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“The story of why a company is performing as it is isn’t going to come from HR data. Period. Full stop.”
That’s an odd statement from someone who makes his living studying the impact of HR and human capital metrics, among other things. But data science can be a strange business—and Alec Levenson, senior research scientist at the University of Southern California’s Center for Effective Organizations, says one of the most important things HR professionals should understand about HR data is its limitations.
While scores of articles have been written about the potential for people analytics to transform human resources from an operational function into a strategic resource, so far HR’s fundamental hurdle has been its inability to quantify its ultimate impact.
A key to changing that, experts say, is to go beyond the HR numbers. To demonstrate how workforce measures can impact the bottom line, HR professionals must meld those metrics with business data.
“Metrics essentially give us a way of qualifying the health of our organization, and, to that end, HR metrics are now different,” says Ross Sparkman, head of strategic workforce planning for Facebook in Menlo Park, Calif. “They’re measuring how the HR function is doing as a whole and also how we’re leveraging the people in the organization to maximize the performance of the company.”
That’s the goal, but the reality often falls short. “Most people use data the way drunks use the lamppost: for support rather than for illumination,” says Alexis Fink, Intel’s Portland, Ore.-based general manager of talent intelligence and analytics. HR, she believes, should focus less on responding to decisions that have been made and more on training executives “to come to you further upstream, to influence the decision.” For that to happen, HR needs to have a better grasp of how metrics and analytics work.
Start with these nine steps.
First, you have to recognize the difference between metrics and analytics. “HR metrics are operational measures, addressing how efficient, effective and impactful an organization’s HR practices are,” Fink explains. “Talent analytics, on the other hand, focus on decision points, guiding investment decisions” that impact the workforce and related matters.
Essentially, metrics use data to assess things like efficiency and performance, while analytics harness those measures to help people understand or predict how changes will affect an outcome. For example, analytics that combine information on employee performance levels and retention data may show you that, once workers reach a certain level of proficiency at their jobs, they’re prone to leave. That, in turn, can help you look for ways to address whatever dynamic is nudging those employees toward the door. You may find that your competitors offer better compensation or have better career advancement programs and opportunities.
Put another way, metrics tell you what is going on, while talent analytics get at what to do about it, driven by both good data and good science. “Metrics are about getting the numbers right, and analytics are about finding answers in the data,” Fink says.
“Ultimately, metrics define what you’re shooting for. They define your objective,” says Michael Housman, workforce scientist in residence at people analytics firm hiQ Labs in San Francisco. “They shouldn’t be a moving target. If you want people to stay in their job, for example, you look at things like turnover and attrition. The goal is to know what you’re trying to improve.”
Though metrics can be used to monitor performance, most data scientists emphasize their use in gathering the intelligence needed to resolve an underlying issue or create a new strategy.
“Take turnover. High is bad, low is good. But what story is it telling?” asks Jennifer Currence, SHRM-SCP, president of OnCore Management Solutions, a human resource strategy consulting company in the Tampa Bay, Fla., area. “Why is it high or low? Is it recruiting? Demographics? Who’s retiring? Is it high in just one department? Why? Is there not enough training there? If not, who’s the manager for training in that area? The initial metric gives you a start to digging down deep.”
The point is to “get past the ‘what’ and fully understand the ‘why,’ ” says Cecile Alper-Leroux, vice president of human capital management innovation for Ultimate Software in Weston, Fla. “The benefit of using metrics is that the decisions are better-informed and backed by facts—rather than hunches—and thus make key people decisions far more ‘sellable’ to the business.” That gets to the heart of the next point.
It has been said, and said repeatedly, that human resources struggles for relevance because it doesn’t speak the language of business: numbers. “HR has to be able to show how its dollars impact ROI [return on investment]. It has to stop acting as a cost center,” Sparkman says. “You can’t act like your costs just happen to have a good impact.”
Instead, focus on building the business case for what the department is doing. As an example, Fink talks about digging into employee departures. After calculating the exit rate, look into what might be driving workers to leave your organization. You might find that certain qualities in a manager discourage some types of employees while other characteristics deepen those same workers’ commitment to the company. Delving further may show that matching managerial styles to specific workers’ personality types will reduce turnover dramatically. From there, you can forecast how much money the organization will save in hiring and training costs.
‘Ultimately, metrics define what you’re shooting for. They define your objective.’—Michael Housman, hiQ Labs
“Nothing matters unless you can prove it,” says Greta Roberts, CEO of Talent Analytics Corp. in Cambridge, Mass., emphatically. “HR makes mistakes by focusing on a metric and not tying it into the business. For instance, you can’t assume higher engagement leads to less turnover. You have to demonstrate it.”
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“You can’t look at metrics in isolation. They have to be looked at holistically,” according to Rishi Agarwal, San Jose, Calif.-based partner and people analytics leader for PricewaterhouseCoopers. That’s why most data scientists believe HR measurements by themselves rarely provide much value to the organization as a whole.
“Really, cost-to-hire should be cost-to-good-hire,” says Ben Yurchak, president of KnowClick, an analytics company in Bryn Mawr, Pa. “One’s an operational metric, and the other’s a quality metric.”
A caveat: Though quality measures might get you closer to tracking real business goals, compiling them may be easier said than done. “To determine the cost of a quality hire, you need time and information from other departments,” Yurchak notes. “Cross-functional measurement is harder to do, but it connects the dots.”
Here again, the process of connecting the dots should be done to solve a business problem that has already been identified.
For example, if executives find that your company’s work contains too many errors, the solution may lie in studying turnover and performance data, Roberts says. “You’re not using data to figure out the issue,” she notes. “You’re figuring out the issue and then knowing what metrics to examine to find the answer.” Is revenue down because turnover is exceptionally high and thus impacting production? Are performance and productivity down because workers have too much to do in too little time?
Facebook closely monitors its number of daily active users. That information hints at whether the company is hiring the kinds of engineers who generate a positive user experience. “Whatever drives the business has to drive HR numbers,” Sparkman says. “HR should be waiting for the top-down strategy to be put in place, then strategize about how HR can help the company deliver.”
“You have to parse the business challenge to figure out where the workforce element comes in,” says Andrew Courtois, a strategic workforce planner with a California-based global technology company he asked not to be identified. “If you can do that, you can make a better case to management. Show how improving the workforce element improves what management wants to improve.” Remember, he adds, “senior leaders understand things from the cost and finance perspective. HR has to think much like the CFO thinks about ROI.”
Another trick to using metrics successfully is to be curious. “We have to always ask why, why, why?” Sparkman says. When you get into that habit, you start to ask better questions and learn to challenge the answers.
If your organization’s executives decide to hire 10 people in Zimbabwe, for instance, inquire about the business reasons driving the decision and whether they’ve considered all of the skills the new hires will need to succeed in such a remote location.
By doing that, “you’re helping the managers understand how far-reaching their decisions are,” Sparkman says. “Why this location? Why these skills? Why this time frame? It helps you get more clarity.”
While you’re doing this, be sure to consider the context. As you approach each challenge, Agarwal says, use a “metrics map” as your guide:
As you work through the process, it’s important to have a clear understanding of individual roles, adds Chris Gagnon, senior solutions partner at global management consultant McKinsey & Co. in the New York City area. “Analytics can identify traits of success to identify who’ll succeed, especially for pivotal jobs that drive a lot of value,” he notes.
It’s vital to collaborate with people in other departments to get the information you need to develop meaningful measures. Given that many organizations regard the human resource department as more operational than strategic, that means being proactive. “HR’s going to have to go and get the numbers,” Fink says.
Incorporating HR data into business strategy requires something of a cultural shift. “You’re changing the paradigm,” Sparkman explains. “This is a commitment, and it’s really important that HR both knows and works with others to understand this. There’s going to be a lot of education involved.”
Exactly how the department gets its message out depends on the size of the employer. “At smaller companies, you just start doing it,” says Cezary Kuziemski, head of HR for insurance underwriter Hamilton USA in Princeton, N.J. “Go to Finance and say, ‘Here’s what I can do for you,’ then do it. You have to speak the other’s language. You have to be humble and admit what you don’t know and ask questions. You have to come to the other function with more than thoughts, but something concrete.”
At bigger organizations, try running a pilot program with a function whose manager has an interest in data, Kuziemski suggests. The effort could be about improving sales performance or reducing turnover in IT. What’s important is that you create a case study that proves you can solve an issue in a measurable way.
Also, be ready to take advantage of opportunities as they arise. “People come to HR and ask for reports,” Fink says. “Use your answer as a Trojan horse to demonstrate the amount of information you can get from analytics. By studying the data, you’ll be able to answer questions that the business didn’t know how to ask.” For example, you might use metrics to better identify ready-to-hire candidates who can decrease cycle times to fill jobs and thus save money.
HR professionals often argue that much of their work is intangible and hard to measure, but there’s always a way to quantify people metrics. It just requires time, experimentation and setting your own benchmarks if published ones aren’t available. For example, you might train people in new procedures and compare the outcome with the results of previously established practices, Courtois suggests.
One thing is certain: Developing base lines can require a significant investment of time and effort. As an example, Sparkman talks about time-to-hire—how many days, weeks or months it takes to acquire top talent in a particular area. “With these numbers in place, trends can be established,” he says. “Has the time to hire increased or decreased over time? Does it vary by month, quarter or season? How do university graduation dates affect the number?”
‘[I]t makes sense to go out and solicit feedback on how the business leaders perceive HR’s performance on the metric in question.’—Ross Sparkman, Facebook
Once you’ve identified such trends, “it makes sense to go out and solicit feedback on how the business’s leaders perceive HR’s performance on the metric in question,” Sparkman says. “So HR leaders could ask their clients if their top talent was being sourced fast enough to meet the demands of the business.” That information will help to establish if the base line needs to be improved and, if so, by how much.
And you may have an average cost-per-hire metric for all positions, but the real meat lies in how the number varies by role. “A clerk’s different from an engineer,” notes Andrew Mariotti, a senior researcher at the Society for Human Resource Management in Alexandria, Va. “You have to look at all of the expenses involved—like ads and travel—and look at each position to figure out how large is the candidate pool and the competition.”
Of course, as you examine all this data, you’ll need to decide what trade-offs to make in developing an optimal solution for your company. “Let’s say your organization enjoys a high profit per employee (PPE), but turnover is higher than industry average and long hours and lack of work/life balance are cited as primary reasons for employee dissatisfaction,” Alper-Leroux says. “If you decide to focus on improving the employee experience by adopting more-flexible policies and discouraging overtime, it’s possible your PPE will drop. The impact of this must be measured against the average cost per hire and your own corporate culture and values.”
“You can measure. You just have to figure out how to do it,” Courtois says.
Admittedly, that’s easier in some cases than others. For example, linking the right type of call center employee directly to customer satisfaction is pretty straightforward. “But how do you measure lawyers, scientists and the like?” Housman asks. “It really depends on what you’re trying to do as an organization.”
Finally, always bear in mind this basic point: The intelligence you glean from analytics will only be as good as the data included in your metrics.
In his book Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness (Berrett-Koehler Publishers, 2015), Levenson notes, “More often than not, balanced scorecards use the ‘best available’ metrics—that is, the least worst of what is sitting around.”
Metrics’ value “is about actively choosing things you track because you want to manage your business better,” he says.
To help identify what those things should be, find data that meets the “CARE” criteria created by Levenson; include only information that is:
Consistent—The data underlying the metric must be measured steadily over time.
Accurate—Information should be precise, with few to no errors in recording it.
Reliable—Your metrics must be a dependable proxy of what you’re ultimately trying to assess.
Efficient—The cost of collecting the data must be minimal.
“One thing people don’t realize is it’s all a system,” Fink observes. “Data has to be of high quality. We’ve invested in making sure it’s good, quick and accessible. Otherwise, it’s like building a seaside mansion without a foundation.”
Mark Feffer is a freelance business writer based in Philadelphia.
Illustration by James Fryer for HR Magazine.
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