Stevenson recruited five-person teams made up of a business expert, a statistician, a predictive modeler, a machine learning expert and a data scientist. "Each person on the team had a slightly different perspective on the problem we were trying to solve. Doing it in six months was our way of earning the right to prove the capability was there to really change the way we do things," she says.
In addition to the projects that reduced testing time and pinpointed lucrative resellers, 13 other analytics projects have been completed using that approach. So Stevenson has upped the ante by finding $100 million problems and challenging teams to solve them.
"When you have a track record, you can ratchet up," she says. Other ongoing projects include a predictive engine for streamlining Intel's chip design and debugging process and another to predict new information security threats.
But Stevenson cautions enterprises not to underestimate the skills required for analytics initiatives and the time it may take to nurture those skills.
"When I think about our learning curve with Hadoop and some of the more advanced presentation layers that are very different from SAP or traditional BI, I'd emphasize that there is a learning curve there for technical skills that isn't insignificant," she warns.
Her other piece of advice: "Develop an appetite for experimentation," especially since analytics technology is still evolving. "The winners and losers on the tech side are not completely shaken out yet," she says. "Keep your aperture wide."
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