five ways of data presentation in research

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

More tips with ahaslides.

• Marketing Presentation
• Survey Result Presentation
• Types of Presentation

Start in seconds.

Get any of the above examples as templates. Sign up for free and take what you want from the template library!

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

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

Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

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

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

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

Partner Center

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 !

More zenpedia articles

Engage your audience: The secret weapon of active listening in presentations

5 Essential leadership presentation ideas to master

Empathy in communication: The role of empathetic communication for successful business

Get the latest from Prezent community

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

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

Share this:

• Share on Tumblr

Top tips for presenting your data according to research

What do we really know about how to present complex data in ways that are easy to understand and have impacts that might help address complex issues such as climate change? Dr Lucy Richardson explores some of the useful tips provided by data visualisation and communication research that can help you effectively communicate complex information.

This article is part of the ISC’s Transform21 series, which features resources from our network of scientists and change-makers to help inform the urgent transformations needed to achieve climate and biodiversity goals.

Over the last year or so, many people across the world have become used to seeing charts and graphs with COVID-19 statistics in their news feeds, but all charts are not created equal when it comes to effectively communicating a key message.

Researchers have been examining how different aspects of data presentation influence audiences for many years. They have looked at the issue from diverse angles such as which components are viewed in what order and why, and whether text, graphs or maps are more engaging and easily understood. These diverse research questions have been addressed using a wide variety of methods ranging from tracking audience eye movements to surveys and social media polls. From this collection of research, we have gained valuable insights that can help make data visuals more effective communication tools.

A useful framework to think about when designing data visualisations follows the broad process of audience interaction with the presented information: (a) first the audience perceives the information (b) then they think about the information, and (c) then some sort of change or impact occurs due to those thoughts.

Perceiving the information (Perception)

Assuming that your data visualisation is presented to your target audience in a time and place where they are likely to see it, your audience needs to be able to perceive and differentiate each of the key components of your visualisation in order to discern its meaning.

Perception tends to happen in sequence, following a visual hierarchy of attention based on the following characteristics of any object (including maps and graphs): size, colour, contrast, alignment, repetition, proximity, whitespace, and texture and styles. Within each of these elements are further sub-hierarchies. For example, people tend to notice large elements before smaller ones, and bright colours before muted ones. Similarly, dramatic contrasting components are noticed more than those with less contrast.

The effect of these hierarchical elements can be impacted by perception challenges and should be carefully considered to ensure that they promote your message rather than confusing or distracting your audience. There are a range of different perception challenges that can impact on the effectiveness of data visualisations, but did you know there are actually seven different forms of colour blindness ? You can even run your data visualisation through a colour blindness simulator to see how it might be viewed by someone with these challenges.

Thinking about the information (Cognition)

When your audience thinks about and derives meaning from information they perceive, this is known as cognitive processing. It includes thinking, knowing, remembering, judging, and problem-solving; any number of which may be used when processing information associated with visualised data.

Some things you can do to help encourage the desired interpretation of meaning from your data visualisation include providing chart titles that are the main message rather than just a description of the content. A title such as ‘Higher amounts of green vegetation in cities is associated with lower summer temperatures’ is much more effective at guiding meaning-making than titling the same chart as ‘Green vegetation and temperature in Australian cities’.

Some topic areas that may require data visualisations can also have underlying psycho-social (psychological, social and/or political) factors that should be considered. This is particularly the case for climate change, a heavily politicised issue that is quite polarising in some countries. When presenting data relating to climate change, some valuable tips include:

• Avoid catastrophic messaging that can cause people to shut down as a coping response to their fear.
• Include solutions-based information can help counteract fear by promoting a sense that climate change can be addressed.
• Provide locally relevant information where possible, as this will resonate more strongly. People are naturally most interested in what happens in their local area.
• Where possible, consider if there are other ways to cover the issue without mentioning ‘climate change’ if communicating to audiences who may not accept current scientific evidence of its existence and urgency. This is easier for messages relating to adapting to changes in climate than mitigation, as there are often diverse benefits beyond climate change that can be used to frame adaptation information.

It’s also important to recognize that people are generally more likely to remember meaning than detail. This means that people are more likely to remember a trend—such as it’s getting ‘worse’ or ‘better’, ‘increasing’ or ‘decreasing’—but may not remember the specific amount or rate of that increase or decrease.

Changes effected (Impact)

There are a range of possible impacts that might arise from audiences viewing your data visualisation. These could be changes in thought (for example, awareness, understanding, attitudes or concern), or changes in behaviour (for example, information seeking, discussion with others, or even adoption of climate-friendly behaviours). The likelihood of change being effected due to your data visualisation will be enhanced by ensuring your messages are clear and relevant, where clarity will come from effectively addressing perception and cognition considerations and relevance will come from appropriate message framing and consideration of psycho-social factors. Knowing the kind of change you want to achieve will be critical in determining how best to integrate these various factors into your work.

Alternative formats

While most people wishing to present complex scientific data tend to think of charts, graphs, maps, and infographics, it is also possible to present information for perception by other senses such as through sound. Some researchers have been testing data sonification as an alternative to visual data representation. Sonification takes each data point and applies a mix of sound elements that can allow trends to be distinguished—for example, pitch, volume, and choice of instrument—to provide an audio representation of the information. NASA has done this so that people can ‘listen’ to the Milky Way Galaxy , and researchers at the Monash University Climate Change Communication Research Hub have sonified cyclone Debbie ’s movements around Australia in 2017.

A free best practice guide has been developed based on a review of data visualisation research. Hopefully, it will help you decide how you can best present your data for effective perception, cognition and impact. You can access the Best practice data visualisation: Guidelines and case study on the Monash Climate Change Communication Research Hub website .

Lucy Richardson

Dr Lucy Richardson is based at the Monash Climate Change Communication Research Hub, Monash University, on the lands of the Kulin Nations, Melbourne, Australia, and a member of the  Commonwealth Futures Climate Research Cohort  established by The Association of Commonwealth Universities and the British Council to support 26 rising-star researchers to bring local knowledge to a global stage in the lead-up to COP26.

The header image was created by NASA’s Scientific Visualization Studio to support a series of talks from NASA scientists for COP26. It is a still from a video that shows the atmosphere in three dimensions and highlights the accumulation of CO 2  during a single calendar year. You can watch the visualisation and find out more about the data on which it’s based here .

Related Items

Call for experts to participate in the scoping meeting for the ipcc seventh assessment report | deadline: 3 june.

INGSA 2024: Science advisors of the world united

Asia and the Pacific call for better science advice links to government

Insights from Dr. Pedro Jaureguiberry, Frontiers Planet Prize National Champion, on biodiversity loss and planetary boundaries

Plastic Treaty negotiations must prioritize health

2024 National Champions in line for the Frontiers Planet Prize announced

publications

A guide for policy-makers: evaluating rapidly developing technologies including ai, large language models and beyond.

Disaster risk reduction: UNDRR and ISC to review Hazard Information Profiles ahead of 2025 Global Platform

The ISC’s Centre for Science Futures secures over one million dollars grant to explore the impacts of AI on science systems in the Global South 

New report on future of wood supply highlights importance of innovation

1 in every 10 women in the world lives in extreme poverty

2024 World Data System Scientific Committee (WDS-SC) Nominations are Now Open

Pacific Islands Academy of Sciences and Humanities: A Pivotal Step Towards a Resilient Future

Spacecrafts burning in the upper atmosphere: what consequences on climate?

Establishment of the ISC expert group on plastic pollution

World Climate Research Programme launches a Lighthouse Activity on Climate Intervention Research

Kigali Declaration: Climate science for a sustainable future for all

Climate inequality: The stark realities and the road to equitable solutions

Biodiversity data is distorted by past inequities. Scientists are wrestling to get a clearer picture.

Early-career researchers' insights on climate

Science reading list from the International Science Council

Readers' choice: our most shared science blogs this year

Seizing the momentum: a year of climate science advocacy

Large Ocean States and climate change: a plea for adaptation

COP28 agreement: embracing the urgency of the scientific consensus?

Fostering tomorrow’s science: the ISC's engagements with Early and Mid-Career Researchers in 2023

"What's holding us back?": how economists and social scientists might hold the key to climate action

Podcast with Cory Doctorow: Science Fiction and the Future of Science: Leveraging Digital Advancements for the Future

"Beyond weathering the storm": rethinking climate resilience

For science-based decision-making on the climate emergency: 10 new insights in climate science

COP28: The International Science Council's engagement in climate science

Scientific communication: Join the Ethnografilm festival for a filmmaking experience in Paris

Policy Brief: Global Sea-Level Rise

The costs of shifting scenarios: why the ipcc should maintain consistent vocabulary in climate assessments.

New ISC policy brief: A call for a formal scientific voice in the global fight against plastic pollution

Converging and interdependent crises are amplifying the impacts of one another with often devastating consequences

Reframing Trust in Science for Multilateral Policy: Insights from the Science Journalists Forum

One Planet Polar Summit, scientists striving to bridge the science-policy gap for urgent action: "every tenth of a degree Celsius matters"

Science Fiction and the Future of Science

Call for nomination of experts to draft an outline of the IPCC Special Report on Climate Change and Cities - deadline 15 November

Kigali declaration pledges to bridge climate injustice

The human dimension of disaster risk reduction: social sciences and climate adaptation 

From monsoon joy to fear: a climate crisis awakening

Global solidarity for climate justice: perspectives from an early-career researcher

One world, one climate: a planetary call to action

Transdisciplinary research for sustainability solutions in urban Africa

Cultivating a proactive approach to crises: first meeting of the UNEP/ISC foresight expert panel

Clearing the air: is ammonia our key to cleaner skies?

Unearthing sustainability: soil science for the SDGs

Helping newsrooms get disaster risk ready, an International Science Council and World Editors Forum collaboration

Our Future Depends on Us: Antarctic climate change and the environment

Unveiling the health benefits of forests and trees

How do we talk about science and uncertainty?

From Authoritarian Threats to Funding Disparities: Key Challenges in Global Science

Frontiers Planet Prize, second edition: celebrating the world’s most innovative sustainability scientists by 1 November

Protecting the ocean: 5 essential reads on invasive species, overfishing and other threats to sea life

The Frontiers Planet Prize unveils its champions

ISC's nomination track ensures transdisciplinary and global representation in UNEP's Scientific Advisory Group for GEO-7 Assessment

UN 2023 Water Conference carries new engagements towards realizing SDG6 and future avenues for a decade of action

IPCC report: the world must cut emissions and urgently adapt to the new climate realities

ISC-BBC StoryWorks partnership ends on a high note, delivering some of the highest engagement for the BBC

Youth perspectives on climate ‘COP’ negotiations and ways to get involved 

Public participation in scientific programmes: Citizen science for biodiversity  

The ISC and UNEP to cooperate on advancing the use of science in environmental policy and decision-making

Science in Times of Crisis Episode 3 - The Fallout of Conflict: The Arctic and Outer Space

Innovative approaches to storytelling in science celebrated at Women in Science Film Festival in Cape Town

Ten key messages for the Convention on Biological Diversity

COP27 ends with commitment on financial support

There are 8 years left to meet the UN Sustainable Development Goals, but is it enough time?

Special interview series on COP 27- Interview with Nick Perkins about climate change and science communication

People on the front lines of climate change must be included in climate action

Are we in a new era of climate adaptation implementation? The role of regional governments in facilitating local action

In the face of climate threats, we cannot afford not to act

The Role of Integrated Science in Understanding the Earth-Human System

In the face of extreme weather events, coordinated global action to address climate change is needed at COP27

Perspectives from the ISC network on expectations for the COP

Professor Carlos Lopes on why Africa needs to stick to renewables despite the temptation of gas

How can scientists make a difference at COP27?

Call for contributions for the Sustainability Research & Innovation Congress (SRI 2023)

Frontiers Planet Prize: Science for a Sustainable Planet

The scientific community can support the call for decisive action at cop27.

Stories of transformations to sustainability

Happy birthday to the Montreal Protocol – the most successful environmental treaty of all time?

Risk of passing multiple climate tipping points escalates above 1.5°C global warming

World Youth Skills Day 2022: From Resilience to Fearlessness 

WorldFAIR: Global cooperation on FAIR data policy and practice – Kick-Off Meeting introduces major new initiative to advance implementation of the FAIR data principles

Six Takeaways on Science Communication from our Talk Back Better Webinar Series

What's on the horizon for scientific data services? The latest from the World Data System

Implementing FAIR data principles – what’s behind the acronym?

WorldFAIR: Global cooperation on FAIR data policy and practice

A letter to our fellow citizens of Earth

The Biggest Carbon Sink of All

Policy Brief: Harnessing data to accelerate the transition from disaster response to recovery

CODATA and ISC celebrate Metrology in the Digital Era on World Metrology Day

Managed retreat from areas threatened by floods can catalyse positive social transformations

‘Now or never’ to limit warming to 1.5°C, according to latest IPCC report

How Science Affects my Everyday Life as a Fourteen-Year-Old

Joint Statement of Intent on the Digital Transformation in the International Scientific and Quality Infrastructure

The African Open Science Platform begins to take shape

Video tutorials on science ethics and science communication

Women Leading on Equitable and Inclusive Solutions to address the Climate Emergency: Webinar

Principles and Structures of Science Advice: An outline

The window for climate action to avoid dangerous systemic risks is narrowing, warns latest IPCC report

The transformative potential of managed retreat in the face of rising sea levels

The seven warmest years on record were the last seven

The Paris Agreement is working as intended, but we’ve still got a long way to go

An early career perspective on the science-policy interface in the decade of climate action following COP26

Holiday binge-watching: Science edition

Our most popular stories from 2021

Global Risks Perceptions Report 2021 released

Staying below 1.5°c: what are the chances.

Picturing the future of complex, cascading climate risks

If universities want to hit climate targets, they should use their land for carbon offsetting

Emerging Climate Risks and what will it take to limit global warming to 2.0°C?

What Antarctica can teach us about global climate change

How to teach energy transition and climate in business schools

Convening the scientific knowledge required to boost climate action

Major scientific assessment of the Amazon region issues urgent call to end deforestation and avert tipping points

Urban Health and Wellbeing in the Anthropocene

Compelling stories, curious science: #UnlockingScience launched

Building resilience in a climate challenged world.

Climate change projections for Pakistan: the need for sustainable solutions to protect its people and biodiversity

Four considerations for accelerating progress on climate change at the science-policy interface

Ten New Insights in Climate Science 2021 report highlights critical research and policy implications for addressing the climate crisis

Ahead of COP26, Ekanem I. Braide shares her perspective on the priorities for action and the role of science

Climate risk assessment gaps: seamless integration of weather and climate information for community resilience

Increasing the participation of women in the climate change debate, including as leaders, is essential for a carbon-zero future

Deepening interactions between science and policy on the way to COP26: What role for science publishers?

Predicting the climate of the next decade

Global citizens and scientists on how to achieve a thriving net zero future

Big Earth Data Advances Science and Engineering for SDGs

Call for emergency action to limit global temperature increases, restore biodiversity, and protect health

Professor Karen O'Brien: People are the most powerful solution to climate change

Climate justice and the decarbonization of shipping

A call for reconceptualizing carbon pricing policies with both eyes open

Coastal communities in the Arctic rely on structural measures to adapt to climate change, but should they?

Enabling positive tipping points towards global sustainability in uncertain times

Solar Geoengineering at a Standstill?

Scientific advances underpinning latest IPCC report demonstrate need for rapid action

Deep and sustained emissions reductions required to head off rapid climate change affecting all regions of the world

The carbon skyscraper

What we are reading

The mental health burden of climate change is growing – now it’s time to act

Climate finance - a sticking point for the COP26?

Call for papers: Discussion meeting on statistical aspects of climate change

World Youth Skills Day 2021: resilience and creativity

Tell me a story – why climate change communication needs to embrace our childlike curiosity

Climate change solutions in focus ahead of COP26

Four insights on collaborating at scale to advance climate adaptation

We're in the midst of a global wake-up call

A Global Survey of Science Offers Hope and Challenging Lessons

Can zero emissions and economic growth go together? Yes, but conditions apply

Climate explained: why is the Arctic warming faster than other parts of the world?

Why water-driven migration and displacement must be part of the climate agenda

COP26 Climate Action Champion Nigel Topping on creating an 'ambition loop' for bolder pathways to change

Climate scientists: concept of net zero is a dangerous trap

Building a climate future requires a regional approach

Designing responsible policy pathways for a zero-carbon transition

Earth Day 2021: we need bold, creative, innovative solutions

Strengthening the links between science and society for action on climate change in France

Target high-carbon emitters to accelerate green transition, say leading experts on behavioural change

What would a 3°C warmer world mean for Australia?

Citizen scientists: perhaps without a degree but certainly making a difference

The new climate change activism is emotional, and it’s a good thing

ISC Science Communications Network

Top ten insights in climate science from the past year

Mary Robinson - No Time to Spare for the Paris Climate Promise

Global change research and the COVID-19 pandemic

Children’s comic book series introduces solar-terrestrial physics

Working together: Future Earth and WCRP announce partnership to jointly address major societal challenges

Global Carbon Budget 2020 Finds Record Drop in Emissions

What are the climate breakthroughs in 2020?

Taking the temperature of the Paris Agreement: perspectives from our community

The “How” of transformation

Project Syndicate talks with Mary Robinson on climate change and her new podcast

Taking stock of progress on global change: what to expect from the unep global assessments synthesis report.

Ozone hole over Antarctica ‘largest’ and ‘deepest’ it has been in years

Redefining business as usual for scientific publishing

Learning from COVID-19 and building more resilient food systems

Global Science TV: Arctic ice keeps shrinking. Here’s what that means for all of us

The COVID-19 Pandemic Illustrates the Need for Open Science

World’s first high-level science initiative dedicated to the survival of the Amazon

Statistical thinking as an essential skill for reading the news

Global Science TV: Why can't we deal with climate change as urgently as COVID-19?

Why can’t we deal with climate change as urgently as COVID-19?

Earth day 50th anniversary calls for climate action.

A Data Ecosystem to Defeat COVID-19

Making data work for cross-domain grand challenges

Tackling Climate Change with COVID-19 Urgency - by ISC Patron, Mary Robinson and ISC President, Daya Reddy

Four major international data organizations join forces to optimize the research data ecosystem, launching a COVID-19 appeal as their first joint action

Call for Expressions of Interest to Host the World Data System International Programme Office (Partial Submissions Allowed)

Join the first ‘Transformation Talks’ webinar to explore how research communication can be transformative

TROP ICSU - Climate Change Education Across the Globe

A rapidly-evolving new normal: Pep Canadell comments on Australia’s Fires

ISC joins WCRP in celebrating its 40th year of international climate science

COP25: Time for action

Call for nominations of experts to serve on the Editorial Board of the IPCC Emission Factor Database

Why we need a UN charter

World Data System Data Stewardship Award 2019

Achieving Risk Reduction Across Sendai, Paris And The SDGs

Disaster Loss Data In Monitoring The Implementation Of The Sendai Framework

The political challenge of achieving transformations to 1.5ºC – the role of social justice

A vision for the African Open Science Platform

COP24 side event on The CitiesIPCC Research and Action Agenda for effective urban responses to climate change

International data week gets underway in Gaborone, Botswana

Transforming southern African cities in a changing climate – Q and A with Alice McClure from the University of Cape Town

Review of the World Climate Research Programme (WCRP)

Ten essentials for research that responds to the climate challenge

Vacancy: executive director of the icsu world data system (wds) (re-advertised).

Vacancy: Communications Intern for the LIRA 2030 programme

News from LIRA2030: Seedbeds of Transformation conference, South Africa

World data system workshop held in rio de janeiro.

ICSU World Data System International Technology Office to open in Canada

Pavel Kabat appointed WMO Chief Scientist and Research Director

Why the IPCC's upcoming 1.5°C report offers an unexpected glimpse of hope

The IPCC at 30: Is the 1.5°C Special Report a turning point?

The origins of the IPCC: How the world woke up to climate change

The state of biodiversity in the regions: What to expect from the IPBES in 2018

Highlights of 2017

Why 2018 is a big year for global environmental assessments

Heide Hackmann recieves award for science diplomacy

IAMAS urges United States to continue support of Earth Observation systems

Cop23 side event on climate change- when and where will habitability limits be reached.

Largest Ever Science Gathering in the Middle East for World Science Forum 2017

Belmont Forum announces Mustapha Mokrane as new Co-Lead of Open Data Initiative

Future of science: voices from our partners.

Future of science: Voices from our partners

Call for scientific and non-academic reviewers for lira 2030, early career scientists gather for lira trans-disciplinary workshop in uganda.

Focus on Interactions: Second Nexus Conference Announced for 2018

International Council for Science calls on United States to support international efforts to combat dangerous climate change

ICSU President to receive International Meterological Prize from WMO

ICSU at the U.N. Ocean Conference

Science Plan on Global Environmental Change – ICSU Regional Office for Africa

Climate change.

"Open Data in a Big Data World" accord passes 120 endorsements

NZ Government thanks IRDR and CODATA groups for their help following 2016 Kaikoura earthquake

Making a case for science at the United Nations

ICSU Unions receive award to launch multi-year initiatives in science outreach and education

New commentary published: climate research must sharpen its view, african open science platform to boost the impact of open data for science and society, icsu co-organizes side event at cop22 on urgent questions in climate research.

Future Earth-PROVIA-IPCC risks and solutions workshop: livestream available

Call for nominations - early career scientists at habitat iii, open data in a big data world, world data system marks fifth anniversary of international programme office, advisory note: science communication (2010/2016), leading science groups urge global accord on open data in a big data world, 30 years pioneering collaboration on global change research: igbp closes down dec 2015, the international council for science and climate change.

Twelve things we've learned on the Road to Paris

Science International to agree international accord on open data

New scientific committee and chair appointed for icsu world data system, landmark scientifc data conference ends with strong support of data sharing for sustainability, future earth 2025 vision.

Future Earth Strategic Research Agenda 2014

Open access to scientific data and literature and the assessment of research by metrics

Icsu-endorsed initiative sustainable deltas 2015 launches in rotterdam.

International Council for Science endorses open access to scientific record; cautions against misuse of metrics

Celebrating 30 years of global change research, call for proposals: transformations to sustainability.

Review of CODATA, the Committee on Data for Science and Technology

Future earth initial design report: executive summary.

Annual global carbon emissions set to reach record 36 billion tonnes in 2013

World social science report 2013: changing global environments.

Scientists meet at the UN for Expert Group Meeting on Sustainable Development Goals

Black carbon report from igbp project generates significant media coverage, ad-hoc strategic coordinating committee on information and data (sccid report), how to describe nanomaterials – an icsu workshop in paris, the international council for science pledges support for scientists in the l’aquila case, icsu-unesco regional science and technology workshops for rio+20, climate research in the next decade of earth system research, icsu's new world data system opens new international progamme office in tokyo, international programme office of icsu’s new world data system opened, icsu foresight analysis peer-reviewed, workshop on the description of nanomaterials, advisory note on access to shared data to reduce global inequality, rio+20 policy briefs released by the gec programmes, advisory note on sharing scientific data, with a focus on developing countries, icsu releases statement on the controversy around the 4th ipcc assessment, polar year comes to a close, a vision for earth system research: have your say, polar research reveals new evidence of global environmental change, upcoming release of new evidence about change in the polar regions, review of the international geosphere-biosphere programme, review of the world climate research programme (2009), ipy polar day focusing above the polar regions, international science community agrees on first steps to establish a global virtual library for scientific data, icsu launches new programme to understand the human impact on earth’s life-support systems, international council for science (icsu) launches major research programme on natural disasters, ipy polar day focusing on people, ipy polar land and life day, report from the ad hoc strategic committee on information and data, ipy day focusing on changing earth, ipy day focusing on ice sheets, ipy presents sea ice day, global launch of international polar year (ipy) 2007-2008, co2 rise heightens concern over vulnerability of polar regions, icsu hosts conference on hazards and disasters, icsu and climate science (2006), at pivotal event in china, the international council for science releases new strategy to strengthen international science for the benefit of society, international experts call for new approach to ensure challenges to data access and management don’t slow scientific progress, icsu pursues new initiative that challenges science to do more to prevent natural disasters, socioeconomic data in relation to the integrated global observing strategy partnership igos-p (2004), priority area assessment on scientific data and information, cern announces major conference on the information society, icsu launches an agenda for action in advance of the world summit on the information society, science in the information society: policy issues for scientific information (2003), science in the information society: optimizing knowledge (2003), science in the information society: decision making and governance (2003), science in the information society: universal access to scientific knowledge (2003), icsu/codata launch online forum for world summit on the information society.

VIEW ALL RELATED ITEMS

https://council.science/current/blog/tips-for-presenting-your-data-research/">

• Disclaimer: The translations are automatically generated by Google Translate and may contain errors. The ISC is not liable for any damage or issues that may arise from these translations. You can provide your feedback by emailing us at [email protected] Note: Please note that the ‘Science Summit at the UN General Assembly’ in September 2023 is not an event of the International Science Council. The International Science Council has no association with this event nor with the organizer of the event.

Privacy Overview

Data presentation

Cite this chapter.

• Sotirios Sarantakos 2  

330 Accesses

The process of grouping the data, that is, integrating the responses in terms of their origin and degree of similarity, was introduced in the previous chapter when we were discussing the process of counting. In the discussion that follows, grouping will be dealt with in more detail. In addition, we shall introduce other aspects of grouping and some aspects of arithmetical operations employed by social researchers to gain an overview of the data as a whole system of information per se, well as the relationship between its parts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Unable to display preview.  Download preview PDF.

Author information

Authors and affiliations.

Charles Sturt University, Australia

Sotirios Sarantakos

You can also search for this author in PubMed   Google Scholar

Copyright information

© 1998 Sotirios Sarantakos

About this chapter

Sarantakos, S. (1998). Data presentation. In: Social Research. Palgrave, London. https://doi.org/10.1007/978-1-349-14884-4_15

Download citation

DOI : https://doi.org/10.1007/978-1-349-14884-4_15

Publisher Name : Palgrave, London

Print ISBN : 978-0-333-73868-9

Online ISBN : 978-1-349-14884-4

eBook Packages : Palgrave Social & Cultural Studies Collection Social Sciences (R0)

Share this chapter

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Home Blog Presentation Ideas How to Create and Deliver a Research Presentation

How to Create and Deliver a Research Presentation

Every research endeavor ends up with the communication of its findings. Graduate-level research culminates in a thesis defense , while many academic and scientific disciplines are published in peer-reviewed journals. In a business context, PowerPoint research presentation is the default format for reporting the findings to stakeholders.

Condensing months of work into a few slides can prove to be challenging. It requires particular skills to create and deliver a research presentation that promotes informed decisions and drives long-term projects forward.

Table of Contents

What is a Research Presentation

Key slides for creating a research presentation, tips when delivering a research presentation, how to present sources in a research presentation, recommended templates to create a research presentation.

A research presentation is the communication of research findings, typically delivered to an audience of peers, colleagues, students, or professionals. In the academe, it is meant to showcase the importance of the research paper , state the findings and the analysis of those findings, and seek feedback that could further the research.

The presentation of research becomes even more critical in the business world as the insights derived from it are the basis of strategic decisions of organizations. Information from this type of report can aid companies in maximizing the sales and profit of their business. Major projects such as research and development (R&D) in a new field, the launch of a new product or service, or even corporate social responsibility (CSR) initiatives will require the presentation of research findings to prove their feasibility.

Market research and technical research are examples of business-type research presentations you will commonly encounter.

In this article, we’ve compiled all the essential tips, including some examples and templates, to get you started with creating and delivering a stellar research presentation tailored specifically for the business context.

Various research suggests that the average attention span of adults during presentations is around 20 minutes, with a notable drop in an engagement at the 10-minute mark . Beyond that, you might see your audience doing other things.

How can you avoid such a mistake? The answer lies in the adage “keep it simple, stupid” or KISS. We don’t mean dumbing down your content but rather presenting it in a way that is easily digestible and accessible to your audience. One way you can do this is by organizing your research presentation using a clear structure.

Here are the slides you should prioritize when creating your research presentation PowerPoint.

1.  Title Page

The title page is the first thing your audience will see during your presentation, so put extra effort into it to make an impression. Of course, writing presentation titles and title pages will vary depending on the type of presentation you are to deliver. In the case of a research presentation, you want a formal and academic-sounding one. It should include:

• The full title of the report
• The date of the report
• The name of the researchers or department in charge of the report
• The name of the organization for which the presentation is intended

When writing the title of your research presentation, it should reflect the topic and objective of the report. Focus only on the subject and avoid adding redundant phrases like “A research on” or “A study on.” However, you may use phrases like “Market Analysis” or “Feasibility Study” because they help identify the purpose of the presentation. Doing so also serves a long-term purpose for the filing and later retrieving of the document.

Here’s a sample title page for a hypothetical market research presentation from Gillette .

2. Executive Summary Slide

The executive summary marks the beginning of the body of the presentation, briefly summarizing the key discussion points of the research. Specifically, the summary may state the following:

• The purpose of the investigation and its significance within the organization’s goals
• The methods used for the investigation
• The major findings of the investigation
• The conclusions and recommendations after the investigation

Although the executive summary encompasses the entry of the research presentation, it should not dive into all the details of the work on which the findings, conclusions, and recommendations were based. Creating the executive summary requires a focus on clarity and brevity, especially when translating it to a PowerPoint document where space is limited.

Each point should be presented in a clear and visually engaging manner to capture the audience’s attention and set the stage for the rest of the presentation. Use visuals, bullet points, and minimal text to convey information efficiently.

3. Introduction/ Project Description Slides

In this section, your goal is to provide your audience with the information that will help them understand the details of the presentation. Provide a detailed description of the project, including its goals, objectives, scope, and methods for gathering and analyzing data.

You want to answer these fundamental questions:

• What specific questions are you trying to answer, problems you aim to solve, or opportunities you seek to explore?
• Why is this project important, and what prompted it?
• What are the boundaries of your research or initiative? 
• How were the data gathered?

Important: The introduction should exclude specific findings, conclusions, and recommendations.

4. Data Presentation and Analyses Slides

This is the longest section of a research presentation, as you’ll present the data you’ve gathered and provide a thorough analysis of that data to draw meaningful conclusions. The format and components of this section can vary widely, tailored to the specific nature of your research.

For example, if you are doing market research, you may include the market potential estimate, competitor analysis, and pricing analysis. These elements will help your organization determine the actual viability of a market opportunity.

Visual aids like charts, graphs, tables, and diagrams are potent tools to convey your key findings effectively. These materials may be numbered and sequenced (Figure 1, Figure 2, and so forth), accompanied by text to make sense of the insights.

5. Conclusions

The conclusion of a research presentation is where you pull together the ideas derived from your data presentation and analyses in light of the purpose of the research. For example, if the objective is to assess the market of a new product, the conclusion should determine the requirements of the market in question and tell whether there is a product-market fit.

Designing your conclusion slide should be straightforward and focused on conveying the key takeaways from your research. Keep the text concise and to the point. Present it in bullet points or numbered lists to make the content easily scannable.

6. Recommendations

The findings of your research might reveal elements that may not align with your initial vision or expectations. These deviations are addressed in the recommendations section of your presentation, which outlines the best course of action based on the result of the research.

What emerging markets should we target next? Do we need to rethink our pricing strategies? Which professionals should we hire for this special project? — these are some of the questions that may arise when coming up with this part of the research.

Recommendations may be combined with the conclusion, but presenting them separately to reinforce their urgency. In the end, the decision-makers in the organization or your clients will make the final call on whether to accept or decline the recommendations.

7. Questions Slide

Members of your audience are not involved in carrying out your research activity, which means there’s a lot they don’t know about its details. By offering an opportunity for questions, you can invite them to bridge that gap, seek clarification, and engage in a dialogue that enhances their understanding.

If your research is more business-oriented, facilitating a question and answer after your presentation becomes imperative as it’s your final appeal to encourage buy-in for your recommendations.

A simple “Ask us anything” slide can indicate that you are ready to accept questions.

1. Focus on the Most Important Findings

The truth about presenting research findings is that your audience doesn’t need to know everything. Instead, they should receive a distilled, clear, and meaningful overview that focuses on the most critical aspects.

You will likely have to squeeze in the oral presentation of your research into a 10 to 20-minute presentation, so you have to make the most out of the time given to you. In the presentation, don’t soak in the less important elements like historical backgrounds. Decision-makers might even ask you to skip these portions and focus on sharing the findings.

2. Do Not Read Word-per-word

Reading word-for-word from your presentation slides intensifies the danger of losing your audience’s interest. Its effect can be detrimental, especially if the purpose of your research presentation is to gain approval from the audience. So, how can you avoid this mistake?

• Make a conscious design decision to keep the text on your slides minimal. Your slides should serve as visual cues to guide your presentation.
• Structure your presentation as a narrative or story. Stories are more engaging and memorable than dry, factual information.
• Prepare speaker notes with the key points of your research. Glance at it when needed.
• Engage with the audience by maintaining eye contact and asking rhetorical questions.

3. Don’t Go Without Handouts

Handouts are paper copies of your presentation slides that you distribute to your audience. They typically contain the summary of your key points, but they may also provide supplementary information supporting data presented through tables and graphs.

The purpose of distributing presentation handouts is to easily retain the key points you presented as they become good references in the future. Distributing handouts in advance allows your audience to review the material and come prepared with questions or points for discussion during the presentation.

4. Actively Listen

An equally important skill that a presenter must possess aside from speaking is the ability to listen. We are not just talking about listening to what the audience is saying but also considering their reactions and nonverbal cues. If you sense disinterest or confusion, you can adapt your approach on the fly to re-engage them.

For example, if some members of your audience are exchanging glances, they may be skeptical of the research findings you are presenting. This is the best time to reassure them of the validity of your data and provide a concise overview of how it came to be. You may also encourage them to seek clarification.

5. Be Confident

Anxiety can strike before a presentation – it’s a common reaction whenever someone has to speak in front of others. If you can’t eliminate your stress, try to manage it.

People hate public speaking not because they simply hate it. Most of the time, it arises from one’s belief in themselves. You don’t have to take our word for it. Take Maslow’s theory that says a threat to one’s self-esteem is a source of distress among an individual.

Now, how can you master this feeling? You’ve spent a lot of time on your research, so there is no question about your topic knowledge. Perhaps you just need to rehearse your research presentation. If you know what you will say and how to say it, you will gain confidence in presenting your work.

All sources you use in creating your research presentation should be given proper credit. The APA Style is the most widely used citation style in formal research.

In-text citation

Add references within the text of your presentation slide by giving the author’s last name, year of publication, and page number (if applicable) in parentheses after direct quotations or paraphrased materials. As in:

The alarming rate at which global temperatures rise directly impacts biodiversity (Smith, 2020, p. 27).

If the author’s name and year of publication are mentioned in the text, add only the page number in parentheses after the quotations or paraphrased materials. As in:

According to Smith (2020), the alarming rate at which global temperatures rise directly impacts biodiversity (p. 27).

Image citation

All images from the web, including photos, graphs, and tables, used in your slides should be credited using the format below.

Creator’s Last Name, First Name. “Title of Image.” Website Name, Day Mo. Year, URL. Accessed Day Mo. Year.

Work cited page

A work cited page or reference list should follow after the last slide of your presentation. The list should be alphabetized by the author’s last name and initials followed by the year of publication, the title of the book or article, the place of publication, and the publisher. As in:

Smith, J. A. (2020). Climate Change and Biodiversity: A Comprehensive Study. New York, NY: ABC Publications.

When citing a document from a website, add the source URL after the title of the book or article instead of the place of publication and the publisher. As in:

Smith, J. A. (2020). Climate Change and Biodiversity: A Comprehensive Study. Retrieved from https://www.smith.com/climate-change-and-biodiversity.

1. Research Project Presentation PowerPoint Template

A slide deck containing 18 different slides intended to take off the weight of how to make a research presentation. With tons of visual aids, presenters can reference existing research on similar projects to this one – or link another research presentation example – provide an accurate data analysis, disclose the methodology used, and much more.

Use This Template

2. Research Presentation Scientific Method Diagram PowerPoint Template

Whenever you intend to raise questions, expose the methodology you used for your research, or even suggest a scientific method approach for future analysis, this circular wheel diagram is a perfect fit for any presentation study.

Customize all of its elements to suit the demands of your presentation in just minutes.

3. Thesis Research Presentation PowerPoint Template

If your research presentation project belongs to academia, then this is the slide deck to pair that presentation. With a formal aesthetic and minimalistic style, this research presentation template focuses only on exposing your information as clearly as possible.

Use its included bar charts and graphs to introduce data, change the background of each slide to suit the topic of your presentation, and customize each of its elements to meet the requirements of your project with ease.

4. Animated Research Cards PowerPoint Template

Visualize ideas and their connection points with the help of this research card template for PowerPoint. This slide deck, for example, can help speakers talk about alternative concepts to what they are currently managing and its possible outcomes, among different other usages this versatile PPT template has. Zoom Animation effects make a smooth transition between cards (or ideas).

5. Research Presentation Slide Deck for PowerPoint

With a distinctive professional style, this research presentation PPT template helps business professionals and academics alike to introduce the findings of their work to team members or investors.

By accessing this template, you get the following slides:

• Introduction
• Problem Statement
• Research Questions
• Conceptual Research Framework (Concepts, Theories, Actors, & Constructs)
• Study design and methods
• Population & Sampling
• Data Collection
• Data Analysis

Check it out today and craft a powerful research presentation out of it!

A successful research presentation in business is not just about presenting data; it’s about persuasion to take meaningful action. It’s the bridge that connects your research efforts to the strategic initiatives of your organization. To embark on this journey successfully, planning your presentation thoroughly is paramount, from designing your PowerPoint to the delivery.

Take a look and get inspiration from the sample research presentation slides above, put our tips to heart, and transform your research findings into a compelling call to action.

Like this article? Please share

Academics, Presentation Approaches, Research & Development Filed under Presentation Ideas

Related Articles

Filed under Design • March 27th, 2024

How to Make a Presentation Graph

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

Filed under Presentation Ideas • February 29th, 2024

How to Make a Fundraising Presentation (with Thermometer Templates & Slides)

Meet a new framework to design fundraising presentations by harnessing the power of fundraising thermometer templates. Detailed guide with examples.

Filed under Presentation Ideas • February 15th, 2024

How to Create a 5 Minutes Presentation

Master the art of short-format speeches like the 5 minutes presentation with this article. Insights on content structure, audience engagement and more.

Leave a Reply

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

Manage Cookies

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

Strictly Necessary Cookies

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

Show cookie providers

• Google Login

Functionality Cookies

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

Performance Cookies

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

• Google Analytics

Targeting Cookies

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

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

Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

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

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

Click to jump ahead:

10 Essential data presentation examples + methods you should know

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

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

1. Bar graph

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

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

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

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

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

2. Line graph

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

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

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

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

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

3. Pie chart

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

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

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

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

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

4. Scatter plot

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

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

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

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

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

5. Histogram

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

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

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

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

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

6. Stacked bar chart

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

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

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

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

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

7. Area chart

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

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

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

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

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

8. Tabular presentation

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

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

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

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

9. Textual data

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

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

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

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

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

10. Pictogram

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

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

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

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

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

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

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

1. Title and objective

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

2. Key data points

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

3. Context and significance

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

4. Key takeaways

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

5. Visuals and charts

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

6. Implications or actions

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

7. Q&A and discussion

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

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

Overloading with data

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

Assuming everyone’s on the same page

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

Misleading visuals

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

Not providing context

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

Not citing sources properly

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

Not telling a story

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

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

Ignoring data quality

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

Simplify your visuals

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

Missing the emotional connection

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

Skipping the actionable insights

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

Can you provide some data presentation examples for business reports?

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

What are some creative data presentation examples for academic presentations?

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

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

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

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

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

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

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

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

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

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

What is the difference between data visualization and data presentation?

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

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

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

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

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

Discover popular designs

Infographic maker

Brochure maker

White paper online

Newsletter creator

Flyer maker

Timeline maker

Letterhead maker

Mind map maker

Ebook maker

An official website of the United States government

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

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

• Publications
• Account settings

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

• Advanced Search
• Journal List
• An Bras Dermatol
• v.89(2); Mar-Apr 2014

Presenting data in tables and charts *

Rodrigo pereira duquia.

1 Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) - Porto Alegre (RS), Brazil.

João Luiz Bastos

2 Universidade Federal de Santa Catarina (UFSC) - Florianópolis (SC) Brazil.

Renan Rangel Bonamigo

David alejandro gonzález-chica, jeovany martínez-mesa.

3 Latin American Cooperative Oncology Group (LACOG) - Porto Alegre (RS) Brazil.

The present paper aims to provide basic guidelines to present epidemiological data using tables and graphs in Dermatology. Although simple, the preparation of tables and graphs should follow basic recommendations, which make it much easier to understand the data under analysis and to promote accurate communication in science. Additionally, this paper deals with other basic concepts in epidemiology, such as variable, observation, and data, which are useful both in the exchange of information between researchers and in the planning and conception of a research project.

INTRODUCTION

Among the essential stages of epidemiological research, one of the most important is the identification of data with which the researcher is working, as well as a clear and synthetic description of these data using graphs and tables. The identification of the type of data has an impact on the different stages of the research process, encompassing the research planning and the production/publication of its results. For example, the use of a certain type of data impacts the amount of time it will take to collect the desired information (throughout the field work) and the selection of the most appropriate statistical tests for data analysis.

On the other hand, the preparation of tables and graphs is a crucial tool in the analysis and production/publication of results, given that it organizes the collected information in a clear and summarized fashion. The correct preparation of tables allows researchers to present information about tens or hundreds of individuals efficiently and with significant visual appeal, making the results more easily understandable and thus more attractive to the users of the produced information. Therefore, it is very important for the authors of scientific articles to master the preparation of tables and graphs, which requires previous knowledge of data characteristics and the ability of identifying which type of table or graph is the most appropriate for the situation of interest.

BASIC CONCEPTS

Before evaluating the different types of data that permeate an epidemiological study, it is worth discussing about some key concepts (herein named data, variables and observations):

Data - during field work, researchers collect information by means of questions, systematic observations, and imaging or laboratory tests. All this gathered information represents the data of the research. For example, it is possible to determine the color of an individual's skin according to Fitzpatrick classification or quantify the number of times a person uses sunscreen during summer. 1 , 2 All the information collected during research is generically named "data." A set of individual data makes it possible to perform statistical analysis. If the quality of data is good, i.e., if the way information was gathered was appropriate, the next stages of database preparation, which will set the ground for analysis and presentation of results, will be properly conducted.

Observations - are measurements carried out in one or more individuals, based on one or more variables. For instance, if one is working with the variable "sex" in a sample of 20 individuals and knows the exact amount of men and women in this sample (10 for each group), it can be said that this variable has 20 observations.

Variables - are constituted by data. For instance, an individual may be male or female. In this case, there are 10 observations for each sex, but "sex" is the variable that is referred to as a whole. Another example of variable is "age" in complete years, in which observations are the values 1 year, 2 years, 3 years, and so forth. In other words, variables are characteristics or attributes that can be measured, assuming different values, such as sex, skin type, eye color, age of the individuals under study, laboratory results, or the presence of a given lesion/disease. Variables are specifically divided into two large groups: (a) the group of categorical or qualitative variables, which is subdivided into dichotomous, nominal and ordinal variables; and (b) the group of numerical or quantitative variables, which is subdivided into continuous and discrete variables.

Categorical variables

• Dichotomous variables, also known as binary variables: are those that have only two categories, i.e., only two response options. Typical examples of this type of variable are sex (male and female) and presence of skin cancer (yes or no).
• Ordinal variables: are those that have three or more categories with an obvious ordering of the categories (whether in an ascending or descending order). For example, Fitzpatrick skin classification into types I, II, III, IV and V. 1
• Nominal variables: are those that have three or more categories with no apparent ordering of the categories. Example: blood types A, B, AB, and O, or brown, blue or green eye colors.

Numerical variables

• Discrete variables: are observations that can only take certain numerical values. An example of this type of variable is subjects' age, when assessed in complete years of life (1 year, 2 years, 3 years, 4 years, etc.) and the number of times a set of patients visited the dermatologist in a year.
• Continuous variables: are those measured on a continuous scale, i.e., which have as many decimal places as the measuring instrument can record. For instance: blood pressure, birth weight, height, or even age, when measured on a continuous scale.

It is important to point out that, depending on the objectives of the study, data may be collected as discrete or continuous variables and be subsequently transformed into categorical variables to suit the purpose of the research and/or make interpretation easier. However, it is important to emphasize that variables measured on a numerical scale (whether discrete or continuous) are richer in information and should be preferred for statistical analyses. Figure 1 shows a diagram that makes it easier to understand, identify and classify the abovementioned variables.

Types of variables

DATA PRESENTATION IN TABLES AND GRAPHS

Firstly, it is worth emphasizing that every table or graph should be self-explanatory, i.e., should be understandable without the need to read the text that refers to it refers.

Presentation of categorical variables

In order to analyze the distribution of a variable, data should be organized according to the occurrence of different results in each category. As for categorical variables, frequency distributions may be presented in a table or a graph, including bar charts and pie or sector charts. The term frequency distribution has a specific meaning, referring to the the way observations of a given variable behave in terms of its absolute, relative or cumulative frequencies.

In order to synthesize information contained in a categorical variable using a table, it is important to count the number of observations in each category of the variable, thus obtaining its absolute frequencies. However, in addition to absolute frequencies, it is worth presenting its percentage values, also known as relative frequencies. For example, table 1 expresses, in absolute and relative terms, the frequency of acne scars in 18-year-old youngsters from a population-based study conducted in the city of Pelotas, Southern Brazil, in 2010. 3

Absolute and relative frequencies of acne scar in 18- year-old adolescents (n = 2.414). Pelotas, Brazil, 2010

The same information from table 1 may be presented as a bar or a pie chart, which can be prepared considering the absolute or relative frequency of the categories. Figures 2 and ​ and3 3 illustrate the same information shown in table 1 , but present it as a bar chart and a pie chart, respectively. It can be observed that, regardless of the form of presentation, the total number of observations must be mentioned, whether in the title or as part of the table or figure. Additionally, appropriate legends should always be included, allowing for the proper identification of each of the categories of the variable and including the type of information provided (absolute and/or relative frequency).

Absolute frequencies of acne scar in 18-year-old adolescents (n = 2.414). Pelotas, Brazil, 2010

Relative frequencies of acne scar in 18-year-old adolescents (n = 2.414). Pelotas, Brazil, 2010

Presentation of numerical variables

Frequency distributions of numerical variables can be displayed in a table, a histogram chart, or a frequency polygon chart. With regard to discrete variables, it is possible to present the number of observations according to the different values found in the study, as illustrated in table 2 . This type of table may provide a wide range of information on the collected data.

Educational level of 18-year-old adolescents (n = 2,199). Pelotas, Brazil, 2010

Table 2 shows the distribution of educational levels among 18-year-old youngsters from Pelotas, Southern Brazil, with absolute, relative, and cumulative relative frequencies. In this case, absolute and relative frequencies correspond to the absolute number and the percentage of individuals according to their distribution for this variable, respectively, based on complete years of education. It should be noticed that there are 450 adolescents with 8 years of education, which corresponds to 20.5% of the subjects. Tables may also present the cumulative relative frequency of the variable. In this case, it was found that 50.6% of study subjects have up to 8 years of education. It is important to point that, although the same data were used, each form of presentation (absolute, relative or cumulative frequency) provides different information and may be used to understand frequency distribution from different perspectives.

When one wants to evaluate the frequency distribution of continuous variables using tables or graphs, it is necessary to transform the variable into categories, preferably creating categories with the same size (or the same amplitude). However, in addition to this general recommendation, other basic guidelines should be followed, such as: (1) subtracting the highest from the lowest value for the variable of interest; (2) dividing the result of this subtraction by the number of categories to be created (usually from three to ten); and (3) defining category intervals based on this last result.

For example, in order to categorize height (in meters) of a set of individuals, the first step is to identify the tallest and the shortest individual of the sample. Let us assume that the tallest individual is 1.85m tall and the shortest, 1.55m tall, with a difference of 0.3m between these values. The next step is to divide this difference by the number of categories to be created, e.g., five. Thus, 0.3m divided by five equals 0.06m, which means that categories will have exactly this range and will be numerically represented by the following range of values: 1st category - 1.55m to 1.60m; 2nd category - 1.61m to 1.66m; 3rd category - 1.67m to 1.72m; 4th category - 1.73m to 1.78m; 5th category - 1.79m to 1.85m.

Table 3 illustrates weight values at 18 years of age in kg (continuous numerical variable) obtained in a study with youngsters from Pelotas, Southern Brazil. 4 , 5 Figure 4 shows a histogram with the variable weight categorized into 20-kg intervals. Therefore, it is possible to observe that data from continuous numerical variables may be presented in tables or graphs.

Weight distribution among 18-year-old young male sex (n = 2.194). Pelotas, Brazil, 2010

Weight distribution at 18 years of age among youngsters from the city of Pelotas. Pelotas (n = 2.194), Brazil, 2010

Assessing the relationship between two variables

The forms of data presentation that have been described up to this point illustrated the distribution of a given variable, whether categorical or numerical. In addition, it is possible to present the relationship between two variables of interest, either categorical or numerical.

The relationship between categorical variables may be investigated using a contingency table, which has the purpose of analyzing the association between two or more variables. The lines of this type of table usually display the exposure variable (independent variable), and the columns, the outcome variable (dependent variable). For example, in order to study the effect of sun exposure (exposure variable) on the development of skin cancer (outcome variable), it is possible to place the variable sun exposure on the lines and the variable skin cancer on the columns of a contingency table. Tables may be easier to understand by including total values in lines and columns. These values should agree with the sum of the lines and/or columns, as appropriate, whereas relative values should be in accordance with the exposure variable, i.e., the sum of the values mentioned in the lines should total 100%.

It is such a display of percentage values that will make it possible for risk or exposure groups to be compared with each other, in order to investigate whether individuals exposed to a given risk factor show higher frequency of the disease of interest. Thus, table 4 shows that 75.0%, 9.0%, and 0.3% of individuals in the study sample who had been working exposed to the sun for 20 years or more, for less than 20 years, and had never been working exposed to the sun, respectively, developed non-melanoma skin cancer. Another way of interpreting this table is observing that 25.0%, 91%,.0%, and 99.7% of individuals who had been working exposed to the sun for 20 years of more, for less than 20 years, and had never been working exposed to the sun did not develop non-melanoma skin cancer. This form of presentation is one of the most used in the literature and makes the table easier to read.

Sun exposure during work and non-melanoma skin cancer (hypothetical data).

The relationship between two numerical variables or between one numerical variable and one categorical variable may be assessed using a scatter diagram, also known as dispersion diagram. In this diagram, each pair of values is represented by a symbol or a dot, whose horizontal and vertical positions are determined by the value of the first and second variables, respectively. By convention, vertical and horizontal axes should correspond to outcome and exposure variables, respectively. Figure 5 shows the relationship between weight and height among 18-year-old youngsters from Pelotas, Southern Brazil, in 2010. 3 , 4 The diagram presented in figure 5 should be interpreted as follows: the increase in subjects' height is accompanied by an increase in their weight.

Point diagram for the relationship between weight (kg) and height (cm) among 18-year-old youngsters from the city of Pelotas (n = 2.194). Pelotas, Brazil, 2010.

BASIC RULES FOR THE PREPARATION OF TABLES AND GRAPHS

Ideally, every table should:

• Be self-explanatory;
• Present values with the same number of decimal places in all its cells (standardization);
• Include a title informing what is being described and where, as well as the number of observations (N) and when data were collected;
• Have a structure formed by three horizontal lines, defining table heading and the end of the table at its lower border;
• Not have vertical lines at its lateral borders;
• Provide additional information in table footer, when needed;
• Be inserted into a document only after being mentioned in the text; and
• Be numbered by Arabic numerals.

Similarly to tables, graphs should:

• Include, below the figure, a title providing all relevant information;
• Be referred to as figures in the text;
• Identify figure axes by the variables under analysis;
• Quote the source which provided the data, if required;
• Demonstrate the scale being used; and
• Be self-explanatory.

The graph's vertical axis should always start with zero. A usual type of distortion is starting this axis with values higher than zero. Whenever it happens, differences between variables are overestimated, as can been seen in figure 6 .

Figure showing how graphs in which the Y-axis does not start with zero tend to overestimate the differences under analysis. On the left there is a graph whose Y axis does not start with zero and on the right a graph reproducing the same data but with the Y axis starting with zero.

Understanding how to classify the different types of variables and how to present them in tables or graphs is an essential stage for epidemiological research in all areas of knowledge, including Dermatology. Mastering this topic collaborates to synthesize research results and prevents the misuse or overuse of tables and figures in scientific papers.

Conflict of Interest: None

Financial Support: None

How to cite this article: Duquia RP, Bastos JL, Bonamigo RR, González-Chica DA, Martínez-Mesa J. Presenting data in tables and charts. An Bras Dermatol. 2014;89(2):280-5.

* Work performed at the Dermatology service, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Departamento de Saúde Pública e Departamento de Nutrição da UFSC.

Data Topics

• Data Architecture
• Data Literacy
• Data Science
• Data Strategy
• Data Modeling
• Governance & Quality
• Education Resources For Use & Management of Data

Types of Data Visualization and Their Uses

In today’s data-first business environment, the ability to convey complex information in an understandable and visually appealing manner is paramount. Different types of data visualization help transform analyzed data into comprehensible visuals for all types of audiences, from novices to experts. In fact, research has shown that the human brain can process images in as little as […]

In today’s data-first business environment, the ability to convey complex information in an understandable and  visually appealing  manner is paramount. Different types of data visualization help transform analyzed data into comprehensible visuals for all types of audiences, from novices to experts. In fact, research has shown that the human brain can process images in as little as 13 milliseconds.

In essence, data visualization is indispensable for distilling complex information into digestible formats that support both  quick comprehension  and informed decision-making. Its role in analysis and reporting underscores its value as a critical tool in any data-centric activity. 

Types of Data Visualization: Charts, Graphs, Infographics, and Dashboards

The diverse landscape of data visualization begins with simple charts and graphs but moves beyond infographics and animated dashboards.  Charts , in their various forms – be it bar charts for comparing quantities across categories or line charts depicting trends over time – serve as efficient tools for data representation. Graphs extend this utility further: Scatter plots reveal correlations between variables, while pie graphs offer a visual slice of proportional relationships within a dataset. 

Venturing beyond these traditional forms,  infographics  emerge as powerful storytelling tools, combining graphical elements with narrative to enlighten audiences on complex subjects. Unlike standard charts or graphs that focus on numerical data representation, infographics can incorporate timelines, flowcharts, and comparative images to weave a more comprehensive story around the data. 

A dashboard, when  effectively designed , serves as an instrument for synthesizing complex data into accessible and actionable insights. Dashboards very often encapsulate a wide array of information, from real-time data streams to historical trends, and present it through an amalgamation of charts, graphs, and indicators. 

A dashboard’s efficacy lies in its ability to tailor the visual narrative to the specific needs and objectives of its audience. By  selectively  filtering and highlighting critical data points, dashboards facilitate a focused analysis that aligns with organizational goals or individual projects. 

The best type of data visualization to use depends on the data at hand and the purpose of its presentation. Whether aiming to highlight trends, compare values, or elucidate complex relationships, selecting the appropriate visual form is crucial for effectively communicating insights buried within datasets. Through thoughtful design and strategic selection among these varied types of visualizations, one can illuminate patterns and narratives hidden within numbers – transforming raw data into meaningful knowledge.   

Other Types of Data Visualization: Maps and Geospatial Visualization  

Utilizing maps and geospatial visualization serves as a powerful method for uncovering and displaying insightful patterns hidden within complex datasets. At the intersection of geography and data analysis, this technique transforms numerical and categorical data into visual formats that are easily interpretable, such as heat maps, choropleths, or symbolic representations on geographical layouts. This approach enables viewers  to quickly grasp spatial relationships, distributions, trends, and anomalies that might be overlooked in traditional tabular data presentations. 

For instance, in public health,  geospatial visualizations  can highlight regions with high incidences of certain diseases, guiding targeted interventions. In environmental studies, they can illustrate changes in land use or the impact of climate change across different areas over time. By embedding data within its geographical context, these visualizations foster a deeper understanding of how location influences the phenomena being studied. 

Furthermore, the advent of interactive web-based mapping tools has enhanced the accessibility and utility of geospatial visualizations. Users can now engage with the data more directly – zooming in on areas of interest, filtering layers to refine their focus, or even contributing their own data points – making these visualizations an indispensable tool for researchers and decision-makers alike who are looking to extract meaningful patterns from spatially oriented datasets. 

Additionally,  scatter plots  excel in revealing correlations between two variables. By plotting data points on a two-dimensional graph, they allow analysts to discern potential relationships or trends that might not be evident from raw data alone. This makes scatter plots a staple in statistical analysis and scientific research where establishing cause-and-effect relationships is crucial. 

Bubble charts take the concept of scatter plots further by introducing a third dimension – typically represented by the size of the bubbles – thereby enabling an even more layered understanding of data relationships. Whether it’s comparing economic indicators across countries or visualizing population demographics, bubble charts provide a dynamic means to encapsulate complex interrelations within datasets, making them an indispensable tool for advanced data visualization. 

Innovative Data Visualization Techniques: Word Clouds and Network Diagrams 

Some innovative techniques have emerged in the realm of data visualization that not only simplify complex datasets but also enhance engagement and understanding. Among these, word clouds and network diagrams stand out for their  unique approaches  to presenting information. 

Word clouds represent textual data with size variations to emphasize the frequency or importance of words within a dataset. This technique transforms qualitative data into a visually appealing format, making it easier to identify dominant themes or sentiments in large text segments.

Network diagrams introduce an entirely different dimension by illustrating relationships between entities. Through nodes and connecting lines, they depict how individual components interact within a system – be it social networks, organizational structures, or technological infrastructures. This visualization method excels in uncovering patterns of connectivity and influence that might remain hidden in traditional charts or tables. 

Purpose and Uses of Each Type of Data Visualization 

The various types of data visualization – from bar graphs and line charts to heat maps and scatter plots – cater to different analytical needs and objectives. Each type is meticulously designed to highlight specific aspects of the data, making it imperative to understand their unique applications and strengths. This foundational knowledge empowers users to select the most effective visualization technique for their specific dataset and analysis goals.

Line Charts: Tracking Changes Over Time  Line charts are quintessential in the realm of data visualization for their simplicity and effectiveness in showcasing trends and changes over time. By connecting individual data points with straight lines, they offer a clear depiction of how values rise and fall across a chronological axis. This makes line charts particularly useful for tracking the evolution of quantities – be it the fluctuating stock prices in financial markets, the ebb and flow of temperatures across seasons, or the gradual growth of a company’s revenue over successive quarters. The visual narrative that line charts provide helps analysts, researchers, and casual observers alike to discern patterns within the data, such as cycles or anomalies.    

Bar Charts and Histograms: Comparing Categories and   Distributions  Bar charts  are highly suitable for representing comparative data. By plotting each category of comparison with a bar whose height or length reflects its value, bar charts make it easy to visualize relative values at a glance.

Histograms  show the distribution of groups of data in a dataset. This is particularly useful for understanding the shape of data distributions – whether they are skewed, normal, or have any outliers. Histograms provide insight into the underlying structure of data, revealing patterns that might not be apparent.  

Pie Charts: Visualizing Proportional Data   Pie charts  serve as a compelling visualization tool for representing proportional data, offering a clear snapshot of how different parts contribute to a whole. By dividing a circle into slices whose sizes are proportional to their quantity, pie charts provide an immediate visual comparison among various categories. This makes them especially useful in illustrating market shares, budget allocations, or the distribution of population segments.

The simplicity of pie charts allows for quick interpretation, making it easier for viewers to grasp complex data at a glance. However, when dealing with numerous categories or when precise comparisons are necessary, the effectiveness of pie charts may diminish. Despite this limitation, their ability to succinctly convey the relative significance of parts within a whole ensures their enduring popularity in data visualization across diverse fields. 

Scatter Plots: Identifying Relationship and Correlations Between Variables Scatter plots  are primarily used for spotting relationships and correlations between variables. These plots show data points related to one variable on one axis and a different variable on another axis. This visual arrangement allows viewers to determine patterns or trends that might indicate a correlation or relationship between the variables in question. 

For instance, if an increase in one variable consistently causes an increase (or decrease) in the other, this suggests a potential correlation. Scatter plots are particularly valuable for preliminary analyses where researchers seek to identify variables that warrant further investigation. Their straightforward yet powerful nature makes them indispensable for exploring complex datasets, providing clear insights into the dynamics between different factors at play. 

Heat Maps: Representing Complex Data Matrices through Color Gradients Heat maps  serve as a powerful tool in representing complex data matrices, using color gradients to convey information that might otherwise be challenging to digest. At their core, heat maps transform numerical values into a visual spectrum of colors, enabling viewers to quickly grasp patterns, outliers, and trends within the data. This method becomes more effective when the complex relationships between multiple variables need to be reviewed.  

For instance, in fields like genomics or meteorology, heat maps can illustrate gene expression levels or temperature fluctuations across different regions and times. By assigning warmer colors to higher values and cooler colors to lower ones, heat maps facilitate an intuitive understanding of data distribution and concentration areas, making them indispensable for exploratory data analysis and decision-making processes.

Dashboards and Infographics: Integrating Multiple Data Visualizations  Dashboards and infographics represent a synergistic approach in data visualization, blending various graphical elements to offer a holistic view of complex datasets.  Dashboards,  with their capacity to integrate multiple data visualizations such as charts, graphs, and maps onto a single interface, are instrumental in monitoring real-time data and tracking performance metrics across different parameters. They serve as an essential tool for decision-makers who require a comprehensive overview to identify trends and anomalies swiftly. 

Infographics, on the other hand, transform intricate data sets into engaging, easily digestible visual stories. By illustrating strong narratives with striking visuals and solid statistics, infographics make complex information easily digestible to any type of audience. 

Together, dashboards and infographics convey multifaceted data insights in an integrated manner – facilitating informed decisions through comprehensive yet clear snapshots of data landscapes.     

How-To Geek

6 ways to create more interactive powerpoint presentations.

Engage your audience with cool, actionable features.

Quick Links

• Add a QR code
• Embed Microsoft Forms (Education or Business Only)
• Embed a Live Web Page
• Add Links and Menus
• Add Clickable Images to Give More Info
• Add a Countdown Timer

We've all been to a presentation where the speaker bores you to death with a mundane PowerPoint presentation. Actually, the speaker could have kept you much more engaged by adding some interactive features to their slideshow. Let's look into some of these options.

1. Add a QR code

Adding a QR code can be particularly useful if you want to direct your audience to an online form, website, or video.

Some websites have in-built ways to create a QR code. For example, on Microsoft Forms , when you click "Collect Responses," you'll see the QR code option via the icon highlighted in the screenshot below. You can either right-click the QR code to copy and paste it into your presentation, or click "Download" to add it to your device gallery to insert the QR code as a picture.

In fact, you can easily add a QR code to take your viewer to any website. On Microsoft Edge, right-click anywhere on a web page where there isn't already a link, and left-click "Create QR Code For This Page."

You can also create QR codes in other browsers, such as Chrome.

You can then copy or download the QR code to use wherever you like in your presentation.

2. Embed Microsoft Forms (Education or Business Only)

If you plan to send your PPT presentation to others—for example, if you're a trainer sending step-by-step instruction presentation, a teacher sending an independent learning task to your students, or a campaigner for your local councilor sending a persuasive PPT to constituents—you might want to embed a quiz, questionnaire, pole, or feedback survey in your presentation.

In PowerPoint, open the "Insert" tab on the ribbon, and in the Forms group, click "Forms". If you cannot see this option, you can add new buttons to the ribbon .

As at April 2024, this feature is only available for those using their work or school account. We're using a Microsoft 365 Personal account in the screenshot below, which is why the Forms icon is grayed out.

Then, a sidebar will appear on the right-hand side of your screen, where you can either choose a form you have already created or opt to craft a new form.

Now, you can share your PPT presentation with others , who can click the fields and submit their responses when they view the presentation.

3. Embed a Live Web Page

You could always screenshot a web page and paste that into your PPT, but that's not a very interactive addition to your presentation. Instead, you can embed a live web page into your PPT so that people with access to your presentation can interact actively with its contents.

To do this, we will need to add an add-in to our PPT account .

Add-ins are not always reliable or secure. Before installing an add-in to your Microsoft account, check that the author is a reputable company, and type the add-in's name into a search engine to read reviews and other users' experiences.

To embed a web page, add the Web Viewer add-in ( this is an add-in created by Microsoft ).

Go to the relevant slide and open the Web Viewer add-in. Then, copy and paste the secure URL into the field box, and remove https:// from the start of the address. In our example, we will add a selector wheel to our slide. Click "Preview" to see a sample of the web page's appearance in your presentation.

This is how ours will look.

When you or someone with access to your presentation views the slideshow, this web page will be live and interactive.

4. Add Links and Menus

As well as moving from one slide to the next through a keyboard action or mouse click, you can create links within your presentation to direct the audience to specific locations.

To create a link, right-click the outline of the clickable object, and click "Link."

In the Insert Hyperlink dialog box, click "Place In This Document," choose the landing destination, and click "OK."

What's more, to make it clear that an object is clickable, you can use action buttons. Open the "Insert" tab on the ribbon, click "Shape," and then choose an appropriate action button. Usefully, PPT will automatically prompt you to add a link to these shapes.

You might also want a menu that displays on every slide. Once you have created the menu, add the links using the method outlined above. Then, select all the items, press Ctrl+C (copy), and then use Ctrl+V to paste them in your other slides.

5. Add Clickable Images to Give More Info

Through PowerPoint's animations, you can give your viewer the power to choose what they see and when they see it. This works nicely whether you're planning to send your presentation to others to run through independently or whether you're presenting in front of a group and want your audience to decide which action they want to take.

Start by creating the objects that will be clickable (trigger) and the items that will appear (pop-up).

Then, select all the pop-ups together. When you click "Animations" on the ribbon and choose an appropriate animation for the effect you want to achieve, this will be applied to all objects you have selected.

The next step is to rename the triggers in your presentation. To do this, open the "Home" tab, and in the Editing group, click "Select", and then "Selection Pane."

With the Selection Pane open, select each trigger on your slide individually, and rename them in the Selection Pane, so that they can be easily linked to in the next step.

Finally, go back to the first pop-up. Open the "Animations" tab, and in the Advanced Animation group, click the "Trigger" drop-down arrow. Then, you can set the item to appear when a trigger is clicked in your presentation.

If you want your item to disappear when the trigger is clicked again, select the pop-up, click "Add Animation" in the Advanced Animation group, choose an Exit animation, and follow the same step to link that animation to the trigger button.

6. Add a Countdown Timer

A great way to get your audience to engage with your PPT presentation is to keep them on edge by adding a countdown timer. Whether you're leading a presentation and want to let your audience stop to discuss a topic, or running an online quiz with time-limit questions, having a countdown timer means your audience will keep their eye on your slide throughout.

To do this, you need to animate text boxes or shapes containing your countdown numbers. Choose and format a shape and type the highest number that your countdown clock will need. In our case, we're creating a 10-second timer.

Now, with your shape selected, open the "Animations" tab on the ribbon and click the animation drop-down arrow. Then, in the Exit menu, click "Disappear."

Open the Animation Pane, and click the drop-down arrow next to the animation you've just added. From there, choose "Timing."

Make sure "On Click" is selected in the Start menu, and change the Delay option to "1 second," before clicking "OK."

Then, with this shape still selected, press Ctrl+C (copy), and then Ctrl+V (paste). In the second box, type 9 . With the Animation Pane still open and this second shape selected, click the drop-down arrow and choose "Timing" again. Change the Start option to "After Previous," and make sure the Delay option is 1 second. Then, click "OK."

We can now use this second shape as our template, as when we copy and paste it again, the animations will also duplicate. With this second shape selected, press Ctrl+C and Ctrl+V, type 8 into the box, and continue to do the same until you get to 0 .

Next, remove the animations from the "0" box, as you don't want this to disappear. To do this, click the shape, and in the Animation Pane drop-down, click "Remove."

You now need to layer them in order. Right-click the box containing number 1, and click "Bring To Front." You will now see that box on the top. Do the same with the other numbers in ascending order.

Finally, you need to align the objects together. Click anywhere on your slide and press Ctrl+A. Then, in the Home tab on the ribbon, click "Arrange." First click "Align Center," and then bring the menu up again, so that you can click "Align Middle."

Press Ctrl+A again to select your timer, and you can then move your timer or copy and paste it elsewhere.

Press F5 to see the presentation in action, and when you get to the slide containing the timer, click anywhere on the slide to see your countdown timer in action!

Now that your PPT presentation is more interactive, make sure you've avoided these eight common presentational mistakes before you present your slides.

Published: 5 April 2024 Contributors: Tim Mucci, Cole Stryker

Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.