Making Government Work

Why Local Governments Are Becoming More Data-Driven

June 26, 2014
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Why Local Governments Are Becoming More Data-Driven
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Indiana is the latest state to ramp up the use of data analytics to improve government efficiency.

It’s no secret that data analytics and an emphasis on machine learning are easy ways to fast track efficiency when it comes to navigating the daunting processes of bureaucracy. That concept is gradually catching on, but in the wake of budget cuts and economic recovery, government officials in Indiana are understanding the important role data can play in saving costs.

Earlier this year Indiana Gov. Mike Pence used an executive order to create a Management and Performance Hub (MPH) to streamline and increase the use of data services across state agencies. In an effort to increase productivity and transparency, the MPH uses performance management tools and an analytic platform to identify to examine where agencies can improve and how to save money.

Supported by the governor’s Office of Management and Budget (OMB) and the Indiana Office of Technology (IOT), MPH stands to serve as an example of agency coordination and cutting out some of the drawn-out, bureaucratic procedures that have long been in place. The state’s vast pool of data is now organized in a central place within the IOT, according to Paul Baltzell, chief information officer.

“We are seeing boosted productivity from standardization and cleaning of data, and also from new technology purchases,” said Sara Marshall, the MPH project director for OMB. “For example, a complex query that takes ten minutes on an SQL server takes less than one second on our new in-memory computing platform.”

The reorganization and clean-up of procedures also limits opportunity to corrupt data, Marshall adds. With few steps involved in a query, there’s less of a chance of error. That also adds to a better workflow.

The state agencies are also exploring more ways to implement a data-driven government, including improving real-time statistics on drug and alcohol abuse for local authorities as well as keeping up with updating resident addresses.

The success of Indiana’s implementation of the site is due in part to strong support from state leadership. The city of Chicago is another example where leadership backing has helped foster a successful model. The city’s Department of Innovation and Technology (DoIT), which helps streamline data use across departments, has had strong support from Mayor Rahm Emanuel’s office. The agency is dedicated to identifying problems and making data-driven decisions through use of machine learning and analytics.

For example, the city recently completed a pilot project to predict rodent infestations for the Department of Streets and Sanitation. The department was then able to target areas where it should use rodent-baiting, saving on cost and time.

With more support from government leaders, data analytics can vastly improve the frustrating procedures that hamper government productivity. Perhaps Indiana’s success can serve as a future model for more states to get on board with the power of data.

MORE: Can Big Data Reshape City Governments?

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