1. IS takes on Decision Making: Decision Support Systems (DSS)
ACM 312 MIS Week 10

2. Decision Support in Business
Companies are investing in data-driven decision support application frameworks to help them respond to:
Changing market conditions
Customer needs
Several types of systems
Management information
Decision support
Other information systems

3. Levels of Managerial Decision Making

4. Attributes of Information Quality

5. Decision Structure Business value of improved decision making
Improving hundreds of thousands of “small” decisions adds up to large annual value for the business
Structured (operational)
Procedures can be specified in advance
Unstructured (strategic)
Not possible to specify procedures in advance
Semi-structured (tactical)
Decision procedures can be pre-specified, but not enough to lead to the correct decision

6. Operational managers, rank and file employees
Senior managers:
Make many unstructured decisions
E.g. Should we enter a new market?
Middle managers:
Make more structured decisions but these may include unstructured components
E.g. Why is order fulfillment report showing decline in Minneapolis?
Operational managers, rank and file employees
Make more structured decisions
E.g. Does customer meet criteria for credit?

7. STAGES IN DECISION MAKING
© Prentice Hall 2011

8. Business Intelligence Applications

9. The emerging class of applications focuses on:
DSS Components
The emerging class of applications focuses on:
Personalized decision support
Modeling
Information retrieval
Data warehousing
What-if scenarios
Reporting

10. Management Information Systems

11. Online Analytical Processing

12. GIS and DVS Systems

13. Using Decision Support Systems

14. Market Basket Analysis
Data Mining
Market Basket Analysis
Provides decision support through knowledge discovery
Analyzes vast stores of historical business data
Looks for patterns, trends, and correlations
Goal is to improve business performance
Types of analysis
Regression
Decision tree
Neural network
Cluster detection
Market basket analysis
One of the most common uses for data mining
Determines what products customers purchase together with other products
Other uses
Cross Selling
Product Placement
Affinity Promotion
Survey Analysis
Fraud Detection
Analyze Customer Behavior

15. Executive Information Systems (EIS)
Combines many features of MIS and DSS
Provides immediate and easy information
Identifies critical success factors
Features
Customizable graphical user interfaces
Exception reports
Trend analysis
Drill down capability

16. Enterprise Information Portal
An EIP is a Web-based interface and integration of MIS, DSS, EIS, and other technologies
Available to all intranet users and select extranet users
Provides access to a variety of internal and external business applications and services
Typically tailored or personalized to the user or groups of users
Often has a digital dashboard
Also called enterprise knowledge portals

17. Enterprise Information Portal Components

18. Enterprise Knowledge Portal

19. Attributes of Intelligent Behavior

20. Domains of Artificial Intelligence

21. Components of an Expert System
An Expert System (ES)
A knowledge-based information system
Contain knowledge about a specific, complex application area
Acts as an expert consultant to end users

22. Methods of Knowledge Representation
Case-Based
Examples from the past
Frame-Based
Collection of knowledge about an entity
Object-Based
Data elements include both data and the methods or processes that act on those data
Rule-Based
Factual statements in the form of a premise and a conclusion (If, Then)

23. Expert System Application Categories
Decision Management
Loan portfolio analysis
Employee performance evaluation
Insurance underwriting
Diagnostic/Troubleshooting
Equipment calibration
Help desk operations
Medical diagnosis
Software debugging

24. Benefits of Expert Systems
Captures human experience in a computer-based information system
Helps preserve and reproduce the knowledge
Limitations of Expert Systems
Limited focus
Inability to learn
Maintenance problems
Development cost
Can only solve specific types of problems in a limited domain of knowledge

25. Suitability Criteria for ES Development Tool
Domain: the domain or subject area of the problem is small and well-defined
Expertise: a body of knowledge, techniques, and intuition is needed that only a few people possess
Complexity: solving the problem is a complex task that requires logical inference processing
Structure: the solution process must be able to cope with ill-structured, uncertain, missing, and conflicting data and a changing problem situation
Availability: an expert exists who is articulate, cooperative, and supported by the management and end users involved in the development process
Expert System Shell
The easiest way to develop an expert system
A software package consisting of an expert system without its knowledge base
Has an inference engine and user interface programs

26. Knowledge Engineering
A knowledge engineer
Works with experts to capture the knowledge they possess
Facts and rules of thumb
Builds the knowledge base
if necessary, the rest of the expert system
Performs a role similar to that of systems analysts in conventional information systems development

27. Example of Fuzzy Logic Rules and Query
Resembles human reasoning
Allows for approximate values and inferences and incomplete or ambiguous data
Uses terms such as “very high” instead of precise measures

28. Genetic Algorithms Neural Networks
Genetic algorithm software
Uses Darwinian, randomizing, and other mathematical functions
Simulates an evolutionary process, yielding increasingly better solutions to a problem
Used to model a variety of scientific, technical, and business processes
Useful when thousands of solutions are possible
Modeled after the brain’s mesh-like network of interconnected processing elements (neurons)
Interconnected processors operate in parallel and interact with each other
Allows the network to learn from the data it processes

29. Virtual Reality (VR) Virtual reality is a computer-simulated reality
Fast-growing area of artificial intelligence
Originated from efforts to build natural, realistic, multi-sensory human-computer interfaces
Relies on multi-sensory input/output devices
Creates a three-dimensional world through sight, sound, and touch
Telepresence
Using VR to perform a task in a different location

30. Intelligent Agents
Software surrogate for an end user or a process that fulfills a stated need or activity
Uses built-in and learned knowledge base to accomplish tasks
Software robots or bots
Types of
User Interface Agents
Interface Tutors
Presentation Agents
Network Navigation Agents
Role-Playing Agents
Information Management Agents
Search Agents
Information Brokers
Information Filters

31. Issues in Business Intelligence Deployments
Analyze raw data (e.g., sales transactions)
Extract useful insights
Can transform business processes
Can impact the bottom line
Major impediment - most companies don’t understand their business processes well enough
Uncovering flawed business processes beats merely to monitoring

32. Three main reasons why investments in information technology do not always produce positive results
Information quality
High-quality decisions require high-quality information
Management filters
Managers have selective attention and have variety of biases that reject information that does not conform to prior conceptions
Organizational inertia and politics
Strong forces within organizations resist making decisions calling for major change