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If You Build It…

Smaller companies have shied away from data-warehouse projects, but when done right such projects can stay on course and provide genuine value.

Battle by Battle

While winning user acceptance was critical, Retano didn’t waste much time exploring how users wanted the data to be presented. “We found out early on they didn’t know what they wanted,” he says. “You need to develop something quickly and get it out to them. Then the light will go on and lead you in the direction you need to go.”

That’s part and parcel of what Retano calls the “divide-and-conquer approach,” one that emphasizes piecemeal progress versus a grand rollout of massive capabilities. “You target a specific topic in a given area, such as shipping performance, and just focus on validating that. It goes a lot smoother that way,” he says.

“Divide and conquer” also helped overcome another challenge: maximizing the effectiveness of employee training. Employees were able to learn the system incrementally, easing the strain. “You can’t just put the data out there. People have to get to the point where they’re comfortable using the tool to improve their work lives,” Retano says. Still, mitigating the normal human aversion to change required constant vigilance. Retano adopted the role of evangelist, seizing any opportunity to demonstrate the tool to executives and managers and point out its time-saving benefits.

There were tricky technical aspects to the project as well, like merging data from the two ERP systems and pulling information from a separate system used to track chargebacks for indirect sales by distributors to pharmacies. But Retano found those easier to handle than the more nebulous, people-oriented issues. “Those are defined problems that you can see right in front of you,” he says.

Probably the most vexing problem was, and is, maintaining a high level of data quality. If, for example, a product is assigned to the wrong business segment in the BI tool, reports generated from it will be wrong. Users then will lose trust in the tool and revert to the more laborious manual processes. Bad data is problematic even if there is no BI initiative, of course, but since a data warehouse is intended to provide a widely disseminated “single source of truth,” people expect the data to be correct.

Even a midsize company like Impax has an enormous volume of data, and “data cleansing” can be complex. Retano says it’s not just an IT issue, but a companywide concern, so he frames it around “the four Cs” — everyone responsible for data should regularly check that it is correct, current, consistent, and complete. “Bad data is the quickest way to short-circuit a BI initiative,” he says, “but addressing it is not fun or exciting, so it doesn’t get the attention it deserves.”

Retano organized an unofficial data-quality group that meets every other month. Its members are part of what Impax calls its Business Intelligence Competency Center, a collection of people who have proven adept at getting value from the BI deployment and are available to offer advice to other users.

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