Doing Good with Data at the University of Chicago

Michael Sims's picture

About a month ago, I got the chance to interview Lauren Haynes, Associate Director at the Center for Data Science and Public Policy at the University of Chicago and a fellow presenter at next week’s Predictive Analytics World conference in Chicago. Lauren and I talked about how data science is used in the social sector and the challenges these organizations face in becoming data driven.

Background

The Center for Data Science and Public Policy at the University of Chicago is best known for the Data Science for Social Good Fellowship, which allows master’s degree and PhD students to work on data-related projects in the social sector (i.e., government and non-profit agencies). As part of the fellowship, the organization trains the fellows on how to do real-world data science projects, specifically for this sector.

How Data Science Is Used in the Social Sector

There are a number of different use cases for data science in the non-profit/government space. Many of these organizations work with limited resources—time, money, capacity, etc.—so data science projects often focus on prioritizing those resources. These organizations use predictive analytics to identify which areas to prioritize by forecasting the impact of increasing/decreasing spend in that area.

Another common use case involves synthesizing data from disparate sources. Often, the Center’s clients have rich data sets that they either cannot access or cannot analyze within their existing data infrastructure. The fellows help these organizations surface and make sense of their existing data and start to use that data to glean insights that will help drive business decisions.

Prior to these organizations’ enlisting the help of the Center, many of them made their decisions purely based on the knowledge and expertise of the people on the ground. This existing expertise would prove essential in guiding the Center’s efforts to inject data science into these organizations’ decision-making processes. As Haynes puts it, “humans are great and machines are great, but both of them working together can be more powerful than either alone.”

Overcoming the Challenges of Becoming Data-driven

Often, the point of contact between the Center for Data Science and Public Policy and the partner organization is quite “data progressive,” as Haynes puts it. However, the challenge is convincing the rest of the organization to follow suit.

That’s where APQC comes in. We’ve spent the past two years researching how to prepare organizations for the shift to a more data-driven approach, how to enact that change, and how to sustain the results. To learn about how to make your organization more data driven, check out our research on Change Management Practices for Establishing a Data-driven Culture. It’s the next best thing to flying to Chicago to watch me try to win my first Tony.

Stay up to date with our upcoming analytics research, webinars, and more by visiting our expertise page.

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