The Characteristics of Dss and Bi Assignment

The Characteristics of Dss and Bi Assignment Words: 2043

The characteristics of DSS and BI Background Nowadays, firms are changing and becoming digital firms. If the digital firm is a house, the data warehouse is the foundation and the most important part. In this body of the house, business applications such as (Enterprise resource planning (ERP),Customer relationship management (CRM), Supply chain management (SCM)… re places occuring many actions of firms such as managing, organizing, carrying on business… Business Intelligence (BI), Decision support system (DSS), and Executive support system (ESS) are top of the house(roof), they are knowledge, mind and power of competition of the firm. They support organization in order to make the right decision. In this assignment, we will discuss DSS, BI and explain why DSS are developed mostly in the academic; and BI are developed mostly by software companies. 2 Content First, we will study DSS and then BI. Researching will begin with history view of DSS.

However, before entering history of DSS, we will see some characteristics about DSS. Decision-support systems serve the management level of the organization. DSS help managers make decisions that are unique, rapidly changing, and not easily specified in advance. They address problems where the procedure for arriving at a solution may not be fully predefined in advance. Although DSS use internal information from TPS and MIS, they often bring in information from external sources, such as current stock prices or product prices of competitors.

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Clearly, by design, DSS have more analytical power than other systems. They use a variety of models to analyze data, or they condense large amounts of data into a form in which they can be analyzed by decision makers. DSS are designed so that users can work with them directly; these systems explicitly include user- friendly software. DSS are interactive; the user can change assumptions, ask new questions, and include new data. From studying istory of DSS, characteristics of DSS and BI, we will realize that BI is a model-driven DSS and focuses on helping users make better business decisions. First, we will have a research on Decision Support Systems Origins. The earliest DSS used small subsets of corporate data and were heavily model driven. Recent advances in computer processing and database technology have expanded the definition of a DSS to include systems that can support decision making by analyzing vast quantities of data, including firmwide data from enterprise systems and transaction data from the Web.

Today, there are many types of decision-support systems such as Model- driven DSS, data-driven DSS,  Communications-driven DSS, Document-driven DSS, Knowledge-driven DSS, Web-based DSS. Information Systems researchers and technologists have built and investigated Decision Support Systems (DSS) for approximately 40 years. The developments in DSS beginning with building model-driven DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems, spreadsheet DSS and Group DSS in the early and mid 80s.

Data warehouses, Executive Information Systems, OLAP and Business Intelligence evolved in the late 1980s and early 1990s, knowledge- driven DSS and the implementation of Web-based DSS in the mid-1990s. Around 1970 business journals started to publish articles on management decision systems, strategic planning systems and decision support systems (cf. , Sprague and Watson 1979).. For example, Scott Morton and colleagues McCosh and Stephens published decision support related articles in 1968.

The first use of the term decision support system was in Gorry and Scott- Morton’s (1971) Sloan Management Review article. They argued that Management Information Systems primarily focused on structured decisions and suggested that the supporting information systems for semi- structured and unstructured decisions should be termed “Decision Support Systems”. In this assignment, I emphasize on two basic types of DSS: Model-driven DSS and Data-driven DSS, they will explain why DSS are developed mostly in the academic world; and BI are developed mostly by software companies.

Model-driven DSS Model-driven DSS were primarily standalone systems isolated from major corporate information systems that used some type of model to perform “what-if ” and other kinds of analyses. Their analysis capabilities were based on a strong theory or model combined with a good user interface that made the model easy to use. A model-driven DSS emphasizes access to and manipulation of financial, optimization and/or simulation models. Simple quantitative models provide the most elementary level of functionality.

Model-driven DSS use limited data and parameters provided by decision makers to aid decision makers in analyzing a situation, but in general large data bases are not needed for model-driven DSS (Power, 2002). Early versions of model-driven DSS were called model-oriented DSS by Alter (1980), computationally oriented DSS by Bonczek, Holsapple and Whinston (1981) and later spreadsheet-oriented and solver-oriented DSS by Holsapple and Whinston (1996). The idea of model-driven spatial decision support system (SDSS) evolved in the late 1980’s (Armstrong, Densham, and Rushton. 1986) and by 1995 the SDSS concept had become firmly established in the literature (Crossland, Wynne, and Perkins, 1995). Data-driven spatial DSS are also common. DSS has been using more model-driven. Model-driven DSS will be more complex, yet understandable, and systems built using simulations and their accompanying visual displays will be increasingly realistic. Data-driven DSS In general, a data-driven DSS emphasizes access to and manipulation of a time-series of internal company data and sometimes external and real-time data.

Simple file systems accessed by query and retrieval tools provide the most elementary level of functionality. Data warehouse systems that allow the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators provide additional functionality. Data-Driven DSS with On-line Analytical Processing (cf. , Codd et al. , 1993) provide the highest level of functionality and decision support that is linked to analysis of large collections of historical data. Executive Information Systems are examples of data-driven DSS (Power, 2002).

Initial examples of these systems were called data-oriented DSS, Analysis Information Systems (Alter, 1980) and retrieval-only DSS by Bonczek, Holsapple and Whinston (1981). In about 1990, data warehousing and On-Line Analytical Processing (OLAP) began broadening the realm of EIS and defined a broader category of data-driven DSS (cf. , Dhar and Stein, 1997). Nigel Pendse (1997), author of the OLAP Report, claims both multidimensional analysis and OLAP had origins in the APL programming language and in systems like Express and Comshare’s System W.

Nylund (1999) traces the developments associated with Business Intelligence (BI) to Procter & Gamble’s efforts in 1985 to build a DSS that linked sales information and retail scanner data. Metaphor Computer Systems, founded by researchers like Ralph Kimball from Xerox’s Palo Alto Research Center (PARC), built the early P&G data-driven DSS. Staff from Metaphor later founded many of the Business Intelligence vendors: The term BI is a popularized, umbrella term coined and promoted by Howard Dresner of the Gartner Group in 1989. It describes a set of concepts and methods to improve business decision making by using fact-based support systems.

BI is sometimes used interchangeably with briefing books, report and query tools and executive information systems. In general, business intelligence systems are data-driven DSS. Business Intelligence We define business intelligence (BI) as systems that combine: Data gathering;Data storage; Knowledge management; with analysis to evaluate complex corporate and competitive information for presentation to planners and decision maker, with the objective of improving the timeliness and the quality of the input to the decision process. This definition looks much like the way decision support systems (DSSs) are defined.

Indeed, a good way to think of business intelligence systems is that they are, in Dan Power’s framework (Power 2002), data-driven DSSs. That is, they emphasize analysis of large volumes of structured (and to some extent semi- structured) data. Implicit to this definition is that business intelligence systems provide actionable information and knowledge at the right time, in the right location, and in the right form. In computer-based environments, business intelligence uses a large database, typically stored in a data warehouse or data mart, as its source of information and as the basis for sophisticated analysis.

Analyses ranges from simple reporting to slice-and-dice, drill down, answering ad hoc queries, real-time analysis, and forecasting. A large number of vendors provide analysis tools. Perhaps the most useful of these is the dashboard. Recent developments in BI include business performance measurement (BPM), business activity monitoring (BAM), and the expansion of BI from being a staff tool to being used by people throughout the organization (BI for the masses). In the long-term, BI techniques and findings will be imbedded into business processes. BI is neither a product nor a system.

It is an architecture and a collection of decision-support applications and databases that provide the business community easy access to business data. Analysis of the DSS-BI connection The second type of DSS explains that BI systems are data-driven DSS. It is an important approach to decision support. BI are developed by software companies because they analyze large pools of data found in major corporate systems. They support decision making by enabling users to extract useful information that was previously buried in large quantities of data. Often data from transaction processing systems are collected in data warehouses for this purpose.

Online analytical processing (OLAP) and data mining can then be used to analyze the data. Companies are starting to build data-driven DSS to mine customer data gathered from their Web sites as well as data from enterprise systems. Software companies created Bi in order to use common requirement for many enterprise. Furthermore, trends suggest that data-driven DSS or BI will use faster, real-time access to larger, better integrated databases. BI systems are geared to provide accurate and timely information (indirect support). They has an executive and strategy orientation.

While DSS has been oriented toward analysts. DSS are constructed to directly support specific decision making. BI systems are constructed with commercially available tools and components that are fitted to the needs of organizations; BI emphasize analysis of large volumes of structured (and to some extent semi-structured) data. Otherwise, DSS more programming is used to construct custom solutions to very unstructured problems. Summary, BI is only a Data-driven DSS, which is better-integrated database, suitable for enterprises. Meanwhile, area of researching DSS is extremely big.

DSS 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. By using more model: not only data-driven DSS (BI) but also model-driven DSS, communications-driven DSS, document- driven DSS, and knowledge-driven DSS.

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. Conclusion One of these three reasons is especially salient to our contention that the DSS realm is a central facet of the IS field: “Success as a business executive depends critically on innovation and creativity in the use and application of data for decision making” (Dhar and Sundararajan 2006).

Who will build intelligence into your business processes? Organizations that need to gain more efficiency and manage or reduce costs are looking to Business Intelligence (BI) to address their requirements. Who did creat the foundation to assist decision-making and planning? The DSS pioneers created particular and distinct streams of technology development and research that serve as the foundation for much of today’s work in DSS. Decision support pioneers include many academic researchers from programs at MIT, University of Arizona, University of Hawaii, University of Minnesota and Purdue University.

DSS and BI are top of house (roof), they are the mind of competitive organizations. Nevertheless, organizations are bureaucracies with limited capabilities and competencies for acting decisively. When environments change and businesses need to adopt new business models to survive, strong forces within organizations resist making decisions calling for major change. Decisions taken by a firm often represent a balancing of the firm’s various interest groups rather than the best DSS or BI solution to the problem.

Reference 1. Professor CHANG TO, Seminar on Information Management 2. http://dssresources. com/history/dsshistory. html 3. Solomon Negash1 and Paul Gray2, Business intelligence, Frada Burstein Clyde W. Holsapple (Editors), Handbook on Decision Support Systems, 2007 1Department of Computer Science and Information Systems, Kennesaw State University, Kennesaw, GA, USA 2School of Information Systems and Technology, Claremont Graduate University, Claremont,CA, USA

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