Data for People Analytics: Clean, In Context, and In demand

If you work with HR data, you want it be useful for the business. And so, if you’re like me, you keep up on the latest in People analytics. Here’s a worthwhile article from SHRM reassuring you that lots of folks aren’t where they want to be in People Analytics.

We’re nuts and bolts data consultants. So, we keep up with the latest but focus on the basics. To be successful in People analytics, your data must be:

  1. Clean
    The article lists several excellent best practices for people analytics. First on the list: Examining the quality of your data. If you gave your company an HONEST grade on data cleanliness, what would it be? My sample is skewed. Folks don’t hire consultants if everything is clean and orderly. But would you give yourself better than a C? You can’t have any kind of understanding if you’re systems aren’t set up correctly and if your data isn’t consistent. Which goes back to the basics of HR Data Management.
  2. In Context
    If you’re data is clean and reliable, it still may not be meaningful. How do you know if a given KPI is good, bad or indifferent? Case in point. We looked at turn over for a large non-profit client. In this case, 40% of new front-line workers left within 8 weeks. Which is not what a good number. But how bad is it? These are difficult jobs that paying close to minimum wage. So, how much better can they expect? The ease of getting comparatives depends on your industry. But it’s essential if you know that you have a solvable problem?
  3. In Demand
    Who’s asking for this data? Have you built reports that no one in the business looks at? And so, behavior doesn’t change. You need to think how folks with OTHER priorities will work with your data. What will make sure that the knowledge you provide delivers the actions you desire.

As with many things, you need to get the basics right before you can do the cool stuff.

Where are you on your People Analytics journey? Let me know in the comments. And if you want to talk about how to get clean, in context, and in demand data, let’s talk.

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