4 Approaches to Making Your Data Memorable
Who would remember the data picked out of overloaded excel sheets and slapped onto a PowerPoint? Mountains of dry stats, numbers, and analysis? We need these to make informative business decisions, but the presentation won’t be one you will recollect or that will inspire you when you’re back at your desk.
However, it’s not impossible to inspire with numbers when you give more meaning to them. Consider, data contains facts, and “the shortest distance between truth and a human being is a story.” –Anthony de Mello, One Minute Wisdom. Data is simply disguised stories waiting to be uncovered. After you make sense of the numbers, then it’s a matter of picking out which story is useful to bring your point across to the audience.
Four Story Approaches
Best-selling author, Tom Davenport describes four key dimensions, time, focus, depth and methods that determine the type of story you can tell with data and analytics. Here is our spin on them:
1.) Time: A typical analytical story takes place in the past and reports on what has happened in the last month, quarter or year. This may not be the most star-striking type of story, but it is one of the easiest and useful forms for explaining statistics and figures.
This can also lead you into predicting future analytics, in which case you should consider including a survey. Talk about new trends that are approaching, or better yet, what is coming next in your industry. What’s the leading story here?
2.) Focus: What is the central question of your story: what, why or how? Knowing this will keep you headed in the right direction for answering it. For example, think about the numbers from the last quarter. Reporting informs us about “what happened.” “Why” addresses the underlying factors of the outcome. “How” refers to approaching the problem by exploring various ways to improve the “what” and “why” stories. Decide which angle you want to take your data and what you want to tell the audience.
3.) Depth: Examine the small details. Davenport gives an example of Expedia being involved in “discovering why some Irish customers were dropping online transactions when they got to the postal code input form. It turned out that some rural Irish locations don’t have postal codes.” This is a great example of researching the stories behind the numbers. See a weird trend? Investigate! Or perhaps everything is going smoothly, in which case you can highlight some achievements that your marketing or sales colleagues have done to contribute to this progress. Recognize people as much as possible.
4.) Methods: Finally, Davenport questions what type of story you are telling. For example, you can illustrate a correlation (in which for example the variables rose and fell simultaneously) or a cause and effect story (where one variable affected the other). Having a clear understanding of the relationship between numbers can point you to the bigger picture and help tell your story better.
Read more about these points in Tom Davenport’s article, 10 Kinds of Stories to Tell with Data.
Tip: these four approaches are not mutually exclusive. Consider all of them when examining your data and searching for your story.
Coming up: Visualizing Data-Driven Stories. What visual will suit your data story the best? Be an even more powerful data storyteller by picking out the best visuals.