By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, Data Science for Social Good: Lauren Haynes IMAGE 2How Predictive Analytics Can Help Governments and Non-Profits, at Predictive Analytics World for Business Chicago, June 19-22, 2017, we asked Lauren Haynes, Senior Project Manager at Center for Data Science and Public Policy at The University of Chicago, a few questions about her work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: We work at the intersection of public policy and predictive analytics – at DSaPP we work in healthcare, social services, governments, non-profits, education, transparency, economic development, public safety, and criminal justice. To that end we help organizations identify the inspections organizations should do to find the highest volume of violations for housing and environmental enforcement, students at risk of dropping out of school, individuals at risk of re-entering the criminal justice system, etc.

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: We think predictive analytics can help organizations drive decisions of where to use limited resources – if you are responsible for 100,000 buildings and can only inspect 1,000 of them a year, knowing which 1,000 are highest risk for having a violation helps use those inspections effectively. Similarly, if you can only enroll a small population in an intervention or program, being able to identify those most at risk maximizes the value of the intervention. 

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: Syracuse deals with hundreds of water main breaks, leaks, and other issues that require attention each year, distributed without a clear pattern across the entire city – in partnership with the city, DSaPP built a model to predict water main breaks. Based on the model’s accuracy at predicting past years’ incidents, the team predicted that 32 of the top 50 highest-risk water mains would break in the next 3 years. If you simply used the age of pipes as a way to prioritize which city blocks should be replaced first, only 5 percent of the top 50 water mains on your list would go on to break in the next 3 years. If you used the history of breaks at different locations, looking at the number of occurrences of breaks per city block, only half of your top 50 riskiest mains would break. But most water main breaks are “first-time offenders,” without prior breaks at that location. Going by past breaks alone, you would never predict any breaks that have previously had less than three water mains breaks, and replacement efforts would only focus on a handful of neighborhoods. In the two weeks after the DSaPP team delivered the risk scores to their Syracuse partners, two of the water mains listed in the top 50 ruptured. The Syracuse Office of Innovation has quickly integrated the model into their work, using it to guide an infrastructure planning process and decide where to do “dig once” combinations of water main replacement and road resurfacing.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: Applying data science and predictive analytics to the social sector is as much about change management and organizational readiness as it is about technology.


Don't miss Lauren’s conference presentation, Data Science for Social Good: How Predictive Analytics Can Help Governments and Non-Profits, on Tuesday, June 20, 2017 from 4:45 to 5:30 pm at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World