Continental Airlines Optimizes Revenue and Customer Service with Real-Time BI

Continental Airlines Flies High with
Real-time Business Intelligence
Real-time Business Intelligence1
Uday Kulkarni
Arizona State University
1 This mini case study is adapted for executive seminar
audience from the original full-length case
study: “Continental Airlines Flies High with Real-time Business
Intelligence”, Anderson-Lehman R.,
Watson H. J., Wixom B. H. and Hoffer J. A., Teradata
University Network,
www.teradatauniversitynetwork.com
1

Real-time Business Intelligence
Introduction
Real-time business intelligence (BI) is taking Continental
Airlines to new heights. The company has
dramatically changed all aspects of its business, resulting in
industry-leading customer service and
generating a considerable financial “lift.” Continental’s
president and COO, Larry Kellner 2
, describes
the impact of real-time BI in the following way: “Real-time BI
is critical to the accomplishment of our
business strategy and has created significant business benefits."
Some of the ways that Continental is
using and benefiting from real-time BI are:
• Flight attendants, gate agents, and all “customer-facing”
employees know at all times who
Continental’s high-value customers are, and they provide
outstanding service to these
customers, contributing to Continental’s track record of
customer service.
• The Operations staff at the hubs monitors on-time performance
throughout the day and
makes operational decisions about catering, personnel, and gate
traffic flow, thus solidifying
Continental’s ranking as the most on-time airline.
• Pricing specialists track in real-time the impact of price
changes on reservations and make
adjustments that optimize revenues.

Company Background Information
Continental Airlines was founded in 1934 with a single-engine
Lockheed aircraft on dusty runways in
the American Southwest. Over the years, Continental grew and
successfully weathered the storms
associated with the highly volatile, competitive airline industry.
In 2011, Continental was one of the
top 5 airlines in the USA measured by passengers carried,
destinations, passenger-kilometers, etc. It
carries over 100 million passengers a year to five continents
(North and South America, Europe, Asia,
and Australia), with over 2,400 daily departures, to more than
262 destinations. 3
An Airline in Trouble
In the early 1990’s, Continental was in trouble. There were ten
major US airlines, and Continental
ranked tenth in on-time performance, mishandled baggage,
customer complaints, and denied
boardings because of overbooking. Not surprisingly, with this
kind of service, Continental was in
financial trouble. It had filed for Chapter 11 bankruptcy
protection twice in the previous ten years
and was heading for a third, and likely final, bankruptcy. It had
also gone through ten CEOs in ten
years. People joked that Continental was a “Perfect 10.”
Gordon Bethune and the Go Forward Plan
The rebirth of Continental began in 1994 when Gordon Bethune
took the controls as CEO. He and
Greg Brenneman, who was a consultant at the time, completely
revamped the airline with a “Go
Forward Plan” 4 . It had four interrelated parts that formed the

cornerstone of Continental’s strategy.
• Fly to Win. Continental needed to better understand what
products customers wanted and
were willing to pay for.
• Fund the Future. Continental needed to change its costs and
cash flow so that the airline
2 2004-2009
3 On August 27, 2010, US regulators approved a planned
merger between United and Continental
Airlines. The merger was completed in early 2012, which made
the combined airline the second
biggest airline in terms of fleet size.
4 “Right Away and All at Once: How We Saved Continental” by
Greg Brenneman, Harvard Business
Review, Sep-Oct 1998.
2
could continue to operate.
• Make Reliability a Reality. Continental had to be an airline
that got its customers to their
destinations safely, on-time, and with their luggage.
• Working Together. Continental needed to create a culture
where people wanted to come to
work.
Most employees bought into the plan, and those who didn’t, left
the company. With the Go Forward

Plan and a re-energized workforce, Continental made rapid
strides. Within two years, it moved from
“worst to first” in many airline performance metrics.
Analytics
Continental’s current position is dramatically different
compared to those times. A key to this
turnaround, the Go Forward Plan, continues to be Continental’s
blueprint for success and is
increasingly supported by real-time BI and data warehousing.
Currently, the use of real-time
technologies has been critical for Continental in moving from
“first to favorite” among its customers,
especially among its best customers.
Analytical capabilities give Continental a competitive
advantage. But, these capabilities need to
continuously evolve to maintain the competitive advantage that
they offer. Initial applications in
pricing and revenue management provided a variety of early,
big “wins” for Continental. These were
followed by the integration of customer information, finance,
flight information, and security. They
created significant financial lift in all areas of the Go Forward
Plan.
This mini case study describes strategically aligned applications
of real-time BI in three areas:
revenue management, customer relationship management, and
flight operations. They are among the
many that Continental implemented and illustrate how real-time
BI is affecting almost all of the areas
that Continental does business in.
Real-time BI Applications

From its main operational systems to the warehouse,
Continental moves real-time data (ranging
from to-the-minute to hourly) about customers, reservations,
check-ins, operations, and flights. This
has transitioned Continental from analyzing and reporting what
happened in the past to influencing
current decisions and business processes.
REVENUE MANAGEMENT
The purpose of revenue management is to maximize revenue
given a set of resources. An airline seat
is a perishable good, and an unfilled seat has no value once a
plane takes off. The warehouse
integrates multiple data sources - flight schedule data, customer
data, inventory data, and more - to
support pricing and revenue management decision-making.
Fare Design
Continental understands how important it is to offer competitive
prices for flights to desired places
at convenient times. Continental uses real-time data to optimize
airfares (using mathematical
programming models). Once a change is made in price, revenue
management immediately begins
tracking the impact of that price on bookings. And, knowing
immediately how a fare is selling allows
the group to adjust how many seats should be sold at a given
price. Continental has earned an
estimated $10 million annually through fare design activities.
Prior to the availability of real-time
data, Continental’s pricing was a less effective at filling seats
and optimizing fares.
CUSTOMER RELATIONSHIP MANAGEMENT
In additional to the traditional CRM applications (such as,
customer segmentation and target

marketing, loyalty/retention management, customer acquisition,
channel optimization, and
campaign management), marketing has created other innovative
CRM applications that leverage the
3
warehouse’s real-time capabilities. A customer value model
using frequency, recency, and monetary
value gives Continental an understanding of its most profitable
customers. Every month, the
customer value analysis is performed using data in the
warehouse, and the value is fed back to
Continental’s customer database. Although the value is not
adjusted real-time, it is provided to
Continental’s customer–facing systems so that employees know
who their best customers are.
Marketing Insight
With Marketing Insight, sales personnel, marketi ng
managers, and flight personnel (e.g., ticket agents, flight
attendants) can see customer profiles - what the person’s
value is to the airline. Flight attendants receive the
information by reading their “final report,” which lists the
passengers on their flights, expanded to include value
information. Gate agents are able to pull customer
information up on their screen and drill into flight history
to see which high-value customers have had flight
disruptions. A commonly told story is about a flight
attendant who saw a high-value customer’s recent flight
disruption and apologized on behalf of
Continental. The passenger was floored that she would know
about the incident and then care
enough to apologize.

FLIGHT OPERATIONS
Operations is concerned with all aspects of getting people
to their destinations safely, on-time, efficiently, and with
their luggage. This is where customers have either a good
or bad flying experience, and Continental works hard to
provide excellent service. Good operations also can reduce
costs by ensuring that ground personnel are in the right
place at the right time. Special real-time applications have
been developed to support this capability.
Flight Management Dashboard
The Flight Management Dashboard is an innovative set of
interactive graphical displays developed by
the data warehouse group. These displays are intended to help
the operations staff quickly identify
issues in the Continental flight network and then manage flights
in ways to improve customer
satisfaction and airline profitability.
Some of the dashboard’s displays help Operations to better
serve Continental’s high-value customers.
For example, one of the displays is a graphical depiction of a
concourse, which is used to assess
where Continental’s high-value customers are or will be in a
particular airport hub (see Figure 1).
The display shows gates where these customers have potential
gate connection problems so that gate
agents, baggage supervisors, and other operations managers can
assess where ground transportation
assistance and other services are needed so these customers and
their luggage avoid missing flights.
It can be seen that Flight 678 is arriving 21 minutes late at Gate
C37 and two high-value customers
(3* and 5*) need assistance in making their connections at
Gates C24 and C29, which are 20 and 12

minutes away, resp.
On-time arrival is an important operational measurement at
Continental. Therefore, another critical
set of dashboard displays helps Operations keep the arri vals and
departures of flights on time. One
display shows the traffic volume between the three Continental
hub stations and the rest of their
network (see Figure 2). The line thickness between nodes is
used to indicate relative flight volumes
and the number of late flights so that the operations staff can
anticipate where services need to be
expedited. The operations staff can click on the lines and drill
down to see individual flight
information. Another line graph summarizes flight lateness.
Users can drill down to more detailed pie
charts that show degrees of lateness, and then, within each pie,
to the individual flights in that
4
category. In all of these elements of the dashboard, high-level
views can be broken down to show the
details on customers or flights that compose different statistics
or categories.
Figure 1: Concourse Display of High-Value Customer Activity
Figure 2: Display of Flight Lateness and Current Traffic
From/To Hubs

Gate Flight From Status
C37 #678 SAN 21 min late
To C24 #155 DCA 3*/20 min
To C29 #253 OKC 5*/12 min
5
Value Creation
Regardless of how exciting a strategy and the supporting
applications are, value is not created until
people act. A key to Continental’s success is a service-oriented
culture – to one another and to
customers. Continental’s employees believe that people should
be treated with dignity and respect,
and this tenet is a major component of the Go Forward Plan
(e.g., Working Together). The
combination of a well thought out business strategy, a service-
oriented culture, and real-time
technologies that are consistent with this culture has been
responsible for Continental’s turnaround
and current success.
Continental invested approximately $30 million into real -time
warehousing, of which $20 million
was for hardware and software expenses, and $10 million was
for personnel costs. Although this
investment is significant, the quantifiable benefits from real -
time warehousing are magnitudes
larger. Specifically, over the first six years iteself, Continental
realized over $500 million in increased
revenues and cost savings, resulting in a ROI of over 1,000
percent. Below are some of the benefits
that have been realized.

Marketing:
o Continental performs customer segmentation, target
marketing, loyalty/retention
management, customer acquisition, channel optimization, and
campaign management using
the data warehouse. Targeted promotions have produced cost
savings and incremental
revenue of $15 to $18 million per year.
o A targeted CRM program resulted in $150 million in
additional revenues in one year, while
the rest of the airline industry declined 5 percent.
o There has been an average increase in travel of $800 for each
of the top 35,000 customers.
Corporate
o Continental was able to identify and prevent over $30 million
in Security fraud over the last
three years. This includes more than $7 million in cash
collected.
Information Technology
o The warehouse technology has significantly improved data
center management, leading to
cost savings of $20 million in capital and $15 million in
recurring data center costs.
Revenue
o Tracking and forecasting demand has resulted in $5 million in
Management incremental

revenue.
o Fare design and analysis improves the ability to gauge the
impact of fare sales, and these
activities have been estimated to earn $10 million annually.
o Full reservation analysis has realized $20 million in savings
through alliances, overbooking
systems, and demand-based scheduling.
Honors and Awards
• No. 1 Most Admired Global Airline; Fortune Magazine (2004–
2009) 5
• No. 1 Most Admired U.S. Airline; 'Fortune Magazine (2006–
2007, 2010)
6
• No. 1 Greenest U.S. Airline; Greenopia (2009)
• No. 1 Pet-Friendly Airline; PetFinder.org (2009) 7
5
"Continental Airlines Ranked No. 1 World's Most Admired
Airline by FORTUNE Magazine". Reuters.
March 11, 2008.
http://www.reuters.com/article/2008/03/11/idUS176168+11-
Mar-
2008+PRN20080311. Retrieved May 7, 2014.
6 "Continental Airlines Again Highest-Ranked U.S. Airline on

FORTUNE World's Most Admired List".
Bloomberg,
http://www.bloomberg.com/apps/news?pid=newsarchive&sid=a
mNcVo_udY38,
March 5, 2010. Retrieved May 7, 2014.
http://www.reuters.com/article/pressRelease/idUS176168+11-
Mar-2008+PRN20080311�
Mar-2008+PRN20080311�
Mar-2008+PRN20080311�
http://www.bloomberg.com/apps/news?pid=newsarchive&sid=a
mNcVo_udY38�
6
• Best Executive/Business Class, OAG Airline of the Year
Awards (2003–2007, 2009);
Best Airline Based in North America, OAG Airline of the Year
Awards (2003-2009);
Best U.S Carrier Trans-Atlantic and Trans-Pacific Business
Class, Condé Nast Traveler
(1999–2006)
• Best Airline for North American Travel; Business Traveler
Magazine (2006-2009)
• Best Large Domestic Airline (Premium Seating); Zagat Airline
Survey(2008);
Best Value for the Money (International); Zagat Airline Survey
(2009)
• Highest-Ranked Network Airline; J.D. Power and Associates
(2007)
• Airline of the Year; OAG (2004–2005) 8

• Business Leadership Recycling Award;
American Forest & Paper Association (2010) 9
• Best Technology Awards: #1 Airline, #2 of 500 Companies –
InformationWeek; TDWI 2003
Best Practice Award – Enterprise Data Warehouse, TDWI 2003
Leadership Award, CIO
Enterprise Value Award
Discussion Questions
1. What surprised you most about this case?
2. What is Continental’s business strategy?
3. This case study tries to show how Continental’s investment in
a real-time BI is closely aligned
with its business strategy. Give examples of business processes
that have changed due to their
enablement by real-time BI and discuss how they have helped
its business strategy.
4. How is Continental making use of "analytics" in its
operational decision-making? What more can
they do?
5. Real-time BI needs real-time as well as historical data – real-
time data is needed to recognize a
business event as it happens and historical data is needed to
respond to it comprehensively. For
each application described in this case study, discuss:
o the need for real-time response
o the decision/activity supported

o the real-time and historical data needed and the analysis
performed
o the performance metrics to measure its effectiveness
7 Truong, Alice (July 10, 2009). "Continental Airlines Ranked
No. 1 Pet Friendly". The Wall Street
Journal. http://blogs.wsj.com/middleseat/2009/07/10/which-
airline-is-the-most-pet-friendly/.
Retrieved May 7, 2014.
8 "OAG Airline Industry Awards – Previous AOY". OAG. 2008.
Archived from the original on June 15,
2008.
http://web.archive.org/web/20080702174024/http://oagairlineaw
ards.com/pressroom.html
Retrieved May 7, 2014.
9 "2010 Business Leadership Recycling Award Presented to
Continental Airlines".
http://www.afandpa.org/docs/default-source/default-document-
library/2010-af-amp-pa-
sustainability-report.pdf. Retrieved May 7, 2014.
http://en.wikipedia.org/wiki/OAG�
http://en.wikipedia.org/wiki/Cond%C3%A9_Nast_Traveler�
http://en.wikipedia.org/wiki/American_Forest_%26_Paper_Asso
ciation�
http://blogs.wsj.com/middleseat/2009/07/10/which-airline-is-
the-most-pet-friendly/�
http://blogs.wsj.com/middleseat/2009/07/10/which-airline-is-
the-most-pet-friendly/�
http://web.archive.org/web/20080615204257/http:/www.oagairli

neawards.com/previousAOY.html�
http://www.oagairlineawards.com/previousAOY.html�
neawards.com/pressroom.html�
http://www.afandpa.org/docs/defa ult-source/default-document-
library/2010-af-amp-pa-sustainability-report.pdf�
library/2010-af-amp-pa-sustainability-report.pdf�
library/2010-af-amp-pa-sustainability-
report.pdf�IntroductionCompany Background InformationReal-
time BI ApplicationsValue CreationHonors and
AwardsDiscussion Questions
BI&Data Mining Assignment 01 Marking Rubric
Section 1 : Report / Case 1 /Case 2/Overall presentation
Description
• Excellent physical layout and logical structure.
• The work is presented accurately, concisely and coherently,
with no errors and thorough attention to detail.
• Excellent technical style, avoiding excessive jargon.
• Full acknowledgement of the work of others and correctly
formatted reference section.
•
A clear report with goals, solution overview and methods which
systematically follow from the business requirements.
• The approach is creative and innovative.
• The approach is ethical.
-fully addressed case 1 and case 2
-provided in-depth details analysis of both cases

• Very good physical layout and logical structure.
• Very good attention to detail.
• The work is presented in an accurate, concise and coherent
fashion, with very few errors.
• Few errors in the acknowledgement of the work of others and
a correctly formatted reference section.
• Correct use of equations, footers and headers
•
A planned approach which follows from the business
requirements.
• The approach is systematic.
-Addressed case 1 and case 2 appropriately with details
-provided a detailed analysis of both cases
Good logical structure and physical layout.
• The work is presented in an accurate, concise and coherent
fashion with good attention to detail in most areas and few
errors.
• Good technical style.
• Few spelling mistakes or grammatical errors.
• Few errors in the acknowledgement of the work of others or
the reference section.
• Few errors in equations, footers and headers.
• A satisfactory plan of work is offered.
• The approach is reasonably systematic.
-Addressed case 1 and case 2 in a good manner, provided details
-provided an acceptable detailed analysis of both cases
• Satisfactory logical structure and physical layout.
• Satisfactory attention to detail.
• The work is presented in a satisfactory fashion.
• Occasional errors in the acknowledgement of the work of

others, satisfactorily formatted reference section.
• Occasional errors in equations, footers and headers.
•.
• The plan of work offered is poor, or only partly relates to the
business requirements.
•
• The plan of work offered is incomplete or unclear, or does not
completely relate to the business requirements.
• The approach is ill-considered, and does not logically flow
from the business requirements.
-Addressed case 1 and case 2 in general as an overview,
provided enough details
-provided an acceptable detailed analysis of both cases
Poor structure and inconsistent physical layout.
• The work is presented poorly with little attention to detail.
• Very many errors in the acknowledgement of the work of
others and a poorly formatted reference section.
• Poor (or no) footers or headers.
The plan of work offered is poor, or only partly relates to the
business requirements.
• The approach is ill-considered, and bears little relationship to
the business requirements.
Did not address the cases appropriately.
Excellent
Well done
Very good
OK
Need to review
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Section 2 : Answers
Description
• Excellent, clear definition of requirements.
• Excellent description of the questions, problems, and/or
requirements (including statement of purpose and relevance).
-Excellent analysis of the case shown in questions responses,
in-depth knowledge of the presented case specifications
• Very good definition of requirements.
• Very good description of questions, problems, and/or
requirements (including statement of purpose and relevance).
-very good analysis of the case shown in questions responses,
demonstrated an adequate understanding of the presented case
specifications
Good definition of adequate requirements.
• Good description of questions, problems and/or hypothesis
(including statement of purpose and relevance).
• good analysis of the case shown in questions responses, in-
depth knowledge of the presented case specifications
-Good analysis of the case shown in questions responses,
demonstrated an adequate understanding of the presented case
specifications
•Satisfactory definition of basic requirements /problem.
-satisfactory responses to questions
Unsatisfactory definition of requirements.
-
Unsatisfactory responses on questions
Excellent

Well done
Very good
OK
Need to review
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Business Intelligence, Analytics, and Data Science: A
Managerial Perspective
Fourth Edition
Chapter 3
Descriptive Analytics II: Business Intelligence and Data
Warehousing
Copyright © 2018 Pearson Education Ltd.
Learning Objectives (1 of 2)
3.1 Understand the basic definitions and concepts of data
warehousing
3.2 Understand data warehousing architectures
3.3 Describe the processes used in developing and managing
data warehouses
3.4 Explain data warehousing operations
3.5 Explain the role of data warehouses in decision support
Slide 3-2

is a list of textbook LO numbers and statements.
2
3.6 Explain data integration and the extraction, transformation,
and load (ETL) processes
3.7 Understand the essence of business performance
management (BPM)
3.8 Learn balanced scorecard and Six Sigma as performance
measurement systems
Slide 3-3
Slide 3 is a list of textbook LO numbers and statements.
3
OPENING VIGNETTE
Targeting Tax Fraud with Business Intelligence and Data
Warehousing
Why is it important for IRS and for U.S. state governments to
use data warehousing and business intelligenc e (BI) tools in
managing state revenues?
What were the challenges the state of Maryland was facing with
regard to tax fraud?
What was the solution they adopted? Do you agree with their
approach? Why?
What were the results that they obtained? Did the investment in
BI and data warehousing pay off?
What other problems and challenges do you think federal and
state governments are having that can benefit from BI and data
warehousing?
Slide 3-4

Business Intelligence and Data Warehousing
BI used to be everything related to use of data for managerial
decision support
Now, it is a part of Business Analytics
BI = Descriptive Analytics
Slide 3-5
What is a Data Warehouse?
A physical repository where relational data are specially
organized to provide enterprise-wide, cleansed data in a
standardized format
A relational database? (so what is the difference?)
“The data warehouse is a collection of integrated, subject-
oriented databases designed to support DSS functions, where
each unit of data is non-volatile and relevant to some moment in
time”
Slide 3-6
A Historical Perspective to
Data Warehousing
Slide 3-7
Characteristics of DWs
Subject oriented
Integrated
Time-variant (time series)
Nonvolatile

Summarized
Not normalized
Metadata
Web based, relational/multi-dimensional
Client/server, real-time/right-time/active...
Slide 3-8
Data Mart
A departmental small-scale “DW” that stores only
limited/relevant data
Dependent data mart
A subset that is created directly from a data warehouse
Independent data mart
A small data warehouse designed for a strategic business unit or
a department
Slide 3-9
Other DW Components
Operational data stores (ODS)
A type of database often used as an interim area for a data
warehouse
Oper marts
An operational data mart
Enterprise data warehouse (EDW)
A data warehouse for the enterprise
Metadata – “data about data”
In DW metadata describe the contents of a data warehouse and
its acquisition and use
Slide 3-10

Application Case 3.1
A Better Data Plan: Well-Established TELCOs Leverage Data
Warehousing and Analytics to Stay on Top in a Competitive
Industry
Questions for Discussion
What are the main challenges for TELCOs?
How can data warehousing and data analytics help TELCOs in
overcoming their challenges?
Why do you think TELCOs are well suited to take full
advantage of data analytics?
Slide 3-11
DW for Data-Driven Decision Making
An example of a DW supporting data-driven decision making in
automotive industry
Slide 3-12
A Generic DW Framework
Slide 3-13
13
DW Architecture
Three-tier architecture
Data acquisition software (back-end)

The data warehouse that contains the data & software
Client (front-end) software that allows users to access and
analyze data from the warehouse
Two-tier architecture
First two tiers in three-tier architecture are combined into one
… sometimes there is only one tier?
Slide 3-14
14
DW Architectures
3-tier
architecture
2-tier
architecture
1-tier
Architecture
?
Slide 3-15
15
Data Warehousing Architectures
Issues to consider when deciding which architecture to use:
Which database management system (DBMS) should be used?
Will parallel processing and/or partitioning be used?
Will data migration tools be used to load the data warehouse?

What tools will be used to support data retrieval and analysis?
Slide 3-16
A Web-based DW Architecture
Slide 3-17
17
Alternative DW Architectures
Slide 3-18
18
Each architecture has advantages and disadvantages!
Which architecture is the best?
Slide 3-19
19

Ten Factors that Potentially Affect the Architecture Selection
Decision
Information interdependence between organizational units
Upper management’s information needs
Urgency of need for a data warehouse
Nature of end-user tasks
Constraints on resources
Strategic view of the data warehouse prior to implementation
Compatibility with existing systems
Perceived ability of the in-house IT staff
Technical issues
Social/political factors
Slide 3-20
20
Data Integration and the Extraction, Transformation, and Load
Process
ETL = Extract Transform Load
Data integration
Integration that comprises three major processes: data access,
data federation, and change capture.
Enterprise application integration (EAI)
A technology that provides a vehicle for pushing data from
source systems into a data warehouse
Enterprise information integration (EII)
An evolving tool space that promises real-time data integration
from a variety of sources, such as relational or
multidimensional databases, Web services, etc.
Slide 3-21

Process
Slide 3-22
ETL (Extract, Transform, Load)
Issues affecting the purchase of an ETL tool
Data transformation tools are expensive
Data transformation tools may have a long learning curve
Important criteria in selecting an ETL tool
Ability to read from and write to an unlimited number of data
sources/architectures
Automatic capturing and delivery of metadata
A history of conforming to open standards
An easy-to-use interface for the developer and the functional
user
Slide 3-23
23
BP Lubricants Achieves BIGS Success
What is BIGS?
What were the challenges, the proposed solution, and the
obtained results with BIGS?
Slide 3-24

Data Warehouse Development
Data warehouse development approaches
Inmon Model: EDW approach (top-down)
Kimball Model: Data mart approach (bottom-up)
Which model is best?
Table 3.3 provides a comparative analysis between EDW and
Data Mart approach
Another alternative is the hosted data warehouses
Slide 3-25
Comparing EDW and Data Mart
Slide 3-26
Use of Teradata Analytics for SAP
Solution
s Accelerates Big Data Delivery
What were the challenges faced by the large Dutch retailer?
What was the proposed multivendor solution? What were the
implementation challenges?
What were the lessons learned?
Slide 3-27

Additional DW Considerations
Hosted Data Warehouses
Benefits:
Requires minimal investment in infrastructure
Frees up capacity on in-house systems
Frees up cash flow
Makes powerful solutions affordable
Enables solutions that provide for growth
Offers better quality equipment and software
Provides faster connections
… more in the book
Slide 3-28
Representation of Data in DW
Dimensional Modeling
A retrieval-based system that supports high-volume query
access
Star schema
The most commonly used and the simplest style of dimensional
modeling

Contain a fact table surrounded by and connected to several
dimension tables
Snowflakes schema
An extension of star schema where the diagram resembles a
snowflake in shape
Slide 3-29
The ability to organize, present, and analyze data by several
dimensions, such as sales by region, by product, by salesperson,
and by time (four dimensions)
Multidimensional presentation
Dimensions: products, salespeople, market segments, business
units, geographical locations, distribution channels, country, or
industry
Measures: money, sales volume, head count, inventory profit,
actual versus forecast
Time: daily, weekly, monthly, quarterly, or yearly
Multidimensionality
Slide 3-30

30
Star Schema versus Snowflake Schema
Slide 3-31
Analysis of Data in DW
OLTP vs. OLAP…
OLTP (Online Transaction Processing)
Capturing and storing data from ERP, CRM, POS, …
The main focus is on efficiency of routine tasks
OLAP (Online Analytical Processing)
Converting data into information for decision support
Data cubes, drill-down / rollup, slice & dice, …
Requesting ad hoc reports
Conducting statistical and other analyses
Developing multimedia-based applications
…more in the book
Slide 3-32
OLAP vs. OLTP

33
OLAP Operations
Slice - a subset of a multidimensional array
Dice - a slice on more than two dimensions
Drill Down/Up - navigating among levels of data ranging from
the most summarized (up) to the most detailed (down)
Roll Up - computing all of the data relationships for one or
more dimensions
Pivot - used to change the dimensional orientation of a report or
an ad hoc query-page display
Slide 3-34
OLAP
Slicing Operations on a Simple Tree-Dimensional
Data Cube
Slide 3-35

Successful DW Implementation
Things to Avoid
Starting with the wrong sponsorship chain
Setting expectations that you cannot meet
Engaging in politically naive behavior
Loading the data warehouse with information just because it is
available
Believing that data warehousing database design is the same as
transactional database design
… more in the book
Slide 3-36
Massive DW and Scalability
Scalability
The main issues pertaining to scalability:
The amount of data in the warehouse
How quickly the warehouse is expected to grow
The number of concurrent users
The complexity of user queries
Good scalability means that queries and other data-access
functions will grow linearly with the size of the warehouse
Slide 3-37

37
EDW Helps Connect State Agencies in Michigan
Why would a state invest in a large and expensive IT
infrastructure (such as an EDW)?
What is the size and complexity of the EDW used by state
agencies in Michigan?
obtained results of the EDW?
Slide 3-38
DW Administration and Security
Data warehouse administrator (DWA)
DWA should…
have the knowledge of high-performance software, hardware,
and networking technologies
possess solid business knowledge and insight

be familiar with the decision-making processes so as to suitably
design/maintain the data warehouse structure
possess excellent communications skills
Security and privacy is a pressing issue in DW
Safeguarding the most valuable assets
Government regulations (HIPAA, etc.)
Must be explicitly planned and executed
Slide 3-39
The Future of DW
Sourcing…
Web, social media, and Big Data
Open source software
SaaS (software as a service)
Cloud computing
Data lakes
Infrastructure…
Columnar
Real-time DW
Data warehouse appliances
Data management practices/technologies
In-database & In-memory processing New DBMS

New DBMS, Advanced analytics, …
Slide 3-40
Data Lakes
Unstructured data storage technology for Big Data
Data Lake versus Data Warehouse
Slide # of total
Slide 3-41
Business Performance Management
Business Performance Management (BPM) is…
A real-time system that alerts managers to potential
opportunities, impending problems, and threats, and then
empowers them to react through models and collaboration
Also called corporate performance management (CPM by
Gartner Group), enterprise performance management (EPM by
Oracle), strategic enterprise management (SEM by SAP)
Slide 3-42

42
Business Performance Management
BPM refers to the business processes, methodologies, metrics,
and technologies used by enterprises to measure, monitor, and
manage business performance.
BPM encompasses three key components
A set of integrated, closed-loop management and analytic
processes, supported by technology …
Tools for businesses to define strategic goals and then
measure/manage performance against them
Methods and tools for monitoring key performance indicators
(KPIs), linked to organizational strategy
Slide 3-43
43
A Closed-Loop Process to Optimize Business Performance

Process Steps
Strategize
Plan
Monitor/analyze
Act/adjust
Each with its own sub-process steps
Slide 3-44
44
1 - Strategize:
Where Do We Want to Go?
Strategic planning
Common tasks for the strategic planning process:
Conduct a current situation analysis
Determine the planning horizon
Conduct an environment scan
Identify critical success factors
Complete a gap analysis
Create a strategic vision
Develop a business strategy

Identify strategic objectives and goals
Slide 3-45
45
2 - Plan:
How Do We Get There?
Operational planning
Operational plan: plan that translates an organization’s strategic
objectives and goals into a set of well-defined tactics and
initiatives, resource requirements, and expected results for some
future time period (usually a year).
Operational planning can be
Tactic-centric (operationally focused)
Budget-centric plan (financially focused)
Slide 3-46
46

3 - Monitor/Analyze:
How Are We Doing?
A comprehensive framework for monitoring performance should
address two key issues:
What to monitor?
Critical success factors
Strategic goals and targets
How to monitor?
Slide 3-47
47
Success (or mere survival) depends on new projects: creating
new products, entering new markets, acquiring new customers
(or businesses), or streamlining some process.
Many new projects and ventures fail!
What is the chance of failure?
60% of Hollywood movies fail
70% of large IT projects fail, …
4 - Act and Adjust:

What Do We Need to Do Differently?
Slide 3-48
48
AARP Transforms Its BI Infrastructure and Achieves a 347%
ROI in Three Years
What were the challenges AARP was facing?
What was the approach for a potential solution?
What were the results obtained in the short term, and what were
the future plans?
Slide 3-49
Performance measurement system
A system that assists managers in tracking the implementatio ns
of business strategy by comparing actual results against
strategic goals and objectives
Comprises systematic comparative methods that indicate

progress (or lack thereof) against goals
Performance Measurement
Slide 3-50
50
Key performance indicator (KPI)
A KPI represents a strategic objective and metrics that measure
performance against a goal
Distinguishing features of KPIs
KPIs and Operational Metrics
Strategy
Targets
Ranges
Encodings
Time frames
Benchmarks
Slide 3-51

51
Key performance indicator (KPI)
Outcome KPIs vs. Driver KPIs
(lagging indicators(leading indicators
e.g., revenues) e.g., sales leads)
Operational areas covered by driver KPIs
Customer performance
Service performance
Sales operations
Sales plan/forecast
Performance Measurement
Slide 3-52
52
Balanced Scorecard (BSC)
A performance measurement and management methodology that
helps translate an organization’s financial, customer, internal
process, and learning and growth objectives and targets into a
set of actionable initiatives

“The Balanced Scorecard: Measures That Drive Performance”
(HBR, 1992)
Performance Measurement System
Slide 3-53
53
Balanced Scorecard
The meaning of “balance” ?
Slide 3-54
54
Six Sigma
A performance management methodology aimed at reducing the
number of defects in a business process to as close to zero

defects per million opportunities (DPMO) as possible
Six Sigma as a Performance Measurement System
Slide 3-55
55
The DMAIC performance model
A closed-loop business improvement model that encompasses
the steps of defining, measuring, analyzing, improving, and
controlling a process
Lean Six Sigma
Lean manufacturing / lean production
Lean production versus six sigma?
Six Sigma as a Performance Measurement System
Slide 3-56
56
Comparison of BSC and Six Sigma

57
Slide 3-57
Effective Performance Measurement Should
Measures should focus on key factors.
Measures should be a mix of past, present, and future.
Measures should balance the needs of shareholders, employees,
partners, suppliers, and other stakeholders.
Measures should start at the top and flow down to the bottom.
Measures need to have targets that are based on research and
reality rather than arbitrary.
Slide # of total
Slide 3-58
Expedia.com’s Customer Satisfaction Scorecard
Who are the customers for Expedia.com? Why is customer
satisfaction a very important part of their business?
How did Expedia.com improve customer satisfaction with

scorecards?
obtained results?
Slide 3-59
Plenty of Resources for DW @ TUN
Teradata University Network (TUN)
Slide 3-60
TeradataUniversityNetwork.com
End of Chapter 3
Questions / Comments
Slide 3-61
What happened?
What is happening?

What will happen?
Why will it happen?
What should I do?
Why should I do it?
üBusiness reporting
üDashboards
üScorecards
üData warehousing
üData mining
üText mining
üWeb/media mining
üForecasting
üOptimization
üSimulation
üDecision modeling
üExpert systems
Well defined
business problems
and opportunities
Accurate projections
of future events and
outcomes
Best possible
business decisions
and actions

Q
u
e
s
t
i
o
n
s
E
n
a
b
l
e
r
s
O
u
t
c
o
m
e
s

DescriptivePredictivePrescriptive
Business Analytics
Business Intelligence
Advanced Analytics
1970s1980s1990s2000s2010s
üMainframe computers
üSimple data entry
üRoutine reporting
üPrimitive database structures
üTeradata incorporated
üMini/personal computers (PCs)
üBusiness applications for PCs
üDistributer DBMS
üRelational DBMS
üTeradata ships commercial DBs
üBusiness Data Warehousecoined
üCentralized data storage
üData warehousing was born
üInmon, Building the Data Warehouse
üKimball, The Data Warehouse Toolkit
üEDW architecture design
üExponentially growing data Web data
üConsolidation of DW/BI industry
üData warehouse appliances emerged
üBusiness intelligence popularized

üData mining and predictive modeling
üOpen source software
üSaaS, PaaS, Cloud Computing
üBig Data analytics
üSocial media analytics
üText and Web Analytics
üHadoop, MapReduce, NoSQL
üIn-memory, in-database
Data Warehouse
One management and analytics platform
for product configuration, warranty, and
diagnostic readout data
Reduced
Infrastructure
Expenses
2/3 cost reduction through
data mart consolidation
Produced Warranty
Expenses
Improved reimbursement
accuracy through improved
claim data quality
Improved Cost of
Quality
Faster identification,

prioritization, and resolution
of quality issues
Accurate
Environmental
Performance
Reporting
IT Architecture
Standardization
One strategic platform for
business intelligence and
compliance reporting
Data
Sources
ERP
Legacy
POS
Other
OLTP/Web
External
Data
Select
Transform
Extract
Integrate
Load

ETL
Process
Enterprise
Data warehouse
Metadata
Replication
A
P
I
/
M
i
d
d
l
e

w
a
r
e
Data/text
mining
Custom built
applications
OLAP,
Dashboard,
Web
Routine
Business
Reporting
Applications
(Visualization)
Data mart
(Operations)
Data mart
(Marketing)
Data mart
(Finance)
Data mart
(...)
Data

Marts
No data marts option
Tier 2:
Application server
Tier 1:
Client workstation
Tier 3:
Database server
Tier 1:
Client workstation
Tier 2:
Application & database server
Web
Server
Client
(Web browser)
Application
Server
Data
warehouse
Web pages
Internet/
Intranet/
Extranet
Source

Systems
Staging
Area
Independent data marts
(atomic/summarized data)
End user
access and
applications
ETL
(a) Independent Data Marts Architecture
Source
Systems
Staging
Area
End user
access and
applications
ETL
Dimensionalized data marts
linked by conformed dimentions
(atomic/summarized data)
(b) Data Mart Bus Architecture with Linked Dimensional
Datamarts
Source
Systems

Staging
Area
End user
access and
applications
ETL
Normalized relational
warehouse (atomic data)
Dependent data marts
(summarized/some atomic data)
(c) Hub and Spoke Architecture (Corporate Information
Factory)
Source
Systems
Staging
Area
Normalized relational
warehouse (atomic/some
summarized data)
End user
access and
applications
ETL
(d) Centralized Data Warehouse Architecture
End user

access and
applications
Logical/physical integration of
common data elements
Existing data warehouses
Data marts and legacy systmes
Data mapping / metadata
(e) Federated Architecture
Packaged
application
Legacy
system
Other internal
applications
Transient
data source
Data
warehouse
Data
marts
ExtractExtractExtractExtract
Product
T
i
m

e
G
e
o
g
r
a
p
h
y
Sales volumes of
a specific Product
on variable Time
and Region
Sales volumes of
a specific Region
on variable Time
and Products
Sales volumes of
a specific Time on
variable Region
and Products
Cells are filled
with numbers
representing

sales volumes
A 3-dimensional
OLAP cube with
slicing
operations
VISION &
STRATEGY
Internal
Business
Process
Perspective
Customer
Perspective
Financial
Perspective
Learning and
Growth
Perspective
Business Intelligence, Analytics, and Data Science: A
Managerial Perspective
Fourth Edition
Chapter 3
Descriptive Analytics II: Business Intelligence and Data

Warehousing
3.1 Understand the basic definitions and concepts of data
warehousing
3.2 Understand data warehousing architectures
3.3 Describe the processes used in developing and managing
data warehouses
3.4 Explain data warehousing operations
3.5 Explain the role of data warehouses in decision support
Slide 3-2
2
3.6 Explain data integration and the extraction, transformation,
and load (ETL) processes
3.7 Understand the essence of business performance

management (BPM)
3.8 Learn balanced scorecard and Six Sigma as performance
measurement systems
Slide 3-3
3
OPENING VIGNETTE
Targeting Tax Fraud with Business Intelligence and Data
Warehousing
Why is it important for IRS and for U.S. state governments to
use data warehousing and business intelligence (BI) tools in
managing state revenues?
What were the challenges the state of Maryland was facing with
regard to tax fraud?
What was the solution they adopted? Do you agree with their
approach? Why?
What were the results that they obtained? Did the investment in
BI and data warehousing pay off?
What other problems and challenges do you think federal and
state governments are having that can benefit from BI and data
warehousing?

4
Business Intelligence and Data Warehousing
BI used to be everything related to use of data for managerial
decision support
Now, it is a part of Business Analytics
BI = Descriptive Analytics
Slide 3-5
What is a Data Warehouse?
A physical repository where relational data are specially
organized to provide enterprise-wide, cleansed data in a
standardized format
A relational database? (so what is the difference?)
“The data warehouse is a collection of integrated, subject-
oriented databases designed to support DSS functions, where
each unit of data is non-volatile and relevant to some moment in
time”
Slide 3-6

A Historical Perspective to
Data Warehousing
Slide 3-7
Characteristics of DWs
Subject oriented
Integrated
Time-variant (time series)
Nonvolatile
Summarized
Not normalized
Metadata
Web based, relational/multi-dimensional
Client/server, real-time/right-time/active...
Slide 3-8
Data Mart
A departmental small-scale “DW” that stores only

limited/relevant data
Dependent data mart
A subset that is created directly from a data warehouse
Independent data mart
A small data warehouse designed for a strategic business unit or
a department
Slide 3-9
Other DW Components
Operational data stores (ODS)
A type of database often used as an interim area for a data
warehouse
Oper marts
An operational data mart
Enterprise data warehouse (EDW)
A data warehouse for the enterprise
Metadata – “data about data”
In DW metadata describe the contents of a data warehouse and
its acquisition and use
Slide 3-10

A Better Data Plan: Well-Established TELCOs Leverage Data
Warehousing and Analytics to Stay on Top in a Competitive
Industry
What are the main challenges for TELCOs?
How can data warehousing and data analytics help TELCOs in
overcoming their challenges?
Why do you think TELCOs are well suited to take full
advantage of data analytics?
Slide 3-11
DW for Data-Driven Decision Making
An example of a DW supporting data-driven decision making in
automotive industry
Slide 3-12
A Generic DW Framework

13
13
DW Architecture
Three-tier architecture
Data acquisition software (back-end)
The data warehouse that contains the data & software
Client (front-end) software that allows users to access and
analyze data from the warehouse
Two-tier architecture
First two tiers in three-tier architecture are combined into one
… sometimes there is only one tier?
Slide 3-14
14

DW Architectures
3-tier
architecture
2-tier
architecture
1-tier
Architecture
?
Slide 3-15
15
Data Warehousing Architectures
Issues to consider when deciding which architecture to use:
Which database management system (DBMS) should be used?
Will parallel processing and/or partitioning be used?
Will data migration tools be used to load the data warehouse?
What tools will be used to support data retrieval and analysis?
Slide 3-16

A Web-based DW Architecture
Slide 3-17
17
Slide 3-18
18

Each architecture has advantages and disadvantages!
Which architecture is the best?
Slide 3-19
19
Ten Factors that Potentially Affect the Architecture Selection
Decision
Information interdependence between organizational units
Upper management’s information needs
Urgency of need for a data warehouse
Nature of end-user tasks
Constraints on resources
Strategic view of the data warehouse prior to implementation
Compatibility with existing systems
Perceived ability of the in-house IT staff
Technical issues
Social/political factors
Slide 3-20

20
Process
ETL = Extract Transform Load
Data integration
Integration that comprises three major processes: data access,
data federation, and change capture.
Enterprise application integration (EAI)
A technology that provides a vehicle for pushing data from
source systems into a data warehouse
Enterprise information integration (EII)
An evolving tool space that promises real-time data integration
from a variety of sources, such as relational or
multidimensional databases, Web services, etc.
Slide 3-21
Process
Slide 3-22

ETL (Extract, Transform, Load)
Issues affecting the purchase of an ETL tool
Data transformation tools are expensive
Data transformation tools may have a long learning curve
Important criteria in selecting an ETL tool
Ability to read from and write to an unlimited number of data
sources/architectures
Automatic capturing and delivery of metadata
A history of conforming to open standards
An easy-to-use interface for the developer and the functional
user
Slide 3-23
23
BP Lubricants Achieves BIGS Success
What is BIGS?

obtained results with BIGS?
Slide 3-24
Data Warehouse Development
Data warehouse development approaches
Inmon Model: EDW approach (top-down)
Kimball Model: Data mart approach (bottom-up)
Which model is best?
Table 3.3 provides a comparative analysis between EDW and
Data Mart approach
Another alternative is the hosted data warehouses
Slide 3-25
Comparing EDW and Data Mart
Slide 3-26

Use of Teradata Analytics for SAP

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