When you think of people analytics you likely think of dashboards. And most dashboards are full of similar stats… time to fill, cost per hire, source of hire etc etc. Which made us wonder — isn’t it time to turn some critical thinking onto the tired concept and delivery of analytics dashboards?
As metrics have an increasingly important impact on talent acquisition decisions, we asked the CXR Analytics Community to take a closer look at the data itself. Using a Reverse Brainstorming exercise we first asked the group to define what they see as the biggest problems facing people analytics today. While a number of problems surfaced, it quickly became clear that many could be grouped into the areas of integrity and accuracy of data.
Problem defined: Our data is too accurate
As part of the reverse brainstorming, we took the extreme situation: that the data metrics were too accurate to work with. In that scenario, we then discussed how we could make the data worse. Yes… worse. Laughter aside, the small group work came up with some pretty illuminating ideas.
- Build inconsistent ATS workflows
- Require people work in different systems
- Silo data across systems
- Let people choose to work in the system or not
- Fire the data architect!
The discussion that came out of reviewing these ideas was a fun one and as we shifted to reversing them, the community came up with some truly actionable ways to improve a key data analytics issue. CXR Members can view the entire exercise with all its points in the board below and stored in the CXR Library.
Key takeaways:
- Standardize inputs: whether it’s locking down options or forcing people to go through the system, anything TA teams can do to standardize the activities of their recruiters and sourcers will improve the data — and thus, the decisions that come out of that data.
- Improve training: Refresher courses, training aids, automated reminders and adequate time to implement systems all fall under the category of helping the end-user provide more accurate and consistent data.
- Set expectations: This one flows in both directions. Set clear expectations for those who are using the system day-in and day-out but also set clear expectations for the upper levels that will be reviewing data sets. Setting expectations also allows a team to establish common definitions and data usage – another big hurdle to acquiring accurate data.
