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Chapter 3 • Data Warehousing 111

What We can Learn frOm this vignette
The opening vignette illustrates the strategic value of implementing an enterprise data
warehouse, along with its supporting BI methods. Isle of Capri Casinos was able to
leverage its data assets spread throughout the enterprise to be used by knowledge
workers (wherever and whenever they are needed) to make accurate and timely deci-
sions. The data warehouse integrated various databases throughout the organization
into a single, in-house enterprise unit to generate a single version of the truth for the
company, putting all decision makers, from planning to marketing, on the same page.
Furthermore, by regularly funneling slot machine data into the warehouse, combined
with customer-specific rich data that comes from variety of sources, Isle significantly
improved its ability to discover patterns to re-imagine/reinvent the gaming floor opera-
tions and forge ever deeper and more lasting relationships with its customers. The key
lesson here is that an enterprise-level data warehouse combined with a strategy for its
use in decision support can result in significant benefits (financial and otherwise) for
an organization.
Sources: Teradata, Customer Success Stories, teradata.com/t/case-studies/isle-of-capri-casinos-executive-
summary-eb6277 (accessed February 2013); www-01.ibm.com/software/analytics/cognos.

3.2 Data Warehousing Definitions anD ConCepts
Using real-time data warehousing in conjunction with DSS and BI tools is an important way
to conduct business processes. The opening vignette demonstrates a scenario in which a
real-time active data warehouse supported decision making by analyzing large amounts of
data from various sources to provide rapid results to support critical processes. The single
version of the truth stored in the data warehouse and provided in an easily digestible form
expands the boundaries of Isle of Capri’s innovative business processes. With real-time
data flows, Isle can view the current state of its business and quickly identify problems,
which is the first and foremost step toward solving them analytically.
Decision makers require concise, dependable information about current operations,
trends, and changes. Data are often fragmented in distinct operational systems, so manag-
ers often make decisions with partial information, at best. Data warehousing cuts through
this obstacle by accessing, integrating, and organizing key operational data in a form that
is consistent, reliable, timely, and readily available, wherever and whenever needed.

What is a Data Warehouse?
In simple terms, a data warehouse (DW) is a pool of data produced to support decision
making; it is also a repository of current and historical data of potential interest to man-
agers throughout the organization. Data are usually structured to be available in a form
ready for analytical processing activities (i.e., online analytical processing [OLAP], data
mining, querying, reporting, and other decision support applications). A data warehouse
is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of
management’s decision-making process.

a historical perspective to Data Warehousing
Even though data warehousing is a relatively new term in information technology, its
roots can be traced way back in time, even before computers were widely used. In the
early 1900s, people were using data (though mostly via manual methods) to formulate
trends to help business users make informed decisions, which is the most prevailing pur-
pose of data warehousing.

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