How can you do big HR data analytics when you eliminate performance rating?
A well-known people analytics person posted this question on LinkedIn a week ago. The discussion that followed was fascinating. The question took my breath away for a couple of reasons: (1) performance ratings are one of the most bias-ridden data points on any employee, so it makes me queasy to think such data would be relied upon heavily in an analytic, and (2) so much more data exists that can provide better insights.
Let’s take the biased data first. While I am no Marcus Buckingham devotee, his piece for Harvard Business Review back in February 2015 encapsulates the idea that HR data – particularly performance data – is bad data. By in large, managers rate employee performance on how they would perform the job, not on the actual performance of the individual employee. If, as Mr. Buckingham points out, 61% of a rating is really a rating of the rater, then ratings aren’t that useful in making people decisions. (In a post for another time, performance management doesn’t even need ratings.) This bad data makes for bad analytic results.
This is furthered by oodles of studies that show unconscious bias is baked into performance data. There is no escaping it. The unconscious bias of managers – which is not to shame managers, we all have bias – is active in performance reviews and data. If women and minorities are rated more harshly because of the bias, then an analytic tool that relies on performance data also contains the bias in those harsher critiques and could perpetuate or exacerbate discrimination. This can occur even when we remove demographic data through data proxies. Who wants biased data in their analytic tool?
Next, the idea that HR analytics must rely on performance data misses the plethora of other data that we could use to make people decisions. Here are but a few examples:
- Great leaders have connections throughout companies. We can find out who has developed a network within a company by reviewing email connections, social media connections, and other network analysis.
- Internal threats sometimes start with emailing themselves information on personal email accounts. Monitoring access (authorized and unauthorized) along with other retention analysis can help identify who could be stealing our trade secrets or confidential information to take to a competitor.
- We can better predict how to scale hiring and what skill sets are needed based upon productivity and sales projections.
- Things like weather, productivity, date, and time can all be factors in safety incidents. If we analyzed these items, we could develop a work schedule that reduces worker injuries.
These examples show employers could do better without touching the bad and biased performance data. If we didn’t include biased and bad data, would it be the end of people analytic tools? I emphatically answer “no.” We could do even better without it.
Image by Markus Spiske available at unsplash.com