Chapter 9 : Enabling The Organization - Decision Making

REASONS for the growth of decision-making information systems:

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


Model
♦a simplified representation or abstraction of reality IT systems in an enterprise

TRANSACTION PROCESSING SYSTEM
♦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 SYSTEM
Decision support system (DSS)
♦models information to support managers and business professionals during the decision-making process.

Three quantitative models used by DSSs include:
♦Sensitivity analysis
♦What-if analysis
♦Goal-seeking analysis

Interaction between a TPS and a DSS

EXECUTIVE INFORMATION SYSTEM
Executive information system (EIS)
♦ A specialized DSS that supports senior level executives within the organization.

Interaction between a TPS and an EIS

ARTIFICIAL INTELLIGENCE
Intelligent system – various commercial applications of artificial intelligence.
Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn.
Advantages : can check info on competitor.

Four most common categories of AI include:
♦Expert system
♦Neural Network
♦Genetic algorithm
♦Intelligent agent

DATA MINING
♦Data-mining software includes many forms of AI such as neural networks and expert systems.

Common forms of data-mining analysis capabilities include:
♦Cluster analysis
♦Association detection
♦Statistical analysis


CLUSTER ANALYSIS
♦Cluster analysis – a technique used to divide 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.

CRM systems depend on cluster analysis to segment customer information and identify behavioural traits.


ASSOCIATION PROTECTION
♦Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information.

  Market basket analysis – analyses such items as Web sites and checkout scanner information to detect customers’ buying behaviour and predict future behaviour by identifying affinities among customers’ choices of products and services.


STATISTICAL ANALYSIS
♦Statistical analysis – performs such functions as information correlations, distributions, calculations, and variance analysis.
Forecast – predictions made on the basis of time-series information.
     Time-series information – time-stamped information collected at a particular frequency.