Data Analytics is a Team Sport—What Position Do You Play?

Posted on by Kevin Troyanos

There’s no “I” in data.

Despite the inarguable importance of data and analytics in today’s marketplace, our country’s demand for analytical talent has quickly outpaced the supply of talent. In other words, although the value and volume of data continue to grow, we’re facing a significant shortage of people who know how to work with and deploy it in the workplace.

data analytics
What position do you play? Data strategist, analyst, scientist?

There are a number of factors contributing to this shortage, but foremost among them is the unfortunate reality that people—especially students—simply don’t understand what a career in “analytics” entails. Furthermore—the breadth of skills required by analytics organizations today spans multiple academic disciplines and areas of domain knowledge—it’s nearly impossible for a single analytics professional to “do it all” with a high degree of excellence.

To the uninitiated, differentiating among the various positions that fall under the “analytics” umbrella can be challenging. Like any great football team, a team built on diverse, complementary skillsets is invaluable in driving disruptive insights for clients.

The Quarterback: Data Strategist

Data strategy is the art of translating business problems into the language of analytics. They are responsible for identifying and defining key performance metrics across a variety of marketing channels, tactics, and sources—a job that requires a nuanced understanding of the relationships between real-world outcomes and quantitative analyses. In short, whenever a company assigns its analytics department a project, it’s the data strategist’s job to orchestrate the project from beginning to end and guarantee that the team produces any and all required deliverables. Just like a quarterback, a data strategist touches the ball during every play.

The Tackle: Data Engineer

Data engineers are in charge of everything related to data integration, automation, and manipulation, and are thus the sine qua non of analytics as a whole. They use complex set theory and statistics embedded within programming languages like SQL to make sense of voluminous raw data and make it usable for everyone else on the analytics team. Not only do data engineers have to consider how to most efficiently store massive consumer datasets, but they also have to optimize code in order to reduce database computation times and costs. They function as the tackle of the analytics team by deftly bearing the significant weight of massive raw data sets and empowering their teammates to do their jobs.

The Wide Receiver: Data Visualization/BI Expert

Data visualization experts “catch” whatever structured data and insights their team members produce and organize it in an aesthetically pleasing, compelling, interactive way. Their role is essential to bringing clients, consumers and stakeholders into what can often be an esoteric, data-based conversation—whether through the development of an intuitive dashboard or the design of engaging infographics.

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