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Chapter 9 Business Intelligence Systems
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parts. The company would earn minimal revenue from the parts themselves; the designs would
have to be priced considerably lower, and that would mean almost no revenue.
In spite of the low revenue potential, the company might still decide to offer 3D designs to
customers. It might decide to give the designs away as a gesture of goodwill to its customers;
this analysis indicates it will be sacrificing little revenue to do so. Or it might do it as a PR move
intended to show that it’s on top of the latest manufacturing technology. Or it might decide to
postpone consideration of 3D printing because it doesn’t see that many customers ordering the
qualifying parts.
Of course, there is the possibility that the team members chose the wrong criteria. If they
have time, it might be worthwhile to change their criteria and repeat the analysis. Such a course is
a slippery slope, however. They might find themselves changing criteria until they obtain a result
they want, which yields a very biased study.
This possibility points again to the importance of the human component of an IS. The hard-
ware, software, data, and query-generation procedures are of little value if the decisions that the
team made when setting and possibly revising criteria are poor. Business intelligence is only as
intelligent as the people creating it!
With this example in mind, we will now consider each of the activities in Figure 9-3 in
greater detail.

Q9-3 How Do Organizations Use Data Warehouses

and Data Marts to Acquire Data?

Although it is possible to create basic reports and perform simple analyses from operational data,
this course is not usually recommended. For reasons of security and control, IS professionals do
not want data analysts processing operational data. If an analyst makes an error, that error could
cause a serious disruption in the company’s operations. Also, operational data is structured for
fast and reliable transaction processing. It is seldom structured in a way that readily supports
BI analysis. Finally, BI analyses can require considerable processing; placing BI applications on
operational servers can dramatically reduce system performance.
For these reasons, most organizations extract operational data for BI processing. For small orga-
nizations, the extraction may be as simple as an Access database. Larger organizations, however,
typically create and staff a group of people who manage and run a data warehouse, which is a
facility for managing an organization’s BI data. The functions of a data warehouse are to:

• Obtain data
• Cleanse data
• Organize and relate data
• Catalog data

Figure 9-12 shows the components of a data warehouse. Programs read operational and
other data and extract, clean, and prepare that data for BI processing. The prepared data is stored
in a data warehouse database using a data warehouse DBMS, which can be different from the
organization’s operational DBMS. For example, an organization might use Oracle for its opera-
tional processing, but use SQL Server for its data warehouse. Other organizations use SQL Server
for operational processing, but use DBMSs from statistical package vendors such as SAS or SPSS in
Collecting and selling data about the data warehouse.
consumer shopping habits is big Data warehouses include data that is purchased from outside sources. The purchase of data
business. But what information about organizations is not unusual or particularly concerning from a privacy standpoint. However,
about you is being collected? And
how is it being used? The Ethics some companies choose to buy personal consumer data (e.g., marital status) from data vendors such
Guide on pages 384–385 considers as Acxiom Corporation. Figure 9-13 lists some of the consumer data that can be readily purchased.
these questions. An amazing (and, from a privacy standpoint, frightening) amount of data is available.