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14 CHAPTER 2 Definitions and concepts

marketing types realized that other subjects, besides customer, required gold copies. Items, products,
vendors, etc., are all areas where companies tend to have multiple versions, which are inconsistent or
too contextual. In the old days, we called these files master filesdhence, master data management.
The DMBOK states that master data is, “.The data that provides the context for transaction data.
It includes the details (definitions and identifiers) of internal and external objects involved in business
transactions. [It] Includes data about customers, products, employees, vendors, and controlled
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domains (code values).” Accordingly, master data management (MDM) represents the “Processes
that ensure that reference data is kept up to date and coordinated across an enterprise. The organi-
zation, management, and distribution of corporately adjudicated data with widespread use in the
iv
organization.”
Obviously, if MDM represents the process to manage a category of data across an enterprise, then
DG needs to come into the picture. Later on, we will talk about DG being mandatory for MDM.
Data governance visibly supports MDM in several ways:
1. Ensures that standards are defined, maintained, and enforced.
2. Ensures that MDM efforts are aligned to business needs and are not technology-only efforts.
3. Ensures that data quality, process change, and other new activity that are rooted in MDM are
accepted and adapted by the organization.
Data Quality

Data quality is probably the single most discussed term or concept in the EIM/DG universe. This is
easy to comprehend once you understand what it really represents. Data quality is simply the root
cause of the majority of data and information problems. Remediating data quality is one of the main
drivers of data governance and MDM.
The DMBOK addresses data and information quality separately. As you already know, this book
does not separate the two concepts, as governance is governance for both of them. Both are presented
here:
• Data quality is the degree to which data is accurate, complete, timely, consistent with all
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requirements and business rules, and relevant for a given use.
• Information quality is the degree to which information consistently meets the requirements and
expectations of knowledge workers in performing their jobs. In the context of a specific use, the
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degree to which information is meeting the requirements and expectations for that use.
Obviously, while the two definitions are different, they are certainly pointing in the same direction. The
best way to understand data quality is that the content in question has to be effective or fit for its
purpose. This means if your organization feels that customer data is not of “good quality,” you need to
understand what purpose, action, or context is involved and how the shortfall is measured. Does bad
customer data mean a wrong address or excessive duplication? You need to understand that “bad data”
does not just appear, and is almost always corrected by a change in processes or habits, or both. That is

iii
Mosely, Mark, Editor, “The DAMA Dictionary of Data Management.”
iv
Ibid.
v
Ibid.
vi
Ibid.