Beyond Performance Measurement
Governments are beginning to depend on data analytics not just to identify problems but to solve them.
Many governments have discovered one tool that's indispensable in delivering better government: good information. Performance data shines a bright light, for example, on how well cities are addressing the public's diverse concerns—from potholes to playgrounds. And the best data efforts draw not just on agency records and systems, but also on citizens themselves, including their calls to the 311 hotlines. Combined, this information helps agencies manage workloads, leverage strengths and close gaps.
But the smartest cities are moving way beyond simple performance measurement. They're weaving together data from sources across local, state and federal agencies to gain unprecedented insight into public conditions and trends. And they're applying the latest technology and predictive analyses to get ahead of the thorniest issues facing their communities.
In New York City, for example, Mayor Michael Bloomberg has assembled an expert analytics team in his Office of Policy and Strategic Planning to lead the city into this new era of data-centered innovation. The team conducts the kind of aggressive data mining and analysis that brings the complete "digital fingerprint" of just about any complex urban problem into focus—and helps determine which tools of government, across agency boundaries, can best address it.
With today's budgets stretched to the limit, this sort of cutting-edge data work will only become more essential, as governments hunt for clever ways to target scarce resources where they'll be most effective. They will depend on data not just to identify problems, but also to solve them.
Those include some of the knottiest problems—those that don't fit neatly within agency portfolios and that seem insurmountable given the strain on existing resources. Take illegal conversions: apartments that unscrupulous landlords have illegally subdivided to cram tenants in for greater profit.
Every year, New York City receives thousands of complaints about these properties, which are often unmonitored and unsafe for the families who live in them. But for a long time, city agencies had no way to home in on the properties that posed the greatest risk of fire where residents could be hurt or killed. Then the city tried something new: Analysts began looking at several previously unexamined sources of data about fires across the city—and quite a story emerged. The property owner's financial condition, the building's history of complaints, the construction date and neighborhood demographics all showed a link to fire risk.
The analytics team used the data to create a powerful, computerized model that now routinely highlights the riskiest properties for enforcement agencies. Where in earlier years inspections uncovered serious hazards only 15 percent of the time, joint teams from the fire and buildings departments are now finding those hazards in 75 percent of illegal conversions they investigate—five times as often.
Another great example of the use of analytics is in fighting mortgage fraud. The city's multi-agency Financial Crime Task Force found ways to detect likely cases of fraud by looking at a range of property information for the first time: erratic sales histories, title transfers at suspicious prices, transactions just below reporting thresholds and more. The result? The task force has led prosecutors and enforcement agencies to more than 1,000 potential fraud cases, together worth over $200 million. And the cases are now identified much earlier on—giving prosecutors a chance of catching the fraudsters and getting some relief for victims, many of them among the elderly and vulnerable.
Next up for New York City analysts is prescription drug abuse. Data on Medicaid reimbursements helps reveal suspicious distribution patterns among physicians and pharmacies. As with unsafe housing and mortgage fraud, the city aims to use this data to target the worst offenders—and thus do more to curb abuse and crime, without spending an additional dime on enforcement.
Data analysis is sharpening more than just enforcement. For example, the city is improving health and human services by employing information across agencies to deliver care and benefits better tailored to each individual citizen's needs—while also cutting costs. And the city is finding new efficiencies using data analytics to examine operational challenges that range from routing garbage trucks to defending against tort litigation.
New York is just one of many cities that have learned what data can do for them. Mayor Bloomberg recently convened a new working group of analytics leaders from several major cities, providing a forum to exchange strategies and experiences. The group's efforts will span three core areas—fraud and abuse, risk management and government efficiency—that build on each city's ongoing efforts.
Boston, for example, is identifying likely absentee landlords using a mix of property, tax, and complaint records. Levying tough penalties on the landlords, Boston has been able to get neighborhoods relief from dangerous, unmaintained properties in their midst.
In Chicago, analysts there are mining 311 call data in new ways to track neighborhood hazards like gang activity and broken streetlights, as well as to get rapid public-transportation updates to the public.
As Mayor Bloomberg says, "If you can't measure it, you can't manage it." These stories mark just some of the first successes that we can expect to see across the country as data-driven solutions become the hallmark of creative government.