How to build a data analytics dream team
Data analytics have become a key part of business success — and capitalizing on data depends on building the right team. From high-tech roles to business-facing experts, here’s where to start.
The days of companies wondering whether they should jump on the data analytics bandwagon, or having a single data analyst on staff, are gone. For firms today, the focus has turned to building the right team to fully harness all that data has to offer.
As companies add to their data teams, analytics jobs are increasingly popular — “data scientist” and “data engineer” were both in the top 10 of LinkedIn’s 2020 Emerging Jobs Report, determined by earnings potential, job satisfaction, and number of job openings. Companies are expected to continue to add and grow data teams amid the rise of the “internet of things,” artificial intelligence, and machine learning.
Ryan Sutton, a district president for tech staffing firm Robert Half Technology Group, said more companies are embracing what he calls a highly organized data approach. “A lot of larger companies, for example insurance companies, have a progressive approach to data overall, and the structure of their organization is focused on data — a data team, we could even argue,” he said. “[Companies are] even creating dedicated org charts specifically to tackle this, whether that is a department or whether it is a group of people that are allocated to different departments that collaborate frequently on data.”
“The overarching trend in data in the last 10 years is just growth,” he added. “It’s how it’s evolved and how it’s grown.”
Digital roles are changing along with increased adoption and advances in technology, and titles are imprecise as different companies and sectors use different names for similar jobs. Some might have one go-to digital analyst who does a range of tasks, while others are on their way to building a team of experts in different areas.
As companies look at fully embracing data and ramping up their analytics departments, creating the right operation is key. Here are some key roles to consider when building a data dream team.
Data engineers are a core part of a data analytics operation. Engineers collect and manage data, and manage storage of the data. Their work is the foundation of a data operation as they take large amounts of raw data and prepare it for others who make business decisions, write prediction algorithms, and the like.
“I would really want a pair of really, really good data engineers. Those people are the heart and guts of your pipeline,” said Althea Davis, the enterprise data governance manager at Etihad Aviation Group, who previously worked in senior data governance positions at Heineken and Ing bank.
Data engineers tend to have software engineering or computer science backgrounds, according to Michelle Li, the program director of MIT Sloan’s Master of Business Analytics program and former director of the Global Technology Group at UBS Investment Bank. “The data engineers are really the backbone,” she said. “If you’re building a house, they’re the structural engineer.”
While engineers maintain the data, data scientists figure out what to do with it. The two positions are the starting point for most companies with big data strategies, Sutton said.
Data scientists are highly sought positions. “I would say data scientist is the most popular title that students want coming straight from a program like ours,” Li said. Some companies have changed traditional roles like operations research, marketing, or data analyst into the data scientist or research scientist title, she said.
Data scientists tend to be a bit more business-facing, while data engineers focus more on infrastructure, scale, and data quality, said Tim Valicenti, a 2018 graduate of the MIT Sloan MBAn program and senior analyst at McKinsey, a role that blends data scientist with classic management consultant.
