The missing analytics executive
https://benn.substack.com/p/the-missing-analytics-executive
There’s a treadmill at the mountain top.
Among the data leaders I know, many of their stories are the same. After rising up the ranks of their organizations, from junior analysts and data scientists, through positions as team leads and managers, and eventually to directors and VPs, their data careers stalled. Somewhere between senior management and the executive team, they found themselves caught at sea, adrift between the land they loved and the aspirational new world they left it for.
The shoreline behind them—the ones many of us leave, in the name of career advancement—are those of our intellectual homeland: The creative work that attracted many of us to the data industry in the first place. By making the leap into management, we gave up data’s puzzles and problems to lead teams, set OKRs, and manage vendor contracts;1 we gave up giving in to curiosity; we gave up, in a more subtle sense, the work that makes many of us us.2 We traded them away for pay and prestige, and for a sense of professional progress.3
Always stuck one door away from the inner sanctum, many of the data leaders I know bounced from company to company, looking for new ways in. But each time, they were trapped under the same mandate: Hire a team; rehabilitate broken data infrastructure; build a data culture. … The terminal point of an analyst’s career, it seems, is the data leader treadmill, hopping between different VP roles, always running but never moving forward.
The executive analyst
The lack of analytical leadership on the executive team isn’t just a problem for analysts, however; it’s also a problem for the executive team.
At their best, analysts are curious investigators, observing the problems around them and proactively looking for opportunities and solutions. Their value scales with what they can see: We can’t solve problems we don’t know about.
But there are no analysts in the rooms with the widest and most strategic views. Board meetings rarely have analytical observers, and there is no official designation for “senior data advisors” to the executive team. Nobody is directly responsible for helping a company make its most important decisions, or for exploring its uncharted strategic opportunities. There is no role committed to this work, and no title that acknowledges its value.
A better chief analytics officer
A similar problem exists in technical organizations. Engineering leaders have to oversee both teams and technology, and those who are good at one aren’t necessarily good at the other. Companies solved this problem by splitting the role in two.
Traditionally,7 the VP of engineering sits at the top of the engineering org chart, and is responsible for the day-to-day operations of the team. They hire managers, manage directors, and direct the machine that builds the company’s technology.
The CTO, by contrast, is shaped like an individual contributor. They don’t typically manage large teams (or, in some cases, any team at all). Instead, they’re “an architect, a thinker, a researcher, a tester and a tinkerer.” Their job is to evaluate the organization’s most important technical decisions and to push its technological frontier outward. They derive their authority from expertise and influence, not an official reporting structure.
Data departments should follow the same pattern. Rather than being led by a single ambiguously defined and overburdened CDO, data teams should have two representatives in senior management: A VP of data responsible for managing the team’s daily operations, and a chief analytics officer.8
Much like a CTO, a chief analytics officer would be charged with working on their company’s most important and far-reaching problems, but without the management responsibilities (or organizational authority) of a department head. In exchange for their wide latitude and generous leash, they’d be expected to deliver impact commensurate with that of a CTO.
This redefined chief analyst offers a way to step off the data leader treadmill without walking away from data entirely. Rather than having to chase impact and influence in other departments, great analysts can climb all the way to top by working on what they’re good at—and, usually, what they love to do.