Michael Housman, Chief Data Science Officer, RapportBoost.AI

Michael Housman was the Chief Analytics Officer at Evolv, Inc. (acquired by Cornerstone OnDemand, Inc.) where he applied state-of-the-art statistical methodologies and econometric techniques to databases consisting of hundreds of millions of employee records in order to understand: (1) what keeps people on the job longer; and (2) what enables them to perform better. He has published his work in a variety of peer-reviewed journals, presented his work at dozens of academic and practitioner-oriented conferences, and has had his research profiled by such media outlets as The New York Times, Wall Street Journal, The Economist, and The Atlantic. Dr. Housman received his A.M. and Ph.D. in Applied Economics and Managerial Science from The Wharton School of the University of Pennsylvania and his A.B. from Harvard University.


What are the key aspects of employee performance that are critical to the success of HR Analytics?

Organizations that are trying to improve employee performance through the use of people analytics must make sure that the performance measures they’re improving are:
a. Subjective: productivity numbers are more effective than performance reviews based on supervisor opinions.
b. Granular: performance must describe an individual as opposed to team or company performance.
c. High periodicity: annual numbers aren’t as useful as daily or weekly numbers.
Organizations can use whatever KPIs they want but the extent to which those KPIs check these three boxes means that they’ll produce HR analytics that is more trustworthy and insightful.

How can HR Analytics enhance employee performance?

I think the process here is actually pretty simple and straightforward:
1) Link up data on employee characteristics (e.g., experience, training, supervisor, location) with whatever employee performance is being tracked (and hopefully improved).
2) Perform exploratory data analysis and, eventually, multivariate regression to understand the drivers of performance.
3) Work with stakeholders to share the results of these analyses and begin to take steps that will improve performance.
That’s the blueprint, although it’s much harder than it sounds (especially the first item).

What do CEOs/CHROs look for in employee performance analytics?

They’re looking for analytics that are:
1) Insightful: share something interesting that may fly in the face of conventional wisdom.  My team did research that found job-hopping behavior wasn’t connected to someone’s tenure in the company.  It really made people sit up and take notice.
2) Accessible and intuitive: don’t share complicated regression results with high-level executives; they want results that they can understand.
3) Actionable: like the tree falling in the proverbial forest if you generated some insight and presented it to stakeholders but they did nothing with the results, did you even do the work in the first place?  Probably not.

What is missing in terms of employee performance data that could make HR Analytics even more meaningful?

Organizations can track whatever employee performance metrics and KPIs that they want as long as they check the boxes that I outlined earlier. When they use employee performance metrics that are subjective, lack specificity, and possess low periodicity, you end up with muddled or biased results that can lead organizations astray. Good data science starts with the data.

Can HR Analytics play a prescriptive role in helping employee fine-tune performance real-time?

Absolutely.  At Evolv, we developed a real-time hiring system that would provide red/yellow/green recommendations about job applicants just seconds after they had applied for a job. Recruiters could use this information to decide whether to offer an interview almost immediately after they received the application. The same principles can be applied to optimizing performance in real-time, though it should be noticed that building a real-time system is a fairly massive undertaking. I often advise organizations to start small, build off of quick wins, and show value before trying to do anything in real time. There are tons of low-hanging fruit available before tackling real-time performance enhancement.


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