7 sure-fire ways to fail at data analytics

Bob Violino

Building a data management foundation takes sustained effort, often over multiple years, Clark says. “Some of the work a data and analytics team needs to drive will not have obvious immediate results, which may be out of alignment with business partner expectations. This requires strong leadership buy-in and efforts to educate business partners to enable a more data-driven future.”


6. Ignore middle- and lower-level managers

Analytics performed in a vacuum by data scientists and other experts without solid input from the business managers who are closest to the need for analytics will likely not be as successful.

“Without the active involvement of mid- to lower-level managers, the information delivered by the analytics team often fails to actually help the management team do their job better each day,” says David Giannetto, COO at Astea International, a provider of service management software.

“The information will be directional, point out larger process flaws or areas that can be improved, but management will get to that someday — when they have time,” Giannetto says. “And most managers never have extra time. It is only when the team is comprised of people who actually know the business and the information the business actually needs access to each day that the information delivered becomes tangible enough to positively impact the business.”

If analytics tells users where a real problem is — where they are likely to fail — in enough time for them to prevent it, they will use this information each day, and the initiative will be successful, Giannetto says.


7. Lack the culture and skills to support good data analytics

This is a common problem for organizations, in large part because skills such as data science are so hard to come by. But if data literacy is not central to a company’s culture, the chances of failure with analytics is greater.

“For folks who are not familiar with analytics, data science is perceived as some sort of magical way of solving problems,” Miglani says. “The concept of prediction and self-learning is very hard for people to grasp. It will be hard to convince your business partners to make decisions on opaque algorithms. You will need to educate them first.”

And organizations continue to struggle to find data scientists and other professionals with analytics skills. “One of the best ways to develop this capability is to groom this talent, instead of scouting out superstars outside your organization,” Miglani says. “Many projects fail or get delayed because [companies] are not able to hire analytics folks on time, or lose them to high attrition.”

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