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What does it take to move from HR reporting to workforce analytics?

What skills are needed on a workforce analytics team? How much data cleanup is enough? Discover five steps to mature HR analytics initiatives.

HR analytics has certainly reached buzzword status. But does that mean it has achieved widespread adoption?

Not according to Bersin by Deloitte research. At the firm's 2014 Impact conference, held in Fort Lauderdale, Fla., Bersin Vice President of Benchmarking and Analytics Research Karen O'Leonard revealed that most organizations haven't progressed from data reporting to workforce analytics. On a four-step maturity model, 86% of companies surveyed fell on the bottom two rungs: operational and advanced reporting. Only 10% had reached advanced analytics, the third level, and a mere 4% had achieved the highest stage of maturity: predictive analytics.

But O'Leonard pointed out that the buzz can be beneficial to boosting these numbers.

"All the hype around big data and predictive analytics has generated a lot of enthusiasm and support for these types of initiatives," she said. "But on the downside, I think a lot of organizations don't know what it takes to get there." O'Leonard said most companies get stuck in the advanced reporting stage, where effort is high and apparent value is still relatively low.

To address the uncertainty, O'Leonard presented five core considerations that can help organizations move from reporting to workforce analytics: data quality, dashboarding capabilities, team capabilities, data-driven decision-making culture and IT support. Workforce analytics practitioners also shared their tips for success during the conference.

Concentrate data cleansing efforts on key metrics

Experts are quick to remind HR practitioners that analytics are only as good as the underlying data. With this in mind, cleaning and organizing data is at the top of many HR analytics leaders' to-do lists. O'Leonard recommended creating data standards, as well as a data dictionary, at the start of a workforce analytics project to ensure consistent measurement and interpretation.

However, perfect data quality is an unattainable goal.

"At some point, you need to ask yourself or your statistician [or] researcher, 'What's good enough data quality that we can still make good decisions on this data?' And it might vary from project to project," O'Leonard said. Instead of waiting for spotless data, O'Leonard recommended identifying which metrics are truly important for decision making, and then focusing cleanup efforts there.

But the work shouldn't be taken lightly, warned Kathleen Blake McCann, vice president of human resources and administration at Liberty Mutual.

"Be very careful not to ignore the work to set up the first couple of [maturity model] levels because that's where the [validation] and scrubbing takes place," McCann said. "The last thing you want is to get really good data scientists that spend 80% of their time cleaning data."

O'Leonard stressed that dashboards should be customized by audience and offered with self-service capabilities, so HR managers can reallocate time spent fulfilling data requests to more advanced analytics tasks. She also suggested HR managers take pains to highlight key takeaways to make data as easy to understand as possible.

How can HR managers find out if their reports are truly valuable? O'Leonard had a creative suggestion.

"Stop sending them and see if anyone notices," she said. If they don't, HR managers have the opportunity to touch base with stakeholders and find out what's important to them, and consequently adjust the reports.

Assemble a diverse workforce analytics team

While a workforce analytics team undoubtedly needs statistical knowledge, O'Leonard said other skills, such as consulting, HR, IT, data visualization and business familiarity, are also critical. With this in mind, she encouraged HR leaders to build multidisciplinary teams, as well as involve other functions within the organization, such as finance, operations and sales.

This support can be especially important for small analytics teams. For instance, a finance contact "can not just help you with getting access to financial systems and data, they can [also] be a great resource for talent," O'Leonard said. "If you don't have a big analytics team -- which a lot of companies don't -- you can leverage people from finance to help with some of your projects because a lot of [them] are data-savvy."

During a panel discussion on HR analytics teams, Brett Winchell, director of workforce measurement and technology at Allstate Insurance Company, offered his opinion on what skills are most important. Although industrial and organizational (I/O) psychology skills were most important to session attendees, as gauged through a poll, Winchell ranked them second. Instead, his top spot went to consulting experience, and statistical ability was third most important. Winchell's team also includes people with finance, engineering and IT backgrounds.

Fellow panelist Akil Walton, vice president of talent management and organizational effectiveness at manufacturer Aleris Corporation, also underscored the importance of consulting ability.

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"As you become more mature, you're going to need some talent that is presenting and publishing information in a way that makes sense for the multiple types of clients you have," Walton said. He added that these team members should work with stakeholders on an ongoing basis to make sure they understand the root causes of data trends.

As organizations progress from reporting to analytics, IT support increases in importance simply because data management issues also become more complicated, O'Leonard said. And while O'Leonard gave a nod to the fact that HR hasn't historically been a data-driven function, she said it's not an impossible goal. But the discomfort with this new way of thinking is real, according to McCann.

"For so long the area of human capital didn't have data analytics around it, [so] it's a little scary to people," she said.

Aleris created a program to help HR employees bulk up their data skills. Walton explained that the two-and-a-half day seminar introduces basic statistical concepts, and covers how to perform analysis in Microsoft Excel and create appropriate graphs for different types of analytics. At the end of the program, HR leaders use their new skills to solve a fictional business problem that is based on real Aleris issues; they then present their projects in front of company leaders.

Organically grow workforce analytics functions with early wins

Besides the five steps, O'Leonard also offered a few tips for organizations just starting out with workforce analytics initiatives.

"Start with a few small wins to build your credibility," she said. "A lot of analytics leaders tell me, 'We didn't even ask for any resources; we put together a skunkworks team [and] focused on a business problem, added some insights based on data, helped the business leaders solve those problems and then [they] started asking us for more and more." She also recommended creating a one- to three-year plan illustrating the trajectory of the workforce analytics initiative.

Winchell offered similar advice.

"Start with naturally occurring experiments," he said. "Find a leader that [has] got a really good business problem, [recognize] there is [an] opportunity to help them make better decisions about this business objective by applying evidence-based HR practices and analytics, and then sell them on that vision."

And as the value of HR analytics starts to become apparent, it will be easier to grow the team. "It's difficult to say, 'I need 10 people to do this fancy thing that no one [has] seen the value of yet,'" Winchell said.

Winchell also echoed O'Leonard's tip on defining strategy early on.

"Begin with a vision and an aspiration because the deeper you get into this particular rabbit hole, the more you find out you should be doing," he said.

Emma Snider is the associate editor for SearchFinancialApplications. Follow her on Twitter @emmajs24 and the site @SearchFinApps.

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Excellent article. But too much data is also bad. Data should be precise and purposive, objective driven.