People Analytics have come a long way in the last few years. It has come so far that the way that HR institutes systems and processes for internal policy making has fallen far behind, and there is no end in sight to how much this trend will probably continue.
The main problem is in the way that policies are crafted without the full use of the data available to practitioners and in many cases without a full understanding of the analysis of that data.
In our experience, we see that sound policies have to follow 3 basic principles:
- Relevancy: New policies must be the relevant to the challenge that is being faced at that very moment or a challenge that is expected to come at a future time and date. In other words, there needs to be some method to the policy creation narrative.
- Consistency: We see so many policies that immediately conflict with other policies once they are implemented, and worse, they originally conflicted with existing policies before implementation. In certain instances, new policies conflicted with the data and analysis itself. Consistency is key to gaining the promised value inherent in new policy creation in the organization. Conflicting policies is like swimming with an arm going one way, and the other going the other way. All you’ll really accomplish is that you’ll be going in circles.
- Clarity: A policy is intended to change or reinforce behavior. The clearer the policy the better it is able to achieve its objective of changing or reinforcing behavior. This means clearly studies, written, and communicated. Policies are only as good as they’re understood in the end.
When new HR policies are implemented, the success (or failure) in their adoption so often comes from these general principles. When it comes to people analytics, and using the insights gained from data analysis to create new HR policies or update existing ones the same rules apply.
What does your data really say? Is it relevant to the challenges that you are trying to tackle or are you simply creating policies based on data because you can?
Often times we also don’t look at the level of consistency of what that data and its analysis are telling us. Data changes quite frequently, and therefore its conclusions can too. If one does not pay close attention to the overall picture, whether it’s by looking at longer timelines, or being observant about changes in related patterns that can affect your data’s integrity and thus conclusions, it is important to ensure that your data, its analysis, and therefore your conclusions are consistent and sound.
If this is accomplished, you can ensure that your new policy has maximum chance of also being right on!