Realizing the Promise of Data Analytics.

• Position: News & Numbers

Everywhere we look, governments are being urged to make more, and better, use of analytics. Cities have always used analytics to help improve municipal policy and performance; what's new is the amount of data and the technology available for collecting, sorting, and presenting it. The Ash Center for Democratic Governance and Innovation at the Harvard Kennedy School's Civic Analytics Network series provides suggestions, research, and case studies aimed at helping local governments use data to make the best decisions possible.

GETTING STARTED ON A DATA ANALYTICS PROJECT

Any project a government undertakes requires a systemic approach to project development. "Analytics in City Government," a report in the Ash Center's Analytics in City Government series, highlights five steps that cities can use in developing data analytics projects.

1. Identify the Problem. "While data may abound, matching an area of need with the right data resources within an organization is vital. Developing an analytics project typically places data scientists in an internal consultant role; by working with a department or agency to identify their key issues or problems, data scientists can support mission-critical needs."

2. Assess Data Readiness. "The success of an analytics project depends not only upon whether there is a need for data analytics, but also, and more importantly, on having the right personnel, data collection and storage practices, and stakeholder buy-in within and outside of the department or agency."

3. Scope the Project. The report provides the steps one city uses:

   Goals--Define the goal(s) of the project.

   Actions--What actions/interventions will this project inform?

   Data--What data do you have access to internally? What data do you need? What can you augment from external and/or public sources?

   Analysis--What analysis needs to be done? Does it involve description, detection, prediction, or behavior change? How will the analysis be validated?

4. Pilot the Project. "This is where the trial and error of testing a new project happens." The report notes that "piloting an analytics projects, like any effort to innovate in the public sector, is somewhat at odds with the bureaucratic preference for consistency and risk avoidance, but it is a critical phase that can yield important insights for improving performance when it is time for implementation on a larger scale."

5. Implement and Scale the Model. Given the vast differences among analytics projects...