Workforce data is everywhere. In all different formats using multiple languages, inconsistent terminology, and living in different systems. Given this complexity, it’s not surprising that most HR and talent teams access and utilize only a small portion of their data’s power. This is the data that’s visible, on the surface, and easily reported. But this is only the tip of the iceberg...and below the surface is where we really need to focus to deliver results for the business.
Above the Surface: Lights on HR Metrics and Reporting
Above the surface we find foundational metrics and standard reporting. These metrics are important for HR to understand basic efficiency metrics, but deliver limited value to the business. Many of us spend the bulk of our time here, preparing data and focusing on the things which are only important to us (i.e., HR). Meanwhile, your business colleagues want you to help them hire better talent, retain key talent, and deliver impactful HR and people programs with great ROI. While your work above the surface is good, it’s not good enough.
Below the Surface: Solving Business Problems with People Analytics
Under the surface is where about 80% of the value and insight lies. Locked away, untapped, or hidden from view. This is where connections across data sources happen, where hypotheses can be tested through endless permutations, and where business-relevant analytics come to light.
And it’s only when your people and talent data is connected and unified across all of your HR, talent, and operational systems - past, present, and future - you get to see the benefits and power of the single VIEW of the truth.
Let’s go beyond the surface and use the example of Top Performer Turnover. Your unified dataset, smartly used by your data-driven workforce and talent team, can now help you:
- Understand the patterns around those top performers who turned over such as tenure, career path, recognitions received, compa and market ratio, where they went to, and the reasons why they left.
- Gain insight into the managers that these high performers worked with, the development programs these managers have been on, their manager career path, or recruitment source.
- Drive a more accurate estimate of your cost of turnover (using multiple factors including recruitment and training costs, time to productivity, the opportunity cost of unfilled positions, etc.).
- Understand the demographic differences across this turnover, whether there were stage of life differences (like young parenting years, an under-represented demographic group. or with elderly parents).
- Understand and forecast your revenue/margin impact and identify risks of not meeting your business plan.
- Understand the root causes of the triggers for this segment turning over, when it became an issue, and your financial impacts to date.
- Create more accurate plans and targeted strategies for recruitment.
- Initiate additional research (such as targeted surveys, specific data collection, and focus groups) related to top performer turnover in places where it is most likely to occur (i.e., New York Operations, Business Unit A).
Bottom line, there typically is a lot more horsepower available in your analytics capability, and this horsepower tends to be locked away in your data - versus the functionality of your toolset.
