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Ted Cuzzillo

The story made me do it Scapegoating data storytelling to cover bad data analysis

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Now and then a data analyst pipes up with a warning about storytelling. It’s a menace, they say. It’s so potent, they say, that it stands to corrupt even data, the twenty-first century’s supposedly immaculate compass.

Most recently, that corruption touched even a hackathon involving Canadian women’s rugby, as documented in a recent blog post. In “The Dangerous Allure of the Narrative,” Oscar Goodloe spells out how narrative seduced him and his fellow hackers.

Toward the end of a “lighthearted hackathon,” he and his data analytics team realized their results were invalid. They blamed the irresistible influence of storytelling.

The competition began as competing analytics teams received a set of data. Within that data, Goodhoe and his team identified one variable as the strongest predictor of injury: “degree of soreness,” as reported by each athlete on a scale of zero to ten. Naturally, the analytics team said, higher values signified greater soreness. A ten of course meant “ouch!”

They combined data for the soreness variable with data they’d found that ranked teams by strength. A simple bivariate scatter plot showed a surprising correlation: the opposing team’s anticipated strength in an impending match tended to be in inverse proportion to each players’ reported soreness. For example, if the next opponent was ranked stronger, the players’ average self-reported soreness tended to be in the lower half of the scale. Stronger opponent, lower soreness.

That seemed odd to the data hackers. Why would players feel stronger ahead of tougher games? The reason, they decided, was that the self-reported soreness wasn’t the actual soreness felt. But why? What would make these athletes deliberately underrate soreness? The reason, they hypothesized, must be that lower soreness might improve the chance the coach would entrust the player with an important role in the game plan.

That’s the story the data hackers would present, but the story was wrong. The data hackers had made a fundamental error in their analysis. They had interpreted the zero-to-ten scale backwards. They had assumed that the scale should be read like the usual zero-to-ten pain scale, with zero meaning no pain and ten meaning a ton. They had failed to check their interpretation of the data with the coaching staff, who had designed the soreness scale and harvested the data.

As Goodhoe tells it, the hackers discovered the error the day they were to present their findings. Did the analytics team persist and tell their intended story anyway? Or did they do the courageous thing and stand down? He doesn’t say — though he does go on to disparage storytelling for its intoxicating sway.

Storytelling is indeed a strong influence on data analysts — so strong that it can corrupt data analysts. Storytelling is so strong that it stands in the same league as money, sex, and power in its ability to corrupt. But that’s only a reason to use it wisely, not to avoid it.

“The Dangerous Allure of the Narrative — How a desire for strong data storytelling can bite you.” Towards Data Science, April 1, 2021

Real cities don’t start at “ping” Smart City Diaries has gone on the road to find a new definition for smart cities

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What do diaries have to do with smart cities? And who do two women think they’re doing trying to reset the agenda for smart cities? Doesn’t everyone know that smart cities are about data technology that goes “ping”?

“Ping” is the notion satirized by Monty Python in its sketch about a team of doctors more concerned with fancy machines than with the patient. The Acostas are part of nascent trend to supplant that notion.

Late last year, this mother-daughter duo set off on a trek from city to city across the U.S. to answer the fundamental question of what smart cities are. What people should expect from the community they live in? How should cities be run and how can data technology help?

For all the talk about smart cities, few have put serious effort on such questions. Even fewer, perhaps none, have been two such disparate women. The result is a polished, entertaining, and important audio podcast series, and soon a video series, called Smart City Diaries.

Deborah Acosta was chief innovation officer for five years at the Bay Area city of San Leandro, and led it from drab to cool. Her daughter, Anna Acosta, is a Latinx millennial described on the Diaries site as “social justice warrior” with widely published articles about music.

Their audio podcast, and soon a video series, will put on lively, at times even hilarious street-level experiments combined with challenging conversations with diverse experts.

So far, they’ve produced three audio-podcast episodes. Soon, they’ll expand to a TV show modeled after Top Gear, the funny and controversial BBC series about cars.

First stop, Portland. It’s a race across the city on city transportation, and along the way there’s a scavenger hunt. But they don’t what it’ll be until immediately before the race begins, when their production team reveals it.

It’s to be the kind of fun in which the audience learns something as it watches this millennial and baby boomer duo compete.

The audio podcast won’t be as funny as the video, but the audio will dig deeper into equity, individuals’ needs, and perhaps a new interpretation of the Declaration of Independence’s “life, liberty, and the pursuit of happiness.” Should everyone have a guarantee of adequate housing, food, medical care, and education?

More typical city topics they consider are the importance and implications of 5G, fiber optics, along with the equity or inequity. Then there’s the recent power-grid failure in Texas. And data and what it has to do with infrastructure. And unlike most discussions of these topics, it comes with entertaining stories.

And what better time? COVID peeled the eternal scab off of inequity. The elder Acosta says, “Of course, inequity undergirds everything about technology.”

Tune in, and stay tuned. Smart City Diaries.

Two recent “storytelling” tools for public audiences Toucan spoonfeeds data’s insight while Juicebox cultivates data skills

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The data-shy among us have two friends in the software business. One a few years old and one new this year. Nashville, Tennessee-based Juice Analytics rolled out Juicebox in February to join Paris, France-based Toucan Toco’s product Toucan for a pain-free “story” based entry into data analysis.

Important as such products are for the business audiences, they’re critical for the public as governments at all levels, particularly local. Understanding data will quickly become a basic skill of city life. The pandemic only accelerated that requirement.

On the surface, Toucan and Juicebox appear similar. Each touts “data storytelling,” and both come clothed in bright colors. But the differences quickly appear. Each one takes users on a radically different path, and each leads to distinctly different destinations and implications.

In the simplest terms, Toucan Toco’s product Toucan channels data’s meaning from a data analyst to data consumers. As Toucan puts it, it focuses on “one aspect of data and analytics, which is the communication of insights only.”

Compared with business users served by the likes of Tableau, Qlik, and Microsoft’s Power BI, Toucan users are passive. They’re fed what Toucan Toco describes as “only what you need to know.” The consuming audience absorbs information that’s as easy to consume as dinner in a restaurant.

The Juice Analytics’s product, Juicebox, is more like dinner at home with Blue Apron or other meal kit service.

Toucan encourages dependence while Juicebox makes data consumption easy while it prods the data-shy into gradual self-confidence. A chef’s knife lends much more confidence than a butter knife.

Each approach pays off differently for the enterprise. Juicebox’s active approach grows analysts. A certain portion of its users will no doubt sprout legs, such as when they see that cooking by number is just a short leap to cooking by touch, smell, and sizzle. They may have not quite as satisfying a dinner the first few times they try that, but in the end they’re better cooks.

On the other hand, restaurant meals have a place. They can be faster and tastier. A Toucan’s audience will absorb the data’s meaning fast, even meaning that comes out of the most sophisticated analysis.

The enterprise intent on quick results may prefer Toucan. But the enterprise that wants to breed deep, enterprise-wide intelligence will prefer Juicebox.

The choice made by local governments will vary by local culture. Those with a population dominated by people who prefer receiving knowledge from authoritative sources will lean toward Toucan. Those with a population that’s confident in their ability to understand data will lean toward Juicebox.

I’ll let you ponder red regions and blue regions on your own.