People Analytics: How to Take a Balanced Approach to Talent Management

With all good things come risks. With all opportunities, come responsibility. The same can be said for people analytics. Data is powerful. Knowledge of what that data means can help your company tremendously — but it can also hurt you. According to a survey by Deloitte, 64 percent of respondents are actively dealing with legal liability issues in relation to their people data.

One of the main dangers is legal risks. Those typically come in two ways:

The risk of your stored data being hacked and stolen.

This makes you liable to your employees. There are various insurance plans to help with this, but prevention is key. Make sure you have good security measures in place, and keep no more data than necessary, for no longer than necessary.

Data algorithms can show bias.

For example, through no fault of your own, your data could always recommend hiring male employees, or just white employees. It could look over women and people of color every time. That is one reason why it is important to not completely trust your data and to always have a human being analyzing it. If it were to continue unchecked, you could certainly have some legal hiring issues coming your way.

Other risks that can come from people analytics:

Your data can lie to you.

Data uses programed algorithms looking for certain words, patterns, activities, etc. Based on data alone, many things can be overlooked, go unnoticed or be misunderstood.

For example, if you use people analytics to determine potential candidates for promotions, you could find that certain people are always getting overlooked. Additionally, some valuable traits aren’t even being considered.

Perhaps you want people with managerial experience to promote into executive management, thus your data is looking at people currently in management roles. At the same time, the most qualified candidate is not currently in a role with a title of manager. They have all the other experience you are looking for: the right attitude, work ethic, and have held management positions elsewhere.

Basically, this is the exact person you’re looking for, but based on the data you asked for, they are not going to be recommended. So, your data will tell you the best person for the job. And it will be wrong.

You’ve got data analyzing attendance.

You flag certain people for having a high number of report offs. They are planned and excused, but they still show up in the data. This is one of your best employees. When it comes time for a raise, though, their personal file shows a little strike against them for attendance.

This is a good example of why it’s important to have a real person that analyzes this data and reconciles it with things they know. Perhaps someone had a very sick family member or were sick themselves. They usually don’t have an attendance problem and the issue was only temporary.

Failing to recognize their contribution to the company, due to a few approved extra off days, could result in their looking elsewhere in the company. It could also result in low morale and less productivity.

Basically, anything that is good about data could also be bad if it is misunderstood and not properly analyzed. Good data systems are important, but having a human being to go over them as well can make a world of difference.

Want to learn more about people analytics best practices? Feel free to get in touch with us to learn how Aspirant can help you achieve your organizational goals.