Chapter 9 : Enabling the Organization - Decision Making.

Nursuhada Bt Abd Ghafar 4C BM111.

Decision Making.
* Reasons for the growth of decision-making information systems
- people need to analyze large amounts of information
- people must make decision 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 system in an enterprise
                                                      Executive      > Executive information systems ( EIS)                                               Managers       > Decision support systems (DSS)
                                    Analysis         > Transaction Processing Systems (TPS)
                                                                                 Organizational Levels
Transaction Processing Systems
* Moving up through the organizational pyramid users move from requiring transaction information to                analytical.


Transaction Processing Systems
> 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 Analysts 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 professional 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

Interaction between in a TPS and a DSS

* Executive Information System (EIS) - 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
> Advantages: can check info on competitor.
* The ultimate goal of AI is the ability to build a system that can mimic human intelligence
* Four most common categories of AI include: 
1. Expert system - computerized advisory programs that imitate the reasoning processes of expert in solving      difficult problems.
2. Neural Network - attempts to emulate the way the human brain works
>  Fuzzy logic- a mathematical method of handling imprecise or subjective information.
3. Genetic alogrithm - an artificial intelligent system that mimics the evolutionary, survival of the fittest process    to generate increasingly better solution to a promblem
4. Intelligent agent - special-purposed knowledge-based information system that accomplishes specific tasks     on behalf of its users
=  Multi-agent systems
=  Agent-based modelling
* Data-mining software includes many forms of AI such as neural networks and expert system

Data mining 
* Common forms of data-mining analysis capabilities include:
    i) 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. CRM systems depend on cluster analysis to segment customer information and        identify behavioral traits.
    ii) Association detection - reveals the degree to which variables are related and the nature and frequency          of these relationships in the information. Market basket analysis - analyzes such items as Web sites and          checkout scanner information to detect customers' buying behavior and predict future behavior by                   identifying affinities among customers' choices of products and services.
    iii) Statistical analysis - performs such function as information correlations, distributions, calculations, and           variance analysis
       1. Forecast- predictions made on the basis of time-series information
       2. Time-series information- time-stamped information collected at a particular frequency.