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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.

Technically, a data scientist is supposed to be a “unicorn” that can do all of this simultaneously, Miglani says. “But unicorns don’t exist,” he says. “Successful data science teams are diverse, where individuals bring in these competencies that need to come together.”


Change management and the value of IT

If an analytics project involves prescriptive or operational analytics (for example, if the results will be tied into a business process or a set of jobs), there is also a need for someone to manage the change process, Davenport says. “The ORION project at UPS, which led to dramatic changes in driver routing, devoted a massive amount of time and energy to change management,” he notes.

Given that the team will be leaning heavily on technology infrastructure such as big data tools, having the IT department represented on the analytics team in some capacity is also important. “Even if the analytics group doesn't report to IT, it's usually a good idea to have some representation of the IT function on the team,” Davenport says.


Emphasize experience — with data and tools

Whoever’s on the analytics team should have lots of experience in their role, Nimeroff says.

“Data analytics is both an art and a science, and more experienced individuals are better able to leverage tools in a creative and effective way than novices,” he says. “I have also found that novices rely on tools to do heavy lifting that they may or may not be fully comfortable in doing themselves. On the flip side, I have met great data scientists who do everything by hand. They don't scale or help a team accelerate. Finding individuals who can execute without tools but understand and embrace the value of modern tools is what I focus on.”


Outside expertise and embedded teams

Many companies turn to outside expertise for help with analytics projects. That’s fine, but it’s important to ensure that the efforts of the project are actually meeting organizational needs.

“If there are some members of the team who are outsourced workers, try to make sure there is at least one [internal] employee on every project, who can help to ensure that the results of the analytics are adopted,” Davenport says.

And whenever possible, the analytics team should either be a formal part of the business that's doing the analysis, or at least embedded within it for the period of the project. Consumer goods company Procter & Gamble used to do this through "embedded" analysts, Davenport says, but now has them report to the head of the relevant business function or unit.


‘Ruthless prioritization’

Once your team is in place, finding an operational model in which everyone can work is next, Nimeroff says.


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