Simply put, data quality management entails the establishment and deployment of roles, responsibilities, policies, and
procedures concerning the acquisition, maintenance, dissemination, and disposition of data. A partnership between
the business and technology groups is essential for any data quality management effort to succeed. The business
areas are responsible for establishing the business rules that govern the data and are ultimately responsible for
verifying the data quality. The Information Technology (IT) group is responsible for establishing and managing the
overall environment – architecture, technical facilities, systems, and databases – that acquire, maintain, disseminate,
and dispose of the electronic data assets of the organization.
Organizations of all kinds make decisions and service customers based on the data they have at their disposal. A
data warehouse is often used to examine business trends to establish a strategy for the future; within the scope of a
customer relationship management (CRM) program, data about the customer is used to make appropriate decisions
concerning that customer; and data in the financial systems is used to understand the profitability of past actions. The
viability of the business decisions is contingent on good data, and good data is contingent on an effective approach to
data quality management.
The initial emphasis of many new data quality management initiatives launched in recent years has been on customer
data, and technology has stepped up to this challenge by automating solutions to many of the data quality problems
associated with customer data. Business data consists of much more than just customer data and the technology to
support it. For instance, standardizing part codes and names and combining product information that is stored
differently in various systems poses some new data challenges. The technology that deals with this non-name and
address data must include an engine that consistently learns and evolves with the new data types, enabling it to
SUGI 29 Data Warehousing, Management and Quality
2
clean, reconcile, and match any type of information.