Kick-Starting Data-Driven Government

There are some key questions a city should ask itself before moving forward.

This story was originally published by Data-Smart City Solutions.

As the civic data field matures, more cities are discovering the value of data to improve government and asking how they can more fully join the movement. The path a public-sector leader should take to move from analog government to analytics-driven decision-making requires following sound advice and proven examples. Purchasing or building a data solution is only worth the investment when accompanied by the right planning, staffing and support to ensure that the tools not only solve an immediate civic problem but will generate long-term improvement.

One source of advice is provided by What Works Cities (WWC), a Bloomberg Philanthropies initiative to help mid-sized cities leverage the power of data and evidence to improve outcomes such as increased public safety and a healthier fiscal bottom line. By working with 100 mid-sized cities in varying stages of their data journeys, the Johns Hopkins' Center for Government Excellence (GovEx), one of the WWC partners, is helping them build capacity for more advanced analytics. And for a small group of qualifying cities, GovEx is piloting an Analytics Kick Start service to identify those that are ready to complete an advanced project and sustain an advanced analytics program.

Some of these projects are already underway. The core elements the GovEx team uses to evaluate the viability of advanced projects involves a series of key questions that allows GovEx to narrow the pool of cities and issues to select a pilot cohort to work with on analytics projects:

Is there a clear challenge for which data and analysis can add value? The best way to ensure that an analytics pilot project leads to further innovation is to focus the initial efforts on areas of value that address high-priority issues and create meaningful, visible results. Front-line workers will not use new tools or insights if they are not designed to meet their needs and do not fit into their processes. GovEx conducts in-person assessments to identify each city's pressing challenges and determine the value of data-driven solutions for different departments.

Is there enthusiasm and support from top officials, including the willingness to use resources and to set expectations of collaboration and information sharing? Institutional change is never straightforward and requires the investment of resources and the leadership to alter longstanding processes. The team from GovEx begins its assessment of potential cities with this in mind, looking for strong commitments from the mayor, city manager, department heads and high-level change agents.

What underlying data infrastructure is in place?
The GovEx team visits city departments to learn more about their challenges and the technical capacities they already have. They want to determine what data exists, whether the data is of sufficient quality, and whether the technology infrastructure allows routinized, automated access to key data -- to enable processes and tools that can be used on an ongoing basis -- rather than requiring manual data pulls. A city needs the underlying data collection and storage systems to be in place before more-advanced tools can be built on top of the data.

What is the plan for scale? Data tools and services require ongoing investment, updates and staff support. Even more important is the ability to use the pilot work and the accompanying investment in data and skills to increase the city's data-analysis capacity. The GovEx pilot projects are designed to "grow the market" in a city, so the infrastructure needs to be able to support further data use beyond the initial projects. GovEx's goal is to help the cities complete pilot analytics projects that can both showcase the value of analytics-driven decision-making and permanently increase the capacity and appetite for further data projects.

Any city can apply these factors to identify the right institutional place to start that is prepared for successful data work. It is vital to ensure that these pillars are in place before investing in data services or tools.