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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
.

STUDENT LEARNING OUTCOMES

1.
Compare and contrast decision support
systems and geographic information
systems.

2.
Describe the decision support role of
specialized analytics (predictive and
text analytics).

3.
Describe the role and function of an
expert system in analytics.

STUDENT LEARNING OUTCOMES

4.
Explain why neural networks are
effective decision support tools.

5.
Define genetic algorithms and the types
of problems they help solve.

6.
Describe data
-
mining agents and multi
-
agent systems.

ONLINE LEARNING

Notice the increase in online learning and the
decrease in traditional enrollments.

Questions

1.
Have you taken or are taking an online
course? Fully online or hybrid?

2.
Why do students opt to take online courses
over traditional classroom courses?

3.
Is this transformation occurring at the K
-
12
level?

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

DECISIONS AND DECISION SUPPORT

Carry out the chosen solution and
monitor the results

Examine the merits of each solution
and choose the best one

Consider ways of solving the
problem

Find or recognize the problem, need,
or opportunity

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

Types of Decisions You Face

EASIEST

MOST
DIFFICULT

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

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

Components of a DSS

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

Google Earth as a GIS

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

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


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:


Predictive Analytics

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


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

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


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...


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

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



Traffic Light Expert System

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

Expert System Examples


Problem Solving ES



Diagnosing Chronic Fatigue



Knowledge Base Example


Soil Classification



Complex Diagnostic System
-

MEDgle



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

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

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

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

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

AGENT
-
BASED TECHNOLOGIES

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

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

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 email)

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




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

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