Information Systems Management, 25: 102–112
Copyright © Taylor & Francis Group, LLC
ISSN: 1058-0530 print/1934-8703 online
Continental Airlines Continues to Soar with Business Intelligence
Continental Airlines Continues to Soar with BI Barbara H. Wixom1, Hugh J. Watson2, Anne Marie Reynolds3, and Jeffrey A. Hoffer4
1McIntire School of Commerce, University of Virginia, Charlottesville, Virginia
2Terry College of Business, University of Georgia, Athens, Georgia
3Continental Airlines, Houston, Texas
4School of Business Administration, University of Dayton, Dayton, Ohio
Abstract As the business intelligence industry matures, it is increasingly important to investigate
and understand the nature of mature data warehouses. Although data warehouse research is prevalent,
existing research primarily addresses new implementations and initial challenges. This case study of
Continental Airlines describes how business intelligence at Continental has evolved over time. It identifies
Continental’s challenges with its mature data warehouse and provides suggestions for how companies
can work to overcome these kinds of obstacles.
Keywords data warehousing, business intelligence, maturity model, case study
What words come to mind when you think of Continental Airlines Successful company, preferred airline, good service, on-time airline, top carrier, financially solvent, happy employees. These are all true; however, this was not always the case. Just six short years ago, probably not one of those descriptions would even be said in the same breath as Continental Airlines. In fact, in 1994, Continental ...
We have a very mature data warehouse at Continental—we have
been doing business intelligence for a long time, about a decade.
New ideas for leveraging the warehouse keep coming up as we
continue to evolve. Many are not the kinds of major applications
that would have justified our initial investment in warehousing.
But, these are the kinds of things that create a constant return on
– Anne Marie Reynolds, Data Warehousing Director,
Much has been written about the potential for data warehousing;
how to build a data warehouse; and how some
companies are benefiting from business intelligence; but
most of the writings have been focused on companies’
initial experiences. Data warehousing is known to be a
journey, not a destination. Although it clearly is important
to start off strongly, it is equally important to know
how to move ahead over time. Managers need to understand
how to evolve data warehouse initiatives to meet
the changing and growing needs of the business. Companies
need to continuously extract value from their significant
on-going warehousing investments.
On a journey, if you know what lies ahead, the more apt
you are to overcome obstacles and take the most satisfying
route. In data warehousing, many folks are blind to what
lies ahead when they begin. We have much to learn
about the opportunities and challenges posed by a mature
data warehouse. How do early decisions, such as data
modeling technique or platform selection, impact later
capabilities? What mechanisms best enable a warehouse
to expand its reach and business impact? What obstacles
do warehousing teams need to overcome over time?
Answers to these questions may better guide even the
most adventuresome of warehousing journeymen.
To explore these questions, the authors conducted a
series of interviews with business intelligence users and
technologists at Continental Airlines. The airline has had
1. DATA MINING 1. 1 INTRODUCTION TO DATA MINING The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The ...
a data warehouse in place since 1998 and is considered a
leader in business intelligence (Watson, Wixom, Hoffer,
Anderson-Lehman, & Reynolds, 2006).
Over time, the
warehouse has grown and evolved in exciting ways,
spreading out across the enterprise’s business areas and
geographical locales. Applications have moved from
being strategic and tactical in nature to ones that are
highly operational. The numbers of users, applications,
and data increase annually.
In this article, we begin by discussing the characteristics
of a mature data warehouse. The characteristics are
drawn from the maturity or stage models that have been
developed for data warehousing. Next, we describe the
current state of Continental Airlines’ data warehouse,
highlighting evidence of the warehouse’s maturity.
Then, we discuss three ways in which Continental has
expanded the reach of its data warehouse, and we
explore the mechanisms that enabled the expansion. We
Address correspondence to Hugh J. Watson, Terry College of
Business, University of Georgia, Athens, Georgia 30602, USA.
Continental Airlines Continues to Soar with BI 103
conclude with challenges that Continental now faces
with its mature data warehouse and with some suggestions
for how companies can work to overcome these
kinds of obstacles.
Mature Data Warehouses
An organization does not develop a mature data warehouse
overnight; rather it is the result of a progression
through a series of earlier stages. It is rare that a warehouse
skips stages. Each stage builds on a previous stage
and provides a greater set of capabilities. A “mature”
warehouse is one that has evolved to the point that it
is part of the institutional fabric and integral to the
functioning of the organization.
In order to better understand mature data warehousing,
it is useful to step back and look at antecedents of
the concept. Maturity, stage, and evolution models, as
they are variously called, have been popular in disciplines
Starbucks is a company that is specialized in offering a range of products including coffee, handcrafted beverages, merchandise, and fresh food. As an enterprise, they require a proper data management to enable them serve their customers efficiently. Data on sales, customer views, customer information, market analytics, products, and production needs a proper storage and retrieval system hence the ...
for many years. The fundamental concept is that
things change over time, in sequential, predictable ways.
Stage models have been used to describe, explain, and
predict organizational life cycles, product life cycles, and
biological growth. They also have been popular in the
information systems field. The classic model is Nolan’s
Stages of Growth Model that identifies specific characteristics
that firms demonstrate as their IT departments
mature (Nolan, 1979).
Though various stage models may differ in terms of
the number of stages and what the stages are called, they
are all similar in that they break down a phenomenon’s
evolution into a series of distinct phases with characteristics
associated with each phase. For example, Nolan originally
had a four stage model (i.e., Initiation, Expansion,
Formalization, and Maturity) that he later expanded to a
six stage model to reflect advances in practice.
Each stage model is composed of component stage
models, which collectively identify the stage of the
higher-level model. For example, Nolan’s model includes
dimensions of application growth, personnel specialization,
and management techniques. Possibly, each of
these components reflects a different growth stage; application
growth may be mature, while personnel specialization
is expanding. However, overall and collectively,
the components define a stage of evolution.
The data-warehousing field also has maturity models.
Watson, Ariyachandra, and Matyska (2001) suggest that
business intelligence has Initiation, Growth, and
Maturity stages. Eckerson (2006) proposes a five-stage
model (i.e., Prenatal-Infant, Child, Teenager, Adult, and
Each of these models uses characteristics or dimensions
in categorizing the stages. For example, Watson,
et al categorize the stages based on a data warehouse’s
data, stability of the production environment, warehouse
staff, users of the warehouse, impact on users’
skills and jobs, use of the warehouse, organizational
impacts, and costs and benefits. Eckerson uses scope,
funding, warehouse staff, governance, standards, architecture,
The basic reasons organizations implement data warehouses are: To perform server/disk bound tasks associated with querying and reporting on servers/disks not used by transaction processing systems most firms want to set up transaction processing systems so there is a high probability that transactions will be completed in what is judged to be an acceptable amount of time. Reports and queries, ...
executive perception, data latency, and business
intelligence focus. Together, the models provide a comprehensive
list of characteristics to use in categorizing
various maturity stages; see Figure 1 for a list of characteristics
of data warehouses in mature stages.
The research around these models teaches us that
maturity is an elusive state. Often, maturity means that
an organization repeats and likely improves upon
what has worked well in the past in new areas of the
organization. Maturity means that the initiative is part
of the culture. There is learning from success and failure,
Figure 1. Characteristics of mature data warehouses.
Eckerson Maturity Model
Watson et al Maturity Model
• Scope. Enterprise. • Data. Enterprise, well integrated, for multiple time periods.
• Architecture. Warehouse, with dependent data marts.
• Stability of the Production Environment. Procedures are routine
• Warehouse Staff. Experienced in-house personnel, with well-defined
roles and responsibilities.
• Users. Organization-wide, suppliers and customers may be users.
• Impact on Users’ Skills and Jobs. Users throughout the organization
need improved computer skills to perform their jobs.
• Applications. Reports, predefined queries, ad hoc queries, DSS, EIS,
data mining, and integration with operational systems.
• Costs and Benefits. Benefits include timesaving, new, and better
information, improved decision-making, redesigned business processes,
and support for corporate objectives.
• Organizational Impact. Organization-wide and often strategic,
tactical, and operational.
• Funding. IT/Business.
• Team. Corporate IT.
• Governance. Business Intelligence Stewardship Team.
• Flexibility/Standards. Plan global, act local.
• Architecture. Enterprise data warehouse.
• Type of System. Strategic System.
• Executive Perception. Drive the business.
• Data Latency and Freshness. Low latency, high freshness.
• Business Intelligence Focus. What should we do?
Abstract Database Systems has a practical, hands-on approach that makes it uniquely suited to providing a strong foundation in good database design practice. Database design is more art than science. While it's true that a properly designed database should follow the normal forms and the relational model, you still have to come up with a design that reflects the business you are trying to model. ...
• Business Intelligence Output. Action.
104 Wixom et al.
but also there is a perception of enough success that the
initiative is allowed to continue and grow, until some
radical event or new opportunity makes the initiative
According to the characteristics listed in Figure 1,
Continental Airlines has a mature data warehouse. The
rest of the article will describe the warehouse, with a
focus on its maturity in terms of the growth of its applications
and usage. As data warehouses mature from an
application perspective, their scope and reach increase.
The warehouse delivers the most direct business value by
maturing in this way.
Continental’s Mature Data Warehouse
Continental’s original data warehouse team was formed
in 1998 and charged with integrating a diverse set of data
from the key mainframe source systems throughout the
company onto one platform. Two specific areas drove the
original project: revenue management and marketing.
The Revenue Management department is responsible
for determining the number of airlines seats offered at a
particular price point (i.e. booking class) for every Continental
flight. In 1998, they needed to capture detailed
booking data at the lowest level of detail for analysis.
Improvements in the ability to understand passenger
behavior in order to optimize pricing decisions were
expected to result in significant revenue opportunities
for the airline.
At about that same time, the Marketing department
needed to understand customers better. The existing frequent
flyer system measured customer activity based on
miles flown, but the Marketing department wanted
insight into the revenue associated with each customer.
They also wanted to know which customers were
affected by flight delays and cancellations and other service
interruptions. Understanding customers better was
expected to improve Continental’s profitability and its
ability to respond to customers’ needs, thus increasing
A Teradata platform was chosen, primarily for scalability
Executive Summary mySupermarket is a grocery shopping and comparison website which aims to provide customers with the best price for their shopping. This report examines how data warehousing provided mySupermarket with the foundation in which to build a successful enterprise, and allowed a subsequent expansion into the ‘business intelligence’ sector. The research draws attention to the problems ...
and ease of administration reasons. Even though
two specific departments were funding the initial investment,
the executive sponsors had the vision to conceive
of a data repository that would ultimately encompass the
entire population of corporate data required to support
decision making, so scalability was critical.
Continental recognized the need and planned for realtime
business intelligence at the outset of its data warehousing
initiative. The warehouse group built a data
acquisition infrastructure whereby source systems would
feed a queue in either batch mode or in a constant flow.
A warehouse loading process continuously monitored data
sources and pulled data into the warehouse. The initial
feeds of data were all performed in batch. Currently, fifteen
percent of the subject areas are loaded continuously,
yet the original data acquisition infrastructure is
still a highly effective loading mechanism.
The data within the warehouse was modeled using a
strict third normal form modeling technique, and the
data used standard naming conventions. A dedicated
and strong-willed database administrator ensured that
there were few exceptions to these policies over time,
regardless of short-term pressures. The intent was to create
an underlying data layer that was not shaped by
unique needs, but instead was kept “vanilla” so that it
could easily evolve over time to meet a wide breadth of
The general philosophy was to grant users access to all
data, unless there was a reason not to do so. Thus, when
users received a warehouse ID, they immediately were
able to access a common enterprise-wide data layer. Some
data in the data warehouse, such as credit card numbers
and employee salaries were restricted, for obvious reasons.
This “open data” philosophy remains intact today.
By 2002, the Revenue Management and Marketing initiatives
were successful, more than offsetting the data
warehouse investment (see Anderson-Lehman, Watson,
Wixom, and Hoffer, 2004).
Both functional areas worked
with the data warehouse team to calculate and document
the most tangible and significant benefits, which
established credibility for future funding.
Over the nine years that the data warehouse has been
in existence, the scope has grown to include more than
50 subject areas and 1400 named users writing ad-hoc
queries. In addition, the data warehouse is the “single
source of the truth” for 70 applications developed by
Continental. These applications fall into three general
categories (see Figure 2).
In 2007, the Continental data warehouse had truly
achieved a global scope. About half of the user community
accesses the warehouse from the company headquarters
in Houston, while the other half is spread across
75 cities throughout the world. The data warehouse staff
has conducted international training sessions in London,
Guam, and Tokyo, which are some of the airline’s centers
for international sales and pricing; and there are plans to
visit Latin America. These user groups are becoming data
warehouse analysts, rather than simply consumers of
A team of 15 people supports the Continental data
warehouse, and this group is responsible for the
data transformation development, application interface
development, user support and training, database administration
and production support. The 24 × 7 production
support is shared by all of the members of the group and
rotated weekly. Production incidents are reviewed each
week to ensure that the root cause of each incident is
Continental Airlines Continues to Soar with BI 105
The data warehousing team size remains relatively
small because application development is primarily done
within the functional areas. Each department has at least
one fairly technical employee who serves as a liaison
between the warehouse group and the department. This
employee ensures that the appropriate business needs
are communicated to the warehouse group; he or she
builds warehouse queries, reports, and/or applications
for use within the department.
Initially, a steering committee prioritized warehouse initiatives
and actively managed the warehouse evolution;
however, once the enterprise data foundation was firmly in
place and new projects became more focused on niche functional
needs, the steering committee evolved into an advisory
body. The warehouse management now prioritizes
projects, with input from steering committee members.
Annually, the warehouse management presents the core
development projects to upper management for approval.
The funding model for the warehouse also changed
over time. Initially, areas like revenue management
and marketing built a business case for the warehouse
and provided its funding. Each time the warehouse
required a significant upgrade to the infrastructure, a
different functional area would make the necessary
investment although all areas would benefit. A significant
shift occurred recently when the warehouse director
secured a multi-million dollar capital investment for
the warehouse through the IT budget. The belief is that
the warehouse has become a critical part of the IT infrastructure
and should be funded as such.
Over time, Continental’s data warehouse application
portfolio has grown, and with this growth Continental
has realized considerable business value. The airline
has generated warehouse application growth in three
unique ways: by constantly adding new business groups
to the warehouse user base, with new applications to
meet their needs; widening the user base across the
globe; and integrating warehouse capabilities into operational
business processes. The following sections provide
examples of how each type of expansion has occurred.
One way to expand data warehouse applications and
usage is by increasing the number of departments or
sub-groups that use the warehouse. Initially, the revenue
management and marketing groups championed the
development and use of the warehouse. Over time, usage
spread to other groups, such as Human Relations and
Technical Operations. The following sections describe
how this expansion occurred.
Continental, like many organizations, has legacy systems
that are cumbersome to use for querying and reporting.
Processes that require data from legacy applications often
are time-consuming and require manual intervention.
For example, prior to the data warehouse, a person in the
Payroll Department was responsible for ensuring that
the amount Continental is billed for benefits matches the
amount of benefits actually being used by its employees.
Each Monday, this employee received a benefits report
and then worked all week to reconcile the numbers.
Being a part of the overall Human Relations (HR) Department,
Payroll had watched the HR area leverage the data
warehouse, and over time, the Payroll Department began
to realize they, too, could tap into warehouse data for their
own needs. At first, paycheck information was loaded into
the data warehouse. The group created simple queries
Figure 2. Types of applications at Continental.
The application is a recipient of data provided through a regularly scheduled batch process.
For example, the warehouse provides revenue data to a Flight Profitability System and a Travel
Agency management system, called Sales Insight.
The application is the recipient of data provided in real-time through a web service or direct query to the data
For example, the warehouse provides a real-time look at today’s flight operations through a web
interface that includes the capability to drill-down to flight detail.
The application resides on the data warehouse. The warehouse performs a predefined function using the data, usually
from several data sources.
For example, the data warehouse performs statement processing for the small business Reward One
program, combining frequent flyer and revenue information with the Reward One profiles.
106 Wixom et al.
against that data to review payroll data. This was easy for
Payroll as the users were already familiar with the data.
“Once we were comfortable with that, we started realizing
we could get a lot more out of the data warehouse. That is
when we started thinking of having the data warehouse do
some payroll calculations for us.” Currently, the warehouse
group is designing a data warehouse report to perform
benefit deduction reconciliations against YTD
coverage information from Continental’s Benefits Provider.
This one task will free up an employee to spend his week
doing more value-added tasks for the department.
And, there are additional plans for the data warehouse.
Payroll is working to transfer time and attendance data to
the warehouse to meet more unique departmental needs.
The current goal is to be able to input an employee number
and immediately have access to all related HR data so
that the group can answer employee questions within the
duration of a phone call. Once time and attendance data
exist in the warehouse, Mullane predicts that the company
as a whole will have the capability to even better
understand what happened at the airport on a specific
date. “The more data we put in for ourselves, it seems like
other divisions get more and know more about how
things are happening at the company level.”
Reliability Engineering Department
Reliability engineers currently are moving their processes
from a legacy COBOL Focus system to queries and
applications based on the data warehouse. Reliability
engineers ensure that the Continental fleet operates
reliably, and they work to reduce delays, cancellations,
and aircraft returns. Although their previous Focus system
was highly useful, it was extremely costly to use.
EDS maintained the system as an outsourcer, so each
time the engineers asked for a report or query in the
past, they had to write a check to EDS. Continental as a
company began to push the group to lower costs; the
data warehouse offered the engineers a much better
value proposition, with expected savings of about
The engineers also anticipate that the warehouse will
increase their capabilities. The Focus tables had a very narrow
scope; the warehouse allows reliability engineers to
join tables like never before possible. The engineers suspect
that having access to a larger scope of enterprise data
may allow them to tap into special needs, some that currently
may be unknown. “The warehouse should allow us
to think outside the box . . . or think into a different box.”
Although the reliability engineers are still in transition
over to the warehouse, they have used it enough to
believe in its value. Recently, Reliability Engineering
received a call from a related area within Technical
Operations, inquiring about options for storing their
data. “We recommended that they go with the data warehouse,”
explains a Sr. Systems Analyst. “The warehouse
group is knowledgeable and easy to communicate with.
They generally recognize what our role is at Continental
and what our needs for the data are. They provide data
access pretty rapidly.” The data warehouse group’s high
level of service and close working relationship with Reliability
Engineering, along with the warehouse’s value
proposition, will encourage the reliability engineers to
recommend that others also leverage the data warehouse.
Continental continuously adds new organizational
groups and users to the warehouse, and this happens in
a fairly predictable way (see Figure 3).
First, an organizational
group observes a related area using the data
warehouse to do things easier, cheaper, or better. Typically,
the groups rely on similar kinds of data and systems;
therefore, it is reasonable to deduce that if the
related area is leveraging the warehouse, then the new
group can derive value from the warehouse as well.
Next, the group begins by asking simple data queries of
known data. This important step builds user skills, confidence,
and trust in the data warehouse. Finally, the
group fully leverages the warehouse by developing custom
applications based on the warehouse and by designating
a liaison within the group who will ensure that
business needs align with the data and capabilities of
A second way to expand data warehouse applications
and usage is by rolling out applications globally. The
initial warehouse applications were headquarters-centric;
they best met the needs of employees based in
Houston. At times, these same applications also served
global needs; but this was not always the case. The
Figure 3. Enterprise expansion.
Continental Airlines Continues to Soar with BI 107
following sections explain how Continental was able to
engage international areas to leverage warehouse
Revenue Management, Japan
Aaron Sacharski, Continental’s manager of pricing and
revenue for Asia Pacific, began working for the airline in
its Headquarters Revenue Management group in 2002.
When Aaron arrived, Revenue Management was heavily
involved with the data warehouse, using it to make micro
and macro types of decisions. The warehouse enabled important
capabilities; its value to the Revenue Management
group was obvious. During this time, he met people in
the data warehouse group and built up his own querying
and data analysis abilities.
In 2005, Aaron moved to Japan to manage pricing
and revenue for Asia Pacific, fully intending to continue
leveraging the data warehouse in his new role.
Two obstacles hindered his progress. First, the technical
environment in Asia was not as mature; bandwidth
was narrow, and software and hardware were
The second and most challenging hurdle was that
the data, queries, and analytics that worked at headquarters
were not applicable to the Asian market. For
example, in Continental’s domestic market, there is a
smooth booking curve that can be analyzed to predict
flight demand behavior. In Japan, there is a high percentage
of travel agent bookings, and agents are not
required to migrate their bookings to the Continental
reservation system until 30 day prior to departure.
Thus, until a month in advance, analysts have minimal
knowledge of flight bookings; it is nearly impossible to
create a demand curve based on current flight booking
Another reason that Japan analysts could not effectively
use data, queries, and analytics from headquarters
was the time difference. Some of the data warehouse revenue
management tables used to predict future bookings
were populated by batch loads each night at midnight.
This was fine for analysts located in Houston, but the
data was not as useful to analysts in Asia who received
the data a day and a half later.
For these reasons, the analysts in Japan perceived that
the warehouse could not meet their unique needs, and
there was little interest to learn about the tool. Aaron,
however, had deployable knowledge of the warehouse
data acquired in Houston. Although the existing techniques
from Headquarters did not fit Asia as well, Aaron
used his data warehousing knowledge in working with
Japan to identify data that could improve pricing and
Since 2005, the Japan pricing and revenue management
team worked in earnest to understand what warehouse
tables were relevant to Japan, and a warehouse team member
built specialized tables to directly meet their needs.
They began to develop queries using PNR (passenger name
records) data because it is loaded into the warehouse in realtime.
At the same time, the technology group for Asia
invested in extra bandwidth for the analysts to improve
querying efficiency. As the warehouse increasingly helped
the group to make better decisions, usage of the warehouse
Tax Department, London
In the United Kingdom, Continental must pay a departure
tax for passengers who leave the U.K. on Continental
flights. Each month, employees in the London office
calculated this departure tax by manually reviewing the
records for every passenger who traveled out of London,
and the employees submitted the appropriate amount to
the government. If passengers are passing through the
U.K. in less than 24 hours, they are exempt from the tax,
but the manual process could not always identify those
individuals. Thus, Continental regularly overpaid the
departure tax, which equated to a $300,000 annual cost
for the airline.
Last year, several members of the Continental London
office were visiting Houston for routine training, which
included a presentation by the warehouse group. During
the presentation, the London employees noticed that
data in the warehouse potentially could identify passengers
who were exempt from the departure tax. They
approached the warehouse team to build a specialized
application. Now, the group runs a monthly query to the
warehouse, prints out a report with an accurate departure
tax amount, and submits the report. The application
eliminates significant time and overpayment.
The global spread of warehouse applications begins with
the development of core capabilities in a headquarters or
central locale. As skills in the base location mature,
employees transfer knowledge by physically transferring
to global locations or by conducting training programs
for global groups. Seasoned warehouse users can use
their strong skills to identify gaps between existing warehouse
capabilities and unique global needs. Over time,
applications can be developed to meet those unique
needs. This builds confidence in the warehouse by global
users and encourages use. Figure 4 illustrates the global
108 Wixom et al.
A third opportunity for expansion includes changing
processes, looking for ways in which warehouse data can
change the way business is done, making things easier
and faster—or doing things differently. Potentially this kind
of change can create a competitive edge for Continental.
Reservation Complaint Handling
In the past, when a customer called a Continental reservation
agent with a complaint, the agent would simply
listen to whatever the customer told them and send the
information through an antiquated communication process
to the Customer Care department. Members of the
eighty-person Customer Care team would then take the
information, print it, scan it, re-key it into a customer
care system, and then work to resolve the case. The process
was time consuming, and it left open the possibility
that customers could game the system and receive duplicate
compensation for the same incident. Continental estimates
that this duplication cost the company $1 million
Other airlines have addressed this issue by only allowing
customer complaints via web or email; complaints
are no longer accepted by phone. Continental has a different
If the customer is calling us with a complaint, then they want us
to listen to them. If we can put in place a process that reduces the
overhead and so streamlines the process that literally there really
is no reason for us to discontinue telephones as a channel for
customers to communicate to us, we are better off.
– John Brinker, Senior Manager CRM Strategy and
Continental created an automated process that takes a
variety of data inputs from the warehouse, runs those
inputs through a proprietary rules engine, and generates
a recommendation on how a particular customer
should be handled (see Figure 5).
A reservation agent
can trigger this process while a customer is on the
phone, and act on the automated recommendation,
which is available within seconds. The automated
process eliminates several steps from before, and it has
reduced the customer-care group count by ten people.
This new process reduces costs so that Continental can
continue serving customers in a manner that meets
customer needs. The customer complaint application
is now in pilot with 50 reservation agents, and will
be rolled out to the entire Reservations department in
Prior to the data warehouse, Continental Operations built
and managed their own information and reporting systems.
The systems support staff was very small; when a
support employee went on vacation or was sick, systems
were put on hold until the person returned to work. Eventually,
management mandated a move to the warehouse
to improve continuity of the support operations.
Steve Hayes, a manager within this operations support
group, has leveraged the warehouse for his area in
significant ways. For example, he has built a real-time
status application that communicates up-to-the-minute
performance statistics on how the airline is operating.
And, when Jet Blue and American Airlines were criticized
for incidents that involved stranding passengers
in planes for long periods of time (Cummings, 2006;
Zeller, 2007), Hayes was able to adapt his application, and
help Continental avoid similar situations. Continental’s
old process for detecting these kinds of events was manual
and time consuming. Hayes explains, “You had to hunt
and peck through flight logs. In the middle of a snow
storm, you don’t have time to do that.”
Once Operations identified the need to monitor
planes on the tarmac, Steve added an alert to the realtime
performance statistics application. Now, flights that
sit on the ground away from a gate for at least two hours
immediately appear on the screen. In real-time, Operations
can work to get those flights off the ground, or get
them back to the gate in a timely manner.
The warehouse also has helped streamline Operations
reporting processes. In the past, Continental
manually tracked the reasons for flight delays (e.g.,
weather, part failure); there are about a hundred delay
codes. Sometimes stations forgot to record the reason
for delay, so Operations regularly ran a query on the
legacy system, downloaded the results into Excel,
emailed the results to the general managers, who
would then fill in the blanks and send information back
by email or telex. According to Hayes, “It would take
Figure 4. Global expansion.
Continental Airlines Continues to Soar with BI 109
forever to track down the information and update the
codes into the legacy system.”
Using the warehouse, Hayes built an application one
weekend that automatically lists flights that need delay
codes for each station. The general manager now directly
logs into the application, clicks on a flight, and enters
the delay code. The new process eliminates multiple
steps, and creates much more accurate results. Hayes
explains that this situation is representative of how he
now can quickly develop simple applications or application
enhancements using the data warehouse that have
high impact to Operations processes.
Process change can be difficult because people often are
reluctant to change. However, users are much more
willing to embrace change when warehouse applications
clearly reduce steps, eliminate costs, or save time. To
recognize change opportunities, users must first understand
the breadth of data that exists within the
warehouse. Then, when key business needs arise, users
can better match these needs to warehouse capabilities;
at that point, they can leverage data for business process
improvement, or even re-engineering (see Figure 6 for
the steps of process expansion).
Maturity does not equate to easy. Thus, once organizations
establish mature data warehouses, they should not
expect a guaranteed smooth journey. Although early
obstacles have been overcome, new challenges emerge as
Figure 5. The warehouse-enabled customer complaint process.
Business Rules Engine Fair Compensation
Data Warehouse Customer Complaint Application
Figure 6. Process expansion.
110 Wixom et al.
a warehouse matures. Below we discuss some of the
maturity challenges faced by Continental.
Only recently, since the mid-1990s, has database marketing
gained a stronghold in the marketing industry. The
supply of people who have database marketing skills,
such as SQL and query writing, data warehousing, and an
appreciation of data has not yet caught up with demand.
Continental’s marketing group proactively hires people
with those skills; in fact, new marketing employees must
take an SQL test as standard operating procedures. But,
the right hires are hard to find. And, once employees are
hired and trained, they can be difficult to retain. Once
employees develop strong analytical and data skills, they
can leave for higher salaries and promotion opportunities.
The staffing problem is not limited to functional areas
like Marketing; the IT group grapples with these same
issues. Talented data warehouse professionals are valuable,
and the turnover in the group has been fairly high.
The warehouse management strives to address this by
providing opportunities for cross training and advancement
where possible, and to keep employees challenged.
Scalability and Performance
Continental currently views technology as a major
hurdle for continued success. For one, applications, such
as the customer complaint application, must perform
quickly. Reservation agents are uncomfortable if their
system does not respond in at least five seconds; thus,
much effort has focused on ensuring the data warehouse
can deliver fast performance. Global locations increasingly
need to adapt their technical environments to
expanding usage and more data moving across the
network. In short, Continental understands that the data
warehouse must be viewed as an operational tool.
The warehouse team invests in significant hardware
upgrades so that the processing power of the warehouse
sufficiently meets usage requirements.
Recently, the warehouse group scheduled a system
upgrade that required a window for system outage. Bad
weather was predicted for that same time period. Operations
contacted the warehouse group and asked them to
reschedule the upgrade; the warehouse had to be available
for use during the bad weather to help Operations
manage airline performance. The data warehouse was
critical for managing the severe weather event.
The warehouse management acknowledges that
business continuity and disaster recovery are clearly
becoming mandatory now that the warehouse enables
important operational processes. It costs a lot of
money to maintain an alternate data warehouse
location for an emergency, but if Operations needs
the warehouse to manage a critical weather event,
this implies that investments must be made in warehouse
disaster recovery. The data warehouse management
is still assessing the alternatives to address these
The expansion of warehouse usage and applications
leads to a growing volume of data within the warehouse.
Brinker senses that the incredible amount of marketing
data can impede Marketing’s ability to perform true analysis.
Large volumes of data are “hard to digest.”
This observation is consistent with the increased interest
in predictive analytics within the data warehousing
community. At the 2006 The Data Warehousing Institute
conference, business leaders ranked predictive analytics as
their number one area for opportunity in business intelligence.
Predictive analytics may be one solution when data
volumes grow too large for the human mind to manage.
Common wisdom says that as users become accustomed
to business intelligence, they increasingly want to see
data in real-time. Rarely do we think of global expansion
as a driver of real-time data, but that certainly was the
case at Continental. Batch loaded data was typically fine
for analysts working in headquarters in Houston, but
that same data did not meet the needs of analysts located
around the globe. As organizations roll out applications,
they must be aware of the latency needs of their analysts
and impact of time zones on the freshness of the warehouse
data. It may be that latency plans have to be
“refreshed” with global expansion.
Initially, the warehouse team developed the applications
for the warehouse users. The users were unfamiliar with
data warehousing, and they had a hard time conceptualizing
the kinds of applications that would be possible. Thus,
the warehouse team used a prototyping approach to
develop user applications for them. Sometimes the
approach resulted in applications that were highly
successful. Other times, the approach was unable to capture
Continental Airlines Continues to Soar with BI 111
the underlying user requirements; users found the application
too hard to use, or not useful for their needs.
As the user skills around the data warehouse matured,
the warehouse team moved away from application development,
encouraging functional areas to develop their
own warehouse applications when possible. Currently, the
warehouse team plays much more of an advisory role,
traveling around the world to educate users, listen to
needs, and ensure that the warehouse remains relevant to
the user base. This approach is highly effective. Functional
areas perceive that the warehouse team delivers high quality
service and responds quickly to their needs.
Although Continental continues to face challenges with
its data warehouse, the organization has realized an
enviable level of warehouse maturity. Continental
realizes significant cumulative benefits from the use of
applications that cross the enterprise, span the globe,
and enable fundamental business processes.
Stage models are sometime criticized for failing to
explain how organizations move from one stage to
another. The experiences at Continental, however, provide
insights into what facilitates the movement into a
mature stage, particularly from an applications and
usage perspective. Several facilitators make the wide variety,
growth, and spread of applications possible. We
conclude by discussing and illustrating these facilitators,
each of which are at their own stage of evolution.
A Common Data Foundation
Continental warehouse users advise not to underestimate
the value of standard naming conventions, a normalized
data structure, and user-friendly metadata. Steve
Hayes from Operations explains, “Uniform naming conventions
across tables is a big deal. This means that I can
access a table that I have never worked on before, but
because we have standard naming conventions and a
web-based data dictionary, I can determine the field’s
purpose, value, and be able to include it in my queries
and reports.” Warehouse management observes that the
deeper and broader a users understanding of the data,
the more creative and usefully the data is applied.
“Open Data” Philosophy
Most companies initially give users limited access to data
in the warehouse, likely restricting access to a subject
area or two most appropriate for their function. At Continental,
the philosophy is exactly the opposite. When
users receive a warehouse ID, they immediately have
access to all data in the warehouse that does not require
special permission. Although only some users can access
human resources data or passenger credit card numbers,
everything else is fair game.
This philosophy helps generate new uses for the warehouse.
According to Brinker, “If I had not had access to the
broad scope of information that was in the warehouse, I
probably would never have drawn the conclusions that I
did regarding how we could automate the customer complaint
process.” Brinker explains that in other organizations,
a typical marketing user would have visibility only
into the company’s customer loyalty program, and he or
she would not interact with operational performance data,
check-in information, or passenger name records. Because
Brinker did have access to the latter information, he was
able to match the known business need of improving customer
care with warehouse capabilities.
A Culture of Data
Continental is fortunate to have a data-driven culture
that was initially developed by former CEO Gordon
Bethune and his management team back in the late
1990’s. Employees across the enterprise view data as a
corporate asset that can be turned into competitive
advantage when used properly. Functional areas intentionally
hire employees who will perpetuate this view.
Thus, using the warehouse as a decision support aid is
consistent with the fundamental employee mindset.
In some companies, the warehousing staff has strong
technical skills but limited business knowledge, and the
business side has limited technical skills but good business
knowledge. At the intersection of the warehousing
and business organizations, there is a dramatic change
in the technical/business skills and knowledge mix. At
Continental, liaisons work in each functional areas and
manage the communication between the warehouse
group and business unit. These liaisons are highly technical
people with deep functional area knowledge and
expertise. They eliminate disconnects between the technology
and business groups and ensure that the right
business needs are met, in the right way.
As organizations begin or continue their data warehousing
journeys, they are advised to assess the current maturity
of their efforts, and then look ahead to better prepare
112 Wixom et al.
for what is to come. There is a chance that making the
right moves now will avoid future missteps. Clearly, Continental
provides excellent insights for warehouse journeymen,
as the airline soars ahead with business intelligence.
Barbara H. Wixom is an Associate Professor at the
McIntire School of Commerce at the University of
Anne Marie Reynolds is the data warehouse director at
Continental Airlines. (email@example.com)
Hugh J. Watson holds a C. Herman and Mary Virginia Terry
Chair of Business Administration in the Terry College of
Business at the University of Georgia. (firstname.lastname@example.org)
Jeffrey A. Hoffer is the Sherman-Standard Register Professor
of Data Management at the University of Dayton.
Anderson-Lehman, R., Watson, H. J., Wixom, B. H., and Hoffer,
Continental Airlines Flies High with Real-
Time Business Intelligence. MIS Quarterly Executive, 3(4),
Cummings, C. (2006, December 30).
Passengers Stuck on
Plane Over 8 Hours. The Dallas Morning News.
Nolan, R. (1979).
Managing the Crises in Data Processing.
Harvard Business Review, 57(2), 115–126.
Eckerson, W. (2006).
Performance Dashboards: Measuring, Monitoring,
and Managing Your Business. John Wiley & Sons: Hoboken, NJ:
Watson, H. J., Ariyachandra, T., & Matyska, R. J. (2001).
Warehousing Stages of Growth. Information Systems Management,
Watson, H. J., Wixom, B. H., Hoffer, J., Anderson-Lehman, J., & Reynolds,
A. -M. (2006).
Real-time Business Intelligence: Best Practices
at Continental Airlines. Information Systems Management,
Zeller, T. (2007, February 16).
Held Hostage on the Tarmac: Time for
a Passenger Bill of Rights? The New York Times.