11-09-2011, 06:51 PM
DSS practice, research and technology continue to evolve. By 1996, Holsapple
and Whinton had identified five specialized types of DSS, including text-oriented
DSS, database-oriented DSS, spreadsheet-oriented DSS, solver-oriented DSS, and
rule-oriented DSS. These last four types of DSS match up with some of Alter’s
(1980) categories. Arnott and Pervan (2005) traced the evolution of DSS using
seven sub-groupings of research and practice: personal DSS, group support
systems, negotiation support systems, intelligent DSS, knowledge managementbased
DSS, executive information systems/business intelligence, and data
warehousing. These sub-grouping overlap, but reflect the diverse evolution of
prior research.
This chapter used an expanded DSS framework (Power, 2001, 2002) to
retrospectively discuss the historical evolution of decision support systems. The
Web has had a significant impact on the variety, distribution and sophistication
of DSS, but handheld PCs, wireless networks, expanding parallel processing
coupled with very large data bases and visualization tools are continuing to
encourage the development of innovative decision support applications.
Historians use two approached to apply the past to the future: reasoning by
analogy and projection of trends. In many ways computerized decision support
systems are like airplanes, coming in various shapes, sizes and forms,
technologically sophisticated and a very necessary tool in many organizations.
Decision support systems research and development will continue to exploit any
new technology developments and will benefit from progress in very large data
bases, artificial intelligence, human-computer interaction, simulation and
optimization, software engineering, telecommunications and from more basic
research on behavioral topics like organizational decision making, planning,
behavioral decision theory and organizational behavior.
Trends suggest that data-driven DSS will use faster, real-time access to larger,
better integrated databases. Model-driven DSS will be more complex, yet
understandable, and systems built using simulations and their accompanying
visual displays will be increasingly realistic. Communications-driven DSS will
provide more real-time video communications support. Document-driven DSS
will access larger repositories of unstructured data and the systems will present
appropriate documents in more useable formats. Finally, knowledge-driven DSS
will likely be more sophisticated and more comprehensive. The advice from
knowledge-driven DSS will be better and the applications will cover broader
domains.
Decision Support Systems pioneers came from a wide variety of backgrounds
and faced many challenges that they successfully overcame to demonstrate the
value of using computers, information technologies and specific decision support
software to enhance and in some situations improve decision making. The DSS
pioneers created particular and distinct streams of technology development and
research that serve as the foundation for much of today’s interest in building and
studying computerized decision support systems.
The future of decision support systems will certainly be different than the
opportunistic and incremental innovations seen in the recent past. Decision
support systems as an academic discipline is likely to follow a path similar to
computer architecture and software engineering and become more rigorous and
more clearly delineated. DSS consulting, teaching and research can be mutually
supportive and each task can help establish a niche for those interested in
building and studying DSSwhether in Colleges of Information, Business or
Engineering
and Whinton had identified five specialized types of DSS, including text-oriented
DSS, database-oriented DSS, spreadsheet-oriented DSS, solver-oriented DSS, and
rule-oriented DSS. These last four types of DSS match up with some of Alter’s
(1980) categories. Arnott and Pervan (2005) traced the evolution of DSS using
seven sub-groupings of research and practice: personal DSS, group support
systems, negotiation support systems, intelligent DSS, knowledge managementbased
DSS, executive information systems/business intelligence, and data
warehousing. These sub-grouping overlap, but reflect the diverse evolution of
prior research.
This chapter used an expanded DSS framework (Power, 2001, 2002) to
retrospectively discuss the historical evolution of decision support systems. The
Web has had a significant impact on the variety, distribution and sophistication
of DSS, but handheld PCs, wireless networks, expanding parallel processing
coupled with very large data bases and visualization tools are continuing to
encourage the development of innovative decision support applications.
Historians use two approached to apply the past to the future: reasoning by
analogy and projection of trends. In many ways computerized decision support
systems are like airplanes, coming in various shapes, sizes and forms,
technologically sophisticated and a very necessary tool in many organizations.
Decision support systems research and development will continue to exploit any
new technology developments and will benefit from progress in very large data
bases, artificial intelligence, human-computer interaction, simulation and
optimization, software engineering, telecommunications and from more basic
research on behavioral topics like organizational decision making, planning,
behavioral decision theory and organizational behavior.
Trends suggest that data-driven DSS will use faster, real-time access to larger,
better integrated databases. Model-driven DSS will be more complex, yet
understandable, and systems built using simulations and their accompanying
visual displays will be increasingly realistic. Communications-driven DSS will
provide more real-time video communications support. Document-driven DSS
will access larger repositories of unstructured data and the systems will present
appropriate documents in more useable formats. Finally, knowledge-driven DSS
will likely be more sophisticated and more comprehensive. The advice from
knowledge-driven DSS will be better and the applications will cover broader
domains.
Decision Support Systems pioneers came from a wide variety of backgrounds
and faced many challenges that they successfully overcame to demonstrate the
value of using computers, information technologies and specific decision support
software to enhance and in some situations improve decision making. The DSS
pioneers created particular and distinct streams of technology development and
research that serve as the foundation for much of today’s interest in building and
studying computerized decision support systems.
The future of decision support systems will certainly be different than the
opportunistic and incremental innovations seen in the recent past. Decision
support systems as an academic discipline is likely to follow a path similar to
computer architecture and software engineering and become more rigorous and
more clearly delineated. DSS consulting, teaching and research can be mutually
supportive and each task can help establish a niche for those interested in
building and studying DSSwhether in Colleges of Information, Business or
Engineering