Things weren’t going well for you as a data analyst in your organization last year. Many of your charts, reports, and data-centric presentations weren’t well received by the management team, who were routinely bored, confused, unimpressed, or unconvinced by your efforts, or clearly misunderstood the data that you were trying to communicate to them.
You then came across a “data storytelling” book or course, which offered to teach you how to make charts and data presentations that were easier to understand, and more persuasive, engaging, and memorable. You read the book or took the course and started applying the techniques that you learned.
After applying data storytelling techniques to your charts and data presentations, your success rate is now definitely higher, which is great, but it’s not 100%. The management team still regularly gets confused or bored by your charts, misinterprets them, or just ignores them altogether. What gives?
Unfortunately, there seems to be a widespread belief that a lack of data storytelling is always the main—or possibly only—reason why charts and data presentations don’t go over well with audiences, and that turning charts and data presentations into “data stories” virtually guarantees that they’ll be effective and well received. Even though that view isn’t promoted by most data storytelling thought leaders, it seems to have taken hold among many data professionals anyway.
IMO, though, there are at least five common reasons why charts and data presentations flop with audiences, one of which has to do with data storytelling:
Poor data visualization “spelling and vocabulary”
Insufficient domain knowledge
Insufficient situation/audience knowledge
Poor statistical or data handling choices
Not using data storytelling techniques
If you use data storytelling techniques, then, you can expect a certain degree of improvement, but, if you don’t also address the other issues, you should still expect a poor response to your charts and presentations fairly often.
Let’s have a closer look at each of these issues, starting with…
Poor data visualization “spelling and vocabulary”
Most data stories are centered around data visualizations (i.e., charts). Data visualization is, in many ways, a language, and, just like any other language, it has a basic “spelling and vocabulary” that must be learned in order to communicate effectively. The basic spelling and vocabulary of data visualization consists of guidelines for choosing chart types, choosing colors, choosing scale ranges, and making many other basic design choices.
Like the spelling and vocabulary of English or any other language, the spelling and vocabulary of data visualization can be unintuitive, and most of us are unlikely to figure them out on our own without reading a good book or taking a good course.
Most books and courses on data storytelling that I’ve seen do have a few dozen pages or an hour or two of course content on “data visualization fundamentals,” but, in my opinion, that’s not nearly enough to allow chart creators to design charts competently.
What makes me think that? Well, my Practical Charts course and associated book only cover the basic spelling and vocabulary of data visualization and some “enhanced data communication techniques” (adding visual highlighting, adding comparison/reference values, etc.), and they don’t cover advanced storytelling techniques such as using characters, suspense, or narrative arcs. Despite their narrower focus, the Practical Charts book is over 300 pages, and the course lasts two full days.
Practical Charts, then, has five or ten times as much “data visualization fundamentals” content as a typical data storytelling course or book. Do you really need to know that much about data visualization to create data stories? Well, Practical Charts contains what I consider to be the bare minimum guidance that’s necessary to handle the chart design decisions that I see regularly in practice and avoid the most common chart design mistakes that I see in the wild. If the book and course were shorter, they’d fail to address many important design decisions and leave chart creators open to making many common chart design mistakes.
If you haven’t taken a multi-day data visualization fundamentals course or read a comprehensive book on the subject, you’re probably still making charts with inappropriate chart type choices, confusing color choices, misleading quantitative scales, and many other basic chart design problems that are confusing or misleading your audience and torpedoing your data stories.
Insufficient domain knowledge
Another common reason why I see data stories flop is that the people who created them didn’t know enough about the domain from which the data came. They were, for example, making data stories about insurance data without knowing enough about insurance.
If you don’t have a fairly good understanding of the domain from which the data came, your data stories will likely misrepresent subtle but important nuances about that domain. In many situations, your audience will be aware of those nuances and will instantly discount or ignore your data stories when they realize that you don’t really understand the data that you’re presenting.
If that’s the reason why your data stories are failing, no amount of data storytelling skill will help; you just need to spend the (potentially significant) time to learn about the domain of the data with which you’re working.
Insufficient situation/audience knowledge
Other common reasons why data stories flop with audiences include:
Showing the audience insights that they were already aware of
Showing insights that might have been important last month, but are irrelevant this month because things in the organization have changed
Showing insights that assume that the audience has background knowledge that they don’t have, leaving them confused
Featuring insights that aren’t useful to the audience or failing to feature insights that would be important for them to see
The root cause of all of these problems is the same: not knowing enough about your specific target audience and their specific problems, objectives, priorities, projects, etc., at the moment.
As analysts, we often want to “live in the data” and tend to avoid HR meetings, planning meetings, sales meetings, and other “non-data” discussions and email threads. If we want to be truly useful to our audiences, though, we need to get out of the data and into our audience’s world by getting up to speed on specifically what’s going on with them at the moment.
Again, if this the reason why your charts and data presentations are flopping, applying data storytelling techniques won’t help.
Poor statistical or data handling choices
Yet other common reasons why data stories flop with audiences or end up misleading them include:
Presenting correlation as causation
Failing to identify survivorship bias in data
Failing to mention that the data being shown was filtered in some way
Showing too much data or too little
The root cause of all of these problems (and many others) is a lack of statistical or data handling expertise. Once again, applying data storytelling techniques won’t help if this is why your charts and data presentations aren’t going over well with your audience.
Not using data storytelling techniques
Finally, yes, there are plenty of situations in which charts and data presentations flop because:
The way in which the information was presented wasn’t engaging or persuasive enough.
Key insights weren’t obvious enough.
Too much detail was presented.
Key takeaways weren’t memorable enough.
These are the kinds of problems that can be addressed with good data storytelling, so I do think that data storytelling is a very useful skill to develop. That’s different, however, than saying something like, “The reason why charts flop with audiences or mislead them is because of a lack of data storytelling.” It may be one of the reasons, but it’s almost certainly not the only reason.
Agree? Disagree? Awesome! Let me know on LinkedIn or Twitter.
By the way…
If you’re interested in attending my Practical Charts or Practical Dashboards course, here’s a list of my upcoming open-registration workshops.