storytelling

Civic tech projects need storytellers

October 22, 2019 by Ted Cuzzillo

I asked a leader of one civic tech group the other day what his team lacked. Among the group’s half-dozen or so core contributors and about 20 occasional ones, they have a coder. They also have a guy who’s tied into local politics. And the guy I talked to, Nick Brooks, is the user-interface designer.

The group is Open Budget: Oakland, one of nearly a dozen active projects in OpenOakland, a local “brigade” associated with Code for America. Open Budget Oakland aims to provide everyday citizens with an understanding, mostly with data visualization, of the city budget and budget-making.

Brooks said, “People need to know how to use this.”

A fun, lively path to smarter cities

May 2, 2017 by Ted Cuzzillo

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.

The six genres of data stories

December 6, 2016 by Ted Cuzzillo

This appeared originally on the TDWI site in September behind a paywall. It’s still there, but today they’ve had the 90 days of exclusive use that I agreed to.

Survey after survey reveals that about 80 percent of business users don’t use data analysis—despite all the marketing and “easy to use” tools.

As if in response to this sad showing, renowned author and academic Tom Davenport proclaimed that data scientists should know “data storytelling.” He’s right. Storytelling has transmitted knowledge and motivated action in every medium we’ve ever known. Stories around a fire, stone tablets, Gutenberg’s books, news, and e-books have all made use of stories. Data is a natural.

The data community lost no time swarming all over it. Trouble is, most of them seem to have heard “data” but not “story.” Even now, years into the data story trend, they still play mostly to each other with the only genre they seem to know, the parade of visualization—a waste of time for all but the already initiated.

It’s not so hard to reach non-data users with other genres, which are just sets of conventions that satisfy different audiences and moods. War movies, for example, deliver noise, action, and beefy male heroes. Romantic comedies deliver jokes, pastel scenery, and romance. Each genre satisfies different needs.

Here are six data story genres. The “naked data” genre seems to have become the default; search Google for “data story” and that’s what you find. Although the other five genres are barely recognized as data stories—I’ve never found any labeled “data story”—that is what they are.

Genre 1: Naked Data

The naked data genre lets data march alone. It is ideal for those who find data exciting. Search Twitter for #datastorytelling and this is the type you’ll find.

The naked data storyteller is like the host of a stone soup lunch. “Here’s the data,” guests are told. “Now make of it what you will.” The data-loving guests unpack the sack of knowledge they carry with them and apply it with their own curiosity and determination.

Genre 2: Narrated Data

Naked data transforms easily into the narrated data genre. The mother of them all is Hans Rosling’s 2006 rendition on childhood mortality around the globe. Rosling’s animated bubble chart now seems dated, but his presentation is timeless. His passionate narration explains the movement of the bubbles like a sports announcer at a football match. The data is more than interesting—it is thrilling. In a later instance, he told another story, this time not with computer visualizations but with pebbles on parking lot pavement. A parking lot space never looked so good.

Genre 3: Explainer

The explainer genre consists almost entirely of words. It uses one or two visualizations, if any.

The Upshot column in the New York Times makes frequent and effective use of this genre. A recent story on the U.S. economy, for example, runs about 800 words with a single, simple visualization. In “GDP Better Than It Looks,” the author explains that, although growth in the second quarter of 2016 was just 1.2 percent, this was almost entirely the result of a contraction in business inventories. That’s not a good predictor of future growth, according to the author. A much better rate of 2.4 percent shows up when looking at GDP excluding inventories, because final sales are a better measure of underlying growth. The bad news comes in shrinking investment and poor growth in productivity.

That offers plenty of data about GDP and is about as much as many people want to know or have time to think about.

Genre 4: Executive

This is for the executive suite. It is brief, perhaps just a minute long, and it may contain little data—sometimes none at all aside from footnotes that cite the underlying data.

A monthly report at financial services firm Charles Schwab, for example, is compressed into a 60-second story in several steps. First, a data analyst dives into the period’s data and comes up with questions and preliminary conclusions. Then a bigger group with representatives from marketing, HR, and other functions joins the discussion. Each person has a take on the period’s events and results.

From there, John F. Carter, senior vice president of analytics and business insight, distills the story further, as he described it to me. The presentation begins with the main conclusion, similar to news reports. Less is more, he explains, “but the right less.” Executives don’t have time to get into the weeds.

Another executive I spoke with—a veteran Silicon Valley CFO who requested anonymity—dismissed “the illusion of certainty that numbers provide…. Execs, at least the good ones, know they are dealing with a messy and uncertain world.”

Genre 5: Detective Story

This one starts out as an explainer but ends with a question. We have a mystery, the storyteller says, but we don’t know what it is. As in a traditional detective story, the audience gets all the facts—nothing’s hidden. Yet this is no game and the storyteller needs help.

Take the declining balances case that longtime TDWI instructor Dave Wells and I have used in our data storytelling class at TDWI conferences. Bank executives have come to recognize declining balances across multiple account types. Why? What can be done to reverse it? The keys are the customer stories behind this behavior, many of which weave into a bigger story. It all leads to the answers.

Genre 6: Scenarios

With scenarios, storytellers start with data and imagine a reality that may develop from it. The data sets the stage and imagination takes it from there. German data scientist Joerg Blumtritt approvingly described to me this kind of data story as “fiction.”

Fiction shouted to an audience of data people empties the room—even though many of them already create stories that are actually fiction. They are extrapolations of data to imagine future events. For example, credit scores are based on data of past behavior to predict default.

An even more sophisticated kind of fictional data story underlies scenario planning. When presented, scenarios may offer mere crumbs of the underlying data. In the 1970s, Royal Dutch Shell famously predicted several trends that competitors hadn’t foreseen. Scenario planning helped warn Shell’s leadership of the 1973 energy crisis, the late ’70s oil shock, the fall of the Soviet Union, and the rise of Islamic radicalism.