Data Quality is a measure of the value of data in relation to
potential data problems:
Data quality can be analyzed in relation to a particular
Data Quality Domain,
making it possible to determine the
importance of different data problems.
A Data Quality Domain is an application or use of data that
imposes a set of
Data Quality Rules,
each of which is associated
with a degree-of-importance for the domain.
A Data Quality Rule is a specification of one or more data
For example, a data quality rule might specify that in the
Data Profiling can refer to:
Data Quality Profiling
or
Database Profiling.
Data Quality Profiling is the process of analyzing
a database
Database Profiling is the process of analyzing
a database to determine
Database Profiling can also include analysis of:
Database profiling can be an initial step in defining a
quality problems which should not exist in a set of data.
EMPLOYEE table, EMPLOYEE_NAME must be set and
that it must contain only letters and spaces. Another rule
might specify that EMPLOYEE_NAME should not contain
multiple consecutive spaces. These two rules might be specified
separately so that, in a particular
Data Quality Domain,
they can be assigned different degrees-of-importance.
in relation to a
Data Quality Domain,
to identify and prioritize
data quality problems. The results can include:
Data quality profiling can be useful when planning and managing
data cleanup projects.
its structure and internal relationships:
Database profiling can be useful when planning and managing
data conversion and data cleanup projects.
Data Quality Domain,
which is used in
Data Quality Profiling.
This page was written by Brian Marshall of Calgary.
Brian started the
ChkDB
Open Source project.