Introduction: Data doesn’t speak for itself

This video tells the story of Alex, a data analyst who knows her information well, but fails to deliver an effective presentation because of common mistakes she makes when presenting data.

Alex made a few mistakes in her presentation that affected the impact it had with her audience.

This chapter will help you avoid those same mistakes by focusing on the following key principles:

1. Know your audience
2. Keep it simple
3. Harness the power of storytelling

What are data visualization and data storytelling?

Throughout the chapter there are two terms you will need to know: data visualization and data storytelling.

Data visualization takes data and represents it in graphical ways. The visualization of data helps an audience digest information quickly by showing patterns, trends, or relationships.

While data visualization helps make the data easy to digest, data storytelling uses elements of narrative to help make the data memorable and increase the odds that the data translates into action by your audience.

Data + Visuals + Narrative = data storytelling

This chapter is aimed at helping you understand the elements necessary to create and use effective data visualization and to help you craft data stories to illuminate the significance of your insights for your business audience.

LET’S BEGIN!

Your team has done their research and collected a lot of data that you are excited to share with your audience, but you don’t know where to start. Let’s go through the process of building your team presentation data story together. At times you may have to go back to a previous step if you find that something has changed, or a new insight is revealed. The more flexible you are at this stage the better. Below are the key steps we will explore, with hyperlinks to each section: use these to travel through the material easily.

Who is your audience?

In addition to the audience analysis you have performed to identify useful psychographic and demographic information, you should ask a few questions about your audience that focus on their data literacy.

1. 1. • Do they work with a lot of quantitative data as part of their normal work? If so, you can probably have more elaborate, detailed data (think about presenting it to a group of statisticians).

1. 1. • Do they not work with as much detailed data in their day-to-day work. They might appreciate more narrative and pictures to help represent the data.

For example, let’s say our introductory data analyst, Alex, was addressing an audience of other analysts. This audience might have an easier time quickly digesting more complex data because they deal with it all the time. But let’s say Alex’s audience is a group of managers who rarely interact with complex data. They might appreciate something that cuts to the chase and lets them know the key finding without requiring them to do their own analysis on the spot.

You need to keep your audience in mind throughout the whole process, from planning to presentation. Audiences are no different from you! They are busy and they are distractible. Therefore, you should focus on efficiency–how can you make your message clear and avoid overloading the audience with unnecessary distractions, like too much information or too much visual noise in your presentation? Think about sitting in a lecture on a topic that isn’t interesting to you. Do you pay attention or do you quickly zone out? What happens if your instructor shows how the information they are sharing is relevant to you? Now you might be inclined to listen more. Audiences in business settings are no different. Your priority needs to be showing how what you have to say is relevant to them. How does what you are sharing help them make decisions and enact courses of action that are meaningful to them? If you’ve done a good job, at the end of the presentation your audience should be able to easily remember what it is they need to do.

What are the main, relevant insights from your data?

After collecting your research data, it’s time to organize your findings. Follow these steps:

• Document your insights
  • create a single Word document to write and organize your insights using the dot-dash method from McKinsey (dot=insight/main point; dash=evidence). Avoid starting with slides or narratives.
• Analyze your data
  • spend time analyzing your data to identify patterns and insights. This can be the most mentally taxing part of the process, so try to do this during your peak performance time for best results. Remind yourself of the situation or the problem you are trying to solve. Are you sharing information and letting the audience draw their own conclusions? Or a you proposing a solution to a problem? What does the audience already know and what do they need to know?
• Team Collaboration
  • work with your team to avoid bias. Ensure you are not just looking for data that supports your existing viewpoint.
  • together, select the data that is most helpful in guiding your audience toward your solution.
  • make sure your insights clearly identify:
    • why your audience should care.
    • what you want your audience to do based on the data.
    • the business impact of your insights.
  • Your data should clearly answer the question, “so what?” Quantifying the pain or opportunity for your audience helps.

Illuminating insights: Finding your story line

Now that you have identified your insights and understand their significance to your audience, you can begin to create your story. Break down your findings into a logical sequence and pick a theme. Have each team member independently come up with an organizational structure/theme. When you meet, choose the structure that the team collectively thinks works best. Starting independently can foster greater creativity, as brainstorming together may inadvertently limit creative output.

Data Storytelling

Stories are powerful tools for communication. We are more likely to remember facts and ideas that are wrapped in stories, rather than presented as mere statistics or even statistics with a visualization. A study from The London Business School (2014) showed that a statistic alone will be remembered by only 5-10% of an audience. Adding a visual increases this to 25%.  Combining a statistic with a visual and a story boosts retention to 65-75% of an audience. Harnessing the power of stories for your data can make your presentation more memorable and persuasive.

Crafting a Compelling Data Story

All stories share a fundamental structure, and data storytelling is no different. The goal of your data story is to guide your audience from their problem to your solution. To make your data story clear and engaging for your readers, you can structure it using this universal narrative framework (for a video explanation of the process, you can watch this video).

Beginning/Setup→Middle/Conflict→End/Resolution

Beginning/Setup: Background, set the scene, hook the audience, name the key players

Middle/Conflict: supporting data that culminates in an “aha” moment (your insight)

End/Resolution: Solution and steps forward, ending with call to action.

Recalling Freytag’s pyramid from previous chapters, you can also break down the narrative structure like this:

Creating your visualizations

You encounter hundreds of data visualizations daily. Let’s explore some common types and when to use each.

Types of Visualizations

1. Bar Graph

 

 

 

• Purpose: useful for comparison
• Orientation: horizontal
• Variations: cluster bar chart, stacked bar chart, 100% stacked bar chart
• Tip: Avoid 3D effects because they make graphs hard to read

2. Column Graph

• Purpose: shows change over time
• Orientation: Vertical

3. Pie Chart

• Purpose: Displays value against the total
• Benefit: Easy to understand relative to sizes since a pie equals 100%
• Variation: Donut chart

4. Line Chart

• Purpose: Illustrates trends and outliers in data over time

5. Scatterplots

• Purpose: Shows the relationship between two variables
• Details: Can indicate positive/negative correlation or no correlation, and highlight outliers

6. Infographics

• Purpose: Combines various visual elements to present information succinctly and engagingly
• Components: icons or images for each element, brief descriptions of each element
• Benefit: Makes information engaging and easier to understand at a glance

 

 

 

Design Principles

A key aspect to remember for your data slides is that they need to be understandable at a glance. Below are some guidelines to help you edit and make choices to enhance visual ease for your audience.

• Convey a single idea: Make sure your data slide conveys 1 idea and 1 idea only. This will help prevent information overload. If you want to convey another idea, either add a new slide, or incorporate an animation to introduce the next idea.
• Less is More
  • Limit colors, fonts, and text
  • Max of 2 fonts
  • Choose easy to read fonts
  • Bold can be used for headlines and titles or words you want to draw attention to
  • Bright colors attract attention
  • Top and center tend to be most important locations
  • You can always link to the full data set and/or put it in your appendix
  • Eliminate gridlines when possible
  • Direct labeling is more audience-centered
• Show your audience where they should look
  • Utilize Preattentive Visual Attributes: Pre-attentive attributes are attributes we notice without conscious effort. For example, your brain quickly notices contrasting colors or differences in height. Consider the following pre-attentive attributes in your design: USE A VIDEO
    • Length
    • Width
    • Orientation
    • Size
    • Shape
    • Curvature
    • Enclosure
    • 2-D spacing
    • Spatial grouping
    • Color
    • Texture
    • Positioning and alignment
• Take advantage of Gestalt principles. Gestalt principles relate to how we perceive visual elements as a whole. Utilize these principles to enhance your design.
  • Law of proximity—elements that are close to each other are perceived as a group.
  • Law of similarity—elements that are similar are perceived as related
  • Law of closure—the mind tends to fill in missing information to create a complete image
  • Law of symmetry—symmetrical elements are perceived as part of the same group
  • Law of continuity– the eye is drawn along paths, lines, and curves, preferring continuous figures.
  • Law of past experience—elements are perceived based on past experiences

• Use headlines for your slides instead of simply labels.Often we default to simply titling our slides with labels. What’s more effective, however, is to avoid simply labeling and instead create a headline. An effective headline:
  

  • o Describes the data clearly
  • o Does not make logical leaps
  • o Contains a verb

  A good test to make sure you have a headline and not a label is to identify a verb. If it has a verb, it is a headline. If not, it is probably just a label.

  Examples of Labels versus Headlines

  • LABEL		HEADLINE
    Quarterly sales report	→	Q3 sales show mild increase
    Sales Trends	→	Sales trends are declining since COVID
    Market Share Analysis	→	Market share has remained stagnant for past 3 years.
    Retail e-commerce sales worldwide from 2014-2027	→	Worldwide sales are up 8-fold from 2014-2027

  Additional Resources

  The Economist has great examples of data visualizations.

  Datawrapper is a tool you can use to build your own visualizations.

  Rubric that can be used to check your data visualization

  Want to learn more about data visualization, but would prefer to listen? Check out storytelling with data’s podcasts.

  Exercises you can use to practice from storytelling with data.

  Example of an advanced use of data storytelling.

  20 best data storytelling examples

  Top 8 data storytelling tips

   

  Example Exercise

  Here is a pie chart with fabricated data (remember, pie charts can be helpful visualization when you want to show a part of the whole. We know that a full pie=100% so we can easily see a rough idea of relative share of the pie.

  

  In this first iteration, when you look at the pie chart, what do you notice?

  If you were given a max of 15 seconds, do you think you could glean the main point What, if any aspects get in your way?

   

  

  Above is a revamped version of the same pie chart. What changes do you notice? Do you find this chart easier to read quickly?

  For more practice, follow along with this video to watch experts make design choices.

   

References

Chamorro-Premuzic, Thomas. “Why Group Brainstorming is a Waste of Time.” Harvard Business Review, 25, March 2015, https://hbr.org/2015/03/why-group-brainstorming-is-a-waste-of-time. Accessed 26, February 2025.

Future of Storytelling. “Empath, Neurochemistry and the Dramatic Arc: Paul Zak and the Future of Storytelling,” YouTube, www.youtube.com/watch?v=q1a7tiA1Qzo. Accessed 25 Feb. 2025.

Galletta, Dan. “Storytelling in PowerPoint: Learn McKinsey’s 2 step framework,” YouTube, . https://www.youtube.com/watch?v=CY1Y367KEko. Accessed 23 Jun. 2024.

Harvard Business Review. “Telling Stories with Data in 3 Steps (Quick Study). YouTube, www.youtube.com/watch?v=r5_34YnCmMY. Accessed 26 Feb. 2025.

MIT Sloan Executive Education. “4 Foundational Tactics for Better Visual Data storytelling,” www.youtube.com/watch?v=MZf5YTdSRnE&t=1518s. Accessed 26 February 2025.

Monarth, Harrison. “The Irresistible Power of Storytelling as a Strategic Business Tool,” Harvard Business Review, 2014, March 11. www.hbr.org/2014/03/the-irresistible-power-of-storytelling-as-a-strategic-business-tool. Accessed 26 Feb. 2025.

Nussbaum Knaflic, Cole. “How to turn data into Stories, Storytelling with Data,” Youtube, www.youtube.com/watch?v=Hfx1X9WSGYQ. Accessed 26, February 2025.

Nussbaum Knaflic, Cole. Storytelling with Data: A Data Visualization Guide for Business Professionals, Wiley, 2015.

Reynolds, Garr. “Why Storytelling Matters.” TEDxTalks Kyoto, YouTube, www.youtube.com/watch?v=YbV3b-l1sZs&t=234s. Accessed 26 Feb 2025.