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More HR leaders shift focus to developing staff who can interpret big data

One of the biggest challenges human resource executives face is increasing the analytics capabilities of their staff. With the abundance of workforce-related information made available through big data, HR leaders have a prime opportunity to use that data in ways that help business leaders make more informed, cost-effective talent decisions.

But without HR staff who can extract and correlate the right data sets from disparate systems and then translate that information into compelling stories for business units, big data does HR departments little good. According to a 2013 study by research and consulting firm Bersin by Deloitte, 86 percent of companies say they have no analytics capabilities in their HR function, compared to 81 percent who have analytics acumen in finance and 58 percent who have it in sales.

To Train or Not to Train

HR leaders looking to add more data science skills to their staff face a decision: Do they hire those capabilities from outside the human resources group or train existing staff in analytical skills? A 2013 study by the Institute for Corporate Productivity (i4cp) and the American Management Association (AMA) found that more than twice as many organizations choose to train existing staff versus hiring outside for analytics skills—47 percent to 17 percent, respectively. One reason HR leaders opt to “build” over “buy” analytics capabilities is the shortage of qualified data scientists in the human capital field.

“It’s important HR have enough staff with quantitative backgrounds who understand how to interpret data and ask the right questions about it,” said Gretchen Alarcon, vice president of human capital management product strategy at Oracle. “It’s an area where we still see some of our customers struggling.”

When asked what analytical skills are the most important today and will be three years from now, 92 percent of study respondents said critical thinking skills. And as the need for HR to move beyond preparing or reporting workforce data to interpreting it for others grows, so too does the need for storytelling skills in human resources. Today a much-needed competency is an ability to create actionable insights from data for those outside of HR, the i4cp/AMA study found.

Assessing In-House Analytics Skills

In the human resources group at ConAgra Foods, most of the analytics heavy lifting falls to two industrial/organizational psychologists with data science backgrounds, said Mark Berry, vice president of people insights at the Omaha, Neb.-based company. But given a growing need for broader analytics competency in the group, Berry has been evaluating who among the HR generalists has the acumen or desire to take on more analytics-related work.

“We’re trying to understand the degree to which nature versus nurture comes into play in developing data-based capabilities and the curiosity required to succeed with workforce analytics,” Berry said.

He’s divided his HR staff into three groups in terms of analytics aptitude:

The “Get It and Go” faction shows a natural curiosity about analytics, seeks to know the “why” behind data and takes on challenges, embraces tools and demonstrates capabilities in data-based decision-making.

A second group, dubbed “Tentative, But Has Potential,” embraces the value of analytics in making improved workforce decisions. “But [analytics] isn’t something that is part of their natural capabilities,” Berry said.

Berry spends much of his time trying to determine who in this category might be moved to the first group. “We’re not looking to create data science experts from our HR generalist community,” he said. “What we are looking for instead, particularly in regard to use of our self-service tools, are people with the desire to understand relationships between data sets and to ask the right questions. As a hypothetical example: ‘What might be happening that’s keeping us from retaining people in critical job roles?’ ‘What data might best tell that story?’ ”

Berry calls the third group “Don’t Get It and Don’t Want It.” This is the smallest group that tends to view use of analytics as a “mechanistic” approach to HR that devalues the “people component” of the field, he said. “They might have the capability for analytics, but they don’t see the importance of data-driven decision-making,” Berry said.

Hiring From Outside HR

Another school of thought: The best way to build analytics competency is to hire it from non-HR sources, recruiting those with finance, marketing or IT backgrounds who have a facility with numbers. The thinking is that it’s more effective to teach these workers HR-specific skills than to attempt to train HR generalists in statistical analysis.

Bersin by Deloitte’s study suggests there is value in adding such “outliers” to the HR staff—those with titles like business intelligent specialist, econometrician or demographer. They bring a different perspective to HR challenges along with numerical analysis skills.

In a 2014 Forbes magazine article, Bertrand Dussert, vice president of HCM Transformation with Oracle, recommended that HR add “scary” data people to their staffs. Known as “quants” or data scientists, these specialists don’t think in traditional HR terms, are masters of mathematical modeling and are trained to unearth insights from data. They are skilled at “answering the question behind the question behind the question,” Dussert wrote.

At Cameco, a Canadian uranium producer, much of the ability to interpret workforce data for those outside of HR rests with Sean Junor, manager of workforce planning and talent acquisition.  Junor comes from a research and policy planning background, and his current job is his first role in HR.

“There are people on my staff who know more about our HRIS [human resource information system] data than I do, so what I focus on is the story I want to tell the business units about things like turnover data or employee absenteeism,” Junor says. “I rely on my staff to ensure the data is accurate and has integrity, and I take more responsibility for interpreting that data in a way that’s meaningful and actionable for our non-HR leaders.”

For example, while high employee absenteeism has HR-related costs to organizations, such data often won’t get line managers’ full attention until it’s couched in terms of reduced productivity.

“That enables you to talk to your operations people eye to eye and say that this metric is killing us in term of leaving production on the table,” Junor said.

Dave Zielinski is a freelance business journalist in Minneapolis.

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