Dub Dubs at SystematicHR has a great post starting with his own personal story to illustrate the point that waiting for complete data is not the best way to go about making decisions.
I'd like to add that while technology would make really mini-transactional data possible, more and more we'll need the skill of looking at the trees, the leaves, the flowers and then zooming out and gaining an understanding of the forest. Sometimes the difference between a tree and another can seem fascinating, but the question we need to ask ourselves is "How does this change the nature of the forest"
The question is relevant for HR professionals because I have seen fellow professionals looking at spreadsheets of data - rows and columns - of salary data - and then saying "When I became a HR manager I didn't realise that I'd be spending more time with numbers than with people"
It's time to remember that the numbers represent people and should not be the only way to define them.
Read the great post here:
We often talk about analytics and how it changes how we operate in HR. High quality data leads to high quality choices – and often times that is true. But it is also true that we don’t always have all of the data that we need at any specific point in time – if we had everything we needed to know, we might make vastly different choices.
I’ll take succession planning as an example. We know who the top 10 succession candidates are for top positions (hopefully). We know when they will be ready, what their relative skills and competencies are, and how their strengths compare to one another. But we don’t know which of them are going to jump ship and go to another company before the position becomes vacant. We don’t know which of them are going to stop growing, regardless of our best efforts to continue developing them. The best that we can do, is to invest in a pool of candidates, and hope that one of them, the right one, is ready when the time comes.
We use decision support and analytics to crunch the numbers for us, but at the end of the day, it’s still serendipity – it’s still luck. The hope here, is that while analytics and decision support can’t be a perfect predictor, we can in fact “make our own luck.” We can improve our odds at getting the best outcomes. At the end of the day, it is not serendipity versus decision support, but a combination of the two that will make our best data work for us.
