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
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.