When doing analysis, data scientists try to generate actionable insights about the data. But actionable insights by themselves, do not matter. What matters is that those insights actually lead to positive business impact. One way to increase the likelihood that your analysis will positively change business actions and decisions is by improving how you communicate about your data insights. In this blog post, I will discuss the skill of “storytelling.” Good storytellers are fantastic at getting their analysis over that “last mile” – from actionable insights to action – because they build narratives that change people’s motivation and thinking.
Good storytellers know what a data story is.
A data story is a narrative about the world that is justified by data. It tells people about something important that is happening with their users, their product or the company. A story explains why it matters, and why it is happening. If your audience understands the story, they should be able to project what the will lose out on if they don’t act and how they stand to gain if they do.
If you want some good examples of how to build a narrative around data, look at how journalists do it. This article, for example, discusses narratives about how the pandemic is changing the primary locations of the tech industry. If you believe this story, you can use it to decide where would be a good place to locate a new start-up or where you can move to if you both want a lower cost of living than San Francisco but still have a lot of opportunities for jobs in tech.
Good storytellers know what a data story is not.
A data story is not a list of findings from an exploratory data analysis (EDA). Sometimes, when you are new to a product or a data set, you need to engage in EDA before you can start to build a story and sometimes it can be worthwhile to present this “lay of the land” to your audience, such as when you need to build up context or provide baselines before you move into your story. A whole presentation about EDA, however, is like a novel that describes a world (“The mountains are surprisingly tall and there is more ocean than land!”) but doesn’t tell you anything about what is happening there or why it matters.
A data story is also not a list of findings even if they are all actionable and interesting, because data findings themselves are not a story. The story sits above the data findings. If you look at the narrative about tech migration, there are multiple data findings underlying that narrative, but you can potentially not remember even a single one of them but still know the narrative (e.g., SF is on the decline; Austin and Miami will be the new hot spots!). A list of data findings must all be tied together into a cohesive narrative, otherwise they are just a list of findings.
A data story is not about your personal journey to discover data insights. If you focus your narrative on the path you took to arrive at your conclusions, you risk people remembering your journey instead of the narrative about the data. If you realize that the first questions and hypotheses you set out to explore aren’t as important as newly discovered ones, reset your story with the new problem and present it like that was the story from the start.
Good storytellers use a key set of building blocks to help tell their story
In a business setting, a data story should contain some key building blocks. It should start with a clear business problem (ideally only one) and a question or set of key questions about that problem. The story should provide the big picture information and context on the problem (why is it a problem?), before zooming in on details. Good storytellers postulate concrete hypotheses to answer their key questions and make it clear what they think that hypothesis predicts (i.e., what you expect to see in the data if it is right). Hypotheses and predictions most of the time should come before the data. After validating hypotheses with data finding, stories will then zoom back out to provide a clear summary (hammer the take-home points!) and provide recommendations for concrete actions to take.
Good storytellers do not worry about explicitly labeling these building blocks with technical terms (e.g., “my hypothesis is”), because this is not a school presentation and some audiences may not know or care what a hypothesis is. They still, however, use them to structure their thoughts and guide the narrative.
A longer story sometimes needs to zoom in and out and touch multiple sub-questions. A good story, however, will not lose track of the key problem and question being addressed or what it set out to do. At any point in the story the audience will clearly know how the current point ties to the overall problem that is being addressed and ultimately feel like the main question has been answered. Good storytellers will step back from their slides and examine the organizing narrative and make sure the building blocks are appropriately placed to emphasize the right points in the needed places.
See this previous blog post on the difference between questions, hypotheses, and predictions.
Good storytellers build expectations in their audience’s minds
Bad storytellers present their data and only then begin to explain how it fits into the broader narrative. Good storytellers start by postulating a clear hypothesis and prediction before showing data. That way, when your audience looks at a data point or figure, they will have the mental framework needed to know what they should be looking for and why you are showing them that data. Good storytellers get their audience to ask themselves “is the prediction confirmed?”
Setting expectations about what to look for in the data decreases the likelihood that audience members will create their own explanations of the data when you show them data. Good storytellers control their audience’s attention so that it revolves around their story.
Good storytellers sometimes build expectations to create some suspense. Suspense also makes people pay attention and, in turn, remember. Even data stories can be fun! For the right audience, feel free to even ask your audience guess the answer before you reveal it.
Good storytellers know that the audience will remember the narrative, not the data.
Your audience, the majority of the time, does not leave your talk thinking about specific data findings. After a week, most people, except maybe your data-science teammates, will have forgotten your visualizations (see exception below). What people remember about your presentation is the story. The implications of the story is what excites them and drives actions.
Good storytellers are aware of this fact, and it is why they don’t make data findings the focus of the presentation. They identify the narrative they want their audience to leave the talk thinking about, and all the data, figures, and slides they present, are used to support this narrative.
Bad storytellers, in contrast, make data points the star of their talks. They present data and then try to explain why it is interesting. They present data and then hope that the audience will create their own stories about it. They present lots of data – too much data – on a slide because they hope that it will generate an interesting conversation. Do not do this.
Good storytellers know that the visual that does get remembered is the one that best distills the narrative
Sometimes, if you’ve done a great job, a data point or a figure will be remembered. It will make its way around the office or get added to other presentations. The visualizations that succeed this way are typically the visualizations that clearly distill the crux of your narrative into one figure. They tend to be figures that are easy to interpret and cannot be interpreted in any other way (i.e., they don’t support competing narratives). In other words, these figures are successful because they make it very easy for people to learn and remember the narrative. Do you have a figure like this in your presentation?
Good storytellers present the least complex figures possible.
I know the temptation to use complex, cool-looking figures. So many interesting findings pop out of them! And they are beautiful! Besides, you used this figure in your analysis so it is the easiest thing to do at this point, right? But do not do this. When you add a figure to a slide, you should always ask yourself “is this the simplest figure I can possibly use here?”
How do you know what is the simplest figure you can use? Well, first, you have to know exactly what point you are trying to make on that slide. What part of your story are you supporting with this figure? You cannot simplify your figures if you aren’t quite clear how it supports this part of your narrative. Second, the simplest figure is the one that best distills down the point you want to make on that slide. If your figure contains information unrelated to the point you want to convey on that slide, then it is too complex. Even if that information is very interesting, in this context, it is noise; it distracts from your story.
Good storytellers try to not make their audience think when explaining data
Take a page from design principles: “don’t make me think.” You want to make it as easy as possible for your audience to understand the story. Do not expect them to think through the problem or to figure out the implications of a data finding on their own. Spell it out for them slowly, clearly, and repeat it throughout the presentation.
When presenting figures, control the audience’s attention. Make sure everyone understands the figure – usually start by explaining the y- and x-axes. Define exactly how you calculate the metrics used in the figure. Nobody should need to ask for clarification. Walk people through the figure. If you do need to include a more complex figure, focus their attention on one part at a time. Tell them exactly where to look – use slide transitions. Find ways to visually highlight the part of the figure you want them to focus on in that slide.
Explicitly state if a data finding supports or does not support the prediction of a hypothesis. If your data point helps elucidate something about a question, then say how. Do not expect your audience to tie the pieces together. Do it for them.
In many talks about data, you have to introduce new concepts or new terms. Define and explain them well early in the presentation so that you do not have to do so repeatedly later on. Give concepts and segments intuitive names; avoid acronyms or abbreviations. In many talks, your metrics or analysis may be limited in some sort of way. It may also be good to deal with this at the start so that you do not have to keep adding caveats to all your statements.
You should simplify, organize, and standardize your slides as much as possible. For example, I like to have a slide title that contains the prediction or question, a figure, and the text with the slide’s key finding/point/learning (hypothesis validated!). It enables you to mentally flow through the figure in the correct order (i.e.,, prediction -> finding -> learning). Minimize text. Try to avoid having multiple bullet points and a graph on the same slide; people won’t know where to look and will spend their time exploring your slide instead of listening to you. Avoid tangential points. Feel free to add extra information to the presenter notes for people to read later on, but resist the urge to keep it in the slide.
Good storytellers make sure their story is justified.
If you get your audience to believe in the wrong narrative, it can have a serious negative business impact. Stories need to be justified with data and good analysis. For example, the article I shared earlier tried to challenge the common narrative about the impact of the pandemic on the location of the tech industry. But the analyses were flawed (see if you can identify reasons why!). This failure means that the narrative may not only be wrong but, if you were using it to decide where to live or start a business, potentially quite dangerous. Select metrics and make comparisons carefully. Know the limitations of your metrics and comparisons. A key part of data storytelling, in a business context, is building trust with your audience that your narrative is correct.
Good storytellers put stories together before conducting analysis.
Good storytellers use their business and product sense to try to tell a compelling and important story before they start their analysis. Good storytellers will work with business partners to test out these first-pass stories and get buy in before starting their analysis. When the data scientist starts the analysis, the goal is to try to justify that story and to examine competing narratives. Usually, in the course of an analysis, as new insights and ideas emerge, the story changes or becomes more fleshed out. You may realize that there are key follow-ups and take the story down further along than originally planned. As you realize this, step back again. Try to recreate your full story. At that point you may realize what parts are still not justified (e.g., unclear if X is the right action to take). Focus on justifying them. Create a presentation only after the narrative is justified.
I can often tell when analysts have created their story after doing their analysis. One key symptom of this is a presentation containing bullet points of findings. Another symptom is a narrative that is not sufficiently justified – if you create your story after you are done analyzing data, then you will be stuck stitching together whatever findings you have. Generally, ad-hoc narratives feel disjointed and uncompelling and they are more likely to be forgotten by the audience. If you want to tell a good story try figuring out up front what pieces of evidence you need to assess it. Iterate your story as you examine new data points. Again, start with your story.
Good storytellers are aware of prior stories
If you want to spin a narrative, it is critical that you be aware of whether there is already a current narrative answering that question. This prior story may be based simply on people’s intuitions or assumptions and not be data-driven or it may be a data story previously told by another data scientist. If this prior story exists, it is helpful to address it explicitly. If there is strong belief in this prior story, just as in Bayesian statistics, you will need more evidence – more justification – to overcome it. If there are no prior beliefs, you have a blank canvas and it is easier to write a story on a blank canvas.
One reason why you need to provide extra justification for a new narrative that competes with an old one is that you risk persuading only part of your audience that the new narrative is right, which could lead to half of your team believing in one story and the other half believing in another. If you work in science, conflict is a great impetus for pushing knowledge forwards. But in business, conflict and uncertainty reduces likelihood of action and impairs strategy.
In this context, you really do want to think twice before challenging the narratives created and sold by other data scientists. The team will become overall ineffective in driving impact if they go back and forth on narratives because the team will be viewed as less trustworthy. Ideally, a team of data scientists will align on and build upon each others’ narratives. Over time, this can lead to organization clarity around strategy and actions. That said, of course, previous narratives do sometimes need to be challenged. If you think you are in this situation, make sure you understand the past analysis well and exactly why it led to different conclusions. Talk to the data scientist who did the analysis, if possible, and work through it together. If you still want to challenge it, then, again, bring extra justification.
Good storytellers know their audience.
What does your audience care about? What problems are they facing? What decisions are they trying to make? Your data story should help your audience. You need a clear sense of their needs to do so. Make sure you understand the world view and the language of your audience, including the specific jargon they use; present from that perspective and use that language. If you are presenting to executives, you may need to tailor your talk to them in particular – how you present, how much data you present, the speed at which you move through data, the level of detail you go into – based on how they prefer to consume information (see this excellent article by Will Larson for more on presenting to executives). The more that you can customize your narrative for your audience, the greater the likelihood that they will be engaged and persuaded by it.
Good storytellers encourage conversation.
I have talked a number of times about controlling your audience’s attention as a way to communicate your narrative. This does not mean that you do not want them to think at all. What you want is for them to carry your narrative forwards. What are the implications you may have missed? If this is right, what actions should they be taking? What surprises them about it? Should they re-evaluate their current approach? The conversation your story elicits is often the most impactful part of the meeting. It helps the team build consensus and excitement about your ideas.
One of the best ways to encourage this type of thinking is to give space for conversation. Do not wait until the end of the presentation to provide this space, because it will be accidentally cut if your talk goes long. People may also forget important points that would have led to good debate if raised earlier. A great time to provide space is right after you present data validating the prediction of a hypothesis. It encourages people to discuss what they should do differently given that this hypothesis seems to be true. When you give space, give more time than you think. It can feel uncomfortable but people need time to collect their thoughts.
** Thanks to the data-science team at Calm, particularly Ben Paul, Will Downey and Heather Lin, for conversation, suggestions and feedback on the first draft of this article.