HRMS and the limits of Big Data
It had to happen. Data became sexy. Or, more precisely, Big Data. And even HR, never the most technology-friendly of functions, has been jumping on the data bandwagon in the last few years, looking to use Big Data-driven predictive analytics to identify who is your best employee, who might be your worst, and foretell likely workforce events, such as who might leave next…
However, the shiny data-driven future promised by the media has yet to appear for HR and that’s because – like anything else – Big Data is no panacea; it has promising applications but is also subject to a few limitations. And it’s those limitations that HR is struggling to overcome.
‘Big Data’: a brief background
First, let’s quickly define Big Data. In a HR context, organizations are now gathering huge amounts of information about skills, job performance, personal details such as age and education, safety records, attendance, projects managed, previous roles and so on. Add this to other corporate information (sales, marketing, finance, customer data, etc.) and ask the right questions and new, ever more detailed insights are potentially available.
Recommended reading: refine your HR department’s use of Big Data and predictive analytics using our guide to getting more out of your HRMS data
However, in the same way that any technology or process is only as good as its designer can make it (and similarly, a bad workman blames his tools…?) it depends on what you ask and how. With this in mind, it would seem that Big Data’s biggest limitation is HR’s understanding of it and HRMS analytics in general, or more to the point, HR’s lack of understanding…
A case of man vs machine?
We are told by the HR press every January that this is the year that HR gets to grips with HRMS predictive analytics functionality. So why do we never quite seem to manage it? There may be a technological barrier here but first and foremost, the problem is attitudinal. Unsurprisingly, HR culture is people-centric, focused on human relations and responses. Individual human beings are by nature unpredictable (it’s only with bulk data sets that themes can be identified and predictions made) and so HR experts are often doubtful that Big Data metrics can usefully describe or define human behaviour. Much of this doubt comes a lack of understanding of statistics and data use which prevents the right questions from being asked. In other words, it’s a limiting attitude that can become a self-fulfilling prophecy: I don’t believe it will work and therefore it doesn’t.
In a sense this is a language issue. Even with an HRMS, data and statistics work is complex and multi-stage, involving data collection from various sources, formulae and algorithms, question design, data organization, metrics and modeling, and so on. Whereas while people themselves are certainly complicated, the goal when communicating with them (or about them) is to express the message in the simplest possible, most easily understood form. This clash of complex with simple may be preventing HR from developing, using and presenting strategic and predictive information.
One thing the industry pundits do agree on is that HR professionals will only begin to get true value from Big Data and predictive analytics when they start building their skills in this new arena, actively taking part in the design of questions and metrics, combining their deep people knowledge with newer, more technical understanding.
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