Earlier this month, I attended the Wharton People Analytics Conference in Philadelphia, where start-ups, thought leaders, and practitioners networked, shared their work and debated the future. The event was by turns heady (e.g., a panel with Wharton Dean Geoffrey Garrett and Byron Auguste of Opportunity@Work discussing “the why of work”), insightful (e.g., Google’s recent research on teams), and tactical (e.g., solutions profiled by recruiting startup HiQ and other talent analytics vendors).
The conference had many highlights, but here are just a few that stood out to me:
Daniel Kahneman and the Limits of Predictability
In a talk with Dan Pink, psychologist and behavioral economist Daniel Kahneman shared his thoughts about the evolution of talent assessments and behavioral economics. Of interest were his comments about the limits of our ability to predict performance. Though he argued that many failed predictions are due to noise, not bias, he wondered whether our ability to predict performance would continue to improve or if we’ve hit a limit. Maybe there are some things we just can’t model out effectively.
In some ways, that may be true for individual performance. But within CLC, we’ve found that more organizations do a very poor job of predicting performance in a more collaborative, team-based environment. Indeed, two thirds of enterprise contributors—our term for high performers—are misidentified. (CEB Corporate Leadership Council members can read more about enterprise contributors and how to develop them here.)
For talent analytics professionals in general, even if all noise cannot be eliminated from performance models, I would argue that these models still need to be updated from time to time to keep up with the pace of change. The discussion also raises interesting questions about the point of talent analytics, which isn’t just about increasing predictability, but rather about increasing it within a certain timeframe or separating signal from noise when dealing with massive infusions of information (large numbers of job applicants, for example).
Responding to the Skills Gap
Garrett and Auguste debated the skills gap, with Auguste arguing that the system was rigged for most employees and that smarter algorithms could essentially free them. For example, institutional pedigree (where you went to school) is used as a heuristic in the early sorting of candidates, instead of actual skills. Talent analytics should help us overcome that problem. But there are many problems with talent analytics; for example, the ROI of selecting talent is much higher than building it. Robots in general can rob people of work that makes life meaningful. Auguste concluded by saying, “Talent analytics is about to explode. But will it take off like a rocket or explode in our faces?”
Tony Hsieh, the CEO of Zappos, defended Holacracy, comparing it to the original iPhone and insisting that it will improve with later iterations. One very interesting thought he had was the idea of someday opening up a company’s organizational chart to the world in pursuit of radical transparency. With organizations making public diversity statistics and salaries, perhaps the whole org chart is next?
There were many other great panels, such as the panel on teams, where Google argued that team dynamics matter more than composition, and Yale’s Amy Wrzesniewski argued that in evaluating teams, one should measure not only the success of team outcomes, but also how individuals have grown within their teams.
The Wharton Conference is in its third year, and it will be interesting to see how this takes off as talent analytics continues to explode. Currently, the field seems to be putting heavy emphasis on behavioral economics and what we might call the Google School of People Analytics. I would expect the perspectives to continue to multiply as organizations across industries start building out their own capabilities. Anyway, with management algorithms on the rise, woe betide the managers who are still leading from the gut.