5 Tips for Retaining Analysts with Data Science Skills

by   |   October 23, 2012 8:39 pm   |   0 Comments

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Elizabeth Craig of Accenture

Studies and surveys confirm that finding enough talented people is among the toughest challenges facing managers looking to implement big data analytics strategies. That’s why it’s important for managers to focus on the second most important aspect of bringing these analytics professionals on board: keeping them on the payroll.

Even happily employed data scientists are likely to be on the lookout for better paying and more attractive opportunities. Just ask Elizabeth Craig, a research fellow with Accenture Institute for High Performance and coauthor of “Counting on Analytical Talent,” an Accenture research report from 2010 that incorporates the survey responses of nearly 800 data analysts.

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“Data scientists have countless opportunities in the external market so they can jump ship very easily,” says Craig. “Nearly half of the data scientists we interviewed said it was likely or very likely they would be looking for a new job in the next year. So they not only have a huge number of opportunities elsewhere in the market but they’re considering them.”

That’s a scary proposition given that these talented professionals are prized for knowing how to parse complex data for applications and systems designed to predict business outcomes and improve operations.  As more and more companies turn to analytics to gain a competitive edge, attracting and retaining data scientists is expected to become increasingly challenging. This week, Garnter said it projects demand for analytics talent will create 6 million IT and other jobs in the U.S. alone by 2015.

Craig says there are five steps companies can take to keep their data scientists happily employed. Here’s how:

Make a business connection. Companies that assume that their data scientists are far too deep in a mathematical morass to care about business matters are making a huge mistake. Rather, Craig says “data scientists need to have information about the business and be business savvy so that they can see how their work could have a direct impact on business outcomes.” For this reason, Craig recommends that executives arm their analysts with critical and competitive information about the business so that they feel adequately engaged.

Feed their love of tools and technologies.  “Data analytics requires specialists to be up to date on all the latest methods, analytic strategies and approaches to accessing data from multiple sources,” says Craig. Companies that aren’t willing to invest in the training of their data scientists, and to provide them with up-to-the-minute tools, risk losing them to the competition.

Set clear roles and expectations.  Without a doubt, data analysts are expected to make sense of mounds and mounds of unstructured data. But that doesn’t mean they’re comfortable with unclear business processes and haphazardly defined job responsibilities. Instead, Craig says that while “the work itself can be very unstructured, the role of the data scientist requires making sure they have a clear understanding of what’s expected of them. These are people who have a preference for structure and are linear thinkers—they like to understand exactly what is required of them.”

Provide managerial support. Working on predictive models aren’t enough to make data scientists feel as if they’re part of a team. “Data scientists want to feel that their skills are valued and that they’re being supported,” says Craig. “It’s easy for them to become isolated in an organization and to lack that community.” As a result, Craig says that nurturing a relationship between a data scientist and his or her manager is critical “to ensuring he feels valued and has someone who understands the technical aspects of the job to support their development.”

Treat them as distinct. Even as an organization works to assimilate data scientists into a company’s culture, it’s critical that business leaders regard them as a unique workforce with specialized skills, precise demands and very different personalities.  “Research has shown that there are actually personality differences between people who have highly quantitative orientation and skills and the rest of us,” says Craig. As a result, she says, leaders need to “think about how to motivate, engage and retain them differently than how they think about the general employee population.”

Cindy Waxer is a Toronto-based freelance journalist and a contributor to publications including The Economist and MIT Technology Review. She can be reached at cwaxer@sympatico.ca  or via Twitter @Cwaxer.




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