CHAPTER 9: ENABLING THE ORGANIZATION – DECISION MAKING

•   Reasons for the growth of decision-making information systems:

• Model – a simplified representation or abstraction of reality.
• IT systems in an enterprise:

 Transaction Processing Systems(TPS)

• Moving up through the organizational pyramid users move from requiring transactional information to analytical information.

• Transaction processing system - the basic business system that serves the operational level (analysts) in an organization
• Online transaction processing (OLTP) – the capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information
• Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making

Decision Support Systems(DSS)

• Models information to support managers and business professionals during the decision-making process.
• Three quantitative models used by DSSs include:
• Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model. 
• What-if analysis – checks the impact of a change in an assumption on the proposed solution.
• Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output. 
• Interaction between TPS and DSS

Executive Information Systems

• A specialized DSS that supports senior level executives within the organization
• Most EISs offering the following capabilities:
• Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information.
• Drill-down – enables users to get details, and details of details, of information. 
• Slice-and-dice – looks at information from different perspectives.
• Interaction between a TPS and an EIS

• Digital dashboard – integrates information from multiple components and presents it in a unified display

• Intelligent system – various commercial applications of artificial intelligence
• Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn

•  Four most common categories of AI include:
• Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. Eg: Playing Chess.
• Neural Network – attempts to emulate the way the human brain works. Eg: Finance industry uses neural network to review loan applications and create patterns or profiles of applications that fall into two categories – approved or denied.
• Fuzzy logic – a mathematical method of handling imprecise or subjective information. Eg: Washing machines that determine by themselves how much water to use or how long to wash.
• Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem.
• Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users

• Data Mining - includes many forms of AI such as neural networks and expert systems.

- END OF CHAPTER 9-