CHAPTER 9 - ENABLING THE ORGANIZATION - DECISION MAKING

DECISION MAKING

1.Reasons for the growth of decision-making information systems

• People need to analyze large amounts of information
• People must make decisions quickly 
• People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions
• People must protect the corporate asset of organizational information

2. Model - a simplified representation or abstraction of reality.

TRANSACTION PROCESSING SYSTEMS
1. Moving up through the organizational pyramid users move from requiring transactional information to analytical information.
2. Transaction processing system - the basic business system that serves the operational level (analysts) in an organization.
3. 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.
4. Online analytical processing (OLAP) - the manipulation of information to create business intelligence in support of strategic decision making.

DECISION SUPPORT SYSTEMS
1. 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 desired level of output. 

EXECUTIVE INFORMATION SYSTEMS
1. 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.

2. Digital dashboard - integrates information from multiple components and presents it in a unified display.

ARTIFICIAL INTELLIGENCE (AI)
1. Intelligent system - various commercial applications of artificial intelligence.
2. Artificial intelligence (AI) - simulates human intelligence such as the ability to reason and learn.
3. The ultimate gal of AI is the ability to build a system that can mimic human intelligence.
4. 4 most common categories of AI include

• Expert system
• Neural network
• Genetic algorithm
• Intelligent agent

DATA MINING
 1. Common forms of data-mining analysis capabilities include:

• Cluster analysis - a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible
• Association detection - reveals the degree to which variables are related and the nature and frequency of these relationships in the information
• Statistical analysis - performs such functions as information correlations, distributions, calculations, and variance analysis