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How to avoid death by PowerPoint featuring Storytelling with Data by Cole Nussbaumer Knaflic



written by human, not by AI




If you work in a more corporate environment, chances are, you have gone through the following situation: slide after slide of blocks of text and low-resolution images, with squiggly light grey lines that were supposed to be a chart axis, maybe? It usually comes accompanied by a monotone voice reading the blocks of text, unsure of how to convey all the information they’ve just worked so hard to put together. Obviously, everything is important and worth mentioning over this one-hour-long call, right? What if they don’t mention a point, and that’s exactly what comes up during question time? What if someone goes back to the slide deck and can’t find that one particular point they had in mind? You better be safe and include everything.


But just between us: have you ever left one of these calls knowing more than when you came in? Do you even remember what the last one you joined was about? Try to recall the three main takeaways from the shared slide deck. Was there an important message there? Has something in particular stuck with you that will improve your work and help you make better-informed decisions?


I’ll be honest; I answered no to most of the previous questions. And if you did, too, you’ve been struck by the dreadful death by PowerPoint.


Death by PowerPoint doesn’t happen because the analyst is a sadist that likes to torture minds into endless yawning. The issue often lies deeper than that. It originates from a well-intentioned yet misguided belief that more is always better. More data, more points, more slides - all to ensure that every possible question or concern is preemptively addressed. The problem with this approach is that it forgets one critical factor: the audience.


Let's look at it this way. Imagine you're at a buffet with an infinite variety of dishes. Overwhelmed, you start piling everything onto your plate, driven by the fear of missing out on something potentially delicious. But, when you finally sit down to eat, you find you can't taste anything distinctly. Everything is jumbled together, making it impossible to enjoy or even identify the different flavours. Not only that, a lot of things don’t even go well together. That's what Death by PowerPoint feels like: a cognitive overload that turns any chance of discovery into an indistinguishable mess, leaving us feeling full yet unsatisfied.


And this is where the power of strategic simplicity and storytelling comes into play. Effective presentations are not about including everything; they're about focusing on a few right things. They distil complex ideas into a handful of memorable key points, guiding the audience's attention instead of scattering it. They leverage the power of narrative, turning dry data into compelling stories that resonate on a human level. They invite engagement, foster understanding, and inspire action.



Storytelling to the rescue


You’ve probably heard about telling stories with data. And chances are, you are somewhat confused by the concept. Many of us have the perception that, as long as all the data is there, in good shape and accurate, the information will be used. Unfortunately, the work doesn’t end there. You may have the most pristine of all datasets, and your message may still fail if no one can connect with it, relate to it and remember its key takeaways that’ll influence their actions and decisions.


If only there were a resource accessible not only to the data folk but also to the layperson needing to present data well!


Enter Storytelling with Data: a Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic. This book does exactly what it says on the cover: It is a guide to help business people effectively convey the messages from their data. And it does that with a well-balanced mix of practical advice, theoretical flavour, and useful strategies that make it a great companion for a wide spectrum of people keen on improving how they communicate with their data.


As the title suggests, the book is not just about presenting data but rather about telling a story through data. Knaflic emphasises the need to augment raw data with a meaningful narrative, recognising that the real power of data lies not in the numbers themselves but in the insights and stories we can tell with them.


The book’s scope is focused on creating data narratives and explanatory visuals, but it covers examples from multiple industries while being extremely accessible to professionals at all levels in the data journey. You don’t have to be skilled in any particular software to take valuable advice from it, either. It covers the concepts and shows how to apply them through instructional makeovers that can be easily replicable with any tools you may have available to you.



The 6 Steps to good data storytelling


The book is organised into 10 chapters that follow a logical progression from defining the context in which your analysis exists to how to form a compelling narrative. The author then summarises her method into a 6-step framework:


  1. Understand the context of your data story: First, you define who your audience may be. Narrow it down as much as possible since having multiple audiences can confuse your message. Then, determine what you need them to do with the information you’re giving them. What’s the expected outcome of your analysis and story? Last, define how your data will support (or not) your point. One brilliant concept introduced in this section of the book is the concept of defining your Big Idea - where you summarise the main point you’re trying to convey with your data into one full sentence that can clearly communicate what’s at stake and why this matters to your audience. This is an amazing exercise I like to do with all visualisations I create, whether they may be part of a data story or not.

  2. Choose an appropriate visual: The author defines the most common visuals that we see in business environments and takes a healthy dose of inspiration from Stephen Few’s predicaments (such as disapproving the use of Pie Charts). But rather than just presenting each visual and giving them a brief description, Knaflic goes deeper into how each visual can be incremented when used in certain contexts. Examples include brief comments about using data labels, ordering and sorting of categories, or if a secondary Y-axis is sometimes appropriate. I appreciate the insight into these micro-decisions we all have to make as dataviz designers, even if they only appear briefly in the context of this chapter.

  3. Eliminate Clutter: The chapter that channels the author’s inner Edward Tufte. While I have my own personal views on the matter of what “clutter” is in dataviz, the chapter introduces important concepts you’ll carry for life when communicating with data. Here, the author wants to get us familiar with the psychological principles that govern a good layout (also known as Gestalt). She also adds a few other relevant design concepts, such as alignment, visual organisation, contrast and white space. She then takes us through a great example, step by step, of how these principles apply to a chart’s makeover. It is awesome to see a book that doesn’t skip through all the iterations and goes the rest of the f-ing owl to the last step. Good dataviz is iterative in nature, and the author does a great job of showing this throughout the book.

  4. Focus your audience’s attention: On this step, Knaflic takes us through the concepts behind preattentive attributes - or, what makes us focus on certain points and not others on a screen, and how to leverage this to our story’s advantage. Colour, size, line weight and typographic emphasis strategies, such as bolding or underlining, are all put into context, with a range of useful examples to help drive the point home.

  5. Think like a designer: Probably my favourite chapter of the book for the sheer audacity of asking data people to consider that design is much more than aesthetics. This is not a chapter about making things look pretty but rather about seeing their function and their purpose. An awesome addition.

  6. Last, tell a story! Easier said than done! The author takes us through multiple techniques to arrange our visuals and entire presentations or slide decks into a narrative. I personally love the one she calls the Bing, Bang, Bongo: You introduce your point to the audience (Bing!), then you tell them what you came to say (Bang!), and finish with a summary they’re sure not to forget (Bongo!). How memorable is that?!


Finally, the book closes with a few chapters on how to apply all these concepts together and several case studies to help you on your quest to never draw another yawn from an audience!



Let’s practice!


Storytelling with Data: a Data Visualization Guide for Business Professionals was first published in 2015. It soon became one of those seminal pieces of work that everyone in the field has read, referenced and recommended multiple times. A natural follow-up came in the shape of Storytelling with Data: Let’s Practice in 2020.


The complementary book goes hand-in-hand with the content of its predecessor but is more focused on practical exercises and examples of how to apply each concept. Let’s Practice is also more refined when revisiting some of the ideas of the first book, going deeper into how all the advice comes together. It also offers useful extra materials that can be downloaded from storytellingwithdata.com.


One of the things I love the most about it is the amazing illustrations that summarise the chapters and steps from the first book. They became a hallmark of the Storytelling With Data branding too.


This is a serious follow-up if you want to keep going and improving. I recommend grabbing both books together and giving both at least one read-through cover to cover. After that, you’ll surely revisit them a few times until you probably run out of post-its and page markers, like me.



Should you read it?


Whether you’re starting in the field or just want to make better charts, this is a must-have introductory book. It won’t go deep into the ins and outs of the dataviz technique, but it works really well as a quick, practical, no-nonsense guide to improving your data communication skills. It has a very low entry barrier, so it works well for people transitioning from other fields or needing the skills but not necessarily the depth to become experts. I strongly advise that both books be read together, as they complement each other well and you’ll make the best of them this way.


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written by human, not by AI

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