When you pick up any chain saw, swallow any medication, or even back out of your driveway, you’ve already been warned. Be careful with this thing. For all the good it can do for you, it could also mess you up badly.
So it should go with stories about data. Even a mediocre story that explains the origins, meaning, and implications of analyzed data stands a better chance of being remembered and retold than just plain old data. That’s true even if the data is dressed up in pretty visualizations.
Bad data stories can distort valuable analysis. They can throw an entire organization off course. They obscure data’s rightful messages behind ill-founded stories, which become the talk of the office.
Business author Seth Godin sketches part of the hazard in a recent blog post.
Facts alone have little chance in a battle with a good story.
Because a good story feels true.
A good story resonates.
A good story is based on our feelings, long-held and hard-earned.
A good story sticks with us, regardless of the facts.
All true, but there’s more to it.
People use stories to understand things. If they hear no story, they’ll make one up automatically and unconsciously. Without a story, abstract facts like data really are just abstractions. As such, they have stand little chance of sticking. There’s even less chance these abstractions will be talked about.
Don’t assume that you have your audience to yourself. So you have to tell your data’s story before your audience substitutes their own.
Watch your mind the next time you hear any set of facts. Your friend says to you, “Hey, did you hear that that big green house burned down?” Instantly, stories arise in your mind to explain the fire.
You conjure images of injury and loss that prompt questions. “Was anyone hurt?” If the residents you didn’t like anyway all died, then, “Hmm, I hope the dog survived. Maybe I can have it.”
The skilled presenter of data offers a good story to explain things. Let the intended meaning of data stand up and be known before other meaning gets first shot.
Amazingly, some in the data industry express fear of stories. They say it distorts data. These Chicken Littles — of “the sky is falling” fame — say that stories distort data. Sure, sometimes they do. But stories have no monopoly on distortion. Data itself, including data visualizations, is easily distorted.
While we’re at it, let’s ban power tools because they sometimes injure people. Ban cars because some people drink and drive. Ban writing because people use it to spread false information. Ban medicine because it has side effects.
Seth Godin finishes his post with this: “Part of the job of making change” — and I assume that’s what you’re trying to do — “is working to make sure a bad story doesn’t get in the way of good facts.”
Extra doodles
CINDY GALLOP’S “Shame on you, Marc Benioff” on Sunday on Linkedin sums up last weekend’s widespread reaction to the continued, widespread tech layoffs. She and others seethed over the Salesforce boss’s bungled layoff announcement — after Benioff recently disclaimed the possibility.
Why do companies lay people off? In short, says Stanford business professor Jeffrey Pfeffer, it’s because everyone else is doing it. Those that resist the social contagion usually end up ahead.
ON SOME IMPLICATIONS OF AI CHATBOTS, there’s Maggie Appleton. An excerpt:
There’s a swirl of optimism around how [generative AI] will save us from a suite of boring busywork: writing formal emails, internal memos, technical documentation, marketing copy, product announcement, advertisements, cover letters, and even negotiating with medical insurance companies. …
But we’ll also need to reckon with the trade-offs …. These new models are poised to flood the web with generic, generated content.
DATA-INDUSTRY TRENDS WITHOUT THE HYPE. Donald Farmer, knitter and an esteemed elder of the data industry, posted a table excerpted from the recent BARC Trend Monitor 2023 on his newsletter Creative Differences.
As we move into the first quarter of the year, I can’t help but remark on just how much hype is spread by software vendors with their largely self-serving predictions about upcoming trends. If you treat these forecasts as entertainment — like the horoscopes in a newspaper — they are interesting enough. But if you are considering long-term investments that will shape an enterprise’s data architecture for years to come, don’t take them too seriously. Rather, look for solid research.
