people analytics

By Jack Guarneri

Human Resource (HR) Analytics in the context of standard Business Intelligence typically involves worker measurements that directly impact the bottom line: head count; salary/benefits expenses; billable hours, and the like.  The data for these measurements is usually available from a firm’s underlying HRIS (HR Information System), ERP, and time-tracking system (though the latter is sometimes done in kludgy Excel spreadsheets—but that is another story).

Recently, a new BI term has been linked to Human Resources: People Analytics, which is concerned with things like attracting the right employees, retaining them, and helping all staff to feel more satisfied with their jobs. It may seem obvious, but it bears stating: happier staffers will be more productive—which also surely impacts a company’s bottom line.

A key challenge in the pursuit of People Analytics is obtaining measurements for creating successful recruitment and retention strategies, along with understanding what makes current workforce enjoy clocking in each day. Firms will need to collect data not normally considered critical for other analyses; for example, from sources that record demographics, personality types, work history, self-evaluations, and satisfaction with job particulars like work-life balance.

Larger firms will certainly have an advantage in obtaining these measurements, given their resources and larger workforces, not to mention the fact that they are more practiced in conducting internal surveys. An interesting case in point was the subject of a New York Times article about Microsoft, the subtitle for which sums it up nicely: The business unit was doing well, but the employees were sad. Could data offer a clue? In fact, the part about “the business unit was doing well” is arguable, given that the business unit leader detected “a problem on the horizon...[which] came in the form of extensive surveys used to monitor employees’ attitudes. The business unit scored average or above average on most measures — except one. Employees reported being much less satisfied with their work-life balance than their counterparts elsewhere at the company.” The unit leader anticipated a potential staff retention issue, which would weaken the company’s prospects in this key business area (not to mention reflect badly on his leadership!).

The results of Microsoft’s intensive effort were not surprising (which inspired a lot of snarky remarks in the article’s Comment section): lack of opportunity to make internal transfers; the scarcity of time to focus deeply on individual tasks, and; most important, managers requiring staff to attend too many overcrowded meetings. But as seemingly obvious as the causes of dissatisfaction were, “doing the People Analytics” gave the company a solid reason to make fundamental changes in the way the business unit’s staff works. Because of these changes, the article concluded, “work-life balance reports rebounded, the unit suffered no outflow of talent, and the company has continued making gains” in the key market the unit specializes in.

Granted that Microsoft is a huge firm, with enormous resources—including a recently purchased start-up that specializes in analyzing meta data from office software (surely a sign of things to come). But that’s no reason to suggest that smaller firms can’t also benefit from People Analytics. We can construct a very simple example, subject to multidimensional analysis, viewable in a BI dashboard: Company X can obtain (from its HRIS) the number of staff, by age, working in each position; it utilizes a tool that categorizes staff profiles—let’s say Collaborators, Individualists and Mavericks, among others (note: these are borrowed from Predictive Index, “a talent optimization platform”). The company conducts a satisfaction survey about job conditions like—to use the Microsoft example—Staff Meetings with >5 Colleagues…

Results include some which are expected: people with the profile of a "Collaborator" are highly satisfied with time spent in such meetings across all positions and ages. "Individualist" profiles for older people in managerial positions, not so much; though, younger "Individualists" in non-managerial positions were highly satisfied.  Those with a "Maverick" profile trend opposite the "Individualists"--younger staff were not satisfied, but older workers were. 

The positive changes any firm makes based on People Analytics lead back, inevitably (and healthily, I would add) to more standard Business Intelligence: ways to report, analyze and plan based on the most current data available. If People Analytics, combined with other HR Analytics, are successful, the results will show: in more hours billed, more widgets sold, and perhaps even more (and more viewed) blog posts published.

The “end game” of all Business Intelligence is what all businesses are after: success, which most understand to be profitability. People Analytics argues, convincingly, that staff success—happier, more engaged, more productive staff—is an important element in that pursuit.

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