VideoAs buzzwords go, it isn't a stroke of genius: data paired with warehouse unites two of the most mundane words imaginable. Data pales before the far sexier information, and as for warehouse, well, one need only think of the final scene in Raiders of the Lost Ark, in which a treasure of incalculable worth disappears for eternity in just such a place. Add to that the facts that data warehousing is a mature technology (translation: its 15 minutes of buzz are over) and that it's among the most expensive and complicated technology initiatives most companies tackle, and you might conclude that the only wise course is to run from it as fast as possible.
But think again, because data warehousing has several factors in its favor. For one, companies have become smarter about how to deploy it. Costs are still high and it will never qualify as a weekend project, but there are ways to ease the pain and get a faster return on investment. Also, while customer relationship management (CRM) and business analytics are red hot, they aren't replacing data warehousing so much as highlighting what a vital part of IT infrastructure it has become. And companies are finding that data warehousing is applicable to any number of strategic efforts, from providing a unified view of the customer to sorting through terabytes of operational data in search of business insights that can boost profitability.
"Data warehousing has always plodded along quietly," says Lynne Harvey, an analyst at Patricia Seybold Group, a Boston- based consulting firm. "It's hard to understand--even experts have a tough time explaining it--but as companies try to cope with more and more information, all roads lead to data warehousing."
A data warehouse differs from a typical database in that it usually extracts information from a number of sources--perhaps including external sources, such as data banks of competitor information--integrates it, and makes it accessible for analysis by nonspecialist employees, even senior management. That integration, however, is also the most difficult part of constructing a data warehouse. IDC, a market intelligence firm in Framingham, Massachusetts, surveyed 1,000 companies earlier this year and found that data integration was the biggest challenge to building a data warehouse, accounting for 70 percent or more of the effort to build one.
Such work can be expensive. Lou Agosta, director of research in data warehousing for Giga Information Group, in Cambridge, Massachusetts, says that while some projects may cost only in the low six figures, $1 million still qualifies as a small effort, and price tags above $10 million are not uncommon. But there's good news, too, he adds. "Businesses have spent the last two decades trying to manage corporate data as an asset," says Agosta, "and they now understand how to build data warehouses."
Fedex: Overnight Success
A case in point is FedEx Corp. Two years ago, the Memphis-based cargo king surveyed about 200 of its business analysts and found they were spending far too much time simply trying to get their hands on important data, leaving less time to analyze it. One problem was the company's structure: a corporate financial planning group provided support to 10 divisions, all of which shared a mainframe but relied on customized planning and reporting systems focused on a subset of data. Any efforts to take a macro view required lots of manual integration. As Joel Halvorson, FedEx's manager of financial systems, explains, "If we were working on the business plan and needed information from a certain system, we'd have to stop and wait while someone sent us the data through a mainframe file so we could pull it into a consolidated view."
The company's analysts were charged with helping to boost profitability by studying operational data and looking for ways to save or capitalize on opportunities. To do that, they might need to reach into one system that tracks revenue, another that tracks pickup and delivery data, a third that tracks employee pay, and so on. That was not only time- consuming, but also confusing, because units might define operational metrics differently. Cost per package, for example, varied depending on whose system was being accessed.
So FedEx decided to build a data warehouse as a backbone for its business intelligence (BI) efforts. The project began in late 1998, although Halvorson says the real work didn't start until February 1999, when Chicago-based ThinkFast Consulting Inc. was brought in to help guide the effort. That means the system took only about a year to begin producing results--impressive considering its scope.
The company identified 16 key sources of data to feed the warehouse hub. "The mainframe is still critical," Halvorson says; every time a FedEx driver scans a package, that data goes straight to the mainframe. But now when business analysts use that data, they're drawing it from the data warehouse.
And they're tapping it through the company's intranet, a key component of the overall design. "Our global reach made that element essential," says Cathy Ross, vice president of corporate financial planning. But if giving employees around the globe easy access to the system was important, at the same time so was restricting the warehouse effort, at least initially. "We wanted to avoid 'scope creep,'" says Halvorson, "so initially we stayed focused on a system that was tailored for finance." FedEx did a fair amount of internal marketing, letting analysts know what was in the pipeline and soliciting feedback from early adopters. The company also focused heavily on training, and phased in the project, believing that demonstrating limited success early was better than springing a wildly ambitious system on users down the road.
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