Decision-Support Systems

Ask a question; get an answer. Those are the basics of computing, which is why decision-support systems (DSS) can claim a lineage that reaches back to the late 1960s, when businesses began to interactively query data to augment static reports. That they remain in use today underscores their essential function in organizational computing.

DSS emerged from a data processing world of routine static reports, according to Clyde Holsapple, professor in the decision science department of the College of Business and Economics at the University of Kentucky in Lexington. "Decision-makers can't wait a week or a month for a report," he says.



Holsapple says that advances in the 1960s, such as the IBM 360 and other mainframe technologies, laid the foundation for DSS. But, he claims, it was during the 1970s that DSS took off, with the arrival of query systems, what-if spreadsheets, rules-based software development and packaged algorithms from companies such as Chicago-based SPSS Inc. and Cary, N.C.-based SAS Institute Inc.

Those capabilities put DSS on the front burner of many information systems project leaders, and it has remained hot ever since.

Information, Please

Tim Harvic is chief technology officer at ProLogis Trust, a publicly held real estate investment trust in Aurora, Colo. He oversees all of the IT systems used to help manage the 1,727 global properties held by the trust. ProLogis buys land and develops properties of all sizes to serve as distribution centers; it has a total of more than 200 million square feet of leased facilities in 36 cities in the U.S. and 14 other countries.

ProLogis also supplies the IT infrastructure at each site for companies that lease the properties.

"You need DSS capabilities to do what we do," Harvic says. "We need to know not only what we have, but when we're going to get something and where it's got to go."

Harvic says that end users need to have analytical tools that see beyond their business units. "Once you have visibility across the enterprise, you have to be able to make decisions on the data," he says.

Such an interdepartment, or even intercompany, approach to data analysis is becoming the next phase of DSS, says Mike Schroeck, global iAnalytics leader in the data warehousing practice at PwC Consulting, the consulting and technical services business of PricewaterhouseCoopers in New York.

"Most companies have done DSS in silos by business unit," Schroeck says. "But now companies are asking for DSS over their entire value chain."

He says decision-makers are no longer satisfied with having every department and division chart its own DSS course.

"Yes, you can analyze CRM or ERP data today," Schroeck says. "But you need to combine the information into a single data warehouse for a consolidated interpretation of the information.

"Executives are getting frustrated with the number of sets of numbers being used in the organization," Schroeck claims. "They want one version of the truth."

PwC estimates that in companies today, only 10% to 20% of users access DSS tools. Schroeck says that to reach the remaining 90% to 80%, companies are going to need to "embed analytics into core solutions" as IT deploys them.

Holsapple takes a different view. "Decision-support systems are so pervasive in their use that people don't even think of them as DSS," he says. He points to the spreadsheet as one of the most common ones used in business today. And, he says, most executives use corporate planning tools with DSS capabilities in them.

But Holsapple agrees that more work can be done, especially with application data integration. Still, he points to the wide range of DSS tools online that anyone can use as the successful entrenchment of DSS analytics in our lives.

"If you go to and look at the forecast and decide to bring an umbrella with you, you're another successful user of decision-support systems," Holsapple says.

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Copyright © 2002 IDG Communications, Inc.

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