Data Quality: The Field Guide
This guide provides the practical guidance needed to start and advance a data quality program.
TABLE OF CONTENTS Data Quality: The Field Guide
Glossary
- accuracy
- Almost always one of the most important dimensions of data quality. Defined as a measure of the degree of agreement between a data value or collection of data values and a source agreed to be correct. Informally customers say they need "data to be accurate" and mean they require that data values agree with the real-world.
- appropriate
- use An often-important dimension of data quality. Since data may be used for many things, many customers desire that sources indicate which uses the data supports. To illustrate, it may be perfectly appropriate to use a person's age in making a pension calculation, but inappropriate (even illegal) to use that same datum in a hiring decision.
- attribute
- The element of a datum that defines a property of the entity. For example, the attribute in the data triple
is "Sex." For a definition of a datum. See also datum.
- availability
- An important dimension of data quality. Availability is a measure of the degree to which needed data may be easily acquired by a data customer. Availability is particularly important to a data customer that purchases or otherwise acquires data from a data supplier. Such customers expect "the data to be available for use when expected."
- business rule
- A constraint on the values that data may take. For example, the attribute "Sex" may take only two values: M (for male) and F (for female). Another somewhat more involved example involves the rules that make up the so-called postal standard. Certain addresses,...
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Copyright Thomas C. Redman 2001 under license agreement with Books24x7
