The challenge for those interested in HR metrics to is not to rush headlong into "mining" the available data mountain to see what it reveals, but to think carefully about what matters first.
uman resources analytics is a very hot topic among serious practitioners these days. With the widespread adoption of more powerful business software systems and the efficient aggregation of human resources transactions, many HR leaders find that for the first time in their careers they are practically swimming in data.
The challenge to utilize more "data-driven decision-making" has led many companies to begin to build HR analytics expertise. Some have chosen to train or hire quantitative experts to sit in a dedicated analytics center at their headquarters, while others are gearing up to embed analytic skills more broadly in their generalist HR populations. While I generally view the adoption of new skills and behaviors in the HR function as positive, in this case I am also cautious.
I am reminded daily of the simple admonitions of my good friend, business consultant Steven Kerr, that "not everything that matters can be counted, and not everything that can be counted matters." (OK, so Steve paraphrased the original quote by Albert Einstein, but I like Kerr's better.)
I recently facilitated a working group session in London on the topic of HR analytics for the Cornell Center for Advanced Human Resources Studies. Representatives from 10 mostly large companies shared their experiences with analytics so far. While there were varying levels of investment and several different service delivery models, everybody was doing something. But when we asked who really thinks they've got this right, no hands went up in the room. After a period of some reflection, a representative from a well-known major global resource extraction company said he would be comfortable with the title "practiced amateur."
The challenge for those who have started and those who are about to is not to rush headlong into "mining" the available data mountain to see what it reveals, but to think carefully about what matters first. And when I say what matters, I don't mean within the HR function. Too often the first temptation is to look in isolation at traditional HR metrics (turnover, time-to-hire, cost-of-hire, etc.). Here the additional precision that a larger data set offers may raise confidence (and costs), but it is likely to do little to change business outcomes. Utilizing a large data set within HR to improve business outcomes brings us back to the fundamental question that brought me back to academia from the real world: How exactly is HR activity related to business outcomes?
Turnover is a particularly common "usual suspect." If the turnover rate of exempt, salaried employees is 5 percent per year, then 3 percent per year must be a good target, right? Here I'm reminded of a funny moment in a class taught by professor Kerr in my early days at General Electric Co., long before the ascension of HR Analytics. Kerr said, "I know how to get your turnover to zero, and it's easier than you may think." We (an audience of "High Potential" leaders) pondered his challenge. Finally, after we struggled with how this goal might be accomplished, Kerr said, "Hire people that nobody else wants." Light bulbs went on all over the room. Metrics, without an understanding of the context in which they appear, can be meaningless. More worrisome (and the subject of a famous 1995 article by Kerr), the "unintended consequences" of focusing in isolation on a metric can be absurd given the overall goals of the organization. But how often have we seen teams and departments contort themselves to make a number, even when it had little or no impact on a paying customer? You get what's measured, so be very, very careful.
So what really matters? What human capital inputs have potentially significant impacts on business outputs? While asking the question that way can lead to an interesting discussion, it may not lead you to an answer. The better question to start with is, "What business issues or problems have human capital implications, and how might we make better decisions regarding those problems or issues in the future"?
By starting at the more strategic level we can assure ourselves of tight linkage with real business needs. By first asking the question, "Can data help?" we can avoid the lost productivity of mining a pile of (probably imperfect) data in search of the answer to a question that does not really "matter." If we find that the answer to a really important business question does turn on HR data, we have a compelling story to justify collecting and collating that data in ways that facilitate decisions, even if the needed data is not readily available. Our key business partners will become the strongest advocates for building the skills and the systems required to collect and transform data into real information.
The emphasis, therefore, is not on the answers, but on the questions. We need to learn to ask better questions about the linkage between business outcomes and HR activity. I wonder how many are starting with the data, not with a business problem? Asking the right questions requires a deep understanding of the business' strategic intent, and the connections from business strategy to HR strategy and finally to HR activities. To be efficient and effective, HR analytics activity needs to be well-guided from the top of HR.
Here's one example. A great question in the C-suite is, "What is limiting our ability to grow?" Growth can sometimes be limited by the amount of available leadership within a company. Since growth is a generally desirable business outcome, gaining a deeper understanding of what creates good leaders within a particular company can be very valuable information. But in many companies, succession planning is still a fundamentally qualitative process. It amounts to "names on a page," a best guess quite often of who might have what it takes to succeed one or two levels up in the organization.
Some companies have gone further. Using data from a variety of internal systems, they are studying the profiles of currently successful leaders to tease out additional (and possibly counterintuitive) conclusions about "how they are made". In one large technology company, a form of network analysis showed that earlier in their careers many of the most successful current leaders shared a similar role or experience, shared a particular manager or supervisor, or both. The company was then in a position to identify and study these "critical experiences" or "key talent developers" (some of whom labored in obscurity deep in the organization), and more deliberately match early-career high-potential employees with jobs and supervisors who seem to matter much later in a career.
The initial analysis was labor intensive and clumsy. While information about roles and supervisors is generally captured, it is sometimes not archived in ways that preserve the raw data needed for more sophisticated analytic techniques. Having proven the value of more discreet role and supervisor data over the course of a career, nobody objected to the programming costs associated with capturing that data for later (and better) analysis. If the availability of great leaders is restricting growth, what business leader wouldn't make an investment in tools to help make better leadership development decisions?
So before you go too far down the HR analytics path, make sure that you have a very good handle on what really matters to your business. Funny, but that's probably good advice no matter what the subject. It's particularly important with respect to investments in HR analytics.
Thanks to John Haggerty / WorkForce / Crain Communications Inc.
http://www.workforce.com/section/software-technology/feature/be-careful-ask-or-measure!/index_printer.html
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