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!