How to tell the difference between good and great HR analytics – part 1

14/12/2012 at 15:42 9 comments

Question: What is the difference between good and bad analytics?

The answer is probably best illustrated like this;

This is bad analytics:
You have received the latest performance management data from all five divisions. You know from experience that the data is questionable – in two of the divisions many of the inputs are the default settings. You also hear that the managers have a somewhat relaxed attitude towards the accuracy of the data (to say the least). Your report show that absenteeism is flat at a reasonable level. You report this with satisfaction.

This is ok analytics:
You get the latest voluntary employee turnover data and see that the figures are trending upwards. The level is above the 6 month moving average. You dig deeper into the data and see that it is in particular in three division the employee turnover has been higher than expected. These division have three things in common; a new leader has been employed, workload has increased and they are client facing. You contact the relevant HR partner and suggest that they implement the usual retention initiatives.

This is great analytics:
You get the latest voluntary employee turnover data and see that the figures are trending upwards. The level is above the 6 month moving average and 2 %-point higher than sector-adjusted benchmark. You dig deeper into the data and see that it is in particular in three division the employee turnover has been higher than expected. These division have three things in common; a new leader has been employed, workload has increased and they are client facing. You then decide to interview relevant people in those divisions including leaders and employees as well as the HR partners. You read relevant academic research and case studies on effective measures against voluntary turnover in your particular sector and discuss this with experienced managers and HR partners within your company. You suggest three actions; coaching for the leaders, competency profiling to match job demands and team building. Your data suggest that these three initiatives will reduce the voluntary employee turnover from the current rate to 1 %-point below the benchmark at a ROI of 55%

 So what is the difference? Great analytics

• are made with reliable data
• are combined with quantitative data
• use an evidence-based approach
• is predictive

04/01/2013 at 14:17 Leave a comment

In my last post I argued that great workforce/HR analytics share four common traits; they are

1. predictive
2. made on reliable data
3. combined with qualitative data (and perhaps some intuition)
4. used an evidence based approach.

But there is one thing missing and probably the most important thing; It must be based upon a strategic approach. I know that”‘strategic” is such an overused word in HR now and frankly most of what is said about strategic is anything but. However there is no getting away from the fact, that you can do good analytics with the above four traits without actually adding much value.  Without doing analytics on the right things AND in the right way it really amounts to very little.

Or to put it in another way: Workforce analytics without a strategic approach will only with the help of luck turn out to be truly value added.

What does strategic approach mean in practice? The best way to illustrate this is to look at the difference between a bottom-up or top-down approach.

Analytics can be bottom-up (operational) or top-down (strategic). The bottom-up approach is the approach many take. They combine their data into Big Data and look through interesting ways of diagnosing, measuring, illustrating, visualizing, trending and reporting the data. They find interesting links between employee turnover and profits (no kidding!), talent profiles and performance or particular training programs and customer satisfaction. That’s when they are good. Sometimes they just show which divisions are experiencing lower employee turnover!

The top-down approach on the other hand start with your HR strategy (which of course is aligned with the business strategy). You then look at which areas you want to focus your HR efforts. Then you find the desired knowledge you require to make the right decision. The you design the data required to provide you with this knowledge.

Good analytics made from a bottom-up approach can give good results; they can surprise you, show links you hadn’t seen before and even challenge your strategy. BUT that approach must not stand alone. The primary use of analytics should be top-down. That’s the strategic approach and that is likely to support you most.

Remember: Workforce Analytics is ‘just’ a tool to make better HR decisions. It is a great tool for that, but if you are an HR executive looking to make strategic HR decisions, your analytics has to be strategic too. Don’t look at the data you’ve got and make the best of them but instead look at what data you need to create the most value-added predictive analytics.

Entry filed under: Analytics. Tags: HR analytics, Human Capital Strategy.