chapter 9


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Decision Making

Reasons for the growth of decision-making information systems
  1. People need to analyze large amounts of information
  2. People must make decisions quickly
  3. People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions
  4. People must protect the corporate asset of organizational information

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

Transaction Processing Systems

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 Systems

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 Systems

Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization

Most EISs offering the following capabilities:
  1. Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information
  2. Drill-down – enables users to get details, and details of details, of information
  3. 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)

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:
  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 handling imprecise or subjective information
  3. Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest 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;Multi-agent systems, Agent-based modeling

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 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

Association Detection

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

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

Syaireen Shafiq,

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