Putting predictive analytics to work

Contrary to popular opinion, you don't need a huge budget to get started.

By , Computerworld |  Business Intelligence, Analytics

It's also important to keep the scope focused. Mission creep can destroy your credibility in a hurry, Peri says. Early on, P&G developed a model to project future market shares for regional business leaders in a line of business he declined to identify. It was successful until the company tried to use the same model to accommodate the needs of other business leaders.

Those requests required a more granular level of detail, but his group tried to make do with the same model. "The model became unreliable, and that undermined the credibility of the original analysis," which had been spot-on, he says.

New users need to take several steps to get started: Hire a trained analyst who knows how to develop a model and apply it to a business problem, find the right data to feed the models, win the support of both the business decision maker and an executive sponsor in the business who are committed to championing the effort -- and take action on the results.

"Notice I didn't mention tools," Peri says. "Resist the temptation to buy a million-dollar piece of software that will solve all of your problems. There isn't one," he says, and you don't need to make that investment for your first couple of projects. Instead, invest in training staff on advanced spreadsheet modeling.

"All of this can be done with Excel to get started." Only when you are ready to scale up do you need to investigate bigger, platform-level types of tools, he says.

Keeping users close

Bryan Jones started on a shoestring budget -- but that's not why his first effort at predictive analytics was a failure. Jones, director of countermeasures and performance evaluations at the Office of Inspector General within the U.S. Post Office, wanted to use predictive analytics to help investigators determine which health care claims were most likely to be fraudulent.

After eight months he had a working model, but the independent analytics group working on the project wasn't fully engaged with the department that would be using the tool. As a result, the raw spreadsheet output mailed to each office was largely ignored by investigators.

Fortunately, Jones' group had the unwavering support of the Inspector General. "You're dead in the water if you don't have that support from the top," he says.

The second time around Jones hired a consultant to help with modeling and data prep, and embedded an analyst within the group that would be using the results.

Heat maps such as this one, used at the U.S. Postal Service, make data much more actionable than raw numbers alone, implementers say.


Originally published on Computerworld |  Click here to read the original story.
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