Chapter 9- Enabling the organization- Decision making
Reasons for growth of decision making information system:
1. People need to analyze large amounts of information- Improvements in technology itself,
innovations in communication, and globalization have resulted in dramatic increase in the
alternatives and dimensions people need to consider when making a decision or appraising an
opportunity.
2. People must make decisions quickly- Time is of the essence and people simply do not have time
to sift through all the information manually.
3. People must apply sophisticated analysis techniques, such as modeling and forecasting, to make
good decisions- Information systems substantially reduce the time required to perform these
sophisticated analysis techniques.
4. People must protect the corporate asset or organizational information- Information systems
offer the security required to ensure organizational information remains safe.
Model- a simplified representation or abstraction of reality. Models can calculate risks, understand
uncertainty, change variables, and manipulate time.
Executives- Executive information systems (EIS), Managers- Decision Support Systems (DSS), AnalystsTransaction Processing Systems (TSS)
Transaction processing system- The basic business system that serves the operational level in an
organization
Online transaction processing (OLTP)- the capturing of transaction and event information using
technology to process the information according to defined business rules, store the information, and
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 (DSS)- models information to support managers and business professionals
during the decision making process.
Three quantitative models used by DSSs include:
1. Sensitivity analysis- The study of the impact that changes in one or more parts of the model
have on other parts of the model.
2. What-if analysis- checks the impact of a change in an assumption on the proposed solution
3. Goal-seeking analysis- finds the inputs necessary to achieve a goal such as a desired level of
output.
Executive information system (EIS)- a specialized DSS that supports senior level executives within the
organization.
Most EISs offer 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.
Digital dashboard- integrates information from multiple components and presents it in a unified display.
Artificial intelligence (AI)- simulates human intelligence such as the ability to reason and learn
Intelligent system- various commercial applications of artificial intelligence.
The ultimate goal of AI is the ability to build a system that can mimic human intelligence.
4 common categories of AI:
1. Expert System- Computerized advisory programs that imitate the reasoning processes of experts
in solving difficult problems
2. Neural network- attempts to emulate the way the human brain works (fuzzy logic- a
mathematical method of handing imprecise of subjective information)
3. Genetic algorithm- an artificial intelligent system that mimics the evolutionary, survival-of-thefittest process to generate increasingly better solutions to a problem.
4. Intelligent agent- Special purposed knowledge based information system that accomplishes
specific tasks on behalf of its users.