I’m live blogging at #HRTechEurope in London. Next up is Eugene Burke from SHL. In the next 15 minutes 2,250 people will have been assessed for a job role. This is generating a world of data. The problem at the moment is the recruitment process or assessment data is used in isolation. If you enable a flow of data from one department to another by collecting it in one place, and enabling everyone to mine it, and present it back in a visual way that anyone can understand. I’m beginning to see that the “big data” magic lies not in finding the data but making it visual.
When you combine assessment data with performance data and other sources, you can get a picture of what good actually looks like. It is back to the moneyball principle of Billy Bean.
The example Eugene gave asks the questions:
> Are our most talented project managers working on the most demanding projects?
> Where is our L & D spend targeted?
> Are we hiring the right behaviors in to our organisation?
The data gives the answer. I like the take on big data being about making it visual to be understandable. Thats one of the reasons I see sites like Visual.ly as having a big future. I enjoyed the presentation and explanation. I’m not a big SHL fan, but they can’t be ignored in this space. The big question I’m left with is how many organisations are feeding all their data in one direction. That is the first thing question everyone needs to answer.
Bill
My understanding of the Moneyball principle is they were forced to use different data points to understand what good looks like, rather than accepting the uniform view. So I don’t understand the conclusion that the data gives us the answer – we should always challenge it rather than blind acceptance. I’m always troubled about the misuse of “performance” data in these kinds of equations, there are numerous examples of making the data say whatever we want it to say, particularly around the “right behaviours”