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“Tame” the data makers

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I’ve heard of “taming data.” But the week before last at Strata I heard it in a new context: taming behavior.

Taming data has been “nichy,” as fellow TDWI writer Steve Swoyer puts it. He says, “It doubtless explains the etymology of, for example, Tamr.”

But Swoyer pushes on from there, as Swoyer knows how to do.

[Notice the] consonance between to wrangle and to tame. Both are grounded in the same metaphorical frame. Both are grounded in the same metaphor. This pre-conscious framing/understanding of the issue is more interesting than the stupid terms.

Former IBM sales engineer Lamont Lockwood, now the “Integration Expert,” sees two definitions. One is simple: to straighten and calm streaming data. “You don’t have time to fix it later,” he explains. “You need smart models to keep up.”

That leads to Lamont’s second, “nefarious” definition: taming the users who produce the data. “You’ll be trackable every day and every minute,” he muses, “like call-center workers….This is happening.”

Schmarzo: An over-infatuation with data

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The “Dean of Big Data,” Bill Schmarzo, said what I think few of his peers would say on the record: “We have an over-infatuation with data.”

Yet there it was, in the Marriott lobby adjoining last week’s Strata+Hadoop conference in San Jose, CA.

Before you can decide on your data, he insists, you have to decide on your decisions. He said, “That is clearly the biggest challenge in the ‘smart’ conversation,” such as in establishing a “smart city.”

Schmarzo, who is by day Dell EMC CTO, jumped into the “smart city” conversation recently when he joined the San Jose, CA Innovation Advisory Board. His first question was to the airport CTO: what decisions do you face? From there, they could choose data. Must they spot beacons over the terminals, for example, or to use the cheaper and readily available MAC addresses? Such choices could make a difference to that CTO and any tax-starved cities on their way to “smart.”

I asked how people react when he suggests easing up on collecting data willy nilly, consuming it like junk food binges? He paused. “So far, the vast majority are relieved,” he said. “They don’t know what to do with all the data.”

“We over-complicate things, and data vendors are the most guilty,” he said. “They talk about smart this and smart that. And they say ‘You gotta have this technology and these gateways.’ But what will you do with that?”

There’s still one more thing to do: explain the decisions. How can you make the data be made interesting not only for the mayor but the citizens, too? “What I don’t know how to do,” he said, “is how to tell a story around it.”

Data driven? No, you’re story driven, says German data scientist

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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.