At my home, my wife calls it ‘Dutch Prosperity’. Every sunny Sunday morning road cyclist set out to ride their bikes. These bikes are usually made of carbon and quite expensive. In fact, they are of similar quality as the ones that the professional cyclists use.
A while back we undertook a modest bike ride from our offices with some likeminded HR professionals. Making a nice tour along the river ‘Rotte’ near Rotterdam. Fitting with our modest on-the-road performance, most of us had aluminum bikes and only a few rode high-tech carbon bikes.
Everyone was perfectly capable to keep up with the pace and everyone had an enjoyable ride –independently of his or her equipment. This made me think that for HR analytics, more or less the same applies.
When I read through LinkedIn, I often see people mention complex definitions and techniques. However, good analytics can already be achieved with a basic level of knowledge and some solid equipment.
Let me give an example. I was recently involved in a project for which we made an analysis of the inflow of people in a certain labor sector. The question that needed to be answered was: “how does our starting salary at the lowest pay grade compare to the starting salary in other sectors?”
A quick scan of the data revealed that no one in this sector started in the lowest pay grade. This insight alone was sufficient to develop further strategy concerning rewards for new hires. This was an analytics exercise which only needed the relevant data and a very basic skill set of analytical techniques.
This brings me back to my very first endeavor into HR metrics which is more than 7 years ago. I was tasked with analyzing the inflow and performance of warehouse temps at my employer back then. I didn’t have fancy tooling – I only had Excel – and the data from our time management system.
Fairly quickly it became apparent that temps from certain employment agencies performed significantly better than those from other agencies. Of course, more analytics was possible to determine the role of other factors – but that wasn’t necessary. This simple insight was sufficient to make a profitable change.
A good fit?
During our company road bike tour, we made a small pit stop at “Koers.cc”. This bike shop is the ‘go to’ shop for road bikes and MTB’s in Rotterdam.
Serge Posthoorn, the lead mechanic there, gave us a short showcase of the basics of bike fitting, the practice of perfectly adjusting the bike to the rider’s body for optimum performance.
After only a brief investigation he adjusted the saddle height of the bike belonging to my colleague Bas by lowering it just 2 cm. This greatly improved the comfort and pedaling efficiency of Bas. This was a simple, basic and quickly realized result. Just like HR Analytics should be.
While leaving the shop we looked at all the state-of-art road bikes on display. These bikes were suited for the elite sport and Olympic competition. And they were good looking as well!
Of course, it is necessary to have state of the art equipment for HR analytics when engaged in high-level projects, for example, when you make an outflow analysis based on eight variables. Or when you’re merging HR data from 23 different countries into a standardized dataset. But for your basic ‘rides’, solid equipment and a sharp and keen eye on things will more than suffice.