Searching for superstars isn’t the answer

As companies race to exploit the power of analytics and transform themselves into insight-driven organizations, recruiting analytics talent has become a critical business issue. However, many have discovered their recruitment efforts aren’t yielding the results they’re aiming for.

In our experience, it’s often because these efforts create unbalanced teams that don’t meet the business’s current needs. Too many organizations focus on finding the rare superstar with stellar skills—often, a PhD with superb technical or quantitative skills and a vague job title of data scientist. Or they hunt for the elusive, highly experienced data engineers, statisticians, quants, and others who are experts both in finding and modelling the right data and turning that data into powerful insights that drive fast action and rapid results.

But it doesn’t work that way. For one thing, such superstars are rare, costly, and often difficult to retain, creating new risks for the business. Building an entire team of outstanding talent would be ruinously expensive for most organizations, even if they could manage to attract so many in the first place.

In many ways, it’s like a hockey team that needs to operate within the confines of salary caps, wage structures, contractual obligations, and other factors that limit what they can spend on talent. Heavy investment in one or two individuals can leave a team shorthanded in other equally important areas. And we’ve all seen cases where teams that rely on one or two players—or even teams filled with expensively assembled talent—have failed to deliver the expected results. Analytics teams are no different; often, superstars only bring part of what a truly effective team needs to thrive.

To put it another way, a champion team will always be better than a team of champions.

Effective insight-driven organizations know that analytics teams need to comprise a range of capabilities, knowledge, experience, and talents to deliver the results that the business needs. These teams combine top-quality technical staff, such as data engineers, modellers, and statisticians, with those who understand the business side of the equation—people who can identify the questions that need to be answered, tell stories that illuminate data-led insights, and persuade others in the organization to act or change.

It’s time for organizations to stop searching for superstars in an effort to build the “perfect” analytics team. It’s time they start building the best analytics team they can—a combination of talented individuals who work together to get work done and deliver meaningful results today, not at some point in the future.