Storytelling with Data
eBook - ePub

Storytelling with Data

A Data Visualization Guide for Business Professionals

Cole Nussbaumer Knaflic

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Storytelling with Data

A Data Visualization Guide for Business Professionals

Cole Nussbaumer Knaflic

Book details
Book preview
Table of contents

About This Book

Don't simply show your data—tell a story with it!

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.

Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:

  • Understand the importance of context and audience
  • Determine the appropriate type of graph for your situation
  • Recognize and eliminate the clutter clouding your information
  • Direct your audience's attention to the most important parts of your data
  • Think like a designer and utilize concepts of design in data visualization
  • Leverage the power of storytelling to help your message resonate with your audience

Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data— Storytelling with Data will give you the skills and power to tell it!

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chapter 1
the importance of context

This may sound counterintuitive, but success in data visualization does not start with data visualization. Rather, before you begin down the path of creating a data visualization or communication, attention and time should be paid to understanding the context for the need to communicate. In this chapter, we will focus on understanding the important components of context and discuss some strategies to help set you up for success when it comes to communicating visually with data.

Exploratory vs. explanatory analysis

Before we get into the specifics of context, there is one important distinction to draw, between exploratory and explanatory analysis. Exploratory analysis is what you do to understand the data and figure out what might be noteworthy or interesting to highlight to others. When we do exploratory analysis, it’s like hunting for pearls in oysters. We might have to open 100 oysters (test 100 different hypotheses or look at the data in 100 different ways) to find perhaps two pearls. When we’re at the point of communicating our analysis to our audience, we really want to be in the explanatory space, meaning you have a specific thing you want to explain, a specific story you want to tell—probably about those two pearls.
Too often, people err and think it’s OK to show exploratory analysis (simply present the data, all 100 oysters) when they should be showing explanatory (taking the time to turn the data into information that can be consumed by an audience: the two pearls). It is an understandable mistake. After undertaking an entire analysis, it can be tempting to want to show your audience everything, as evidence of all of the work you did and the robustness of the analysis. Resist this urge. You are making your audience reopen all of the oysters! Concentrate on the pearls, the information your audience needs to know.
Here, we focus on explanatory analysis and communication.

Recommended reading

For those interested in learning more about exploratory analysis, check out Nathan Yau’s book, Data Points. Yau focuses on data visualization as a medium, rather than a tool, and spends a good portion of the book discussing the data itself and strategies for exploring and analyzing it.

Who, what, and how

When it comes to explanatory analysis, there are a few things to think about and be extremely clear on before visualizing any data or creating content. First, To whom are you communicating? It is important to have a good understanding of who your audience is and how they perceive you. This can help you to identify common ground that will help you ensure they hear your message. Second, What do you want your audience to know or do? You should be clear how you want your audience to act and take into account how you will communicate to them and the overall tone that you want to set for your communication.
It’s only after you can concisely answer these first two questions that you’re ready to move forward with the third: How can you use data to help make your point?
Let’s look at the context of who, what, and how in a little more detail.


Your audience

The more specific you can be about who your audience is, the better position you will be in for successful communication. Avoid general audiences, such as “internal and external stakeholders” or “anyone who might be interested”—by trying to communicate to too many different people with disparate needs at once, you put yourself in a position where you can’t communicate to any one of them as effectively as you could if you narrowed your target audience. Sometimes this means creating different communications for different audiences. Identifying the decision maker is one way of narrowing your audience. The more you know about your audience, the better positioned you’ll be to understand how to resonate with them and form a communication that will meet their needs and yours.


It’s also helpful to think about the relationship that you have with your audience and how you expect that they will perceive you. Will you be encountering each other for the first time through this communication, or do you have an established relationship? Do they already trust you as an expert, or do you need to work to establish credibility? These are important considerations when it comes to determining how to structure your communication and whether and when to use data, and may impact the order and flow of the overall story you aim to tell.

Recommended reading

In Nancy Duarte’s book Resonate, she recommends thinking of your audience as the hero and outlines specific strategies for getting to know your audience, segmenting your audience, and creating common ground. A free multimedia version of Resonate is available at



What do you need your audience to know or do? This is the point where you think through how to make what you communicate relevant for your audience and form a clear understanding of why they should care about what you say. You should always want your audience to know or do something. If you can’t concisely articulate that, you should revisit whether you need to communicate in the first place.
This can be an uncomfortable space for many. Often, this discomfort seems to be driven by the belief that the audience knows better than the presenter and therefore should choose whether and how to act on the information presented. This assumption is false. If you are the one analyzing and communicating the data, you likely know it best—you are a subject matter expert. This puts you in a unique position to interpret the data and help lead people to understanding and action. In general, those communicating with data need to take a more confident stance when it comes to making specific observations and recommendations based on their analysis. This will feel outside of your comfort zone if you haven’t been routinely doing it. Start doing it now—it will get easier with time. And know that even if you highlight or recommend the wrong thing, it prompts the right sort of conversation focused on action.
When it really isn’t appropriate to recommend an action explicitly, encourage discussion toward one. Suggesting possible next steps can be a great way to get the conversation going because it gives your audience something to react to rather than starting with a blank slate. If you simply present data, it’s easy for your audience to say, “Oh, that’s interesting,” and move on to the next thing. But if you ask for action, your audience has to make a decision whether to comply or not. This elicits a more productive reaction from your audience, which can lead to a more productive conversation—one that might never have been started if you hadn’t recommended the action in the first place.

Prompting action

Here are some action words to help act as thought starters as you determine what you are asking of your audience:
accept | agree | begin | believe | change | collaborate | commence | create | defend | desire | differentiate | do | empathize | empower | encourage | engage | establish | examine | facilitate | familiarize | form | implement | include | influence | invest | invigorate | know | learn | like | persuade | plan | promote | pursue | recommend | receive | remember | report | respond | secure | support | simplify | start | try | understand | validate


How wi...

Table of contents

  1. Cover
  2. Title page
  3. Copyright
  4. Dedication
  5. Foreword
  6. Acknowledgments
  7. About the Author
  8. Introduction
  9. Chapter 1 the importance of context
  10. Chapter 2 choosing an effective visual
  11. Chapter 3 clutter is your enemy!
  12. Chapter 4 focus your audience’s attention
  13. Chapter 5 think like a designer
  14. Chapter 6 dissecting model visuals
  15. Chapter 7 lessons in storytelling
  16. Chapter 8 pulling it all together
  17. Chapter 9 case studies
  18. Chapter 10 final thoughts
  19. Bibliography
  20. Index
  21. EULA
Citation styles for Storytelling with Data

APA 6 Citation

Knaflic, C. N. (2015). Storytelling with Data (1st ed.). Wiley. Retrieved from (Original work published 2015)

Chicago Citation

Knaflic, Cole Nussbaumer. (2015) 2015. Storytelling with Data. 1st ed. Wiley.

Harvard Citation

Knaflic, C. N. (2015) Storytelling with Data. 1st edn. Wiley. Available at: (Accessed: 14 October 2022).

MLA 7 Citation

Knaflic, Cole Nussbaumer. Storytelling with Data. 1st ed. Wiley, 2015. Web. 14 Oct. 2022.