1. CHAPTER 4 ANALYTICS, DECISION SUPPORT, AND ARTIFICIAL INTELLIGENCE
Brainpower for Your Business

2. Opening Case: Online Learning
Notice the increase in online learning and the decrease in traditional enrollments.
What are the causes of this: Universities offering more online classes? More
alternatives to Universities?
What are the business implications of this? More space on campus…what can be done with that if it becomes available? Should colleges move towards online
courses?

3. Phases of Decision Making
Intelligence (diagnostic)
Design (create solutions)
Choice (select solution)
Implementation (apply
solution)
May cycle between these
phases.

4. Types of Decisions
Structured decision : data is available and variables are known. Decision is based on known criteria.
Semi-Structured decision: In between. Some uncertainty.
Nonstructured decision: not sure what data to collect. Variables affecting decision are not known. Not sure what the success criteria may be.
Satisficing:
Not optimal decision but allows
us to satisfy predetermined
Criteria.
E.g., Salary at least $50,000.

5. Decision Support Systems
Types of Decisions
Recurring decision
Nonrecurring (ad hoc) decision
Decision Support Systems
Helps you analyze, but you must know how to solve the problem, and how to use the results of the analysis
Model management component
Data management component
User interface management component

6. Components of a DSS

7. Geographic Information Systems
Geographic information system (GIS)
Spatial information is any information in map form
Used to analyze information, generate business intelligence, and make decisions. It’s easier to see information on a map. Suppose you are going to start a new store selling electronic accessories. Where would you locate it?

8. DATA-MINING TOOLS AND MODELS
Databases and DBMSs: operational data
Multidimensional analysis tools: summarized data
Digital dashboards: managers can get real- time info and drill down or roll up
Statistical tools: regression, summarization, association rules, clustering
GISs: See information on a map
Artificial intelligence: Genetic algorithms, neural nets

9. Data-Mining: Predictive Analytics
highly computational data-mining technology that uses information and business intelligence to build a predictive model for a given business application
Insurance, retail, healthcare, travel, financial services, CRM, SCM, credit scoring, etc

10. Data-Mining: Predictive Analytics Example
Prediction goal
What customers are most likely to respond to a social media campaign within 30 days by purchasing at least 2 products in the advertised product line?
Prediction indicators
Frequency of purchases
Proximity of date of last purchase
Presence on Facebook and Twitter
Number of multiple-product purchases

11. Data-Mining: Text Analytics
uses statistical, AI, and linguistic technologies to convert unstructured textual information into structured information
Gaylord Hotels uses text analytics to make sense of customer satisfaction surveys

12. Data-Mining: Endless Analytics
Web analytics – understanding and optimizing Web page usage
Search engine optimization (SEO) – improving the visibility of Web site using tags and key terms
HR analytics – analysis of human resource and talent management data
Marketing analytics – analysis of marketing-related data to improve product placement, marketing mix, etc

13. Data-Mining: Endless Analytics
CRM analytics – analysis of CRM data to improve sales force automation, customer service, and support
Social media analytics – analysis of social media data to better understand customer/organization interaction dynamics
Mobile analytics – analysis of data related to the use of mobile devices to support mobile computing and mobile e-commerce (m-commerce)

14. Artificial Intelligence
Artificial intelligence (AI)
Types of AI systems used in business
Expert systems
Neural networks
Genetic algorithms
Agent-based technologies
AI systems deliver the conclusion (rather than helping you analyze the options)

15. Expert Systems Expert (knowledge-based) system
Uses if-then rules…lots of them.
Used for
Diagnostic problems (what’s wrong?)
Prescriptive problems (what to do?) E.g., should we give a loan to someone. Or should a credit card customer be allowed to run a charge? If customer older than 40 AND customer income > 100,000 and previous payments all on time THEN allow transaction.

16. Expert System: Components
Information Types
Problem facts
Domain expertise
“Why?” information
People
Domain expert: know the rules to apply
Knowledge engineer: Capture the rules
Knowledge worker: use the rules
IT Components
Knowledge acquisition
Knowledge base
Inference engine
User interface
Explanation module

17. Expert System: Components

18. What Expert Systems Can and Can’t Do
An expert system can
Reduce errors
Improve customer service
Reduce cost
An expert system can’t
Use common sense
Automate all processes

19. Neural Networks and Fuzzy Logic
Neural network (NN)
(or artificial neural network (ANN))
Learns through training data
Finds patterns
Different from expert systems since rules do not have to be spelled out here…it discovers its own rules in the data!

20. The Layers of a Neural Network

21. Neural Networks Can…
Learn and adjust to new circumstances on their own
Take part in massive parallel processing
Function without complete information
Cope with huge volumes of information
Analyze nonlinear relationships

22. Fuzzy Logic Fuzzy logic
a mathematical method of handling imprecise or subjective information
Used to make ambiguous information such as “short” usable in computer systems
Applications
Google’s search engine
Washing machines
Antilock brakes

23. Genetic Algorithms Genetic algorithm (GA)
Takes thousands or even millions of possible solutions, combining and recombining them until it finds the an optimal solution
Needs a fitness function and encoding of the possible solutions in terms of chromosomes. It then attempts to find the best chromosomes, after reproduction and mutation.
E.g., What is the best inventory level for engines for the Ford F150?
Work in environments where no model of how to find the right solution exists

24. Agent-Based Technologies
Intelligent Agents
Multi-Agent Systems

25. Intelligent Agents Software that acts on our behalf
Information agents or shopping/buyer agents
Monitoring-and-surveillance agents
User or personal agents
Data-mining agents

26. Multi-Agent Systems Biomimicry Swarm (collective) Intelligence.
Ants find shortest route to food by laying pheromone trails that decay automatically if they are unfit or the food supply is gone. Based on this, they find theshortest path to the food. Similarly, find simple rules that can create big desirable patterns.