HR's Crystal Ball

Data analytics allows HR to expand its scope from collecting static data to making predictive and strategic analysis.

Wednesday, June 12, 2013
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In some ways, information in HR data systems is like yesterday's newspaper. Interesting? Yes. Helpful? Sure. But an effective tool for planning? Questionable.

Fortunately, there may now be a way HR executives can take a peek at tomorrow's news. This is partly because of the great job HR has done in collecting historic data. But it also involves combining that with information from other departments, such as finance, and adding the special sauce of statistical analysis.

Recently, a few pioneering companies have begun employing HR-data analytics to integrate information from disparate systems in order to create predictive models that can be used by line managers and HR executives to make more informed and strategic decisions.

One company that has a relatively advanced HR-data-analytics project is Hartford-based health insurance company Aetna. HR professionals and others at the company can now use data analytics to make evidence-based predictions about workforce issues that they could only guess at in the past. For example, using demographic, survey and compensation data, Aetna's HR can determine things such as how many people are close to retirement and might likely accept specific retention packages.

"Getting these [retention] offers right is very important. If we offer too little, no one will accept. If it's too high, we can break the program," says Craig Hurty, Aetna's vice president of HR Shared Services Analytics and Reporting. Without an analytics system, Aetna would have a hard time honing the retention package so sharply.

Hurty says data-analytics-based HR reporting can sometimes yield surprising results. For example, in comparing performance data of hires who graduated from top-tier schools with those of people who graduated from less-prestigious institutions, the company found very little difference. "That [information] may allow us to concentrate a bit more on educational institutions that are less competitive," Hurty says. The result might be reduced cost-per-hire and even higher retention rates.

As with virtually all data-analytics projects, Aetna -- for privacy reasons -- analyzes anonymous data. The goal is to view trends, not make predictions about individual employees.

To feed the analytic engine, Aetna's HR and financial data, both housed in a single data warehouse but not always able to communicate with one another, are being configured so they can be tightly integrated and accessed from a single application. "This is a broad partnership -- not just an HR project -- involving finance, software engineers, statisticians and other areas of expertise," Hurty says.

While the project is too new to engender many success metrics, the company is enjoying better retention numbers. Before the project began, turnover was in the teens; now, it is solidly in single digits.

Christopher J. Collins, associate professor of HR studies at Cornell University in Ithaca, N.Y., says data analytics can provide HR with clear, intuitive tools that display a view of the company that, in the past, could only be understood by people trained in statistical analysis. He says initial data-analytics projects at most companies seek to apply the process to three primary areas.

The first is workforce planning: getting a sense of what types of people and skills the business will need -- not just currently but in one, two or even more years in the future. This is based on revenue trends, business strategy, country-specific economic forecasts and other factors.

Second, companies want to measure the level of engagement and make predictions about the effect of various engagement and retention programs -- for example, financial incentives, training, family leave and work-from-home privileges. "Measuring engagement is very important, because increasing it can be the most cost-effective and efficient means of improving retention and productivity," Collins says.

And third, companies employ data analytics to identify the factors that make great leaders. Training, recruitment and compensation all play a role in leadership development. Using analytics, companies can fine-tune their efforts, sometimes curbing costs without decreasing effectiveness. For example, is a three week off-campus training program really more effective than a two-hour-a-week program in a conference room or a distance-learning experience? The answer to such questions can save the company from overspending on programs with questionable benefits.

Despite the benefits of data analytics, Collins -- also the director of the Center for Advanced Human Resource Studies, which recently released a report, State of HR Analytics, showing "centralized data" as being a "critical enabler" in making good use of HR analytics by many of the 30 large employers studied -- is quick to point out that, so far, most projects are in the nascent or planning stages. According to the study of 30 large companies, "centralized data" was cited by many as a "critical enabler" in making good use of HR analytics.

"Very few companies have full-blown, mature data-analytics projects yet," he says. So companies that opt to at least study the potential value and feasibility of implementing data analytics now may eventually enjoy a competitive advantage.

Not Just for Tech Firms

Many of the companies that are further along in data-analytic projects, such as Aetna, are those with workforces possessing robust in-house statistical or scientific skills. However, less technologically oriented companies are also entering the fray, although sometimes starting with a tentative toe in the water.

One such organization is Darden Restaurants, based in Orlando, Fla. Beth McCarty, vice president of business excellence at the company, says Darden's goal is to move past static reporting of the current situation and toward a better understanding of why things are as they are.

"We didn't want to continue to simply measure performance and see how that compared with previous years. Now we want to know specifically what drives performance and what we need to do in order to improve it," she says.

The company's first project, overseen by Shira Spector, the company's director of workforce-reporting analytics, a new position at Darden, is to create a graphical representation of retention and performance correlated with onboarding experience. "We want HR managers and executives to understand how orientation and training programs will affect turnover," Spector says. The company plans to add more analytics soon, including measurements related to compensation, incentives and demographics.

Virtually all of the data Darden needs to power the analytics system are already available in the company's robust data warehouse. But some of it needed a bit of cleaning up before it could be used in the analytical project. For example, while the databases indicated whether employee termination was voluntary or involuntary, in the past Darden managers weren't always diligent about characterizing the separation accurately. So, before using that data, HR and line managers had to manually go into employees' files to ensure that the termination characterizations in the database matched those in the files.

While retention is important, companies also use data analytics to help plan future workforce requirements. For example, Eaton Corp., a power-management company headquartered in Dublin, Ireland, is developing a number of HR analytic projects, including one that uses historical financial data to forecast the number of leaders the company will need in the next 12 months based on revenue trends. "This is really actionable information since we translate it back into our leadership programs," says Shelley McGrail, director of organizational development. For the first time, HR will have a scientific basis to help in developing training programs, determining the number of interns it will need in the pipeline and adjusting headcount in various areas of the company.

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Eaton has a dedicated team tasked with developing the technologies that will allow disparate databases to share data, a first step in developing the predictive models. The team is also designing the system so it's usable by business leaders who don't have a background in statistics. "This has to be easy to use or it will not work," McGrail says. The project team has been working with managers and executives to help create intuitive systems -- including dashboards. "The system does require a little training. But a manager should be able to open this application and very quickly find and understand what the data mean," McGrail says.

Moun Peterson, director of workforce analytics and research at Marina Del Ray, Calif.-based Human Capital Management Institute, agrees that easy-to-understand statistics, and especially dashboards, are keys to successful data-analytics projects. "There are a lot of data that go in to those dashboards, but the end user sees only one small set of numbers on which to base decisions," she says.

For example, says Peterson, "a quality of hire index," a single number or dashboard symbol, would be comprised of the following underlying data.

* 25 percent of the index is based on a 90-day new-hire turnover rate,

* 20 percent is based on the percentage of the job requirement met by the applicant,

* 25 percent comes from the new-hire-satisfaction survey,

* 20 percent is based on the new-hire-performance rate and

* 10 percent is based on the number of qualified people who applied for the job.

Besides creating an easy-to-understand interface, another factor in gaining fast acceptance is focusing initial analytics projects on the company's most pressing needs. Success there might result in faster return-on-investment and encourage buy-in. For example, at Columbus, Ohio-based Battelle Memorial Institute -- an applied science and technology development company that handles projects for the Department of Defense, the Department of Energy and other federal agencies relying heavily on science and technology -- fostering talent is the most important HR goal.

"I would say that most HR organizations are focused on efficiency. But our primary concern is collaboration," says Mark J. Sullivan, Battelle's vice president of talent management. Sullivan says the wide variety of disciplines and demographics at his company creates a challenge when it comes to communications. "Chemists have to be able to communicate with engineers, and 20-something recent Ph.D.s have to work together with octogenarians," he says.

Accordingly, with the help of Chicago-based Knowledge Advisors, Battelle is developing a data-analytics program that incorporates virtually all of the company's initial data-analytic information into a dashboard, measuring such variables as satisfaction, skills gained and job improvement -- all factors in collaboration. The next step is to build in the ability to predict how different types of training will affect collaboration.

Part of the reason companies are moving toward HR-based data analytics, says Peterson, is their recognition that the workforce is the most important resource the company has.

"For years, companies have been spouting about the importance of their employees. Now, with data-analytics projects," she says, "they're putting their money where their mouth is." The end result, she believes, will be a more robust and strategic HR.


See also:

A Three-Tiered Process

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