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The secrets of highly successful data analytics teams

Bob Violino | Oct. 25, 2017
Effective data teams bring diverse, cross-functional skill sets to bear on clearly defined business priorities — without losing sight of the value of experimentation and ongoing education.

“Companies are becoming more agile and, like with software development, finding an approach for prioritizing work, decomposing the execution into digestible chunks, developing specific success criteria for each work effort, and providing a framework for ongoing communication is often the difference between success and failure,” he says.

Also, the team will more likely to succeed if it’s able to demonstrate the business value of what it does, Miglani says.

“Engaging with stakeholders and consumers of data science recommendations helps them showcase this value, and also get a deeper understanding of the key pain points which they should focus on,” Miglani says. “Sharing results sooner than later, and building organizational structures where data science goals are aligned to the [business units] they are tagged to is a great way to create value.”

GE practices “ruthless prioritization” with its analytics efforts. “A commitment to clearly defined business priorities will enable the data and analytics team to be most successful,” Clark says. “When teams can demonstrate an impact in targeted areas, they are more likely to stay motivated and inspire business partner engagement.”

The company has seen significant productivity results from the “Digital League” in its aviation business, where a cross-functional team has come together to define priorities and then deliver insights in two-week sprints.


Emphasize experimentation and innovation

It’s also important to keep an experimental mindset on the team.

“The business case for these projects is not easy; you have to take a step into the unknown,” Miglani says. “Unlike technology projects that begin with a definite scope in mind, data science projects begin with a problem and a set of hypothesis which needs to be tested. There is no clear map of before-and-after processes for these projects, and teams which are new to data science need to understand and get comfortable with that.”

Along those lines, there should be outlets for innovation, Clark says. “There is an enormous amount of emerging technologies in this field,” she says. “Employees will want to know they have time and funding to continue to grow their own skills and try new approaches. We leverage our Global Digital Hubs as a place to incubate new technologies and pilot work in self-organizing teams; an atmosphere of innovation keeps teams motivated.”

As with science and learning in general, curiosity is a key element of analytics. “Curious people have a desire to follow up on their own analysis, whether or not our clients ask for it,” says Stuart Wilson, data scientist and analytics team lead at Paytronix Systems, a provider of reward program services to restaurants and retailers.

“One of our analysts decided to check up on a marketing campaign run six months prior,” Wilson says. “Because of this, we were able to discover an unanticipated result of this campaign, that would have otherwise been inconclusive.”


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