A recent New York Times article described a so-called "emerging field called work-force science:
It adds a large dose of data analysis, aka Big Data, to the field of human resource management, which has traditionally relied heavily on gut feel and established practice to guide hiring, promotion and career planning.
While the practice of human resource management could certainly use stronger foundations in rigorous scholarship, this article is insulting to generations of researchers who have used data to carefully answer critical questions in the field for decades. In 1949, the first director of the precursor to today's Center for Human Resources and Labor Studies at the University of Minnesota, Professor Dale Yoder, launched a series of pioneering benchmarking studies of personnel ratios, salaries, and budgets. In the 1950s, Professor Yoder's colleagues developed of a number of measurement instruments that continue to be used today around the world, including the Minnesota Satisfaction Questionnaire. And so on and so forth right up to today, such as a recent project by some of my current colleagues who worked with data from seven organizations to better understand turnover. In fact, while we can always keep learning from new data sources (especially those using company records, or, even better, field experiments), from my perspective the field sometimes has too much data and not enough conceptual clarity.