Daniel J. Power

Past, present, and future of decision support technology

Since the early 1970s, decision support systems (DSS) technology and applications have evolved significantly. Many technological and organizational developments have exerted an impact on this evolution. DSS once utilized more limited database, modeling, and user interface functionality, but technological innovations have enabled far more powerful DSS functionality. DSS once supported individual decision-makers, but later DSS technologies were applied to workgroups or teams, especially virtual teams. The advent of the Web has enabled inter-organizational decision support systems, and has given rise to numerous new applications of existing technology as well as many new decision support technologies themselves. It seems likely that mobile tools, mobile e-services, and wireless Internet protocols will mark the next major set of developments in DSS. This paper discusses the evolution of DSS technologies and issues related to DSS definition, application, and impact. It then presents four powerful decision support tools, including data warehouses, OLAP, data mining, and Web-based DSS. Issues in the field of collaborative support systems and virtual teams are presented. This paper also describes the state of the art of optimization-based decision support and active decision support for the next millennium. Finally, some implications for the future of the field are discussed.

Model-driven decision support systems: Concepts and research directions

In some decision situations, quantitative models embedded in a Decision Support System (DSS) can help managers make better decisions. Model-driven DSS use algebraic, decision analytic, financial, simulation, and optimization models to provide decision support. This category of DSS is continuing to evolve, but research can resolve a variety of behavioral and technical issues that impact system performance, acceptance and adoption. This article includes a brief survey of prior research. It focuses on model-driven DSS built using decision analysis, optimization, and simulation technologies; implementation using spreadsheet and web technologies; issues associated with the user interface; and behavioral and technical research questions.

Progress in Web-based decision support technologies

World Wide Web technologies have transformed the design, development, implementation and deployment of decision support systems. This article reviews and summarizes recent technology developments, current usage of Web-based DSS, and trends in the deployment of such systems. Many firms use the Web as a medium to convey information about DSS products or to distribute DSS software. The use of Web-based computation to provide product demonstrations or to deploy DSS applications for remote access remains less common. The academic literature on Web-based DSS is largely focused on applications and implementations, and only a few articles examine architectural issues or provide design guidelines based on empirical evidence.

Understanding Data-Driven Decision Support Systems

Abstract It is important for managers and Information Technology professionals to understand data-driven decision support systems and how such systems can provide business intelligence and performance monitoring. Data-driven DSS is one of five major types of computerized decision support systems and the features of such systems vary across specific implementations. Different development packages also impact the capabilities of data-driven DSS and hence criteria for evaluating data-driven DSS development software are important to understand. Overall, this article builds on an historic foundation of prior decision support systems theory.

Supporting Decision-Makers: An Expanded Framework

A conceptual framework for Decision Support Systems (DSS) is developed based on the dominant technology component or driver of decision support, the targeted users, the specific purpose of the system and the primary deployment technology. Five generic categories based on the dominant technology component are proposed, including Communications-Driven, Data-Driven, Document-Driven, Knowledge-Driven, and Model-Driven Decision Support Systems. Each generic DSS can be targeted to internal or external stakeholders. DSS can have specific or very general purposes. Finally, the DSS deployment technology may be a mainframe computer, a client/server LAN, or a Web-Based architecture. The goal in proposing this expanded DSS framework is to help people understand how to integrate, evaluate and select appropriate means for supporting and informing

Specifying An Expanded Framework for Classifying and DescribingDecision Support Systems

This article defines an expanded conceptual framework for classifying and describing Decision Support Systems (DSS) that consists of one primary dimension and three secondary dimensions. The primary dimension is the dominant technology component or driver of decision support. The three secondary dimensions are the targeted users, the specific purpose of the system and the primary deployment or enabling technology. Five generic DSS types are identified and defined based upon the dominant technology component, including Communications-driven, Data-driven, Document-driven, Knowledge-driven, and Model-driven Decision Support Systems. Specific targeted users like individuals, groups, or customers can use any of the five generic types of DSS. Also, a DSS can be created for a decision- specific or a more general purpose. Finally, in the framework, the DSS deployment and enabling technology may be a mainframe computer, a client/server LAN, a spreadsheet or a webbased technology architecture.

Decision Support Systems: A Historical Overview

Academic researchers from many disciplines have been studying computerized decision support systems (DSSs) for approximately 40 years. This chapter briefly summarizes the history of decision support systems and provides examples of DSSs for each of the categories in the expanded DSS framework (Power 2002), including communications-driven, data-driven, document-driven, knowledge-driven and model-driven DSSs. Trends suggest continuing adoption of new technologies in DSS and past events suggest decision support may follow the path of other applied design disciplines like computer architecture and software engineering.

Using ‘Big Data’ for analytics and decision support

People and the computers they use are generating large amounts of varied data. The phenomenon of capturing and trying to use all of the semi-structured and unstructured data has been called by vendors and bloggers ‘Big Data’. Organisations can capture and store data of many types from almost any source, but capturing and storing data only adds value when it has a useful purpose. Big Data must be used to provide input to analytics and decision support capabilities if it is to create real value for organisations. Some bloggers, industry leaders and academics have become disillusioned by the term Big Data. It is a marketing term and not a technical term. More descriptive terms like unstructured data, process data and machine data are more useful for information technology (IT) professionals. Researchers need to study and document use cases that explain how specific, novel data, so-called Big Data, can be used to support decision-making.

Impact of Social Media and Web 2.0 on Decision-Making

Information technology continues to provide opportunities to alter the decisionmaking behavior of individuals, groups and organizations. Two related changes that are emerging are social media and Web 2.0 technologies. These technologies can positively and negatively impact the rationality and effectiveness of decision-making. For example, changes that help marketing managers alter consumer decision behavior may result in poorer decisions by consumers. Also, managers who heavily rely on a social network rather than expert opinion and facts may make biased decisions. A number of theories can help explain how social media may impact decision-making and the consequences.

The Changing Technological Context of Decision Support Systems

Understanding technological change is important for practitioners and academics who want a realistic perspective on information technology’s role in building Decision Support Systems (DSS). To develop this perspective, the DSS technology outlined by Sprague and Carlson (1982) is compared and contrasted with the technological context of current DSS. Their technology framework of dialogue, database, models, and DSS architecture is reviewed. Current technologies like Graphical User Interfaces (GUI), data warehouses, On-line Analytical Processing (OLAP), and client-server technologies are then discussed in terms of that framework. Several examples are used to illustrate the changing technological context of DSS.