals pursue graduate degrees, such as a master's degreee in Data Science or related fields. These programs often focus on advanced topics like machine learning, big data technologies, artificial intelligence and sophisticated statistical methods. 

Those looking to push the boundaries of data science may even opt for Ph.D. programs, which are research-focused and allow for specialization in cutting-edge areas such as deep learning, natural language processing or cognitive computing.

Data Analytics Education

Aspiring data analysts often start with undergraduate degrees in Business Analytics, Information Systems , Statistics or similar fields. These degrees provide a solid foundation for understanding how to collect, process and analyze data as well as apply it in a business context to solve problems and support decision-making.

Advancing in data analytics might involve pursuing a master's degree in Data Analytics, Business Intelligence or Applied Statistics . These programs typically focus on more strategic aspects of data analytics, including advanced statistical methods, predictive analytics and the use of analytics in business strategy. Post-graduate certifications in tools and technologies such as SQL, Tableau, SAS or Power BI are also highly valued in the industry.

Skill Set Requirements

Both data science and data analytics professionals require a specific set of skills; however, some of these skills overlap, reflecting the integrated nature of these disciplines.

Data Science Skills

Data scientists are expected to possess a wide array of skills, each critical for various aspects of their role:

• Programming languages: Python and R for coding and data manipulation
• Machine learning: Application of algorithms and models for complex problem-solving
• Data visualization: Creation of visual representations to clarify and highlight data trends
• Domain expertise: Deep industry knowledge to effectively apply data insights
• Data analysis: Interpretation of complex datasets to extract actionable information
• Big data tools: Proficiency with platforms like Hadoop and Spark for handling large data volumes
• Deep learning frameworks: Familiarity with sophisticated models for advanced data analysis
• Statistical analysis: Application of statistical methods for meaningful data interpretation

Data Analytics Skills

Data analysts require several core competencies to effectively handle data:

• Data manipulation: Proficiency in cleaning, sorting, and organizing data
• Database management: Skilled in using SQL for efficient data retrieval and management
• Data visualization: Ability to create clear and impactful visual representations of data
• Data interpretation: Competence in analyzing large datasets to extract meaningful insights
• Analytical thinking: Strong critical thinking skills to evaluate data and assess implications
• Communication: Capability to translate technical data findings into understandable and actionable business insights
• Statistical analysis: Solid understanding of statistical methods to conduct accurate data evaluations

Career Opportunities

The fields of data science and data analytics offer varied career opportunities, some overlapping while others are unique to each field.

Data Science Careers

Data science offers a range of career paths, including:

• Data scientist
• Machine learning engineer
• Artificial intelligence specialist
• Deep learning engineer
• Big data engineer
• Algorithm developer

These roles typically focus on the development of new algorithms, predictive models and systems for data analysis and interpretation, pushing the boundaries of what organizations can achieve with data.

Data Analytics Careers

In data analytics, the roles are often more directly linked to business applications and include:

• Data analyst
• Business intelligence analyst
• Data engineer
• Marketing analyst
• Financial analyst

These professionals are crucial in analyzing data to inform business strategies, improve operational efficiency and enhance market understanding across various industries, such as finance, marketing and healthcare.

Salary and Job Outlook

The growing emphasis on data has propelled the fields of data science and data analytics into the spotlight, predicting a promising future for both domains. Not only are these roles expected to remain in high demand, but they also offer attractive salaries and substantial career growth opportunities.

Data Dcience Jobs

The role of a data scientist is not only intellectually rewarding but also financially lucrative, with an average salary of approximately $108,020 per year . The job market for data scientists is robust, boasting about 168,900 professionals engaged in this dynamic field. Looking ahead, the outlook for data science is exceptionally bright, with employment projected to grow at a rate of 35% from 2022 to 2032, a pace much faster than the average for all occupations. 

Data Analytics Jobs

Similarly, data analytics professionals enjoy competitive salaries, with an average annual income of around $77,445 . The salary can vary depending on factors such as experience, location and the specific industry in which one works. 

The job market for data analysts is also experiencing a significant upswing and is projected to grow by 25% by 2030. This expansion is a testament to the growing reliance on data analytics to inform and shape business decisions. 

Challenges and Future Trends

Professionals in data science and analytics face numerous challenges, including maintaining data quality, managing privacy concerns and overcoming scalability issues. As data volumes continue to grow, keeping data accurate and clean becomes even more challenging. 

Privacy concerns are also significant as both fields handle sensitive information, requiring compliance with increasingly stringent regulations. Scalability is another hurdle, as systems must be designed to handle large-scale data analysis.

Several trends and technologies are shaping the future of data science and data analytics. For instance, the advancement of AI and machine learning is expanding the capabilities and efficiency of data analysis. Big data analytics is also becoming more sophisticated, providing deeper insights and predictive capabilities. Additionally, there is a growing focus on the ethical aspects of data usage, pushing for transparency and fairness in data-driven decision-making.

Data Science vs. Data Analytics: Which One Should You Choose?

Choosing between a career in data science and analytics depends largely on your interests, strengths and aspirations. Data science might be the right path if you are drawn to complex mathematical models, deep statistical analysis and building algorithms. 

On the other hand, if you are interested in using data to solve business problems, improve operational efficiencies and support decision-making, then a career in data analytics could be more suitable.

To make an informed decision, assess your skills in programming, mathematical analysis and interest in business applications. Reflect on whether you thrive in abstract mathematical concepts or prefer the tangible impact of data-driven solutions in business contexts. Also, consider your long-term career aspirations and how each path aligns with your goals. 

Keep in mind that the fields of data science and data analytics are not mutually exclusive, and there is often overlap between the two. Ultimately, choose the path that resonates most with your passion, strengths and professional aspirations.

All in all, while both data science and data analytics revolve around the use of data, they differ significantly in scope and application. But, regardless of which path you choose, each offers a wealth of opportunities and the potential for a rewarding career in a field that is only expected to grow in relevance and demand.

Remember, in a world increasingly driven by data, your ability to analyze and interpret this vital resource can set you apart and propel you to success. Let UND help you unlock the full potential of data in your professional journey. Explore our cutting-edge programs to gain the expertise and skills necessary to thrive in these fields.

What tools and technologies are commonly used in data science compared to data analytics? ( Open this section)

Data science often utilizes tools such as Python, R, TensorFlow and Apache Hadoop, focusing on machine learning, deep learning and big data technologies. In contrast, data analytics frequently employs SQL, Tableau, Power BI and Excel, emphasizing data manipulation, visualization and reporting.

Are there any specific industries where data science is more prevalent compared to data analytics and vice versa? ( Open this section)

Data science is particularly prevalent in industries like technology, healthcare and finance, where predictive modeling and deep learning are crucial. Data analytics is more widespread in business sectors such as retail, e-commerce and marketing, where operational decisions and consumer behavior analysis are key.

How do data science and data analytics teams collaborate with other departments such as marketing, finance and operations? ( Open this section)

Data science and analytics teams work closely with marketing, finance and operations to provide insights that inform strategies and improve efficiency. They help these departments by analyzing trends, forecasting outcomes and optimizing processes based on data-driven evidence.

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Pathways Study Genotype Data, Alliance A011502 Secondary Analysis Headline Roswell Park ASCO24 Presentations

Breast cancer experts Dr. Arya Roy and Dr. Shipra Gandhi to give invited talks at ASCO Annual Meeting 2024

New insights on improving clinical outcomes and quality of life among breast cancer patients highlight the slate of new research presentations Roswell Park Comprehensive Cancer Center experts will deliver at the American Society of Clinical Oncology (ASCO) Annual Meeting at McCormick Place in Chicago next week.

“We are extremely proud of the great work done by our researchers, who will present results from trials and new data on prognostic biomarkers, quality of life and the impact of stress on outcomes,” says  Renuka Iyer, MD , Vice Chair of Faculty Recruitment & Retention and Professor of Oncology in the Department of Medicine. 

Two physician-scientists from Roswell Park’s Breast Cancer Medical Oncology team will give invited talks on original research during the gathering of global oncology experts, patients and patient advocates, which begins Friday, May 31.

Arya Mariam Roy, MBBS , Hematology/Oncology Fellow, Class of 2024, will present “International medical graduates (IMG) representation at international oncology conference meetings” ( Abstract 9000 ), as part of a Clinical Science Symposium on Conflicts (of Interest) and Conundrums: Perspectives from the Global Oncology Community, Saturday, June 1, 1:15-2:45 p.m. CDT in S100bc, highlighting the differences in speaking opportunities for foreign medical graduates compared to graduates of U.S. medical schools. “This is very important as we strive for equity in our professional societies,” notes Dr. Iyer. 

Dr. Roy, who received an ASCO Annual Meeting Merit Award recognizing the high quality of her work, will also give a second oral presentation, “Breast cancer polygenic risk score and patient survival outcomes in the Pathways study” ( Abstract 10502 ) during the Prevention, Risk Reduction, and Genetics Oral Abstract session on Saturday, June 1, from 3-6 p.m. CDT in S100a. Based on the Pathways Study carried out in collaboration with Kaiser Permanente Northern California, the presentation reports findings of genome-wide genotype data for nearly 4,000 women. “This insightful application of polygenic scores is an important piece of data that has too often been missing in survivorship care, and tells us in a very well-done study the value the score has to predict second cancers and survival,” says Dr. Iyer.

Also speaking during the meeting will be  Shipra Gandhi, MD, MS , Associate Professor of Oncology in the Department of Medicine, who will highlight the findings of “Association of higher baseline stress and pain with clinical outcomes: Secondary analysis from Alliance A011502” ( Abstract 11016 ) during the Quality Care/Health Services Research Rapid Oral Abstract session on Monday, June 3, from 1:15-2:45 p.m. CDT in S102. “This is the first prospective study to show that higher stress and pain levels correlate with poor survival in breast cancer, underscoring the importance of managing these common symptoms in our patients,” says Dr. Iyer. 

Roswell Park Chief of Leukemia  Eunice Wang, MD , is Co-Chair and moderator of the  Hematologic Malignancies – Leukemia, Myodysplastic Syndromes, and Allotransplant Oral Abstract session, happening Friday, May 31, from 2:45-5:45 p.m. CDT in S100bc. And And Rohit Gosain, MD , Roswell Park faculty member and Medical Director of Roswell Park Hematology Oncology Southtowns, will be a panelist on a Trainee & Early Career Lounge Discussion “Leveraging Social Media Early on in Your Career” on Friday, May 31, from 2-2:45 p.m. CDT, Hall C (adjacent to Meeting Registration). 

Other featured abstracts

Thirteen teams will present new Roswell Park-led research during poster sessions at the ASCO 2024 Annual Meeting:

• Yu Fujiwara, MD , Hematology/Oncology Fellow in Roswell Park’s Department of Medicine, is first author on “Pan-cancer B- and T-cell transcriptome analysis of CXCL13 as a predictive marker for immune checkpoint inhibitor response,” to be presented during the Developmental Therapeutics – Immunotherapy session,  Abstract 2626 , poster board 105, Saturday, June 1, 9 a.m. to noon CDT in Hall A. Dr. Fujiwara is also senior author on “Surgical outcomes in patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy versus chemotherapy alone: A systematic review and meta-analysis,” to be presented during the Lung Cancer—Non-Small Cell Local-Regional/Small Cell/Other Thoracic Cancers session,  Abstract 8020 , poster board 282, Monday, June 3, 1:30-4:30 p.m. CDT in Hall A.
• Igor Puzanov, MD, MSCI, FACP , Senior Vice President of Clinical Investigation, The Judith and Stanford Lipsey Endowed Chair in Clinical Cancer Research, Professor of Oncology and Director of Early Phase Clinical Trials, Department of Medicine, is senior author on “A phase 1/1b study of the IL-2 prodrug WTX-124 in patients with locally advanced or metastatic solid tumors after checkpoint inhibitor therapy: Initial results of the combination dose escalation with pembrolizumab” ( Abstract 2623 ), to be presented in the Developmental Therapeutics—Immunotherapy session, poster board 102, Saturday, June 1, 9 a.m. – noon CDT in Hall A.
• Riya Jayesh Patel, MBBS , Hematology/Oncology Fellow in the Department of Medicine, is first author and  Renuka Iyer, MD , Vice Chair of Faculty Recruitment & Retention, is senior author on “Genomic signature analysis and survival outcomes in cholangiocarcinoma (CCA) using oncology research information exchange network (ORIEN) database,” to be presented in the Gastrointestinal Cancer – Gastroesophageal, Pancreatic, and Hepatobiliary session,  Abstract 4101 , poster board 81, Saturday, June 1, 1:30-4:30 p.m. CDT in Hall A.
• Deepak Vadehra, DO , Assistant Professor of Oncology and Co-Leader of the Adolescent and Young Adult Translational Research Group, Department of Medicine/Gastrointestinal Center, is first author and  Sarbajit Mukherjee, MD, MS , Co-Leader of GI Translational Research and Associate Professor of Oncology, Departments of Medicine and Immunology, is senior author on “Non-colorectal GI malignancies in the young: Incidence trends, demographic disparities, and survival analysis,” to be presented in the Gastrointestinal Cancer – Gastroesophageal, Pancreatic, and Hepatobiliary session,  Abstract 4023 , poster board 3, Saturday, June 1, 1:30-4:30 p.m. CDT in Hall A.
• Malak Saud Alharbi, MBBS , Hematology/Oncology Fellow in Roswell Park’s Department of Medicine, is first author and  Saby George, MD, FACP , Professor of Oncology and Medicine and Director of Network Clinical Trials, is senior author on “Impact of obesity on immune-related adverse events and tyrosine kinase inhibitor side effects in patients with metastatic renal cell carcinoma (mRCC),” to be presented during the Genitourinary Cancer – Kidney and Bladder session,  Abstract 4560 , poster board 255, Sunday, June 2, 9 a.m. to noon CDT in Hall A.
• Saby George, MD, FACP , Professor of Oncology and Medicine and Director of Network Clinical Trials, will present “Health-related quality of life (HRQoL) with nivolumab (NIVO) subcutaneous (SC) or intravenous (IV) in patients (pts) with advanced or metastatic clear cell renal cell carcinoma (ccRCC) who have received prior therapy in the phase 3 CheckMate 67T trial,” during the Genitourinary Cancer – Kidney and Bladder session,  Abstract 4535 , poster board 230, Sunday, June 2, 9 a.m.-noon CDT, in Hall A.

Mody Amin, MD, DO, Resident Physician with the Department of Medicine, is first author and  Eunice Wang, MD , is senior author on “Impact of body mass index and karyotype on clinical outcomes in adolescents and young adults (AYA) with acute myeloid leukemia,” to be presented in the Hematologic Malignancies – Leukemia, Myodysplastic Syndromes, and Allotransplant session,  Abstract 6551 , poster board 110, Monday, June 3, 9 a.m.–noon CDT in Hall A.

Dr. Amin is first author and  Francisco Hernandez-Ilizaliturri, MD , Director of Lymphoma Research and Professor of Oncology in the Department of Medicine, is senior author on “Carfilzomib plus rituximab, ifosfamide, carboplatin and etoposide (C-RICE) led to higher response, OS, and PFS in patients with transplant-eligible relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL)” poster in the Hematologic Malignancies – Lymphoma and Chronic Lymphocytic Leukemia session,  Abstract 7042 , poster board 25, Monday, June 3, 9 a.m.-noon CDT in Hall A.

• Muhammad Salman Faisal, MBBS , Hematology/Oncology Fellow in Roswell Park’s Department of Medicine, is first author and  Saby George, MD, FACP , Professor of Oncology and Medicine and Director of Network Clinical Trials, is senior author on “Risk assessment of cardiovascular adverse events with BTK inhibitors in hematological malignancies: Insights from FAERS,” to be presented during the Symptom Science – Palliative Care session,  Abstract 12145 , poster board 274, Monday, June 3, 1:30-4:30 p.m. CDT in Hall A.
• Amy Gallagher, PsyD, MSHCT , Director, Employee Resiliency Program, is first author and  Amy Case, MD , Chief Wellness Officer, is senior author on “Utilizing data to drive solutions for healthcare worker wellbeing,” during the Medical Education and Professional Development session,  Abstract 9012 , poster board 1, Monday, June 3, 1:30-4:30 CDT in Hall A.

Karan Jatwani, MD , Hematology/Oncology Fellow in Roswell Park’s Department of Medicine, is first author on “End-of-life care preferences in patients with gallbladder cancer: Shifting patterns and disparities over two decades,” to be presented during the Symptom Science – Palliative Care session,  Abstract 12036 , poster board 165, Monday, June 3, 1:30-4:30 p.m. in Hall A.

Dr. Jatwani is also first author on, and  Dharmesh Gopalakrishnan, MD , Assistant Professor of Oncology in the Department of Medicine/Genitourinary Center, is senior author on “Risk assessment of myelosuppression with PARP inhibitors in prostate cancer: Insights from FAERS” during the Symptom Science – Palliative Care session,  Abstract 12144 , poster board 273, Monday, June 3, 1:30-4:30 p.m. in Hall A.

Clinical Trials in Progress

Additionally, three Roswell Park teams will update their international colleagues on clinical trials that are currently underway:

• Robert Fenstermaker, MD , Chair of Neurosurgery, is senior author on "A multicenter, randomized controlled phase 2b trial of survivin vaccine SurVaxM plus adjuvant temozolomide for newly diagnosed glioblastoma (SURVIVE)" ( Abstract TPS2099 ), to be presented by Manmeet Singh Ahluwalia of Miami Cancer Institute during the Central Nervous System Tumors session, poster board 393a, Saturday, June 1, 11:30 a.m.-1 p.m. CDT in Hall A.  
• Christos Fountzilas, MD , Associate Director for Solid Tumors/Early Phase Clinical Trials Program, Associate Professor of Oncology and Co-Leader, Gastrointestinal Clinical Disease Team, Department of Medicine, is first author on “A randomized, multi-center, phase 2 study of ivaltinostat (Ival) plus capecitabine (Cape) versus capecitabine alone in the maintenance setting in patients with metastatic pancreatic adenocarcinoma (mPDAC)” ( Abstract TPS4206 ), to be presented in the Gastrointestinal Cancer – Gastroesophageal, Pancreatic, and Hepatobiliary session, poster board 173a, Saturday, June 1, 1:30-4:30 p.m. CDT in Hall A.
• Shipra Gandhi, MD, MS , Associate Professor of Oncology in the Department of Medicine, is first author and  Elizabeth Repasky, PhD , Vice Chair, Distinguished Member, Professor of Oncology and The Lawrence J. Minet Endowed Chair in Immunology, Department of Immunology, is senior author on “A phase II trial to assess the impact of ?2 adrenergic blockade in immune checkpoint inhibitor (ICI)– refractory metastatic triple negative breast cancer (mTNBC)” ( Abstract TPS1141 ), to be presented during the Breast Cancer – Metastatic session, poster board 108b, Sunday, June 2, 9 a.m.-noon CDT in Hall A.

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Calliditas Therapeutics Presents Data at the 61[st] European Renal Association Congress

Calliditas Therapeutics AB (Nasdaq: CALT, Nasdaq Stockholm: CALTX) (“Calliditas”), today announced the presentations of an additional efficacy analysis of Nefecon (TARPEYO® (budesonide) delayed release capsules)) as well as a real-world analysis of the use of systemic glucocorticoids (SGC) in IgA nephropathy (IgAN). These were presented at ERA 2024 virtually and in Stockholm on May 23 – 26, 2024.

The presented efficacy analysis of Nefecon and sparsentan showed that treatment with Nefecon for 9 months was associated with estimated glomerular filtration rate (eGFR) benefit compared with continuous treatment with sparsentan.

Additionally, the findings of a real-world analysis of challenges associated with the use of systemic glucocorticoids demonstrate significant side effects and costs for IgAN patients treated with systemic glucocorticoids (SGC), such as Prednisone and Prednisolone.

“It was wonderful to participate in ERA 2024 and present data contributing to the discussion on the need for effective treatments in IgAN,” said Richard Philipson, Chief Medical Officer of Calliditas, “ We continue to gather evidence that highlights the importance of treating the underlying autoimmune pathogenesis associated with IgAN, and we believe TARPEYO, as the only approved immunomodulating therapy designed to target the production of Gd-IgA1, has the potential to become a cornerstone therapy in IgAN.”

Poster presentation details are below and will be available on the Presentations and Publications page on the Calliditas’ corporate website following the meeting.

Presentation Analyses:

Title: “Matching-adjusted indirect comparison of eGFR in patients with immunoglobulin A nephropathy treated with Nefecon (TRF budesonide) or sparsentan”

A matching-adjusted indirect comparison (MAIC) methodology is a widely accepted and relevant methodology for comparing treatments across trials in the absence of head-to-head comparisons. Here the effects of Nefecon, marketed as TARPEYO® and sparsentan, marketed as FILSPARI™, on kidney function deterioration in patients with IgAN were compared, as assessed by eGFR change from baseline at 9, 12 and 24 months. Results from the MAIC showed significantly favorable effects of Nefecon versus sparsentan on eGFR across all time points analyzed. Mean differences in the absolute change in eGFR of 5.68mL/min/1.73 m2 (95% credible interval [Crl] 3.14, 8.20; p<0.001), 3.48 mL/min/1.73 m2 (95% Crl 0.97, 5.97; p=0.006) and 3.28 mL/min/1.73 m2 (95% Crl 0.02, 6.51; p=0.048) were observed when comparing Nefecon with sparsentan at 9 months vs 36 weeks, 12 months vs 48 weeks, and 24 months vs 106 weeks, respectively. This efficacy analysis showed that treatment with Nefecon 16 mg/day for 9 months was associated with greater eGFR benefit compared with continuous treatment with sparsentan 400 mg/day over 2 years, with significant differences observed as early as 9 months after treatment initiation and sustained up to 2-years of follow-up. While the rigor of well-controlled head-to-head clinical trials cannot be replicated, MAIC is also a widely accepted and relevant methodology for comparing treatments across trials in the absence of head-to-head comparisons 1,2, *

Title: “Real-world challenges associated with the use of systemic glucocorticoids in a US IgAN cohort”

Per Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, patients at high risk of progressive chronic kidney disease despite maximal supportive care can be considered for a 6-month course of systemic glucocorticoids (SGC), although important risks of toxicity and contraindications must be considered. There is currently limited real-world evidence describing the impact of the use of SGC on treatment‑emergent toxicity and healthcare resource utilization (HCRU) in patients with IgAN. Our findings demonstrate significant side effects and costs for IgAN patients treated with SGC compared with patients not treated with SGC. Increases in severe infection incidents, inpatient visits, emergency department admissions, and ambulatory visits, underscore the careful consideration of treatment-emergent toxicity prior to initiating SGC therapy in patients with IgAN.

• * The optimization strategy in NefIgArd (optimized RASi) differed from the optimization strategy in PROTECT (IR), and anchoring of the two trials at optimized RASi/IR might lead to biased results. However, we also evaluated an unanchored MAIC in a sensitivity analysis and found very similar results.
• The MAIC method can only adjust the relative effect estimates for any observed effect modifier available in the data, but it cannot adjust for unobserved or unobservable effect modifiers. A significant number of potential treatment effect modifiers were included in the present analysis: age, sex, race, baseline eGFR, UPCR, UACR, and urinary protein excretion.

TARPEYO is indicated to reduce the loss of kidney function in adults with primary immunoglobulin A nephropathy (IgAN) who are at risk for disease progression.

Important Safety Information

Contraindications: TARPEYO is contraindicated in patients with hypersensitivity to budesonide or any of the ingredients of TARPEYO. Serious hypersensitivity reactions, including anaphylaxis, have occurred with other budesonide formulations.

Warnings and Precautions

Hypercorticism and adrenal axis suppression: When corticosteroids are used chronically, systemic effects such as hypercorticism and adrenal suppression may occur. Corticosteroids can reduce the response of the hypothalamus-pituitary-adrenal (HPA) axis to stress. In situations where patients are subject to surgery or other stress situations, supplementation with a systemic corticosteroid is recommended. When discontinuing therapy or switching between corticosteroids, monitor for signs of adrenal axis suppression.

Patients with moderate to severe hepatic impairment (Child-Pugh Class B and C respectively) could be at an increased risk of hypercorticism and adrenal axis suppression due to an increased systemic exposure to oral budesonide. Avoid use in patients with severe hepatic impairment (Child-Pugh Class C). Monitor for increased signs and/or symptoms of hypercorticism in patients with moderate hepatic impairment (Child-Pugh Class B).

Risks of immunosuppression: Patients who are on drugs that suppress the immune system are more susceptible to infection than healthy individuals. Chickenpox and measles, for example, can have a more serious or even fatal course in susceptible patients or patients on immunosuppressive doses of corticosteroids. Avoid corticosteroid therapy in patients with active or quiescent tuberculosis infection; untreated fungal, bacterial, systemic viral, or parasitic infections, or ocular herpes simplex. Avoid exposure to active, easily transmitted infections (e.g., chicken pox, measles). Corticosteroid therapy may decrease the immune response to some vaccines.

Other corticosteroid effects: TARPEYO is a systemically available corticosteroid and is expected to cause related adverse reactions. Monitor patients with hypertension, prediabetes, diabetes mellitus, osteoporosis, peptic ulcer, glaucoma or cataracts, or with a family history of diabetes or glaucoma, or with any other condition where corticosteroids may have unwanted effects.

Adverse reactions: In clinical studies, the most common adverse reactions with TARPEYO (occurring in ≥5% of TARPEYO treated patients, and ≥2% higher than placebo) were peripheral edema (17%), hypertension (12%), muscle spasms (12%), acne (11%), headache (10%), upper respiratory tract infection (8%), face edema (8%), weight increased (7%), dyspepsia (7%), dermatitis (6%), arthralgia (6%), and white blood cell count increased (6%).

Drug interactions: Budesonide is a substrate for CYP3A4. Avoid use with potent CYP3A4 inhibitors, such as ketoconazole, itraconazole, ritonavir, indinavir, saquinavir, erythromycin, and cyclosporine. Avoid ingestion of grapefruit juice with TARPEYO. Intake of grapefruit juice, which inhibits CYP3A4 activity, can increase the systemic exposure to budesonide.

Use in specific populations

Pregnancy: The available data from published case series, epidemiological studies, and reviews with oral budesonide use in pregnant women have not identified a drug-associated risk of major birth defects, miscarriage, or other adverse maternal or fetal outcomes. There are risks to the mother and fetus associated with IgAN. Infants exposed to in-utero corticosteroids, including budesonide, are at risk for hypoadrenalism.

Please see Full Prescribing Information .

About TARPEYO

TARPEYO is an oral 4mg delayed release formulation of budesonide, designed to remain intact until it reaches the ileum. Each capsule contains coated beads of budesonide that target mucosal B-cells present in the ileum, including the Peyer’s patches, which are responsible for the production of galactose-deficient IgA1 antibodies (Gd-Ag1) causing IgA nephropathy.

About Primary Immunoglobulin A Nephropathy

Primary immunoglobulin A nephropathy (IgA nephropathy or IgAN or Berger’s Disease) is a rare, progressive, chronic autoimmune disease that attacks the kidneys and occurs when galactose deficient IgA1 is recognized by autoantibodies, creating IgA1 immune complexes that become deposited in the glomerular mesangium of the kidney. This deposition in the kidney can lead to progressive kidney damage and potentially a clinical course resulting in end- stage renal disease. IgAN most often develops between late teens and late 30s.

For further information, please contact:

Åsa Hillsten, Head of IR & Sustainability, Calliditas

Tel.: +46 76 403 35 43, Email: asa.hillsten@calliditas.com

The information was sent for publication, through the agency of the contact persons set out above, on May 28, 2024, at 14.00 p.m. CET.

• CALT_ERA Data Release_ENG