How to assemble a highly effective analytics team

Bob Violino

data analytics

Effective data analytics can be a competitive differentiator today, giving companies keen insights into customer preferences, product development and usage trends, and market gyrations that others don’t see.

To get the most out of their analytics efforts, businesses need to assemble a highly talented team to make sense of all the data that’s coming in from multiple sources, and to translate the analyses into real value for the organization.

What does it take to create a crackerjack analytics team? Here are some key best practices provided by experts.


Just the right mix of expertise

Broadly speaking, a high-performing analytics team needs to have three basic skills: technical data skills to empower the team, analytical skills to drive the work itself, and business skills to ensure the right work is being done and that it’s driving business value, says Dan Magestro, senior manager of advanced analytics at consulting firm West Monroe.

“Very few people have this full set of skills,” Magestro says. “In fact, what I just described is truly a unicorn. But a team of people with these three elements can be even more effective.”

The technical data skills can be provided by those who understand how to organize the data. “These are likely the more traditional IT employees, and including a couple of them on an analytics team can be a powerful ingredient to team success,” Magestro says.

Data scientists bring the validity or “science” to an analytics team, Magestro says. “The hard skills of a data scientist are very important,” he says. “We’ve also found that solid problem-solving and critical-thinking skills are incredibly important, often more than deep experience on a certain platform.”

And a dedicated business expert on the team can be essential to making sure the information analyzed is relevant to the business, Magestro says. That person would effectively communicate the insights back to the larger organization. “The business expert bridges the frequent communication gaps between the analytics work and business need,” he says.

On a more granular level, top-notch analytics teams should include people with an understanding of areas such as relational tables, dimensional models, cubes, JavaScript Object Notation (JSON), Extensible Markup Language (XML), and comma-separated values (CSV), says Mike Bowers, principal architect and leader of the data management staff at The Church of Jesus Christ of Latter-day Saints.

“At least one expert needs to understand REST [REpresentational State Transfer] and how to effectively retrieve data over REST,” Bowers says. “At least one expert needs to understand relational databases and how to get data using ETL [Extract, Transform and Load] tools and file exports. The team needs at least one expert for each type of database,” including SQL, NoSQL document, and NoSQL wide column.

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