ISQS 3358: Business Intelligence
Introduction
Spring, 2016
Instructor: Zhangxi Lin
Office: BA E311
Phone: 834-1926
E-mail: zhangxi.lin@ttu.edu
Homepage: http://zlin.ba.ttu.edu
Class meetings: MWF 1-1:50p, BA103
Office hours: MWF 2:30-3:30p, or by appointment
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Course Description
Business intelligence (BI) is referred
to as applications and technologies
which are used to gather, provide
access to, and analyze data and
information about their company
operations.
 Three main topics
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◦ Data warehousing
◦ Big Data
◦ Data analysis
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Driving Force - Big Data
A collection of data sets so large and
complex that it becomes awkward to
work with using on-hand database
management tools.
 Difficulties include capture,
storage, search, sharing, analysis, and
visualization.
 ate smaller sets with the same total
amount of data.
 Videos

◦ What is big data 1’33”
◦ Big Data Analytics 3’05”
8/14/2012
Copyright 2012
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ISQS7339, Fall 2012
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Data Scale
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Big Data Companies
IBM
 Oracle
 Facebook
 LinkedIn
 Cloudera (Hortonworks)
 Yahoo
 Amazon
 Google
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Data, information, and knowledge
Data – a collection of raw value elements or
facts used for calculating, reasoning, or
measuring.
 Information – the result of collecting and
organizing data in a way that establishes
relationship between data items, which
thereby provides context and meaning
 Knowledge – the concept of understanding
information based on recognized patterns in
a way that provides insight to information.
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ISQS 6339, Data Mgmt & BI
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What is Business Intelligence

A Simple Definition: The applications and technologies
transforming Business Data into Action
◦ Business intelligence (BI) is a business management term
 refers to applications and technologies which are
used to gather, provide access to, and analyze data
and information about their company operations.
◦ Business intelligence systems can help companies gain more
comprehensive knowledge of the factors affecting their business,
and help companies to make better business decisions.

YouTube:
◦ What is BI? – B, 2’
◦ Global warming 0’31”
◦ World Economy & Population 2’45”
◦ Microsoft Business Intelligence Surface Demo 6’34”
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The process of BI
Data -> information -> knowledge -> actionable plans
 Data -> information: the process of determining what data is to be
collected and managed and in what context
 Information -> knowledge: The process involving the analytical
components, such as data warehousing, online analytical processing,
data quality, data profiling, business rule analysis, and data mining
 Knowledge -> actionable plans: The most important aspect in a BI
process
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ISQS 6339, Data Mgmt & BI
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Actionable Knowledge

An information asset retains its value on if the converted
knowledge is actionable.
◦ Need some methods for extracting value from knowledge
◦ This is not a technical issue but an organizational one – need
empowered individuals in the organization to take the action
◦ There is an issue of Return on Investment (ROI)
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BI Problems

Structured
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Detecting Credit card fraud
Setting Loan parameters
Market segmentation/Mass customization
Deciding Marketing mix
Customer Churn
Reducing employee turnover
Improving Quality/Efficiency
…
Unstructured
◦ Data exploration
◦ Utilization of resources (stored knowledge) to maximum effectiveness
◦ …
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BI Applications
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Customer Analytics
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Customer profiling
Targeted marketing
Personalization
Collaborative filtering
Customer satisfaction
Customer lifetime value
Customer loyalty
Sales Channel Analytics
◦ Marketing
◦ Sales performance and pipeline
ISQS 6339, Data Mgmt & BI
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BI Applications (2)

Supply Chain Analytics
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Supplier and vendor management
Shipping
Inventory control
Distribution analysis
Behavior Analysis
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Purchasing trends
Web activity
Fraud and abuse detection
Customer attrition
Social network analysis
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The Evolution of Business Intelligence
1st Generation – Traditional analytics (query and reporting)
 2nd Generation – Traditional generation (OLAP, data
warehousing)
 2.5nd Generation – New traditional generation
 3rd Generation - Advanced analytics

◦ Rules, predictive analytics and realtime data mining
◦ Stream analytics
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Stream Analytics*
Real-time, continuous, sequential analysis
Business Intelligence
Classifications
(ranging from basic to advanced analytics)
3rd-Generation BI
* In lieu of stream analytics,
“embedded analytics,”
although architecturally different,
could potentially play the same
Advanced Analytics/Optimization
role
Rules
Predictive Analytics
Real-time and traditional Data Mining
“New Traditional” Analytics
“2.5-Gen” Analytics (In-Memory OLAP, Search-Based)
Source:
Bill O’Connell
IBM, Aug 2007
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Traditional Analytics
1st Generation Analytics (Query & Reporting)
2nd Generation Analytics (OLAP, Data Warehousing)
ISQS 6339, Data Mgmt & BI
Legacy BI
Stream Analytics*
Real-time, continuous, sequential analysis
Business Intelligence
Use Cases
(ranging from basic to advanced analytics)
Focus on what is
happening RIGHT NOW
* In lieu of stream analytics,
“embedded analytics,”
although architecturally different,
could potentially play the same
Advanced Analytics/Optimization
role
Rules
Predictive Analytics
Real-time and traditional Data Mining
Real-Time Threshold
“New Traditional” Analytics
Focus on what did
happen
Turning data into
information is limited by the
relationships which the
end-user already knows to
look for.
“2.5-Gen” Analytics (In-Memory OLAP, Search-Based)
Example Target Solutions:
Fraud Detection / Risk
CRM Analytic
Supply Chain Optimization
RFID / Spatial Data
Other High-Volume
Focus on what will
happen
Analytic applications that
apply statistical
relationships in the form
of RULES
Data mining to determine
why something
happened by unearthing
relationships that the
end-user may not have
known existed.
Traditional Analytics
1st
2nd
Generation Analytics (Query & Reporting)
Generation Analytics (OLAP, Data Warehousing)
Source:
Bill O’Connell
IBM, Aug 2007
ISQS 6339, Data Mgmt & BI
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SAS Enterprise Guide

SAS Enterprise Guide is available in
the Citrix server.
◦ Download Citrix Receiver from
http://www.citrix.com
◦ Use your eRaider ID and password from Citrix
Receiver to access http://citrix.ba.ttu.edu for the
applications.

Potential problem: The Citrix server
could be slow when many users use it
simultaneously.
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Big Data Tools

Pentaho: A company that offers Pentaho Business Analytics, a suite
of open source Business Intelligence (BI) products, founded in 2004
by five founders, headquartered in Orlando, FL, USA, acquired by
Hitachi in 2015 (https://en.wikipedia.org/wiki/Pentaho)
◦ Pentaho Data Integration (PDI)
◦ Pentaho for Big Data
◦ Pentaho Data Mining
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Tableau Software: Founded in Mountain View, California in January,
2003 by Chris Stolte, Christian Chabot and Pat Hanrahan,
headquartered in Seattle, Washington. It produces a family of
interactive data visualization products focused on business
intelligence.
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Qlik: Qlik, founded in Lund, Sweden in 1993 as a software company
in business intelligence (BI), is a software company based in Radnor,
Pennsylvania. Qlik is the provider of QlikView and Qlik Sense,
business intelligence & visualization software.
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Syllabus
Textbook and references
 Deliverables: projects, exercises
 Exams
 Grading policy
 Schedule
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Your checklist
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Website
◦ Class home page, Schedule, online Notes

Shared network drive
◦ \\TechShare\coba\d\ISQS3358\

Citrix application account (download Citrix Receiver at
http://www.citrix.com)
◦ SAS Enterprise Miner
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Downloadable materials
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E-Textbooks
Datasets
Homework assignments
Slides
Exercises
Demonstrative Videos
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Your opportunities to contact BI
industry

Student Poster, SAS Analytics 2015
Conference,
◦ October 26-27, Bellagio, Las Vegas. Check
http://www.sas.com/events/analytics/us/

How about this year?
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