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In this way, as an informational application, a DSS differs from an ordinary operations application, whose function is just to collect data.The DSS can either be completely computerized or powered by humans. The ideal systems analyze information and actually make decisions for the user.
In fact, these systems are primarily used by mid- to upper-level management.
For example, a DSS may be used to project a company's revenue over the upcoming six months based on new assumptions about product sales.
A DSS sifts through and analyzes massive amounts of data, compiling comprehensive information that can be used to solve problems and in decision-making.
A decision support system gathers and analyzes data, synthesizing it to produce comprehensive information reports.
To understand the state of DSS research an empirical investigation of published DSS research is presented.
This investigation is based on the detailed analysis of 1,020 DSS articles published in 14 major journals from 1990 to 2003.At the very least, they allow human users to make more informed decisions at a quicker pace.The DSS can be employed by operations management and other planning departments in an organization to compile information and data and to synthesize it into actionable intelligence.The journal DSS is the major publishing outlet; US ‘Other’ journals dominate DSS publishing and there is very low exposure of DSS in European journals.Around two-thirds of DSS research is empirical, a much higher proportion than general IS research.Due to a large number of factors that surround projected revenue figures, this is not a straightforward calculation that can be done manually.However, a DSS can integrate all the multiple variables and generate an outcome and alternate outcomes, all based on the company's past product sales data and current variables.The primary purpose of using a DSS is to present information to the customer in an easy-to-understand way.A DSS system is beneficial because it can be programmed to generate many types of reports, all based on user specifications.This paper critically analyses the nature and state of decision support systems (DSS) research.To provide context for the analysis, a history of DSS is presented which focuses on the evolution of a number of sub-groupings of research and practice: personal DSS, group support systems, negotiation support systems, intelligent DSS, knowledge management-based DSS, executive information systems/business intelligence, and data warehousing.