KPH First Unit

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 BUSINESS INTELLIGENCE AND BUSINESS DECISIONS

Ans.5: Data Driven DSS: A Data Driven DSS model puts its emphasis on collected data that is then manipulated to fit the decision maker's needs. This data can be internal, external and in a variety of formats. It is important that usually data is collected and categorized as a time series which is a collection of data that forms a sequence, such as daily sales, operating budgets from one quarter to the next, inventory levels over the previous year, etc.

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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

(3) Time oriented processing capabilities. 

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Document Driven DSS: A Document Driven DSS mod uses documents in a variety of data types such a text documents, spreadsheets and database records to come with decisions as well as further manipulate the information to refine strategies.

Document-Driven DSS is a relatively new field Decision Support. Document-Driven DSS is focused on the retrieval and management of unstructured document Documents can take many forms, but can be broken down into three categories: oral. written, and video. Examples oral documents are conversations that are transcribed video can be news clips, or television commercials; written documents can be written reports, catalogs, and letter from customers, memos, and even e-mail.

Knowledge Driven DSS: A Knowledge Driven DSS model uses special rules stored in a computer or used by a human to determine whether a decision should be made.. For instance, for many day traders a stop loss limit can be seen as a knowledge driven DSS model. These rules or facts are used in order to make a decision. This can suggest or recommend actions to managers. These DSS are person- computer systems with specialized problem-solving expertise. The expertise consists of knowledge about a particular domain, understanding of problems within that domain, and skill at solving some of these problems. A related concept is data mining. It refers to a class of analytical applications that search for hidden patterns in a database. Data mining is the process of sifting through large amounts of data to produce data content relationships. Tools used for building Knowledge-Driven DSS are sometimes called Intelligent Decision Support methods.

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Knowledge-Driven DSS, Suggestion DSS, rule-based DSS and Intelligent DSS are overlapping terms for management support systems built using artificial intelligence technologies. We usually use expert systems development shells and data mining tools to create these systems. Business analysts identify relationships in very large databases using data mining or knowledge discovery tools. When a manager or knowledge worker uses a DSS with a data mining tool the results from an analysis may suggest relationships and new knowledge.

Characteristics of Knowledge-Driven DSS: We can identify a number of characteristics that are common to Knowledge-Driven DSS. First, this category of software aids managers in problem solving. Second, the systems use knowledge stored as rules, frames, or likelihood information. Third, people interact with a program when they are performing a task. Fourth, Knowledge-Driven DSS base recommendations on human knowledge and assist in performing very limited tasks. Fifth, Knowledge- Driven DSS and expert systems do NOT think.

 The inference engine is the software that actually performs the reason function. In small systems, this is sometimes called the shell of the expert system, though the shell can be considered to be everything except the knowledge base itself. The inference engine is the software that uses the knowledge represented in the knowledge base to draw its conclusions. The design of the inference engine may limit the ways in which knowledge can be represented in the knowledge base so that certain shells are only suitable for particular types of applications.

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In comparing Knowledge-Driven Model-Driven DSS, we should remember that:

DSS Knowledge-Driven DSS = Knowledge Base + Inference Engine

Model-Driven DSS = Data + Quantitative Models 

Characteristics of Decision Support System: The following is the list of the characteristics of a DSS.

(1) Facilitation: DSS facilitate and support specific decision-making activities and/or decision processes

(2) Interaction : DSS are computer-based system designed for interactive use by decision makers staff users who control the sequence of interactive and the operations performed. Ans

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(3) Ancillary: DSS can support decision makers at level in an organization. They are NOT intended replace decision makers.

(4) Repeated Use: DSS are intended for repeated A specific DSS may be used routinely needed for ad hoc decision support tasks or used as needed for hoc decisions supports tasks.

(5) Task-Oriented DSS provide specific capabilities that support one or more tasks related to decision- making, including: intelligence and data analysis; identification and design of alternatives; choice among alternatives; and decision implementation.

(6) Identifiable: DSS may be independent systems that collect or replicate data from other information systems OR subsystems of a larger, more integrated information system.

(7) Decision Impact: DSS are intended to improve the accuracy, timeliness, quality and overall effectiveness of a specific decision or a set of related decisions.

Difference Between Decision Support System and Group Decision Support System:

        GDSS is a computer based information system that focuses on the group while DSS focuses on an individual for instance, the manager or the supervisor. GDSS and DSS may have similar components in terms of hardware and software structures however, GDSS has a networking technology that is best suited for group discussions or communication. DSS on the other hand, have technologies that are focused for a single user. GDSS maintenance involves a better system reliability and incomprehensible multiuser access compared to DSS because system failures in GDSS will involve a lot of individual.

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Through these programs or computer based information system, company or individual decision making capacities will be enhanced and hasten. This allows not only good communication system but also a positive outcome within a department, group, or company.

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