Ch. 1
Supporting Business Decision-Making

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Traditionally, academics and practitioners have discussed building Decision Support Systems in terms of four major components – 1) the user interface, 2) the database, 3) the models and analytical tools, and 4) the DSS architecture and network (cf., Sprague and Carlson, 1982). This traditional list of components remains useful because it identifies similarities and differences between categories or types of DSS and it can help managers and analysts build new DSS. The DSS framework is based on the different emphases placed on DSS components when systems are actually constructed (see Figure 1.1).

Data-Driven, Document-Driven and Knowledge-Driven DSS need specialized database components. A Model-Driven DSS may use a simple flat-file database with fewer than 1,000 records, but the model component is very important. Experience and some empirical evidence indicate that design and implementation issues vary for Data-Driven, Document-Driven, Model-Driven and Knowledge-Driven DSS. Multi-participant systems like Group and Inter-Organizational DSS also create complex implementation issues. For instance, when implementing a Data-Driven DSS a designer should be especially concerned about the user's interest in applying the DSS in unanticipated or novel situations.

In creating an accounting or financial DSS simulation model, a developer should attempt to verify that the initial input estimates for the model are thoughtful and reasonable. In developing a representational or optimization model, the analyst should be concerned about possible misunderstandings of what the model means and how it can or cannot be used (cf., Alter, 1980, p. 92). Networking issues create challenges for many types of DSS, but especially for Communications-Driven systems with many participants, so-called multi-participant systems. Today architecture and networking issues are increasingly important in building DSS.

DSS should be built or implemented using an appropriate process. Many small, specialized Model-Driven DSS are built quickly. Large, Enterprise-Wide DSS are built using sophisticated tools and systematic and structured systems analysis and development approaches. Communications-Driven and Group DSS are often purchased as off-the-shelf software. Creating Enterprise-Wide DSS environments remains an iterative and evolutionary task. An Enterprise-Wide DSS grows and inevitably becomes a major part of the overall information systems infrastructure of an organization. Despite the significant differences created by the specific task and scope of a DSS, all DSS have similar technical components and share a common purpose, supporting decision-making.

A Data-Driven DSS database is often a collection of current and historical structured data from a number of sources that have been organized for easy access and analysis. We are expanding the data component to include unstructured documents in Document-Driven DSS and "knowledge" in the form of rules in Knowledge-Driven DSS. Large databases of structured data in Enterprise-Wide DSS are often called data warehouses or data marts. DSS usually use data that has been extracted from all relevant internal and external databases. Managing information often means managing a database. Supporting management decision-making means that computerized tools are used to make sense of the structured data or documents in a database.

Mathematical and analytical models are the major component of a Model-Driven DSS. DSS models should be used and manipulated directly by managers and staff specialists. Each Model-Driven DSS has a specific set of purposes and hence different models are needed and used. Choosing appropriate models is a key design issue. Also, the software used for creating specific models needs to manage needed data and the user interface. In Model-Driven DSS the values of key variables or parameters are changed, often repeatedly, to reflect potential changes in supply, production, the economy, sales, the marketplace, costs, and/or other environmental and internal factors. Information from the models is then analyzed and evaluated by the decision-maker. Suggestion DSS use special models for processing rules or identifying relationships in data.

The DSS architecture and networking design component refers to how hardware is organized, how software and data are distributed in the system, and how components of the system are integrated and connected. A major issue today is whether DSS should be available using a Web browser on a company intranet and also available on the Global Internet. Managers and MIS staff both need to develop an understanding of the technical issues and the security issues related to DSS architectures, networks and the Internet. Networking is the key driver of Communications-Driven DSS.

Managers and DSS analysts both need to emphasize the user interface component. In many ways the user interface is the most important component. The tools for building the user interface are sometimes termed DSS generators, query and reporting tools, and front-end development packages. Much of the design and development effort should focus on building the user interface. We need to remember that the screens and displays in the user interface heavily influence how a manager perceives a DSS. What we see is the DSS!!