How can we use information about an employee’s current performance to predict future performance? That’s one of the key questions addressed by the ever-growing use of predictive analytics as a human resources (HR) tool. The application of predictive analytics to the management of baseball teams — brought to life in the book (2003) and then movie (2011) “Moneyball” — made vivid the ways that data-based modeling can be used for more accurate talent acquisition and deployment. The idea that metrics could guide strategy by supporting intuitive decision making has created a boom in the use of predictive analytics in the HR industry.
Ironically, one of the places where predictive analytics hasn’t yet made substantial inroads is in the place of its birth: the halls of academia. Tenure decisions for the scholars of computer science, economics, and statistics — the very pioneers of quantitative metrics and predictive analytics — are often insulated from these tools.
That may soon change. A study we conducted with Dimitris Bertsimas and Shachar Reichman, published in Operations Research, finds that data-driven models can significantly improve decisions for academic and financial committees. In fact, the scholars recommended for tenure by our model had better future research records, on average, than those who were actually granted tenure by the tenure committees at top institutions.
Tenure decisions have impacts that ripple far outside of university campuses. The choices of which scholars are offered permanent posts are the key HR judgments made by academic institutions in the United States. These decisions impact not just the scholars’ careers but the funding of universities and the overall strength of scientific research in private and public organizations as well. On an individual level, a tenured faculty member at a prestigious university will receive millions of dollars in career compensation. At a broader scope, these faculty will bring funding into the universities that house them. The National Science Foundation, for instance, provided $5.8 billion in research funding in 2014, including $220 million specifically for young researchers at top universities.
Despite these factors, academic decision-making processes rely mainly on subjective assessments of candidates. We believe, though, that if analytics is given the opportunity to complement the tenure decision-making process by offering improved predictions about candidates’ future performance and scholarly research, businesses and the public will be better served by the academic community.