Toon DevlooHR analytics finds itself under increasing pressure to deliver tangible results and prove its value to organizations. Hence, this is perhaps a good moment to reflect on the HR analytics journey taken so far, and separate hype from substance. What stages has it passed through in its quest to become an integral part of strategic decision-making? To explore this, the Gartner hype cycle provides an insightful framework to describe its initial emergence to its current state, highlighting the peaks and troughs along the way.
Innovation Trigger
The starting point. Inspired by pioneering organizations such as Google and IBM 10-15 years ago, the potential impact of advanced analytics on employee data sparked intrigue in many companies.
Peak of Inflated Expectations
Drawing inspiration from marketing analytics, the HR field heavily emphasized predictive analytics. The idea was to mirror consumer behavior predictions but within specific HR domains (e.g., which employees have a higher risk of leaving the organization, which leaders will be more successful).
Trough of Disillusionment
The challenges became evident. Identifying robust use cases for complex predictive models proved not always easy, and the costs often outweighed the benefits. Furthermore, a 'black box' approach—predicting behavior without understanding the 'why'—lacks often actionable insights for HR to adopt and optimize employee policies.
Slope of Enlightenment
→ I believe we are currently at this stage.
As the adoption of HR technology evolves, so does the abundance of employee data it generates. The challenge now is to harness this data to address critical HR and organizational needs. For example, in healthcare, where the inflow of adequate candidates and absenteeism prevention are significant concerns, it makes sense to channel efforts and leverage context-specific data to address these talent management challenges.
Plateau of productivity
We anticipate on a more grounded HR data approach by organizations, where the focus is shifting from complex analytical modeling to effective data use and management. This is illustrated by some of the following questions:
- What employee data are we gathering through various systems?
- How do we use, share, and report this data in a GDPR-compliant way?
- What specific metrics and/or analyses are critical to monitor specific HR domains, and what drives those KPI’s (rather than predicting them)?
- How can we capture and quantify important touchpoints across the lifecycle of an employee, and even more importantly, optimize our response time in case interventions are needed?
Conclusion
Exciting times ahead! As we are about to move beyond the hype, and embark the (slippery) slope of enlightenment, many lessons can be learned from the journey taken so far. It’s a reminder to balance our expectations with a grounded understanding of how HR data and technology can be efficiently leveraged to support the broader organization.
Ready to delve deeper into the potential of your HR data and technology stack? Feel free to contact us for some inspirational use cases and advice!