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 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.

 
Melissa Data


 
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