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A Framework for Understanding Computerized Decision Support

Published: 13 September 2007

This article introduces Dan Power’s Decision Support Expert Channel and provides helpful definitions of the different types of decision support systems.

People have been using tools and aids to help in decision making for thousands of years. Recordkeeping was basically a way to have historical information for decision making. Building scale models was a tool to help in planning. Using signal fires or drums for warning or alert in times of war was a tool for communications and decision support. In many ways, some books were written to codify knowledge for the next generation of decision makers. The world has changed, and now, more than ever before, managers need more sophisticated computerized tools for decision support. This article summarizes a framework that can help developers better understand what is possible and can help managers better describe their needs.

Decision support is a broad concept that describes tools to assist in individual, group and organization decision making. The tools may be used frequently in a specific decision process or even used to make decisions. The tools may be used to examine, to communicate, to retrieve decision-related documents, or to capture knowledge.

At one end of the spectrum are tools for making or automating decisions; this is the realm of decision automation. The role of a decision maker is usually limited to handling exceptions and periodically revising rules. These are very well-structured, routine decision situations.

Decision automation refers to computerized systems that make decisions and have some capability to independently act upon them. Decision automation refers to using technologies including computer processing to make decisions and implement programmed decision processes. Typically, decision automation is considered most appropriate for well-structured, clearly defined, routine or programmed decision situations. When human decision makers are kept in the decision-making loop, the system is more appropriately called a decision support system (DSS).

At the other end of the spectrum are tools used for one-time special studies; this is the realm of management science, financial analysis and marketing research. Ill-structured situations can be supported by computerized systems, but the support focuses more on information presentation, summary and support analyses rather than on finding an optimal solution. Managers may define information needs, but specialists often develop a computerized analysis to provide the information. For example, a manager may be concerned about customer turnover and request a special study to identify characteristics of customers who are loyal and frequent buyers and those who are not. This study may involve data mining, statistical analysis or even additional data collection.

Special studies refer to a broad range of computerized decision support. A manager may conduct a one-time analysis using Excel, a marketing researcher may use a data mining tool for a market basket analysis, or a financial analyst may conduct a cost-benefit analysis for a new product.

In the middle of the spectrum is the broad realm of computerized decision support systems. At DSSResources.com, decision support systems (DSSs) are defined as "an interactive computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge and/or 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 group’s ability to make decisions. Also, decision support systems refers to an academic field of research that involves designing and studying decision support systems in their context of use. In general, decision support systems are a class of computerized information systems that support decision-making activities. DSSs do not replace human decision makers.

Five more specific decision support system types (Power, 2002) include:

1. Communications-driven DSS
   
   
2. Data-driven DSS
   
   
3. Document-driven DSS
   
   
4. Knowledge-driven DSS
   
   
5. Model-driven DSS

Communications-driven decision support is a type of DSS that emphasizes communications, collaboration and shared decision-making support. A simple bulletin board or threaded e-mail is the most elementary level of functionality. Groupware is a subset of a broader concept called collaborative computing. Communications-driven DSS enables two or more people to communicate with each other, share information and coordinate their activities. Group decision support systems or GDSSs may be either primarily communications-driven or primarily model-driven DSS that allow multiple users to work collaboratively using various software tools. Examples of group support tools are: audio conferencing, bulletin boards and web conferencing, document sharing, electronic mail, computer supported face-to-face meeting software, and interactive video.

Data-driven decision support emphasizes access to and manipulation of a time-series of internal company data and sometimes external data. Simple file systems accessed by query and retrieval tools provide the most elementary level of functionality. Data warehouse systems that allow the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators provide additional functionality. Data-driven DSS with online analytical processing (OLAP) provide the highest level of functionality and decision support that is linked to analysis of large collections of historical data. These systems are often called business intelligence systems. Early versions of data-driven decision support systems were called data-oriented DSS by Alter (1980) or retrieval-only DSS by Bonczek, Holsapple and Whinston (1981). Some data-driven DSSs use real-time data to assist in operational performance monitoring.

Document-driven decision support systems integrate a variety of storage and processing technologies to provide complete document retrieval and analysis to assist in decision making. Examples of documents that might be accessed by a document-driven DSS are policies and procedures, product specifications, catalogs, and corporate historical documents, including minutes of meetings, corporate records, and important correspondence. A search engine is a powerful decision-aiding tool associated with a document-driven DSS. Sometimes these systems are called knowledge management systems.

Knowledge-driven decision support suggests or recommends actions to managers. These DSSs are person-computer systems with specialized problem-solving expertise. The "expertise" consists of knowledge about a particular domain, understanding of problems within that domain, and "skill" at solving some of these problems. These systems store and apply knowledge for a variety of specific business problems. These problems include classification and configuration tasks such as loan approval, help desk support, risk management and application of company policies. A knowledge-driven DSS does not replace human decision makers in contrast to decision automation which uses some of the same technologies.

Model-driven decision support systems emphasize access to and manipulation of a model, e.g., financial, optimization and/or simulation. Simple analytical tools provide the most elementary level of functionality. In general, model-driven DSSs use complex financial, simulation, and/or optimization models to provide decision support. Model-driven DSSs use data and parameters provided by decision makers to aid decision makers in analyzing a situation, but they are not usually data intensive. Very large databases are usually not needed for model-driven DSSs. Early versions of model-driven DSSs were called model-oriented DSS by Alter (1980) and computationally oriented DSS by Bonczek, Holsapple and Whinston (1981).

Not all decision situations require (nor would managers and groups benefit from) computerized decision support. The key for managers and developers is to have a shared framework for discussing wants and needs. The realm of computerized decision support continues to expand to more and more decision situations, but it is certainly not a solution for all decision situations. In general, computerized decision support promotes rational decision behavior that uses analytical decision processes. Where the situation does not require, expect, encourage or need analysis, then computerized decision support will be unnecessary.

Computerized decision support should be considered when managers are in decision situations characterized by one or more of the following factors: complexity, relevant knowledge, uncertainty, specific goals, multiple groups with a stake in the decision outcome (multiple stakeholders), a large amount of information (especially company data or documents), and/or rapid change in information.

References

Alter, S.L. Decision Support Systems: Current Practice and Continuing Challenge. Reading, MA: Addison-Wesley, 1980.

Bonczek, R. H., C.W. Holsapple, and A.B. Whinston. Foundations of Decision Support Systems, New York: Academic Press, 1981.

Power, D. J., Decision Support Systems: Concepts and Resources for Managers, Westport, CT: Greenwood/Quorum, 2002.