Thought experiment time. I’m going to give you some excerpts from a Harvard Business Review article about how a company sold its workforce on management, and you’re going to work through the implications of what this firm learned for Law Land—where, yes, lawyers are at least as averse to “Management” as were the engineers at this software company.
Since the early days of [the company], people throughout have questioned the value of managers…. As one software engineer, Eric Flatt, puts it, “We are a company built by engineers for engineers.” And most engineers, not just those at [the company], want to spend their time designing and debugging, not communicating with bosses or supervising other workers’ progress. In their hearts they’ve long believed that management is more destructive than beneficial, a distraction from “real work” and tangible, goal-directed tasks.
Since the company has an “overall indifference to pecking order,” top-down directives are not accepted without question. People respect expertise and good ideas over titles and lines of authority. The selection of people who thrive in this kind of meritocratic environment begins with recruiting.
[The company] emphasizes the power of the individual in its recruitment efforts, as well, to achieve the right cultural fit. Using a rigorous, data-driven hiring process, the company goes to great lengths to attract young, ambitious selfstarters and original thinkers. It screens candidates’ résumés for markers that indicate potential to excel there—especially general cognitive ability. People who make that first cut are then carefully assessed for initiative, flexibility, collaborative spirit, evidence of being well-rounded, and other factors.
[The challenge is] if your highly skilled, handpicked hires don’t value management, how can you run the place effectively?
If we can stipulate that this company diverges wildly from Law Land in its use of “rigorous, data-driven hiring,” may we agree that otherwise this sounds like an awfully familiar recruiting model? I would further ask you to consider—just entertain the wild and almost unhinged notion—that there might actually be something to a rigorous, data-driven hiring process, given this company’s stratospheric success?
This company isn’t alone in experimenting with and learning from data analytics in the context of recruitment and hiring:
Until recently, organizations used data-driven decision making mainly in product development, marketing, and pricing. But these days, Google, Procter & Gamble, Harrah’s, and others take that same approach in addressing human resources needs.
The reasoning, dare I add, could not be simpler: If data has improved everything from the speed, efficiency, and quality of decision making for products, marketing, and pricing, why wouldn’t we want to try it on one of the gnarliest organizational challenges out there, hiring and recruitment?
Seizing on this reasoning, our guinea pig company decided to go all-in on this data analytics challenge, hiring “several PhD’s with serious research chops.” The leader of this initiative recalled, “I didn’t want our group to be simply a reporting house…I wanted us to be hypothesis-driven and help solve company problems and questions with data.”
Initial results were scarcely emphatic. Even though they began by comparing managers in the top and bottom quartiles of satisfaction and performance, even the low-scorers were doing pretty well. All the managers, in other words, looked pretty similar. So they rolled out the big statistical guns and applied multivariate regression techniques, which revealed that “even the smallest incremental increases in manager quality were quite powerful.”
The high-scoring managers saw less turnover on their teams than the others did—and retention was related more strongly to manager quality than to seniority, performance, tenure, or promotions. The data also showed a tight connection between managers’ quality and workers’ happiness: Employees with high-scoring bosses consistently reported greater satisfaction in multiple areas, including innovation, work-life balance, and career development.
Now we’re getting somewhere.
But the question remained: Even if we can begin to identify the best managers, what exactly is it that they’re doing differently to distinguish themselves?
Back to the data.