Effective data analytics can give companies a huge competitive edge, because business managers can gain new insights into trends and customer behaviors that might not otherwise be possible.
To get the most out of their information resources, enterprises need to have a strong analytics team in place. What does it take to assemble and maintain a top-notch team, and what should these teams be doing to make themselves successful?
These are not trivial questions. In this heavily data-driven environment, how companies go about building and operating a team of analytics experts could have a big impact on the business for years to come.
But before you put together your data analytics team, you need to formulate the mission and charter of the team, says Jeffry Nimeroff, CIO at Zeta Global, a customer lifecycle management marketing company.
“In too many organizations, data analytics is embedded in the more traditional and bland notion of ‘reporting and analytics,’” Nimeroff says. “In these configurations, it is often the case that reactive reporting takes precedence. As there is always another way to formulate more meaningful reports, this can become a never-ending cycle where the true power of data analytics is never fully realized.”
Data success begins with diversity
When building a team, don’t limit the focus to just finding analytics professionals. Diversity is critical for success, experts say.
“It’s very important to include not only people with analytical skills, but also those with business and relationship skills who can help frame the question in the first place and then communicate the results effectively at the end of the analysis,” says Tom Davenport, a senior advisor at Deloitte Analytics and author of the book Competing on Analytics: The New Science of Winning.
Multinational conglomerate GE values a diversity of capabilities for its analytics teams. “Data and analytics are most effective when world-class technology skills are paired with strong functional domain knowledge,” says Christina Clark, chief data officer at the company.
This can be achieved by having a team with a variety of business backgrounds; a mix of both IT and functional skills, Clark says. “We are making terrific progress in developing innovative solutions to support our finance function,” she says. “The data team supporting this effort is comprised of long-time IT professionals but also financial analysts, former auditors, and finance managers.”
Strong knowledge of data science is of course critical to any analytics team, and there should be statisticians, mathematicians, and machine learning experts on the team who understand algorithms and how they can be applied on data, adds TP Miglani, CEO at Incedo, a technology services firm.
“You [also] need technologists — data engineers who can build the pipelines to get the data in place for completing all the analysis,” Miglani says. “And you also need business experts who understand the complexities of the domain you are solving the problem for. For example, if the problem at hand is building data-driven drugs, then you need quantitative pharmacologists and biologists.”
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.”
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