News
 6
Key Data Quality Dimensions
“To be able to correlate data quality issues to
business impacts, we must be able to both classify
our data quality expectations as well as our
business impact criteria.” So says David Loshin,
president of Knowledge Integrity, Inc,. In his
Informatica white paper, The Data Quality Business
Case: Projecting Return on Investment, Loshin lays
out six common data quality dimensions. Do you know
what they are?
In order for the analyst to determine the scope of
the underlying root causes and to plan the ways that
tools can be used to address data quality issues, it
is valuable to understand these common data quality
dimensions:
• Completeness: Is all the requisite information
available? Are data values missing, or in an
unusable state? In some cases, missing data is
irrelevant, but when the information that is missing
is critical to a specific business process,
completeness becomes an issue.
• Conformity: Are there expectations that data
values conform to specified formats? If so, do all
the values conform to those formats? Maintaining
conformance to specific formats is important in data
representation, presentation, aggregate reporting,
search, and establishing key relationships.
• Consistency: Do distinct data instances provide
conflicting information about the same underlying
data object? Are values consistent across data sets?
Do interdependent attributes always appropriately
reflect their expected consistency? Inconsistency
between data values plagues organizations attempting
to reconcile between different systems and
applications.
• Accuracy: Do data objects accurately represent the
“real-world” values they are expected to model?
Incorrect spellings of product or person names,
addresses, and even untimely or not current data can
impact operational and analytical applications.
• Duplication: Are there multiple, unnecessary
representations of the same data objects within your
data set? The inability to maintain a single
representation for each entity across your systems
poses numerous vulnerabilities and risks.
• Integrity: What data is missing important
relationship linkages? The inability to link related
records together may actually introduce duplication
across your systems. Not only that, as more value is
derived from analyzing connectivity and
relationships, the inability to link related data
instance together impedes this valuable analysis.
Understanding the key data quality dimensions is the
first step to data quality improvement. Being able
to segregate data flaws by dimension or
classification allows analysts and developers to
apply improvement techniques using data quality
tools to improve both your information and the
processes that create and manipulate that
information.
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Melissa Data
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