Document-Driven DSS is a relatively new field in Decision Support. Document-Driven DSS is focused on the retrieval and management of unstructured documents. Documents can take many forms, but can be broken down into three categories: Oral, written, and video. Examples of oral documents are conversations that are transcribed; video can be news clips, or television commercials; written documents can be written reports, catalogs, letters from customers, memos, and even e-mail.
Jane Fedorowicz (1996) estimated that American businesses use store up to 1.
3 trillion documents which can eat up to 50% of their floor space. Yet only 5 to 10 percent of these documents are available to managers for use in Decision making Essay. Fedorowicz defined document as a "chunk" of information.Order now
Unfortunately documents are not standardized in a uniform pattern or structure. Managers and IT/IS people need away to correlate these documents into usable formats that can be compared and processed, as well as incorporating existing databases, to support decision making.
New technology and software is making this concept into a reality.
Basic Document-Driven systems exist in the form of web-based search engines, such as Excite, Alta Vista, and Lycos. Many commercial based web pages contain search engines which allow users to input words or phrases to specify and limit the documents they wish to see. Further advances in client/server technology will allow managers to store, manage, and access these documents
This web page contains links to many sites that contain information related to Data-driven DSS, especially Data Warehousing (DW) and On-line Analytical Processing (OLAP).
Data-driven DSS is a type of DSS that emphasizes access to and manipulation of a time-series of internal company data and sometimes external 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 (OLAP) provides the highest level of functionality and decision support that is linked to analysis of large collections of historical data. Executive Information Systems (EIS) and Geographic Information Systems (GIS) are special purpose Data-Driven DSS.
A Data Warehouse is a database designed to support decision making in organizations. It is batch updated and structured for rapid online queries and managerial summaries. Data warehouses contain large amounts of data. A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management’s decision making process.
On-line Analytical Processing (OLAP) software is used for manipulating data from a variety of sources that has been stored in a static data warehouse. The software can create various views and representations of the data. For a software product to be considered an OLAP application it must contain three key features: 1. multidimensional views of data; 2. complex calculations; and 3. time oriented processing capabilities.
Executive Information Systems (EIS) are computerized systems intended to provide current and appropriate information to support executive decision making for managers using a networked workstation. The emphasis is on graphical displays and an easy to use interface that present information from the corporate database. They are tools to provide canned reports or briefing books to top-level executives. EIS offer strong reporting and drill-down capabilities.
A Geographic Information System (GIS) or Spatial DSS is a support system that represents data using maps. It helps people access, display and analyze data that have geographic content and meaning.
Chapter 1 Introduction
Part 1 Three Case Studies of Decision Support Systems
Case 1 Connoisseur Foods: The Introduction of Modeling and
Data Retrieval Capabilities
Case 2 Great Eastern Bank: A Portfolio Management System
Case 3 Gotaas-Larsen Shipping Corporation: A Corporate Planning System
Part 2 Choices for the User and Implementation
Chapter 2 A Taxonomy of Decision Support Systems
Chapter 3 Using Decision Support Systems to Increase the Effectiveness of Individuals
Chapter 4 Patterns of System Usage
Part 3 Toward Successful Implementation
Chapter 5 Difficulties in System Usage
Chapter 6 Implementation Patterns
Chapter 7 Implantation Risk Factors and Implementation Strategies
Chapter 8 Trends for the Future
Part 4 Additional Case Studies
Case 4 Equitable Life: A Computer-Assisted Underwriting System
Case 5 Interactive Market Systems: A Media Decision Support System
Case 6 The .