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HR Can’t Ignore Big Data

Information remains an organization’s most valuable and underutilized asset. Top performers are investing in analytics to elevate recruitment and performance.

June 28, 2013
Related Topics: Data Collection and Analysis, Assessment and Measurement
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Everyone talks about big data. People talk about being overwhelmed by information, about capturing it, storing it, struggling with it and doing something useful with it.

Talent managers can’t avoid the topic. The digital universe will double every two years, reaching 40,000 exabytes by 2020, according to the December report “The Digital Universe in 2020” from research firm IDC. To put that kind of volume into context, one exabyte is big enough to contain 50,000 years’ worth of DVD-quality video.

In the same report, authors John Gantz and David Reinsel indicate that a “generous estimate” would be that half of 1 percent of these digital assets are actually analyzed. “Herein is the promise of ‘big data’ technology — the extraction of value from the large untapped pools of data in the digital universe,” the authors wrote.

Talent leaders can either survive or thrive within the vortex of big data, and IDC projections demonstrate all of this is going to get bigger. Consider the implications for recruiting.

“This is a new frontier,” said Chris Collins, director for the Center for Advanced Human Resource Studies (CAHRS) and associate professor of human resource studies at Cornell. “It’s so easy to submit an application if there’s a job posting. So where there would be hundreds of candidates in the past, some of these companies now get thousands, especially with the job market being what it has been in recent years.”

Online applications and the resulting follow-through create vast additional knowledge streams that can drive optimal hiring practices. Overall, however, that’s not happening. In “State of HR Analytics: Facts and Findings From CAHRS Topical Working Groups,” a Cornell University report published in spring 2011, Fortune 500 firms working with Cornell’s CAHRS indicated they were using HR data for basic reporting, usually deploying dashboard display technologies or scorecards.

Two-thirds of them said they had senior leadership support for HR analytics projects. However, only 27 percent felt they had a strong team of analytical talent to execute such initiatives. Thirteen percent said they had the necessary technologies or systems to facilitate HR analytics. And one-third agreed with the statement, “Front-line HR generalists understand the value of HR analytics.”

Some organizations have come up with complex algorithms to conduct searches and basic metrics gathering. But those at the top of the analytics curve are using search results to compile pools of candidates who have not necessarily been interviewed, but convey positive, in-demand skill sets. HR managers also can create similar pools based on registration form content gathered from company conferences and contact interactions established there.

“These are the candidates you do not want to go away,” Collins said. “You should launch real conversations with them even if it isn’t for a specific opening, to let them know you have interest in them. These companies will also use analytics-based prompts to remind them to stay in touch on a routine basis. In other words, you can’t depend entirely on auto-responses. You need to make a personalized connection at certain points.”

The Benefits of HR Analytics
“Human Capital Analytics,” a March report from advisory firm CEB, stated high-performing HR teams will improve hiring quality and engagement by 19 percent when they deploy talent analysis tools.

On the other hand, without sustained structural improvements in the talent acquisition process, average first-year turnover among U.S. organizations could reach 29 percent this year, according to PwC Saratoga, a resource for human capital metrics.

“By tracking competencies associated with new recruits — as well as their early performance and length of service — HR teams can build ideal hiring profiles, predict hiring quality and then actively recruit for long-term, high-value talent,” said Sayed Sadjady, a principal in PwC’s people and change practice. “Valuable, historic data already exists on HR information systems. But most organizations fail to use this data to address challenges in recruiting practices.”

It is no longer sufficient for HR leaders to evaluate their programs on a defined set of process-based or transactional metrics. Sadjady said they also should consider key measures of business impact. Using critical business questions as a foundation, high-performing organizations mine analytical data for strategic value — whether resolving issues in sourcing or logjams created during key decision-making stages within the hiring process.

Organizations aren’t doing this type of analytics because they can’t get the data. They have it — it’s piling up every day within network file storage systems. The challenge is deploying proven, metrics-based IT tools to capture, view and make sense of it all.

“The irony is that, when we buy a piece of equipment, we’ll push to measure how quickly it’s brought up to speed from a production-impact standpoint,” said Phil Brandt, CEO and president of the AAIM Employers’ Group, a recruiting strategy and support consultancy. “So why don’t we do the same with our human assets? Why don’t we measure how long it’s taking for employees to get to 50 percent of required productivity, then 80 percent and finally 100 percent?”

He said part of the reason is because some groups of employees — such as call center workers and sales teams — are easier to quantify than others. For instance, it’s tough to apply metrics to relationships-dependent vocations, such as nursing. The nurse who completes more bed checks per hour may also have a rotten bedside disposition.

Top organizations are starting to do this despite the challenges, but there are several potential roadblocks. Some are culture-based, while others stem from a lack of tech capabilities. For instance, many global organizations depend on dozens of disparate, silo-based HR recruitment and retention systems spread throughout office locations and divisions, none of which can sync data to produce targeted enterprise-wide analytics.

In that case, talent leaders aren’t dealing with big data at all, said Jeff Neal, senior vice president for organizational research, learning and performance at consultancy ICF International; it’s a bunch of different small data repositories with limited use.

Even companies that gather all the metrics in a unified, enterprise-wide manner are daunted by the amount of data available, and many don’t know what to do with it, said Neal, former chief human capital officer for HR information technology performance at the U.S. Department of Defense and Department of Homeland Security.

“They’ll collect it all, but they often don’t know what to do next, how to conduct analytics to help them launch meaningful, action steps based upon what it will reveal.”

Make a Little Effort
Ultimately, what HR and business managers don’t know about metrics can hurt them. Useful data can demonstrate commonly held internal assumptions that are wrong. Many organizations invest large sums of time and money trying to resolve human capital issues for which they don’t understand the root cause, Neal said.

For example, they may see an employee survey show alarmingly low engagement levels, “but if they don’t know which subset of employees is bringing down the score — their years of service, overall experience, company standing, compensation, performance evaluations, etc. — then they’re prescribing solutions when they do not understand the problem.”

That can result in mistargeting of limited resources and little improvement.
A proactive approach to analytics can improve a metric the vast majority of HR departments are accountable for: time to hire. In general, organizations that are best in class at using metrics perform 25 percent better than average ones on time to hire, according to Staffing.org, a consulting firm for corporate recruiters.

To continue improving the time-to-hire metric, managers must pursue an in-depth, full-view perspective of the entire talent-recruitment process to understand where bottlenecks exist. For example, how long are candidates held up during the application process, and what is the percentage of potential good hires who drop out during this stage because an avoidable bottleneck triggered a delay, and were subsequently hired elsewhere?

The same techniques can be applied throughout all recruitment cycles, from the preliminary phone screen stage, the interviewing process to the official job offer and acceptance. Then, the analysis can go deeper to provide perspective on retention effectiveness during the critical first and second years of employment and beyond.

Then there are cultural factors. When a higher degree of analytics is readily available, accountability will follow, triggering internal pushback. HR people realize that senior leadership will associate measurement with accountability, said Linda Brenner, founder and managing director at Designs on Talent, an Atlanta-based talent consulting firm.

“But we shouldn’t let that stop us from pursuing these numbers. They’re a part of doing business. Besides, there are also good consequences which can come from these numbers. You can demonstrate how effective your department is in these areas, and then reap the rewards.”

To take full advantage of metrics and analytics, HR must transition from a department mindset to a total organization commitment, ICF International’s Neal said.

“We’re always focusing on time to hire because that’s where HR has been held accountable,” he said. “But the quality of candidate is so much more important. You can hire a bad one quickly. That employee will cost a bundle in terms of bad productivity, impact on other employees’ performance, overall morale and then replacement costs. It’s better to have a vacancy than rush to bring on a bad hire.”

Instead, HR and business managers have to work together on data initiatives to rapidly bring their organizations the most likely sources of promising talent.

“The data will tell you the truth, and debunk false assumptions,” Neal said. “You may have managers who firmly believe HR has to spend more time recruiting at a certain university. But, with sharper metrics and better assessment tools, those HR professionals will be able to demonstrate they’ve had hires who perform at a higher standard — and stay longer — who came from other schools.”

He said too many HR departments are stuck on time to hire when talent leaders can apply the same statistical thinking to categories such as former places of employment, experience level and certifications earned, and then drill down to find factors that will make a difference.

Mike Giuffrida is co-founder and CEO of NGA.NET. He can be reached at editor@talentmgt.com.

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