We use many different terms for the systems labeled in this chapter as Data-Driven DSS and that is OK. What is important is understanding the concepts associated with helping people access, analyze and understand large amounts of complex data. We can call this category of systems and software Data-Driven DSS, Business Intelligence, data warehouse software, multi-dimensional analysis software, OLAP or Executive Information Systems.
Data-Driven DSS support decision-makers who need access to large amounts of business data. These systems have evolved from simple verification of facts to analysis of the data and now to discovery of new relationships in large historical data sets. Data-Driven DSS software products are also evolving to better support this wide range of activities. Managers can verify facts using intuitive, easy to use query and reporting tools. Decision-makers can conduct analyses using On-line Analytical processing and statistical tools. Also, managers and DSS analysts can discover new relationships in data using knowledge discovery and data mining tools.
Data-Driven DSS are also evolving in terms of technology and architecture. During the past four years, we have been delivering these DSS capabilities using World-Wide Web technologies to managers located almost anywhere in the world. DSS developers have been implementing Data-Driven DSS that can be accessed by users on an intranet or on the Internet. The Web is an exciting frontier that is broadening the use of data in decision-making. These Web-Based DSS will be discussed further in Chapter 11.
Data-Driven DSS are crucial systems for providing managers with the information they want, when they want it, and in a format that is "decision impelling". Despite these clear benefits, many companies have difficulties developing, implementing, and maintaining Data-Driven DSS at an acceptable cost. For large, enterprise-wide Data-Driven DSS projects, cost control largely depends on the management and political skills of the Project Manager, the restraint of targeted user managers and the technical skills of the MIS staff on the project team. Once the diagnosis and needs analysis tasks are completed for a project, future DSS users need to restrain their desire for more information and resist changing the requirements for the Data-Driven DSS.
When we build a Data-Driven DSS we are organizing and summarizing data in multiple dimensions for fast retrieval and for ad hoc analysis. The primary goal of these systems is to help managers transform data into information and knowledge. Both management control and strategic planning activities can be supported by such systems.
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