Just when data seems to be approaching maturity as a touchstone in business, along comes a data scientist with doubts. Data is like the stone in stone soup, he says. It has not a single calorie of meaning.
“The beef is in data fiction,” says Joerg Blumtritt, CEO of Munich-based Datarella and consultant to a long string of large European and American clients in manufacturing and media. He comes to his observations after years of consulting and research.
“Fiction” in the same breath as data? Any kind of story at all frightens some data people, and now you talk about fiction! The standard fear seems to be that storytelling will distort data — as if data is bred and born pure like greenhouse cucumbers.
The real value of data, he says, is that it lets us talk about things we couldn’t talk about otherwise without resorting to subjective experience. It’s a touchstone.
It’s where you go from there that’s important, and that’s where things get messy. Meaning comes from context, which comes in stories and metaphors, he says. “I could tell you that the lawn outside my window is green. But that’s just data. It means nothing,” he says. The meaning of the green lawn comes only when he explains that that lawn will make the picnic at noon especially pleasant.
The question of the picnic’s quality, and the hopeful scenario in which people have a good time, determined the choice of data. With that scenario, other data was ignored. The day’s financial market data, the latest sports scores, and the organizer’s checking account balance were obviously irrelevant.
Data is filtered, selected, and rendered according to the needs of those who decide what’s relevant. One crisp example Joerg’s used is an MRI image of a knee. He explains in a 2014 presentation at the Content Strategy Forum in Frankfurt, Germany that the line representing the division of bone and tissue should be rendered differently for a surgeon than for a cardiologist. One needs a blurry line while the other needs a sharp line — two renderings of data reflecting the same knee.
There is so much data available now, he says, that you can prove almost any hypothesis. Big data makes it easy to prove just about any hypothesis. For example, Google data can produce many arbitrary subsets, all with highly significant differences. “What does that mean in the end?” he says. “Nothing.”
But where is all this going? What if, as he says, “the beef” is really in data fiction? It means, I think, that we might be free to play with data, to experiment freely and imaginatively. Insights, he seems to say, come from play and narrative instead of rigid “truth.”
Much of data analysis and predictive analysis is essentially fiction. Predictive analytics is about telling stories. “It’s not facts. It’s taking data and forming a picture from it that guides your decision.” It’s the same with personas, developed to guide marketing. Or credit scores to predict trustworthiness.
Fiction, he explained, is not random. Like data, it has to make sense by conforming to our experience. Data is the stage that constrains fiction, but fiction provides the images and drama.
“I hope that data will transcend from facts to fiction,” he says. “And I want to hear and tell the fairytale where we wake the sleeping beauty in data.”
I think what he’s saying boils down to this: Useful new understanding on which to base business decisions comes not from data. That understanding comes naturally and fluidly from the narratives that arise from data.
Data stories are what we do with data, giving it as little notice as we give our own breath. You hear those stories, though, if you listen carefully to people who analyze data. You hear metaphors, anecdotes, experiences, personas, scenarios and predictions.
The data kisses Joerg’s “sleeping beauty,” who awakens to inspire wise decisions.