HR analytics: Demystifying the buzzwords

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What some of the most commonly-used phrases mean (in lay terms)

“A lot of HR people are actually proud they don’t like numbers as they work in a people-centric field. There is plenty of evidence from as far back as the 1970s about the concept of using numbers to improve employee performance, but they have buried their heads in the sand,” says Guy Ellis, director of HR consultancy CourageousWorkplaces.

But if attitudes don’t change the profession is in danger of being left behind. “The technology is now there to crunch the numbers that HR needs to measure its own effectiveness, but in the past it has typically just measured its own efficiency,” adds Ellis.

Analytics can be used to assess everything from the effectiveness of performance management to determining which workforce groups are the biggest flight risks. But many an HRD is coming up against the barrier of confusing technical jargon.

So here we have demystified some of the buzzwords used in analytics and explained their relevance to HR.

Descriptive analytics

The use of historical data to describe things today. Many organisations already do this for things like absence rates, skills level, and pay and remuneration – even if they refer to data generated by HR systems simply as ‘reporting’. But HR needs to make sure this is done in a way people can understand.

“Most HR systems will have this capability but too many HR people just take a number out of the system. You need to break it down into many components and give it to people in a way they understand,” says Andy Charlwood, professor of human resource management at Leeds University Business School.

Oliver Shaw, CEO at Cascade HR, agrees: “HR should be making the information as instantly available as possible so you can react to exactly what’s happening at that moment.”

Predictive analytics

The use of statistical techniques to understand current or historical facts to then make future predictions. Some regular business flows can be very predictable, such as absence and the time it takes to hire people. But according to Jordan Katz, global head of employee experience transition programs at Qualtrics, HR is still in the dark over what this means: “Many HR practitioners mistake descriptive for predictive.”

So how can it be used? “You can make decisions ahead of time about the impact on the organisation. For example, if you have a predictive model the HR director can prevent the sales director from being understaffed,” explains Shaw. “HR directors often say they don’t have a strategic voice in the boardroom but if they have this ability it can give them one.”

Prescriptive analytics

Examining the outcomes of computerised modelling exercises for predictions using different variables – including data from outside the HR function.

A customer-facing organisation, for example, might look at how weather trends affect footfall and plan accordingly.

Katz says that it’s about using data to find the best course of action. “Lots of HR people are sold pseudo-AI products, which are really just a computer picking a static value from a list,” he says. “But you need AI to do what you can’t do as a human by gathering different elements of data and giving you a recommendation.”

Data lake

A vast collection of data that can be used to provide a consolidated insight. Data cleansing then takes place to help ensure this insight is accurate and up to date.

Such vast amounts of data can relate to many different branches of HR, so insight should be drawn accurately.

“You need a systematic approach for checking data to assess validity, accuracy, consistency and completeness,” says Edward Houghton, head of research and thought leadership at the CIPD.

For James Akers, director of product management at Thomsons Online Benefits, data cleansing is just one part of the picture to ensuring this accuracy. He says: “[It] can help but is reactive in nature, so it’s just as important to have a good governance process to ensure data is captured cleanly at the point of acquisition.”

Data scientist

The individual entrusted with data analytics roles. It’s a role that many believe HR should be investing in.

“Marketing is way ahead of HR when it comes to using data scientists but HR has been catching up. Data scientists are probably the most valuable resource to invest in,” says Houghton.

“The only way to find the right skills is to recruit those with data science backgrounds, train them in the parts of the HR profession that are important and relevant to them, and use them to train others within the company.”

This piece appeared in the March 2018 issue. Subscribe today to have all our latest articles delivered right to your desk

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