In addition to exhibiting facts and figures, analytical data should communicate a comprehensible narrative. Use these methods to construct a clear story arc within data reports.
We all dread the weekly report. A spreadsheet arrives in your inbox crammed full of numbers, perhaps a few charts, maybe even some attempts at "data visualization" but usually without any narrative or explanation. You look at it and think to yourself, "So what?"
So what's it telling you? So what are you going to do about it? We're not short of numbers, we're short of understanding what to do about the numbers.
From an analyst's perspective, we're often complicit in this. Our habit is to send out reports rather than to send out insight. We're good at reading data but not great at telling stories. We need to be telling more stories because people remember stories. They rarely remember a piece of analysis. But what are the ingredients of a good story and how is that relevant to analysis?
Simply put, stories have a beginning, a middle, and an end. When it comes to delivering the results of a project in a presentation, I urge consultants to go by the old adage, "Tell them what you're going to tell them - tell them - and then tell them what you told them." In other words, start off by giving the key highlights, tell the story, then summarize and close. Participants will get a sense of what's coming; they will be able to follow the flow, and the conclusion pulls all the points together.
In addition to a basic structure, a story needs a good narrative. In our data-driven world, it's easy to get hung up on the numbers and to have slide after slide containing tables full of numbers and complex (Read more...). The storytelling analyst will understand the narrative and will find a way to tell the narrative in an engaging and memorable way. Often less can be more. Fewer numbers can give greater insight if they are the right numbers. Less precision can lead to more confidence. "67.3 percent of visitors..." can almost appear to be too precise and can lead to challenges around data accuracy, whereas "two-thirds of visitors..." takes the data issues away. The focus is then on what the two-thirds of visitors did or didn't do.
The storytelling analyst will also develop the flow. Stories have a cause and effect relationship. There are events and then there are consequences. Otherwise the narrative is just a series of events and there is nothing for the listener or the reader to take away at the end. In their book, Elements of Persuasion, Richard Maxwell and Robert Dickman define the elements of a good story as:
- The passion with which the story is told
- A hero to drive the action
- An obstacle or an antagonist to challenge the hero
- A moment of awareness where the hero realises how they can overcome the obstacle
- A transformation in the hero and the world around them
At first take, it can be difficult to see how all these elements might be incorporated into the delivery of a piece of analysis or research, but data storytelling is worth thinking about it in those terms.
Passion is possibly the easiest part of storytelling, in a way. A good analyst will be interested in the business and will have empathy for it. He or she will understand the consequences of the events that they are describing and the relevance of them to the audience.
The hero in the story could be the business's customers. One technique might be even to personalize the story around an individual or a group of individuals by using pen portraits or personas. This can help bring the story to life and increase its relevance.
The obstacle that the hero needs to overcome would be the point of the research or the analysis that's being carried out.
The moment of awareness would be the insight from the research, and the transformation would be the consequences from the recommendations accompanying the analysis.
Technologies like Insight Rocket and Sweetspot Intelligence recognize the importance of narrative and storytelling around data. More "traditional" data visualisation technologies, like Tableau, are also incorporating features into their software to enable the data to be visualized and presented as part of a narrative rather than just a series of dashboards. However, at the end of the day, technologies are just enablers. It's the analyst who has to decide what the narrative is.
So next time you create or send out the weekly report or deliver a piece of analysis, maybe ask yourself, "What's the story here?"
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