The problem with the sorcery of Big Data in HR
Many HR departments are misusing the data they collect on their employees, but there are some simple steps that can be taken to maximise the value of the data at your fingertips
The science fiction author Arthur C Clarke once noted that any sufficiently advanced technology is indistinguishable from magic. The tipping point for some of us is set embarrassingly low: I still struggle to see the difference between the satnav app on my phone and a prosaic form of divine guidance.
But the problem, if we cannot distinguish science from sorcery, is that we fail to understand the limits of these amazing new inventions. Satnavs are all very well, but they will still direct you into the path of an oncoming freight train if someone made a mistake with the mapping.
Among employers, there appears to be a growing sense that analytics is the new magic.
It is perceived as a philosopher’s stone able to transmute the base substances of employment – sickness absence, poor employee engagement, high labour turnover and the rest – into pure gold.
But the groundswell of interest in the HR world in getting to grips with analytics appears to be accompanied by an almost equally widespread absence of any real idea about how to get started. Hence, presumably, the endless series of invites that have been clogging up my email account these past few months to conferences on the topic.
Indeed, when XpertHR recently surveyed HR professionals about their use of workforce data, there were startling numbers who told us that they collected the numbers but did nothing with them. This was either because the data was not up to the job, or because no one was that interested.
Even where there was an appetite for collecting and using good-quality data, only around one in three HR departments benchmarked against other organisations. So they might know that staff typically took five days off sick each year, but they had no idea whether this was good, bad or average.
Data on absence, the time taken and cost to fill vacancies, pay rates and awards, and much else besides, is all vital if, as a manager, you are going to make decisions about what your organisation should be doing based on evidence rather than guesswork. And the results of making the right and wrong decisions will be clearly visible.
So why do more organisations not pay more attention to collecting, understanding and acting on data? Part of the reason could be that many HR departments are not up to the task – and they know it.
HR professionals often find themselves on uncertain ground with numbers. Leaving aside a few industrial-relations specialists, most people in your typical personnel department made their career choice because they enjoyed working with people – not because they were good at maths.
Most struggle with baffling stuff about big data, predictive analytics and modelling. But it sounds important, and fortunately there is always a consultant on hand to play the modern-day magician and, for a modest fee, produce an utterly baffling report which, for a further modest fee, they will then explain.
But there is no need to be a data scientist to start making real progress, and you probably do not need to invest in a major technology solution.
It is a little beyond the remit of this column to come up with a step-by-step guide to basic benchmarking, but ask yourself what data you have – it might be about the number of days people take off sick, the number who choose to leave each year or the time it takes to fill vacancies.
Decide on sensible time frames for any data – you might want to break down sickness absence month by month, but look at salaries once a year. Then think about what you can compare it against: historic data (is absence higher or lower than in the same month last year?), internal comparators (is absence higher in sales than in IT?) and external comparators (do I pay higher salaries than other companies?).
At this point you begin to have a good idea where the problems are that are costing your business money. As Paul Daniels used to say, “now that’s magic” – while knowing full well that it was not.