And, as we’ve seen in this chapter, the problem with almost all data relating to people—including you—is that it isn’t reliable. Goals data that reports your “percent complete”; competency data comparing you to abstractions; ratings data measuring your performance and your potential through the eyes of unreliable witnesses: it wobbles by itself, and fails to measure what it says it’s measuring.

In recent years, large corporate business has focused its marketing and promotion efforts on collecting “vanity metrics”—like social media followers, subscribers, or clicks. But those metrics don’t always correlate with sales, profit, or reputation. That is, they don’t measure engagement or trust—they simply show how many people took some form of marketing bait. By considering “collecting” over “connecting” (with customers), these companies are becoming too caught up in collecting page likers and followers and have forgotten to build relationships with those individual customers who are already listening, following, or buying. Having 100 passionate fans of your business who are eager to buy anything you release is exponentially more effective than having 100,000 followers who simply follow your business to win something like a free iPad.

Humanyze, an MIT spin off led by Ben Waber, who coined the term people analytics, tags employees and leaders with sensors that capture their movements, communications, and even physiological responses (e.g., stress, excitement, and boredom). Just by analyzing anonymous group-level data, the firm can help organizations identify invisible elements of work relations, such as the hidden power dynamics, in a firm.

For example, in a recent study reported in Harvard Business Review, Waber and his team set out to decode the behavioral differences between men and women in a large multinational firm and explore whether such differences could partly explain the underrepresentation of women in the senior leadership ranks (where they accounted for just 20 percent). The researchers gathered email data, meeting schedule data, and location data for hundreds of employees, across all seniority levels, over four months. Of particular relevance was the data collected with sensors some employees wore. The sensors recorded who talked with whom; where, when, and for how long people communicated with each other; and who dominated each conversation. Waber’s team expected to find behavioral differences between men and women pertaining to people’s drive and networking habits: ‘Perhaps women had fewer mentors, less face time with managers, or weren’t as proactive as men in talking to senior leadership.’ However, the results showed no significant differences between what women and men did at work: ‘Women had the same number of contacts as men, they spent as much time with senior leadership, and they allocated their time similarly to men in the same role. We couldn’t see the types of projects they were working on, but we found that men and women had indistinguishable work patterns in the amount of time they spent online, in concentrated work, and in face-to-face conversation. And in performance evaluations men and women received statistically identical scores. This held true for women at each level of seniority. Yet women weren’t advancing and men were.

KEY QUESTION: Are the stakeholders (employees, customers, shareholders) happy and engaged in the business; and would you “rehire” all of them?

(For more practical insights about building Job Scorecards, read Bluewire Media’s excellent blog on the topic.)