Center for Data Innovation

Center for Data Innovation: 5 Q’s for Jason Saul, Chief Executive Officer of Mission Measurement

By Joshua New– May 30 2016

The Center for Data Innovation spoke with Jason Saul, chief executive officer and founder of Mission Measurement, a social-change focused analytics firm based in Chicago. Saul discussed how most social programs don’t use meaningful data to measure their progress and how Pandora Internet Radio inspired him to take a predictive analytics approach to social change.


This interview has been edited.


Joshua New: Mission Measurement focuses on helping organizations make better decisions about how to affect social change. Why is there a need for this?


Jason Saul: My background is in analyzing government programs and policy, and while I was doing bonds for public projects it struck me that there wasn’t any really good way to measure these programs. I started something called the Center for What Works to develop these methods, publishing books and doing consulting on measuring social impact, and then a couple years ago the governor of Illinois asked me to be on a bipartisan commission called the “budgeting for results commission,” which was set up by the Illinois legislature to figure out what government programs actually work so we could fix the state’s budget problem.


We ran into a major quagmire on the commission because we couldn’t figure out how to get the right data for the legislators and the budget analysts to actually make recommendations for the budget. That left us with two unacceptable alternatives. On the one hand we had a bunch of administrative performance metric data, which had really low value in terms of actually determining the impact or value of a program—it was just data about things like “number of miles of road laid,” and “number of carnival rides inspected.” Most governments call this “performance data,” but I call it just low-grade sludge of useless administrative data. On the other hand, we had university researchers that could crawl through these programs for years and perform randomized control trials to giving you an answer about what works five years after the fact. Given that we had to issue a budget in three months, neither of these would be acceptable options.


We had to invent a third option, and I realized that we needed to get government to start generating predictive data about what programs work. Pretty much every field in the world has tools to make decisions about what to do based on predictions about what will work, except for the field of policy. For example, no lawyer would ever go into court without comparing their case against historically similar cases in a legal database like LexisNexis. No investor would ever take a stake in public company without analyzing the company’s performance and making a prediction about its future. In the policy space, without these tools we’re essentially just guessing, which means most of decisions the public sector makes are not supported by good data, benchmarks, and analytics.


Read the full interview here.

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