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Predictive analytics satisfied by skills, training, research shows

Predictive analytics requires support and know-how to create a sustainable program, recent research suggests.

Businesses recognize the potential of predictive analytics, yet there’s a large gap between those who see it as important and those who actually use the technology, according to research by Ventana Research, an analyst firm based in San Ramon, Calif.

The group conducted a study about a year ago which found that only 13% of organizations were using predictive analytics, but 80% indicated it was important or very important, said David Menninger, information technology research director for Ventana.

The reason? While businesses consider it important, those that struggle with predictive analytics lack both the skills and the training, Menninger discovered in a follow-up study.

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“Organizations are least mature in the people aspect,” Menninger said.

That conclusion from the results of a three-month survey of 198 respondents measured against Ventana’s predictive analytics maturity model, used to rate survey responses across the categories of process, information, technology and people.

Lack of predictive analytics skills
The survey reveals that self-service predictive analytics, or end users creating and deploying their own analyses, has not been widely deployed, despite a wave of easier-to-use predictive analytics tools coming to market.

In fact, almost half of the respondents questioned whether users have the background to produce their own analyses. For the nonbelievers, Menninger said it came down to two reasons: 83% reported users didn’t have enough skills, and 58% reported users didn’t understand the mathematics involved.

“[Predictive analytics] requires the specialist skill set -- the data scientist, the statistician, the data mining experts -- to be successful,” he said.

Instead of relying on users, 63% of respondents reported their organization had a specialized team for predictive analytics or that the task fell to the business intelligence (BI) and data warehousing (DW) team. But even then, Menninger’s research indicated that how satisfied content respondents are with the way predictive analytics is used within their organization (two-thirds said they’re satisfied) depends, in part, on who does the work.

The highest levels of satisfaction, 70%, came from respondents who worked for organizations that employed specialists such as data scientists to produce the predictive analytics. The lowest levels of satisfaction, 59%, came from respondents whose BI and DW teams were in charge of the work.

“I think it’s common for organizations to think this will naturally fall out of the BI and DW team,” Menninger said. “But what this tells me is that this is not a generalized skill of BI and DW teams.”

Lack of predictive analytics training
Organizations are not doing a great job providing the ongoing support needed to successfully maintain a strong predictive analytics program, Menninger said.

According to the results, businesses are most successful at providing concept and technique training (44% of respondents felt this was adequate) and have the most trouble delivering help desk support (24% reported this was adequate). More than a third of respondents, 42%, also found product training to be adequate.

Menninger said concept, technique and product training may drive a stronger sense of satisfaction because they require “specialized knowledge” over the broader needs -- and the skills -- required by something like a help desk.

“I think it relates back to necessary skills,” he said. “How do you have people on help desk supporting a more complicated topic? The help desk resources would need to have specialized training and skills to be able to provide meaningful support.”

Yet respondents indicated that, in addition to concept and technique training, the most effective type of support was brought about by help desk resources. Organizations that provided either support feature adequately were given an 89% satisfaction rating, according to the survey results.

“I suspect that organizations probably think first about doing product training and less about this generalized set of skills and help desk resources,” he said.

While the level of satisfaction in a predictive analytics program may wax and wane based on training , Menninger said several times the root of that issue is most likely derived from what he considers to be an even bigger problem -- a lack of skills.

The skills issue is significant,” he said. “It appears to have been preventing organizations in the past from either choosing to tackle predictive analytics or to tackle it successfully.”

Menninger said predictive analytics requires a deeper kind of knowledge, which can’t be handed out on a street corner and be readily understood.

“It’s unrealistic today to expect the technology to deliver self-service capabilities,” he said. “If you have the right skills, the technology is available to be successful with predictive analytics.”

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