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HR Data is powerful but only if it is used correctly

Where HR goes wrong when it comes to people analytics

Data has been the overriding theme of the last half-decade, with stats and figures now used to make all manner of business decisions. Take HR data, people analytics, and workforce analytics, for example. We see these as essential, as well we should, but simply having them at your disposal isn’t enough. You need to know what to do with those numbers.

HR data is well and truly ingrained in global business culture. According to a survey by CIPD, 75% of HR professionals are now using data to understand workforce and productivity issues.

More compelling still, 65% of professionals at organisations with a ‘strong people analytics culture’ claim the business is doing well, compared with 32% working at companies and organisations with a weak approach to workforce analytics.

There are many ways in which HR data and people analytics can really help a business out. Depending on the software, you can get a comprehensive overview of everything from the basics – like punctuality and absenteeism – to more complex or hidden issues. So we’re talking records of qualifications, payroll statistics, labour efficiency levels, costs and more. Better yet, with tools that offer visualisation of data, it’s easy to get a clear picture of factors that may act as variables affecting those areas.

Do your staff suffer from Monday morning fatigue or a Friday afternoon lack of focus? Are there correlations between efficiency and certain types of qualifications, or lack thereof? Plot it, chart it, map it, and then work towards finding a way to combat the problems or develop a better environment to get the most out of any positives.

However, there’s one huge differentiator between companies when it comes to the ability to achieve positive change in this way. Those that understand how to use data can, those that don’t cannot.

That may be stating the obvious, but sometimes the obvious needs stating. And then overstating. There are many ways in which HR departments can fail to make the most of people analytics, and many of the worst have nothing to do with the interpretation of workforce analytics data itself. Instead, it comes down to setting the correct parameters within which to read that HR data and maintaining employee records correctly.

Let’s cut to the chase. Are you setting clear benchmarks and laying out specific, consistent reporting intervals? No? Then it’s a fail. Are SMART objectives in place that give the department something to work towards with all that HR data? Forgot? Sorry – another fail. Do you understand which categories of information are actually relevant to what you are trying to achieve, or are you looking at things from a distance, and getting blinded by the reams of numbers offered by even the most basic people analytics tools?

Getting this framework right isn’t just going to improve how you use the data itself. It’s going to make sure that the data isn’t completely going to waste. Allow us to explain.

Ad hoc reporting, for example, won’t give anyone any clear pictures of what is actually going on in any department or business area. Choosing regular times – perhaps monthly or fortnightly – to run reports is the only way you can truly compare performance. Similarly, if you don’t have targets in place then you obviously don’t know why you’re collecting all this HR data in the first place. So why are you collecting all this HR data in the first place?

To use a simple case in point, there’s no point in comparing the weekend output of a department running on skeleton staff after Friday afternoon with another that has all hands on deck on Saturdays and Sundays. That data is irrelevant, and will only skew your view of the situation, which can lead to blind alleys and cul-de-sacs.

In summary, then, consider what you need from the data, then work backwards ensuring you are using the right data and parameters to provide what is required. All of which sounds easy when you know how, but it’s remarkable how many firms are still getting it wrong.

 

Words: Richard Trenchard.

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