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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.

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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.

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This is Datadoodle

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Where’s Datadoodle? It’s been on everyone’s mind, absolutely everyone’s. Well, not really. Most longtime readers probably assumed that the latest sabbatical was just Datadoodle doing business as usual. Still, the latest pause, from late 2019 until today, was just one of several in the blog’s 14 year history, 

This time was slightly different. I took a full time job as an industry analyst to see if the benefits of a paycheck and camaraderie compensated for lockstep. It didn’t. But I did get to spend 12 months with a remarkably intelligent and humane team. I also learned a few important things about industry analysis, some of which I will apply here. The others I will repudiate.

Datadoodle returns with a few changes

Cadence. I plan to publish every Tuesday and, as soon as I can sustain it, one other day, too. 

Focus. At least to start, Datadoodle will observe and comment on two of my old favorites, data storytelling and smart cities —  from the intersection of the two. As always, Datadoodle will evolve with the industry, always with an eye on the horizon.

Smart cities and data narrative may seem at first glance like an odd couple. But it’s actually a natural and even unavoidable one. 

Smart cities, the crucible. Smart cities rely in part on the willingness and ability to use data by the public, city staff, and others. What portion of these groups are data literate? What portion is willing to use data? The answers to both questions are ominous for smart-city dreamers and planners: Not many of them.

Data narratives, the connector. So then, how will the data-illiterate, data-averse, data-lazy, or just too-busy-for-data communicate and theorize? With stories, of course. 

By the way, I will use “story” and “narrative” interchangeably. “Narrative” has connotations I prefer, and “story” is a sacred and ancient word that needs a rescue. More on that in later posts.

The narrative on stories, or the story on narratives

As data spills out from the relatively disciplined business space to public spaces, people will talk about it with the same medium they’ve always used. Imagine a simple example: A man asks his neighbor if it’s going to rain in the afternoon. The neighbor may pull out a smartphone to read the forecast and give the percent chance. He may only translate the percentage chance of rain into a range. Either way, whether he reads out the percentage or the range, it will be heard as a range like good chance, or fair chance, or no chance. Most data will be related in a similarly casual way. 

Notice that such a story works even without an explicit beginning, middle, and end. His neighbor’s innate knowledge fills in on the fly. 

A data story is much more than a parade of charts

My definition of data stories makes a sharp break from the one imposed on it by a presumptuous handful of writers. A data story is not parade of data charts! Such a pathetic spectacle benefits only those who already know how to interpret data. 

Actual data narratives/stories are bigger, broader, and far more potent. They can even, for example, help analyze data. That includes theorizing about events that underlie metrics and suggesting new paths for analysis. 

Stay tuned for more on the city-story “intersection.” I look forward to your comments!

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