Tom Davenport tells us that there are 10 good ways to tell a story with data.
Fair enough. You'll find his ideas well known and familiar, but there's nothing like a bit of taxonomy to organize thinking.
The first idea is three dimensions of time:
Time: Analytical stories can be about the past, present, or future time
And, then there are two more, addressing depth of detail, or not:
Depth: There is also a depth dimension to analytical stories ... akin to "CSI" forensics.
The alternative I call “Eureka” stories, which involve long, analytically-driven searches for a solution to a complex problem
Tom calls the next three matters of focus, but you could easily say "motivation", the ever popular what, why, and how:
Focus: Are you trying to tell a what story, a why story, or a how to address the issue story
Let us not leave out cause and effect, sometimes called root cause analysis, or it's close cousin -- correlation:
Methods: Finally, there are different types of stories based on the analytical method used. Are you trying to tell, for example, a correlation story—in which the relationships among variables rose or fell at the same time—or a causation story, in which you’ll argue that one variable caused the other
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