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
IT tools can aid in the decision-making process
Use of IT Analytics is now key to the success of any business

7. 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

8. 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

9. Types of Decisions You Face
EASIEST
MOST DIFFICULT

10. Decision Support Systems
Decision support system (DSS)
Highly flexible and interactive system
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

11. 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

12. Components of a DSS

13. 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

14. Google Earth as a GIS

15. 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

16. Data-Mining Tools and Models Support
Association/dependency modeling – identifying cross-selling opportunities, ex: jalapeno chip sales correlate with Arizona Tea sales
Clustering – discovering groups of entities that are similar (without using known structures)
Classification – use historical data to derive future inferences
Regression – find corollary and often causal relationships between data sets
Summarization – descriptive stats, basic but powerful
Sums, averages, standard deviations
Histograms, frequency distributions

17. Predictive Analytics Predictive analytics
computational data-mining technology
uses information and BI to build a predictive model for a given business application
Insurance, retail, healthcare, travel, financial services, CRM, SCM, credit scoring, etc
Prediction goal – the question addressed by the predictive analytics model
Prediction indicator – measurable value based on an attribute of the entity under consideration ex:

18. Predictive Analytics

19. 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 (FP)
Proximity of date of last purchase (LP)
Presence on Facebook and Twitter (FB)
Number of multiple-product purchases (MP)
Predictive Analytics Example
RapidMiner

20. 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

21. Text Analytics Support
Lexical analysis – word frequency distributions
Named entity recognition – identifying peoples, places, things
Disambiguation – meaning of a named entity recognition
“Ford” can refer to how many different things?
Co-reference – handling of differing noun phrases that refer to the same object
Sentiment analysis – discerning subjective business intelligence such as mood opinion

22. 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
CRM analytics – analysis of CRM data to improve customer service and support
Social media analytics, Mobile analytics, etc...

23. 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

24. 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?)
ES Components – Similar to DSS
Model management  Inference / Rule Engine
Data management  Expert Knowledge Base
User interface  Question / Explanation Module

25. Traffic Light Expert System

26. 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

27. Expert System Examples
Problem Solving ES
Diagnosing Chronic Fatigue
Knowledge Base Example – Soil Classification
Complex Diagnostic System - MEDgle

28. Neural Networks
Neural network (artificial neural network or ANN) – AI system capable of finding and differentiating patterns
ANNs can:
Learn / 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

29. Fuzzy Logic
Fuzzy logic – 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

30. Genetic Algorithms
Genetic algorithm – AI system that mimics evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem
Staples – determine optimal package design characteristics
Boeing – design aircraft parts such as fan blades
Many retailers – better manage inventory and optimize display areas

31. 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

32. 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

33. AGENT-BASED TECHNOLOGIES

34. 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

35. 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

36. 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 )

37. Intelligent Agents Shopping Agents – MySimon Chatbots – A.L.I.C.E.
Personality Test through Chat
Driving Agent – AIDA
Virtual Agents – Education / Training
Swarm Intelligent Bots
The Future of Intelligent Robots

38. 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

39. Swarm Intelligence
Swarm (collective) intelligence – collective behavior of groups of simple agents capable of devising solutions to problems as they arise, resulting in coherent global patterns
Attributes
Flexibility – adaptable to change
Robustness – tasks are completed even if some individuals are removed
Decentralization – each individual has a simple job to do