Soft Skills and Hard Science: The Secret to Finding Success as a Data Strategist

Successful data strategists possess a wide variety of technical and soft skills — and know how to put them to use.

As I’ve written about previously, of all the members of a well-built data analytics team, data strategists are best positioned to ensure that the last mile of a project actually reflects the key business questions (KBQs) that were posed during the first mile of the project.

To orchestrate this alignment, data strategists must be included in discussions at every step of the analytics process — from the pitch to the final presentation. Because their role is one of overarching oversight, a data strategist can be conceived of as the quarterback of an analytics team.

Just as a quarterback must understand and coordinate their offensive line’s blocking schemes, their receivers’ routes, and their backs’ running lanes, a data strategist must understand and coordinate how their data engineers are querying, how their data scientists are analyzing, and how their data visualization experts are designing.

Data strategy is clearly a complex, multifaceted endeavor, which is why it bears asking, “What does it take to excel as a data strategist?” Appropriately enough, many of the same things it takes to excel as a quarterback.

Embracing the “Intangibles”

In an interview with CBS This Morning the day after he led his team to a dominant victory in Super Bowl XLVIII, Seattle Seahawks quarterback Russell Wilson characterized his success as follows: “My height does not define my skill-set. I think to be a great quarterback, you have to have great leadership, great attention to detail, and a relentless competitive nature.”

To Wilson’s point, while one obviously needs some physical gifts to play football professionally, more often than not, elite-level players set themselves apart with their “intangibles.” In my view, a similar principle holds for data strategists.

Yes, the most effective data strategists grasp the fundamentals of coding, understand intermediate statistics, are able to spot when a data visualization oversimplifies the underlying analytics, and hold a nuanced view of business strategy. But being an expert programmer or holding a PhD in statistics doesn’t necessarily make an individual a better data strategist than the right generalist — just as the tallest quarterback or the quarterback with the greatest arm strength isn’t necessarily the best quarterback.

“Great leadership” and “great attention to detail” are absolutely critical pieces of a data strategist’s skill-set, and insofar as data strategy is an iterative process, a bit of “relentlessness” doesn’t hurt, either. In short, while a baseline level of technical proficiency is part and parcel of a data strategist’s qualifications, their soft skills are equally important.

Understanding the Playing Field

To fully appreciate the value of soft skills in data strategy, it’s helpful to take a step back and consider the broader decision-making landscape in which data strategists in the health and wellness industry must operate.

In recent years, a growing number of pharmaceutical companies have made a point of diversifying their portfolios. Gone are the days where a brand could ride a single blockbuster drug for years on end; in 2019, developing multiple products for the same general space is the key to sustained success.

This proliferation of products has introduced unparalleled complexity into most if not all analytics projects. Crafting a market strategy that accounts for the interests and priorities of multiple product teams can be incredibly difficult, not least because doing so requires ample data from each team. If, as if often the case, these teams are operating under the same incentive structure, getting them to openly share their data can be a difficult challenge — and can surface precisely the wrong kind of “relentless competitive nature.”

Ultimately, it falls to data strategists to get stakeholders from each of these teams — and possibly several outside agencies — on the same page, not only during initial planning workshops that will chart the course for a project’s first mile, but for the entirety of the project.

While there are certainly formal strategies for fostering this continuous cross-product alignment — reorganizing teams around disciplines instead of products foremost among them — a data strategist’s ability to implement these strategies effectively hinges on the maturity of their soft skill-set.

The All-Star Soft Skills

Irrespective of one’s beliefs regarding the teachability of soft skills, there are several cardinal rules to which data strategists should adhere in order to maximize the outputs of the projects they oversee.

  1. Always Be Prepared

As the quarterback of the data analytics team, a data strategist can ill-afford to be caught flat-footed. Fielding inquiries, resolving conflicts, and providing guidance are all parts of the job, and staying well-informed at all times is the easiest way to excel at each. A data strategist should check in with every project stakeholder on a regular basis, and be fully prepared to elaborate on the status of each project component at any given time.

Of course, no single person is capable of storing every detail of a project in their head at once. However, when it comes to preparation, it’s incumbent upon data strategists to lead by example. It’s not a good look — nor is it anywhere near “great leadership” — for a data strategist to hem and haw when prompted to provide a project update.

Get informed, stay informed, and be able to communicate information to stakeholders with various backgrounds — that’s how a data strategist becomes a great project leader.

  1. Practice Active Listening

Active listening is arguably the most important tool on a data strategist’s tool belt. At the very least, active listening is an efficient way of getting and staying well-informed. A data strategist’s stakeholder check-ins shouldn’t be one-sided recitations of facts, but engaging back-and-forths.

The best data strategists are able to decipher body language, track fluctuations in enthusiasm and buy-in, and read between the lines of what’s being said. One can learn a lot from both spoken things and things left unspoken, and integrating these less explicit insights into one’s project status assessments adds valuable nuance to the practice of data strategy — “great attention to detail,” indeed.

  1. Be a Leader to All

Granted, active listening provides data strategists with the most quantifiable value when it’s practiced during conversations with high-level stakeholders, but that doesn’t mean it’s not in data strategists’ best interest to afford the same level of attention to every person involved with a project.

The most respected leaders — in data analytics, in football, in life — are those who recognize the potential contributions of every “player” and make a concerted effort to make even “role players” feel valued. If for no other reason than the fact that developing talent internally is considerably cheaper than making outside hires, data strategists should strive to be a leader of the many, not a leader of the few.

There’s No “I” in “Data Strategy”

In the increasingly complex pharmaceutical landscape, the success of a data analytics project pivots largely on the sophistication of the planning and execution of the project’s data strategy. And while a data strategist is responsible for managing a project’s strategic progression, they cannot — and should not — go it alone.

Just as Russell Wilson didn’t win Super Bowl XLVIII by himself, a data strategist is never going to carry a project across the goal line by sheer force of will. That said, just as a quarterback can single-handedly lose a game by throwing a handful of interceptions, a data strategist can severely damage a project’s chances of success by committing too many unforced errors.

At the end of the day, the most effective way for a data strategist to minimize their errors is to acquire — and continually refine — a well-rounded set of skills. The best data strategists are detail-oriented leaders as much as they are experienced technicians, and while they may not be the smartest person in the room, they have the power to help analytics teams deliver championship-caliber results that drive key business objectives forward.

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