Warehouse Data Earns Its Keep

MasterCard has converted 50 terabytes of transactional and financial data into a business-intelligence engine for use by banks and MasterCard employees.

Reprinted from Network Computing, May 1, 2003.

Download as a PDF file:https://img.cmpnet.com/nc/1408/graphics/1408centerfold_file.pdf (Includes a chart depicting MasterCard International's data warehouse)

MasterCard International's technology operation is best known for authorizing, clearing and settling transactions the moment a consumer's credit card is swiped. Millions of times a day, that sets off a flurry of electronic handshaking and verification as the transaction executes across 25,000 member banks and financial institutions. But it's how that information is processed when it reaches MasterCard's massive data warehouse that helps the credit-card giant and its bank clients make more effective business decisions.

MasterCard has converted the 50 TB of transactional and financial data into a business-intelligence engine for use by banks and MasterCard employees. The company runs a combination of homegrown and off-the-shelf analysis tools--and uses analysts--to identify buying trends, credit-card fraud and other useful information residing in its data warehouse. The company can correlate and analyze transactions to determine a consumer's interests or detect anomalies that suggest a card has been stolen, for instance.

MasterCard offers bank clients access to these tools, as well as custom reports, over the MasterCard Online portal. "We package our technology and our intelligence into the tools," says Sam Alkhalaf, senior vice president of technology and strategic architecture at MasterCard's IT organization, based in St. Louis.

Financial institutions count on these credit-card transactions to tell a story, which provides information for targeted marketing or business planning. For example, a bank issuing credit cards might notice that many cardholders use their MasterCard accounts to charge flights on a specific airline. The bank then can strike a deal with the airline to offer special offers and incentives to cardholders.

Mining Tools
Although it's not pure data mining--MasterCard stresses that it adds human intelligence to the mix by having in-house analysts review the data--Alkhalaf describes the process as a form of data mining. Each tool reports, extracts and summarizes the information it plucks from the data warehouse.

"It's a way we can observe cubes of data and drill down into them," he says. "The actual magic comes from our statistical analysis team, which figures out trends based on what's gleaned from the information."

MasterCard's IT group runs Business Objects' Analytics software, then builds its own models. One model, for instance, can allow a bank to monitor spending by a particular group of card members it targeted in a direct-mail campaign. The IT group can then recycle that business-intelligence model for other MasterCard bank clients.

These customized tools help banks make predictions based on buying trends. When a cardholder has made a series of purchases at stores carrying baby items, for example, it's safe to assume the buyer, a friend or family member is expecting a baby or has a newborn. A bank could use that information to set up a marketing arrangement with, say, BabiesRUs, to generate more sales from this type of customer.

Crunching the Numbers
MasterCard runs its data warehouse on 12 Sun E Series Solaris servers, and employs such tools as SAS Analytics for crunching statistical algorithms, Crystal Decisions' Crystal Reports for reporting and Oracle Financials for analyzing financial data. "All of these pieces make up the front-end tools we can build an application with," Alkhalaf says. The Business Objects software handles drill-down reporting and analysis.

Getting transaction and internal financial data to the data warehouse quickly and efficiently is paramount. Alkhalaf says MasterCard is gunning for a real-time, load-as-you-go operation for loading, mining and analyzing data. Most loading is still done in batches, though that's beginning to change. MasterCard has started running IBM's WebsphereMQ (formerly MQSeries) and BEA Systems' Tuxedo middleware to post bank transactions in near real time instead of relying on batch data transfers.

Squeaky Clean Data
Transactions between retailers and banks include everything from bulk grocery purchases at Sam's Club to lunch at Sam's Diner. The company then loads the data into the data warehouse.

Since MasterCard doesn't have any control over how the retailer's and financial institution's transaction systems record Joe Smith's charges for bulk foods at the discount store or for a steak and mashed potatoes at the diner, the records in the data warehouse can get awfully messy, awfully fast. A bank couldn't just sample credit-card charges to "Sam's," for instance, since there's more than one retailer by that name.

Maintaining and measuring the quality of the data that lands in the data warehouse is a major challenge for MasterCard. There aren't any mature tools that measure the quality of the data, Alkhalaf says. "When you bring in multiple information from multiple transactional sources, there's no way to reconcile the data correctly," he says. "Your data warehouse isn't much use if the results are invalid because your underlying data is inaccurate and inconsistent."

MasterCard built its own data-scrubbing tool that searches for and analyzes consistencies and anomalies. If it finds 20 Sam's charges from more than one Sam's retailer, it sorts and labels them appropriately. Even after the dirty data

is cleaned, the next step--measuring the data's validity--isn't so simple. "That's a hard one to go after," Alkhalaf says. "How do you know your data quality today is better than it was yesterday?"

Next for MasterCard is a Web interface for all its clients. "It's easier to deploy this way, and then we don't need to support the client software," Alkhalaf says. The company also will add more self-service features for clients, so they can perform more business analysis through the MasterCard portal.

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The Hard Sell
It's not easy to build a business case for a data-warehouse buildout. A data-warehouse project like MasterCard's starts as more of a strategic infrastructure plan. "You start out with a base strategic decision, and your applications grow off of that," says Sam Alkhalaf, senior vice president of technology and strategic architecture for MasterCard.

So when MasterCard conceived of its data warehouse in 1996, the IT guys didn't present a giant business case to upper management. The project went through without a hitch because it was considered a strategic move to give MasterCard a competitive edge. "It was more that this is what we believe is a huge opportunity to keep our strong technology and leadership position," Alkhalaf recalls.

Alkhalaf, who won't reveal how much MasterCard has invested in the project, says it wouldn't be so easy to sell a data warehouse to upper management in today's climate of shrinking IT budgets. "To crank up a data warehouse today would be hard," Alkhalaf says, because most organizations are trying to cut costs rather than launch new initiatives.

With MasterCard's data warehouse well established, the company can begin to measure a return on investment on each application that runs with the warehouse, Alkhalaf says, though he won't say exactly how MasterCard's data warehouse is doing with its ROI.

Meanwhile, MasterCard does have to sell to upper management any extensions to the data-warehouse infrastructure, such as a new storage area network (SAN) for the data-warehouse servers that speeds information retrieval. "A SAN is a tactical decision, so you have to deliver a business case for it," Alkhalaf says.

Next for Alkhalaf and his IT group is to pitch to management a failover and redundancy architecture for the data warehouse, which for now is run at its main data center. "When we first put in the data warehouse, if it went down, it wasn't a major issue. Now that more people rely on it, they expect it to be up all of the time," Alkhalaf says.

It's all about what Alkhalaf calls information velocity. "People want to be able to get to the information more quickly," he says. "We are keeping more data and providing more subjects more quickly to more people."

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15 Minutes
Sam Alkhalaf; Senior Vice President, technology and strategic architecture division; MasterCard International, Purchase, N.Y.

Sam Alkhalaf, 48, is responsible for MasterCard's data warehouse and applications, as well as the company's internal e-mail, desktops, LAN, WAN, helpdesk, security and business applications. Alkhalaf also oversees emerging technology testing and standards development. Alkhalaf has worked in IT for 23 years, seven of them with MasterCard. He holds a B.S. in electronics and an M.B.A. in management.

If I Knew Then What I Know Now: I would have communicated the data warehouse's capabilities more broadly and more often to get it penetrated more quickly into the organization and externally. You have to communicate its functionality to get the masses to jump on it. I could've done more.

When Data Warehouses Become Mission-Critical: It moves them up the food chain because now there are performance expectations. Each year we try to get our data warehouse's availability up. There are a lot of pieces to this, including processing, disk drives and network redundancy.

Biggest Technology Flop: The baggage-handling system at Denver's airport. It was a great lesson in acknowledging that you can have a complex system with lots of great IT knowledge but there might be implementation gaps between the system developers and managers.

Best Advice: Get out of IT consulting. (Before my job at MasterCard, I was with Andersen Consulting.) Also, in an IT project, have a democracy during the requirements definition period and a dictatorship during the execution of it.

Biggest Bet I've Ever Made: The stock market--and the outcome is still to be determined.

For Fun: Golf.

Wheels: Volvo XC90 SUV. It's technologically innovative--a good example of Volvo taking advantage of being a latecomer to the SUV market.

This article originally appeared in Network Computing magazine. www.nwc.com/1408/1408centerfold.html

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