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A fun, lively path to smarter cities

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San Francisco-area designer Steve Pepple wondered whether data could help him explore neighborhoods — on the way to helping city planners build better, perhaps “smarter” cities.

He describes in a fun post on Medium, a genuine “data story,” how he set out to help people find “pockets of activity, vibrance, and new things.”

First, he joined a do-it-yourself project called Sense Your City. Sensors around the world reaped “hyper-local data about environmental conditions, such as noise, dust, light, and pollutants.” That data led to “pockets of activity, vibrance, and new things.” In San Francisco, he writes, “you could see when the fog rolls in.”

That sounds like fun, but could he find a key insights about mobility, housing, or neighborhood change? He had frustrating moments. “I had a bunch of data to work with,” he writes. His many sources included DataSF, San Francisco Planning, SPUR, and the City of Oakland. But he didn’t know what he was doing. Perhaps worse, “I was stymied by trying to find a significant, cumulative insight by cobbling together and analyzing all the data.”

He had been inspired by one man’s discovery who famously found such an insight in San Francisco rental data. Eric Fischer had pored over newspaper ads back to 1948 and surprised urbanists with his conclusion: the usual levers for regulating demand wouldn’t solve the city’s housing crisis.

Smaller experiments

With no big discoveries in sight, Pepple turned to shorter experiments. Apparently as part of a fellowship with Stamen Design and Gray Area Foundation for the Arts, he looked at Instagram photos to track street movement, such as some taken during the 2015 Pride Parade. Data from those photos showed events like parade formation and, in the afternoon, a drift into block parties.

His Medium post includes a short series of visualizations that are more fun and vibrant than you might think any “smart city” has a right to be. With “color quantization,” he created a “spatial database of color” that showed parks and murals in the Mission District. In another map, colors expressed “density of people and establishments,” with brighter and more colorful buildings showing the most active.

Tangible, street-level meaning came in large, street-level screens…that showed passersby how they could “interact with data about their neighborhood and arrive at their own discoveries.”

Clues

It was his work with the urban designers and architects Perkins + Will that his approach had what I find the most interesting and possibly the most meaningful clue. Pepple and team could see and hear neighborhoods with the most activity. Could that be found with data? Indeed, data tracked activity and what people said about the places where it occurred; social media helped characterize those places; mapping tools connected hotspots.

A look at the maps Pepple and team produced shows that transit, living space, restaurants, and other amenities fail to anchor social hotspots. Fine, but why? If the data can reveal this, perhaps it can go the rest of the way.

Keep your eye on Pepple. His playful and often fun analysis puts a bright, even inspiring face on urban data.

Thanks to Stephanie Langenfeld McReynolds, vice president of marketing at Alation, for alerting me to Pepple’s work.

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Bad stories stop good data at the water cooler

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We agree by now that data’s a good compass. One neglected question is tougher: Which map?

Everyone’s known the kind of “grouchy guy” TDWI instructor Kellee M. Franklin, Ph.D tells about. This guy knew better than most of his co-workers about how their Washington, D.C. defense agency worked. And he was frustrated.

Over the years, he had offered a series of genuinely beneficial ideas over years — and saw each one ignored. He had become known as a complainer. No matter what he said, no one listened to a single word anymore.

Ignoring relevant data

“We have blinders on,” she said. Franklin is a longtime innovation consultant, and she’s seen case after case like this. “We can only see what we’ve been educated on and exposed to.”

In fact, she found out when she talked to him, they should have listened. “He was way ahead of the curve in thinking about things within the agency,” she said. Over and over, he came up with options that were then rejected. He saw a way to streamline security clearances, for example. As it was, his agency insisted that even people with existing clearance at other agencies got full clearance over again. But he had seen how other agencies used automation to clear new staff more quickly. He also wanted to create a working group for security issues that would address time wasted.

Those ideas crashed into others, including common ones: “We don’t do things that way around here” and “This will never fly!” Why? No reason, it just won’t work.

Other stories or mental models might have included “We’re rigorous, they’re not.” Or, about Grouchy Guy’s idea to have a working group, “What, another committee?”

Quantitative data on Grouchy Guy from his several dozen years at the agency pegged him as a “low performer.” A loser. But Franklin found much more data to consider.

She ended up presenting some of his ideas — in the way the man’s coworkers finally heard. Several of those who had once dismissed his ideas once now felt remorse.

Better stories force new data into the frame

“Many good ideas are stopped at the water cooler,” she said, when a ruling clique cuts off ideas that don’t fit their model. Those models determine the stories we tell ourselves and each other and have a strong influence on who’s hired and how things get done.

“Stories are powerful in this regard,” she said. New, alternative stories force other data into the frame.

“I’m all for data,” she said. “It moves the needle. But we’ve got carpetbaggers who promote data without a clear understanding of human behavior and storytelling.”

Look past the tech. Look closer at stories behind the data.

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Stupid analytics gets them talking

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If data analysis somehow ends up supporting an unwise choice, should we blame the data? Of course not, no more than we should blame a stethoscope for a bad medical diagnosis or fingerprints for a bad courtroom verdict. Common sense says the only ones responsible are the humans involved.

AllAnalytics contributing editor James M. Connolly wrote last week in defense of common sense in a good blog post, “Analytics won’t cure stupidity.”

Let’s admit it, we know the limitations of data analysis. The obvious truth whispers to us in the quiet of our own little minds, and that truth can also find a voice in private business conversations over a beer or lunch.

And that truth of the limitations of data analysis also finds voice in online comments, such as in those responding to Connolly’s post. Such conversations go around and around, often failing to convince anyone. They may have no effect at all on, say, the foolish executive who insists that data must has a predetermined result. But at least some people test arguments, let others know when they’re really off, refute facts, and even make a few people think again. I find what might be thought of as “campfire behavior” — known these days as collaboration and storytelling — exhausting, often annoying, but also crucial. This is where supposedly hard facts, including data analysis, assume the rightful position: subordinate to review by smart people.

Such dangerous ideas! The first comment in the string following the post sounded like a slap at Connolly himself: “This article shows you have the diplomacy and tact in handling issues from the viewpoint of a toastmaster.” The same person commented again a few minutes later with a myopia that’s useful for close examination of data but only obstructs decision making: “You’ve given me a choice: cursing the data or cursing the stupidity. Neither. I look at the facts and draw a conclusion very carefully. I am mathematically-oriented.”

“Toastmaster” Connolly replied, “No, Judith, you don’t have to choose between data and stupidity. Just be aware that even the best data project could place the results in the hands of the very fallible human…” She replied, seeming to have missed his point again.

Others joined in. One told a story about presenting analysis to executives only to discover that they didn’t care about the analysis, they only wanted a certain conclusion. He wrote, “The endpoint was already decided.”

Another asked what if the analysts are the “stupid ones”? Still another told about the eroded trust that results from predetermined results. And another commenter wrote, “I’ve heard that technology will never truly change the world because the problem isn’t technology, it’s humans.  I guess the same could be said of analytics.”

That’s right, the problem — and the promise — is with humans.

The best remedy for stupidity is a traditional one: conversation. It’s not perfect, and often it takes a long succession of campfires ignited and exhausted before sense emerges. And sometimes the result is still stupid. But it’s always smarter than leaving everything to the data, no matter how “careful” any data analysis may have been.

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Uber and AirBNB hint at BI’s future

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You may have seen yesterday’s New York Times story about ratings on Uber, AirBNB, and similar outfits. Customers rate providers, but providers also rate customers. And guess what: As coarse as a five-point scale may seem, actual ratings tend to be worse. Forget the shades of gray, you’re probably either terrific or terrible.

Welcome to a chilling thought. This is what a “data driven culture” could be among the late adopters— a warning those of us who worship “data-driven culture” should be careful what we wish for. Is the failure of business intelligence’s failure to penetrate beyond the present beachhead such a bad thing? The Times story makes me wonder: Which comes first, tools or intelligence?

As important part of intelligence, we might say, is the ability to perceive subtle meaning. At least that’s the assumption underlying the design of most data analysis tools. No one needs SAS, QlikSense, or any of the others to recognize a gold mine glinting in the sun. But we do need data analysis to reveal the vein that, by the thinnest of margins, could turn a profit. And so-called “big data” offers even finer resolution on that picture, letting analysts study questions from many new aspects.

Perhaps you dispute the Doodler’s point. You say that the Uber example has nothing to do with data analysis. You say that the problem is in data quality! That would imply that those who submit data with hamfisted abandon would analyze that same kind of data with a sensitive touch — very unlikely.

What’s more likely is that, if true, the Uber and AirBNB experience portends a dark future for “data driven” cultures.

Read the Times story here. “Ratings Now Cut Both Ways, So Don’t Sass Your Uber Driver”

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Data served direct-to-exec with Napkyn

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We’ve heard about shortage of data-analysis help, and about senior execs who pooh-pooh the influence of analysis in decision-making, and about the “dead” self-service analysis. So when a vendor tells me about a direct-to-exec service, featuring a sort of concierge of analytics, I pull up a chair to listen.

Napkyn, five years old last month and major brands under its belt, assigns a senior analyst to lead a long-term relationship with one executive. Behind the analyst stands a 13-person team that collaborates on the exec’s behalf, putting forward experts as needed. One team member, for example, has 15 years’ experience in multi-variate testing. The CEO and founder, Jim Cain, comes with 15 years in marketing-data alignment.

Cain explains that it all began as a response to a gap he saw: execs’ inability to find answers in the data analysis tools they had, and their inability to hire people who could help. “It’s impossible,” he said, “to hire people in a discipline that’s no older than a first grader.”

He sketched his idea on a napkin and capitalized Napkyn with a $10,000 severance check, and then spent the first five years “aggressively bootstrapping.” It has served 40 major brands, he says, and now has 15 current clients.

The service runs on a flat fee with no overages — which made me wonder how they protect themselves from unreasonable demands? He says it took time to learn how. In the first few years, they worked with anyone who wanted the service, which apparently attracted some who just weren’t ready. “It’s hard to keep the interest of someone who wears too many hats,” he says. That sounds like some people I know, but that’s a long dark hallway I’ll look into another time.

Now Napkyn concentrates on “tier-one brands,” $10 million-plus organizations and only vice president and up. “They have the experience to know what they want and what to do with it.”

Napkyn also protects itself, he says, by “earning the right” to say no. “We earn the right with senior stakeholders. ‘You’ve asked for five things and we only have time for two.'” Deferring to later is usually a better option.

From the start, the lead analyst wants to know what the exec cares about most. The first question is, “What do you care about? What answers can’t you get and complain about to someone close to you?” From there, the Napkyn analyst plugs in with people below to find the data and the answers. He described the role as “half bartender and half mathematician.”

I suppose the lead analyst is something like what Wayne Eckerson, author of Secrets of Analytical Leaders: Insights from Information Insiders, would call a “purple person.” This is the person in the middle, the non-specialist who’s neither business blue nor IT red and who empathizes with both sides.

I’ve always liked those roles, but not many others seem to. Napkyn analysts get more training in asking questions, Cain says, than in running analysis tools or in complex implementation.

At first, the reaction within the organization is a little bit “Sharks and Jets,” he says, referring to the “West Side Story” street-gang drama. But sooner or later people realize that Napkyn is an asset. Their presence tends to change ways people use information and agreeing what things mean. He says, “Getting organizations to align is surprisingly disruptive. We don’t do it with technology.”

“We’re kind of the KPMG of measurement,” says Cain. When the organization uses Napkyn, “everybody can assume that the information the exec gets is accurate and not biased.” Cain says it’s one of the benefits of a direct path to the top stakeholder mediated by senior analytics people.

Wait, does the analyst have a pistol under the napkin? Does he really exude such adult presence?

Pistol or not, it’s a service to watch.

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