Operationalizing Data Quality through Data Governance - Data Governance: Creating Value from Information Assets - page 67

a value). Completeness can be prescribed on a single attribute, or can be
dependent on the values of other attributes within a record or a message.
Data completeness definition is the “expected completeness.” It is possible
that data is not available, but it is still considered completed because it
meets users' expectations. Every data requirement has “mandatory” and
“optional” aspects. For example, a customer's mailing address is manda-
tory and it is available, while customer's office address is optional; it is
okay if it is not available. Data can be complete, but inaccurate, e.g., all the
customers' addresses are available, but many of them are not correct.
Data Integrity: Integrity of data means that data has a complete or whole
structure. Data integrity is imposed within a database when it is designed
and is authenticated through the ongoing use of error checking and vali-
dation routines. All characteristics of the data must be correct—includ-
ing business rules, relations, dates, definitions, and lineage—for data to
be complete. As a simple example, to maintain data integrity, numeric
columns/cells should not accept alphabetic data.
Data Consistency: Consistency of data means that data should be
synchronized across the enterprise. For example, data should be con-
sistent between systems and no duplicate records should exist. Data can
be accurate (i.e., it will represent what happened in real world), but still
inconsistent. Data is inconsistent when it is in sync in the narrow domain
of an organization, but not in sync across the organization. Data can
be complete, but inconsistent, e.g., data from different therapeutic area
studies using different data formats.
Data Timeliness: Data should be available at the time needed.
Timeliness refers to the degree to which information is up-to-date with
the corresponding real-world entities. “Data delayed” is “data denied.” The
timeliness of data is extremely important and it depends on user expecta-
tion. An example of data not being timely: The courier package status is
delivered, but it will be updated in the system only in the night batch run.
This means that online status will not be available.
Data Traceability: Data traceability means that any transaction, modi-
fication, and report can be tracked to its originating transaction. It is
the property that enables the understanding of the data's lineage and/or
the relationship between an element and its predecessor(s). Traceability
facilitates transparency, which is an essential component in building
confidence in a result or conclusion. It is built by clearly establishing the
path between an element and its immediate predecessor and establishes