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Can we engineer effective decision support systems?

Daniel J. Power

Editor, DSSResources.com

Building effective computerized decision support systems is possible and increasingly common. Understanding how to engineer various DSS and reuse components and methods promises to increase the likelihood of success. Decision support technologies result from creating, combining and modifying computing hardware and software components to solve decision problems, retrieve decision support information and improve decision making processes.

DSSResources.com defines a decision support system as "an interactive computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge and models to identify and solve problems, complete decision process tasks, and make decisions. Decision support system is a general term for any computer application that enhances a person or groupís ability to make decisions.

Decision support systems engineering has been a research topic since Andrew Sage (1991) formalized and differentiated the content area. Engineering is not widely used to describe DSS development, but the concept and methods remain useful. Engineering is a "mindset" or way of viewing the world that brings a scientific perspective to DSS design. Perhaps future DSS designers need to focus more on engineering repeatable decision support solutions.

Sage uses the term decision support systems engineering to mean the systems engineering of decision support systems. Systems engineering relies on 1) design tools and techniques, 2) a systems design methodology, and 3) systems management cognitive tasks needed to produce a useful system.

Engineering is a more mature professional discipline than Information Systems or Computer Science. From an observer's perspective, computer and systems engineering have evolved from industrial and electrical engineering. Reviewing curricula it seems systems engineering programs develop and reinforce problem-solving, analytic thinking and mathematical modeling. In general, an engineer starts with an abstract idea and creates a real instance of the idea.

Sage defines systems engineering "as the need identification, architectural specification, design, production, and maintenance of functional, reliable, and trustworthy systems within cost and time constraints (p. 9)."

Engineers create new technology artifacts. A decision support engineer combines structural components and designs new architectural components to expand the functionality of a specific instance of a DSS. A method is used to customize the system and fit it to organization requirements. The resulting system is specific to an organization and its planned usage. Imagine a familiar physical artifact like a bridge. An engineer designs specific bridges that connect specific physical locations. All bridges have similarities and the engineered design of two bridges may be almost identical, but the actual bridge is at a specific place serving a specific need. Engineering DSS has similarities to any recurring engineering design and development task.

So what is the impediment to building effective DSS? Is it rapid technology change? Is it poorly defined concepts? Is it a lack of research and critical evaluation? Is it a failure to systematize our experiences?

IT staff develop decision support capabilities that fail to meet specifications when one or more of five problems occur: 1) the development method used is inappropriate, 2) the original abstract idea was impractical, 3) technology change made the specifications obsolete, 4) the specifications used immature or emerging technologies, and 5) the problem is misidentified or poorly understood. Some argue DSS are frequently designed haphazardly and that systems engineering tools, methods and approaches reduce problems that lead to failure.

The major purpose of the systems engineering design and development life cycle is to overcome problems and increase DSS effectiveness. DSS effectiveness refers to answering the question "Is the DSS accomplishing its intended purpose?" (cf., Sage p. 201). A DSS developer or engineer learns to apply tools, methods and manage development processes.

Engineering and Information Systems professionals continue to apply scientific, human factors and practical knowledge to create and design integrated hardware and software technology solutions to assist decision makers. Applying the concept of "engineering" to building DSS adds a dimension of system and method to our understanding of what is necessary to successfully build more effective DSS.

Researchers continue to study the tools, methods and management issues associated with building and engineering DSS, as well as the specific applications that decision support analysts and systems engineers develop. A critical evaluation of practice is important to increasing the likelihood of success. Finally, systematizing our experiences provides organized knowledge to help educate the next generation of developers. Research and evaluation of practice can lead to engineering more effective DSS and better use of decision support system technologies.

References

Sage, A. P., Decision support systems engineering, New York: John Wiley and Sons, 1991

Last update: 2012-12-30 04:06
Author: Daniel Power

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