Joel Shapiro, JD, PhD, is clinical associate professor and Executive Director of the Program on Data Analytics at Kellogg's School of Management at Northwestern University.
Joel teaches graduate courses in decision analytics and policy analysis, with a strong focus on how to use data analytic solutions to solve real-life business problems. Prior to joining Kellogg, Joel served as Associate Dean of Academics at Northwestern University School of Professional Studies, leading the creation and growth of myriad on–ground and online programs. Joel holds a PhD in policy analysis from the Pardee RAND Graduate School, a JD from Northwestern University School of Law, and a BS in physics from the University of Michigan.
We sat down with him ahead of his presentation at the Predictive Analytics Innovation Summit, taking place in Chicago this November 29-30.
What first sparked your interest in analytics?
I’ve always been very quantitatively inclined, and loved trying to explain various day-to-day phenomena with math. After I majored in physics in college, I took a 'quant-detour' by going to law school and briefly working as an attorney. I was completely a fish out of water. One senior partner asked me to help assess how our clients would benefit from the big tobacco settlement in the late 90s. I created a cool probabilistic model that was really quite elegant, and – when I presented it to him – he swore and threw the memo at me. He wanted the 'one right answer,' and I knew that there was a range of possible answers. At that point, I knew I needed to find a field that embraced data and the uncertainty inherent in using data for decision-making. I ended up going back to school for a PhD in policy analysis and loved it – it wasn’t easy to go back to school after just completing three years of law school, but it turned out incredibly well for me.
What do you think is the most important thing a company should do to instill a data-driven culture?
They need to think really deeply about what problems they’re trying to solve and what questions they’re trying to answer. If a company doesn’t give sufficient thought to the specific goals they’re trying to accomplish, then they’re going to fail with analytics. You can’t just hire some data scientists and hope that they come up with great insight. The data scientists typically don’t know the business, and the business folks often don’t know the data science. They need to work well together, which means that they both have to know what they’re trying to answer and why.
Can analytics be automated?
Well, yes and no. In the right cases, data can be automatically generated and analyzed. But analytics is fundamentally about using analysis to do something differently. I am very skeptical of off-the-shelf analytics products that claim all you have to do is load in your data and it will spit out actionable insight. That’s really dangerous. Most businesses have unique processes, goals, and contexts that make the link from data to action fraught with nuance. Analytics still rests fundamentally on good critical thinking skills – how to ask great questions and rigorously assess evidence that can lead to action. It’s hard for me to imagine how context-specific critical thinking could be automated - I think good data scientists will serve this role for a long time.
What will you be discussing in your presentation?
I’ll talk about my firm conviction that analytics is a leadership problem, not an IT problem, and not a data science problem. Sure, IT and data science give us important tools to collect, store, and analyze data. But analytics has value when it leads to action and change. And business leaders are the ones who implement business change. When an organization embraces analytics at the leadership level, the IT and data science jobs are more valuable to the company and a whole lot easier to do well. It’s entirely a win-win.
You can hear from Joel, along with other experts in data analytics, at the Predictive Analytics Innovation Summit. View the full agenda here.