Decision Making
Ø Reasons for Growth of Decision Making Information System
- People need to analyze large amounts of information – Improvements in technology itself, innovations in communication, and globalization have resulted in a dramatic increase in the alternatives and dimensions people need to consider when making a decision or appraising an opportunity
- People must make decisions quickly – Time is of the essence and people simply do not have time to sift through all the information manually
- 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
- People must protect the corporate asset of organizational information – Information systems offer the security required to ensure organizational information remains safe.
Ø Model – A simplified representation or abstraction of reality
Ø IT systems in an enterprise
Transaction Processing System
Ø Moving up through the organizational pyramid users move from requiring transactional information to analytical information
Ø Transaction processing system – the basic business system that serves the operational level (analysis) in an organization
Ø 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
Ø Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making
Decision support systems
Ø Decision support system (DSS) – models information to support managers and business professionals during the decision-making process
Ø 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 outputs
What-if analysis
Goal-seeking analysis
Executive information system
Ø Executive information system (EIS) – A specialized DSS that supports senior level executives within the organization
Ø 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 information
- Slice-and-dice – looks at information from different perspectives
Ø Interaction between a TPS and an EIS
Ø Interaction between a TPS and a DSS
Ø Digital dashboard – integrates information from multiple components and presents it in a united display
Artificial intelligence (AI)
Ø The ultimate goal of AI is the ability to build a system that can mimic human intelligence
Ø Intelligent system – various commercial applications of artificial intelligence
Ø Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn
Ø 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
o 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
Data Mining
Ø Data-mining software includes many forms of AI such as neutral 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
- Eg: Consumer goods by content, brand loyalty or similarity
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
Eg: Maytag uses association detection to ensure that each generation of appliances is better than the previous generation.
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
Eg: Kraft uses statistical analysis to assure consistence flavor, color, aroma, texture, and appearance for all of its lines of foods.
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