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266 Part Two Information Technology Infrastructure

the specific policies and procedures through which data can be managed as an
organizational resource. These responsibilities include developing information
policy, planning for data, overseeing logical database design and data dictionary
development, and monitoring how information systems specialists and end-user
groups use data.
You may hear the term data governance used to describe many of these
activities. Promoted by IBM, data governance deals with the policies and
processes for managing the availability, usability, integrity, and security of the
data employed in an enterprise, with special emphasis on promoting privacy,
security, data quality, and compliance with government regulations.
A large organization will also have a database design and management
group within the corporate information systems division that is responsible
for defining and organizing the structure and content of the database, and
maintaining the database. In close cooperation with users, the design group
establishes the physical database, the logical relations among elements, and
the access rules and security procedures. The functions it performs are called
database administration.

ENSURING DATA QUALITY

A well-designed database and information policy will go a long way toward
ensuring that the business has the information it needs. However, additional
steps must be taken to ensure that the data in organizational databases are
accurate and remain reliable.
What would happen if a customer’s telephone number or account balance were
incorrect? What would be the impact if the database had the wrong price for the
product you sold or your sales system and inventory system showed different
prices for the same product? Data that are inaccurate, untimely, or inconsistent
with other sources of information lead to incorrect decisions, product recalls,
and financial losses. Gartner Inc. reported that more than 25 percent of the
critical data in large Fortune 1000 companies’ databases is inaccurate or incom-
plete, including bad product codes and product descriptions, faulty inventory
descriptions, erroneous financial data, incorrect supplier information, and
incorrect employee data. A Sirius Decisions study on “The Impact of Bad Data
on Demand Creation” found that 10 to 25 percent of customer and prospect
records contain critical data errors. Correcting these errors at their source and
following best practices for promoting data quality increased the productivity of
the sales process and generated a 66 percent increase in revenue.
Some of these data quality problems are caused by redundant and inconsistent
data produced by multiple systems feeding a data warehouse. For example,
the sales ordering system and the inventory management system might both
maintain data on the organization’s products. However, the sales ordering
system might use the term Item Number and the inventory system might call
the same attribute Product Number. The sales, inventory, or manufacturing sys-
tems of a clothing retailer might use different codes to represent values for an
attribute. One system might represent clothing size as “extra large,” whereas
the other system might use the code “XL” for the same purpose. During the
design process for the warehouse database, data describing entities, such as a
customer, product, or order, should be named and defined consistently for all
business areas using the database.
Think of all the times you’ve received several pieces of the same direct mail
advertising on the same day. This is very likely the result of having your name

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