Decision support systems are a class of computerized information systems that support decision making activities.

Contents 1 Definitions 2 A brief history 3 Taxonomies 4 Architectures 5 Applications

Definitions

The concept of a decision support system (DSS) is extremely broad and its definitions vary depending upon the author's point of view (Druzdzel and Flynn 1999). A DSS can take many different forms and the term can be used in many different ways (Alter 1980). On the one hand, Finlay (1994) and others define a DSS broadly as "a computer-based system that aids the process of decision making ." In a more precise way, Turban (1995) defines it as "an interactive, flexible, and adaptable computer-based information system , especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker's own insights." Other definitions fill the gap between these two extremes. For Keen and Scott Morton (1978), DSS couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions ("DSS are computer-based support for management decision makers who are dealing with semi-structured problems"). For Sprague and Carlson (1982), DSS are "interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems." On the other hand, Keen (1980) claims that it is impossible to give a precise definition including all the facets of the DSS ("there can be no definition of decision support systems , only of decision support "). Nevertheless, according to Power (1997), the term decision support system remains a useful and inclusive term for many types of information systems that support decision making. He humorously adds that every time a computerized system is not an on-line transaction processing system (OLTP), someone will be tempted to call it a DSS. As you can see, there is no universally accepted definition of DSS.

Additionally, the specifics of it is what makes it less generalized and more detailed. In addition, a DSS also is a specific Software application that helps to analyze data contained with a customer database. This approach to customers is used when deciding on target markets as well as customer habits. As you can see in this specific example, it is obvious that DSS can be used for more than just organization.

A brief history

In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power, 2003). According to Keen and Scott Morton (1978), the concept of decision support has evolved from two main areas of research: the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s. It is considered that the concept of DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS. Beginning in about 1990, data warehousing and on-line analytical processing ( OLAP ) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced.

It is clear that DSS belong to an environment with multidisciplinary foundations, including (but not exclusively) database research, artificial intelligence , human-computer interaction, simulation methods, software engineering , and telecommunications .

DSS also has a weak connection to the user interface paradigm of hypertext . Both the University of Vermont PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload , certain researchers, notably Douglas Engelbart , have been focused on helping decision makers in particular.

DSS has been around for a long time, however, it obviously didn't make a boom until the start of the computer era. Since computers are aging and becoming more and more advanced, so is the use of DSS. However, with this in mind, DSS will continue to advance and coincide with computer advancement. Additionally, they have been around for more than 35 years. DSS began with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid 80s. Additionally, DSS is also responsible for the origin of Executive Information Systems, OLAP and Business Intelligence. Additionally, as computers have advanced, so have the use of Web-based DSS in the mid-1990s. As you can see, DSS will continue to improve.

Taxonomies

As with the definition, there is no all-inclusive taxonomy of DSS either. Different authors propose different classifications. At the user-level, Hättenschwiler (1999) differentiates passive , active , and cooperative DSS . A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to her for validation. The whole process then starts again, until a consolidated solution is generated.

At the conceptual level, Power (2002) differentiates communication-driven DSS , data-driven DSS , document-driven DSS , knowledge-driven DSS , and model-driven DSS . A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by DSS users to aid decision makers in analyzing a situation, but they are not necessarily data intensive. Dicodess is an example of an open source , model-driven DSS generator (Gachet 2004). A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove (Stanhope 2002). A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data. A document-driven DSS manages, retrieves and manipulates unstructured information in a variety of electronic formats. Finally, a knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures. At the system level, Power (1997) differentiates enterprise-wide DSS and desktop DSS . Enterprise-wide DSS are linked to large data warehouses and serve many managers in a company. Desktop, single-user DSS are small systems that reside on an individual manager's PC.

When classifying DSS, it can be viewed as very broad or very narrow. Since it is difficult to classify DSS into only one classification, the taxonomy cannot exactly be pinpointed. However, if it is necessary, a DSS is certainly classified into precise, scientific organizational software that not only contributes, but also performs decision making steps in order to ease the pressure for its users. The fact is in a few words, DSS is an organizational decision making software.

Architectures

Once again, different authors identify different components in a DSS. Sprague and Carlson (1982) identify three fundamental components of DSS: (a) the database management system (DBMS), (b) the model-base management system (MBMS), and (c) the dialog generation and management system (DGMS).

According to Power (2002), academics and practitioners have discussed building DSS in terms of four major components: (a) the user interface , (b) the database , (c) the model and analytical tools, and (d) the DSS architecture and network. Hättenschwiler (1999) identifies five components of DSS: (a) users with different roles or functions in the decision making process (decision maker, advisors, domain experts, system experts, data collectors), (b) a specific and definable decision context, (c) a target system describing the majority of the preferences, (d) a knowledge base made of external data sources, knowledge databases, working databases, data warehouses and meta-databases, mathematical models and methods, procedures, inference and search engines, administrative programs, and reporting systems, and (e) a working environment for the preparation, analysis, and documentation of decision alternatives.

Marakas (1999) proposes a generalized architecture made of five distinct parts: (a) the data management system, (b) the model management system, (c) the knowledge engine, (d) the user interface, and (e) the user(s).

There are several ways to classify DSS applications. Not every DSS fits neatly into one category, but a mix of two or more architecture in one.

Holsapple and Whinston (1996) classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and Compound DSS.

A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes two or more of the five basic structures described by Holsapple and Whinston (1996).

The support given by DSS can be separated into three distinct. interrelated categories (Hackathorn and Keen, 1981): Personal Support, Group Support and Organizational Support.

Additionally, I classify DSS in a similar way. The build up of a DSS is classified into a few characteristics. 1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze. 2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user. 3) Outputs/Feedback: This is used so the user of the system can analyze the decisions that may be made and then potentially 4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use.

Applications

As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.

One of the examples is Clinical decision support system for medical diagnosis . Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.

DSS has many applications that have already been spoken about. However, it can be used in any field where organization is necessary. I am using it now for my fathers surgery center. Additionally, a DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward. DSS has endless possibilities that can be used anywhere and anytime, for its decision making needs.