1. Chapter 4
Analytics, Decision Support, and Artificial Intelligence: Brainpower for Your Business
McGraw-Hill/Irwin
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.

2. STUDENT LEARNING OUTCOMES
Compare and contrast decision support systems and geographic information systems.
Describe the decision support role of specialized analytics (predictive and text analytics).
Describe the role and function of an expert system in analytics.

3. STUDENT LEARNING OUTCOMES
Explain why neural networks are effective decision support tools.
Define genetic algorithms and the types of problems they help solve.
Describe data-mining agents and multi-agent systems.

4. ONLINE LEARNING
Notice the increase in online learning and the decrease in traditional enrollments.

5. Questions
Have you taken or are taking an online course? Fully online or hybrid?
Why do students opt to take online courses over traditional classroom courses?
Is this transformation occurring at the K-12 level?

6. INTRODUCTION Businesses make decisions everyday
Some big and some small
Many IT tools can aid in the decision-making process
Analytics is now key to the success of any business

7. CHAPTER ORGANIZATION Decisions and Decision Support
Learning outcome #1
Geographic Information Systems
Data-Mining Tools and Models
Learning Outcome #2
Artificial Intelligence
Learning outcomes #3, 4, and 5
Agent-Based Technologies
Learning outcome #6

8. DECISIONS AND DECISION SUPPORT
Find or recognize the problem, need, or opportunity
Consider ways of solving the problem
Examine the merits of each solution and choose the best one
Carry out the chosen solution and monitor the results

9. Types of Decisions You Face
Structured decision – processing a certain information in a specified way so you always get the right answer
Nonstructured decision – may be several “right” answers, without a sure way to get the right answer
Recurring decision – happens repeatedly
Nonrecurring (ad hoc) decision – one you make infrequently

10. Types of Decisions You Face
EASIEST
MOST DIFFICULT

11. Decision Support Systems
Decision support system (DSS) – a highly flexible and interactive system that is designed to support decision making when the problem is not structured
Decision support systems help you analyze, but you must know how to solve the problem, and how to use the results of the analysis

12. Components of a DSS
Model management component – consists of both the DSS models and the model management system
Data management component – stores and maintains the information that you want your DSS to use
User interface management component – allows you to communicate with the DSS

13. Components of a DSS

14. GEOGRAPHIC INFORMATION SYSTEMS
Geographic information system (GIS) – DSS designed specifically to analyze spatial information
Spatial information is any information in map form
Businesses use GIS software to analyze information, generate business intelligence, and make decisions

15. Google Earth as a GIS

16. DATA-MINING TOOLS AND MODELS
Business need IT-based analytics tools
Databases and DBMSs
Query-and-reporting tools
Multidimensional analysis tools
Digital dashboards
Statistical tools
GISs
Specialized analytics
Artificial intelligence
Our remaining focus

17. Data-Mining Tools and Models Support
Association/dependency modeling – cross-selling opportunities, recommendation engine effectiveness
Clustering – groups of entities that are similar (without using known structures)
Classification – use historical data to derive future inferences

18. Data-Mining Tools and Models Support
Regression – find corollary and often causal relationships between data sets
Summarization – basic, but powerful
Sums, averages
Standard deviations
Histograms, frequency distributions

19. Predictive Analytics
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

20. Predictive Analytics
Prediction goal – the question you want addressed by the predictive analytics model
Prediction indicator – specific measurable value based on an attribute of the entity under consideration

21. Predictive Analytics

22. 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

23. Text Analytics
Text analytics – uses statistical, AI, and linguistic technologies to convert textual information into structured information
Gaylord Hotels uses text analytics to make sense of customer satisfaction surveys

24. Text Analytics Support
Lexical analysis – word frequency distributions
Named entity recognition – identifying peoples, places, and things
Disambiguation – meaning of a named entity recognition
“Ford” can refer to how many different things?

25. Text Analytics Support
Coreference – handling of differing noun phrases that refer to the same object
Sentiment analysis – discerning subjective business intelligence such as mood, opinion, and emotion

26. 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

27. Endless Analytics
Marketing analytics – analysis of marketing-related data to improve product placement, marketing mix, etc
CRM analytics – analysis of CRM data to improve sales force automation, customer service, and support

28. Endless Analytics
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)

29. ARTIFICIAL INTELLIGENCE
Artificial intelligence, the science of making machines imitate human thinking and behavior, can replace human decision making in some instances
Expert systems
Neural networks (and fuzzy logic)
Genetic algorithms
Agent-based technologies

30. Expert Systems
Expert (knowledge-based) system – an artificial intelligence system that applies reasoning capabilities to reach a conclusion
Used for
Diagnostic problems (what’s wrong?)
Prescriptive problems (what to do?)

31. Traffic Light Expert System

32. 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

33. Neural Networks and Fuzzy Logic
Neural network (artificial neural network or ANN) – an artificial intelligence system that is capable of finding and differentiating patterns

34. 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

35. 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 breaks

36. Genetic Algorithms
Genetic algorithm – an artificial intelligence system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem

37. Genetic Algorithm Examples
Staples – determine optimal package design characteristics
Boeing – design aircraft parts such as fan blades
Many retailers – better manage inventory and optimize display areas

38. Genetic Algorithms Can…
Take thousands or even millions of possible solutions and combine and recombine them until it finds the optimal solution
Work in environments where no model of how to find the right solution exists

39. AGENT-BASED TECHNOLOGIES
Agent-based technology (software agent) – piece of software that acts on your behalf (or on behalf of another piece of software) performing tasks assigned to it

40. AGENT-BASED TECHNOLOGIES

41. Types of Agent-Based Technologies
Autonomous agent – can adapt and alter the manner in which it works
Distributed agent – works on multiple distinct computer systems
Mobile agent – can relocate itself onto different computer systems

42. Types of Agent-Based Technologies
Intelligent agent – incorporates artificial intelligence capabilities such as reasoning and learning
Multi-agent system – group of intelligent agents that can work independently and also together to perform a task

43. Types of Intelligent Agents
Information agents (buyer agents) – search for information and bring it back
Monitoring-and-surveillance agents – constantly observe and report on some entity of interest, a network, or manufacturing equipment
User agents – take action on your behalf (e.g., sorting your )

44. Types of Intelligent Agents
Data-mining agents – operate in a data warehouse discovering information
Important analytics tool for data warehouse data
Can find hidden patterns in the data
Can also classify and categorize

45. Multi-Agent Systems & Biomimicry
Biomimicry – learning from ecosystems and adapting their characteristics to human and organizational situations
Used to
Learn how people-based systems behave
Predict how they will behave under certain circumstances
Improve human systems to make them more efficient and effective

46. Swarm Intelligence
Swarm (collective) intelligence – the collective behavior of groups of simple agents that are capable of devising solutions to problems as they arise, eventually learning to coherent global patterns
A subfield of biomimicry

47. Characteristics of Swarm Intelligence
Flexibility – adaptable to change
Robustness – tasks are completed even if some individuals are removed
Decentralization – each individual has a simple job to do