The New York City Mayor’s Office of Data Analytics (MODA) is among the most watched analytics programs in the nation, both because of its quick adoption and its impact to real-world problems.
Hoping to leverage the program’s groundbreaking insights in the new space of municipal analytics, Code for America, a San Francisco based civic tech supporter, called in MODA’s former Chief Analytics Officer Mike Flowers and the office’s former Head Programmer Lauren Talbot.
The two shed light on the program via webinar, pointing dialog toward pragmatic first steps, how-tos and the treacherous yet avoidable pitfalls of jump-starting a citywide analytics program. Flowers took to a friendly but no-nonsense demeanor as he explained the core hurdles in New York's analytics launch and its real-world political mechanics.
He started the conversation with a note about New York City Mayor Michael Bloomberg's project endorsement.
“It starts from the top,” Flowers said, and emphasized the first principle of any analytics project is a complete buy-in from the administration. Though he only met with the mayor a handful of times during the start, Flowers said Bloomberg’s nod of approval was a match that not only ignited the project, but also doubled as a knife to cut blockades of bureaucratic red tape.
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Beneath the mayor, Flowers credited the program's launch to relationship-building more than technological advancements. A small footprint in departments is better than no footprint all, he said, and underscored the fact that for any IT project to be a success, it has to use current people and resources. In New York City's case, this meant working with its various departmental “tribes,” their territories and office cultures.
“[Department] turf wars are real and some agencies are very protective of their subject matter domains ... they can get uncooperative,” Flowers said. “The question was whether or not we could do this in a way that didn’t get us stuck in a circular firing squad.”
His approach into the city’s rough and woolly internal politics, he said, turned into a few months of get-to-know-you office visits. The point of these, he said, was to familiarize himself with not just the printed hierarchies of departments, but also with the ground zero organization structure of who was controlling and managing data, who has to answer to whom, and what data processes were being implemented.
“You have to understand that terrain,” he said.
Next Flowers said he assembled a team of interested but affordable experts, most of whom he described as young but eager to gain ownership of such a groundbreaking tech endeavor. Opting for this hiring route, as opposed to one that preferred more seasoned employees, kept costs down and creativity up, he said.
“They were so good and so aggressive, and the product they came up with compelled attention. … I don’t think they knew how much I would ask of them when they signed on,” he said.
Talbot and the team began collaborating with city departments to spotlight tangible problems that data analysis could solve. Using predictive analytics, the team improved fire risk assessment for the city’s more than 300,000 buildings, measured 911 call response times, and precisely mapped New York’s commercial business activity with many more projects already slated for 2014.
“Once you get real outcomes, you’ve definitively proven your case,” Flowers said.
Further suggestions included from the two were to hold off on media attention until an analytics program is firmly established (to ease unnecessary fears), to support the analytics program by making it publicly visible online, and always to associate relationships and people as the glue that makes it all possible.
“I think if you’re walking into the conversations with a measure of respect with the people you’re dealing with, that goes far,” Flowers said. “Because quite frankly, they deserve it. And at the end of the day, they’re the ones doing the work.”