And they made those results more 'real' to users. For a team investigating contract fraud, for instance, his team placed the results in a Web-based interactive heat map that showed each contract as a circle, with larger circles representing the biggest costs and red circles the highest risk for fraud.
Investigators could then click on the circles to drill down on the details of each contract, as well as related contracts, that are at risk. "That's when people started to notice that we really had something that could help them," he says.
Now departments come to him with requests, and they assign someone from their own group to work with Jones' team. Once the output is ready, the person returns to their group and teaches others how to use it. Jones doesn't have hard return on numbers yet but will be reporting on increased arrests, dollars recovered and other metrics.
Bryan Jones, who heads up analytics at the Inspector General's office at the U.S. Postal Service, says, "You're dead in the water if you don't have that support from the top."
There were also unanticipated benefits. "Now people understand that we can be proactive. That was a return we didn't have on our list," he says, and new investigators that don't have 25 years of experience and connections can get up to speed quickly. "These tools can level the playing field," Jones says.
Jones's advice: Get close to your customer, get professional help building your first model and present the results in a compelling, easy to understand way. "We didn't have the right people or expertise to begin with. We didn't know what we didn't know," he says, so Jones turned to an outside data-mining expert to help with the models. "That relationship helped us understand why we failed and kept us from making the same mistakes again."
Overcoming business skepticism
While hiring a consultant can help with some of the technical details, that's only part of the challenge, says John Elder, principal with Elder Research, a consultancy that worked with Jones' team. "Over 16 years we have solved over 90% of the technical problems we've been asked to help with, but only 65% of the solutions have gone on to be implemented."
The problem, generally, is that the people the model is intended to help don't use it. "We technical people have to do a better job making the business case for the model and showing the payoff," Elder says.
Organized analytics: Who runs the practice?
Where you locate an analytics practice may be as critical a success factor as the teams skills, experts say. Should you embed it within the IT organization, establish it as an independent group or embed the function within each business unit? Here are the tradeoffs.