Article by Oliver Wise: “Cities are under growing pressure to make better use of data and artificial intelligence to deliver results for residents. And the dominant advice has been consistent: First, lay the groundwork by standardizing your data, setting up governance processes, and investing in central data stores. Only then, the thinking goes, can local leaders begin to roll out more ambitious, data-driven solutions on challenges from housing to public safety to infrastructure.
But the imperative to show value and improve lives in tangible ways is too urgent to wait for all of that groundwork to take hold, which is why the most effective data teams are taking a different path. They start with urgent problems, demonstrate results quickly, and develop lasting capacity along the way. In this model, capacity is not a prerequisite for action. It is a by-product of it.
Ultimately, this is how local leaders can show residents what’s possible and build their support for using data and AI in more ambitious ways in the future.
As executive director of the Bloomberg Center for Government Excellence at Johns Hopkins University (GovEx), I work every day to help cities navigate this tension of when to invest and when to act. As I do so, I lean on my own experience in city hall.
In 2010, five years after Hurricane Katrina flooded 80 percent of New Orleans’ housing stock, the city was still grappling with tens of thousands of blighted properties. These homes depressed property values, discouraged people from returning, and attracted crime. Residents who had fought insurers and navigated rebuilding programs were frustrated to see their neighborhoods still struggling despite their efforts.
When Mayor Mitch Landrieu took office that year, he set an ambitious goal: reduce blight by 10,000 addresses, about a quarter of the total, by the end of his first term. I was asked to lead a data team to support that effort.
Conventional wisdom would have suggested building strong data governance, management systems, and infrastructure before attempting to deliver results. But we didn’t have the luxury of time. The problem was urgent, and waiting years to get the foundations right would have meant years more blight, declining trust, and missed opportunity.
So we reversed the sequence. We focused on solving the problem first.
We created BlightStat, a performance management program that used data and analytics to guide city action, such as “nudging” homeowners to improve their properties after 311 complaints were filed against them. That approach yielded demonstrable results, and with that momentum, we ramped up the sophistication and impact of our data interventions. We built a machine learning-based recommendation tool to help code enforcement officials determine the most effective path for each property. We used A/B testing to understand which interventions best compelled property owners to act. And we developed civic applications that made the remediation process more transparent to residents, helping rebuild trust in city government…(More)”.
