Data quality procedures ensure that data is consistently applied to stringent specifications.
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At first, the term data quality sounds like a platitude, something meant to secure assenting nods at meetings with consultants--"Of course, we can all agree that quality data is important." The recent data quality push in CRM, however, does carry specific meanings.
Data quality practices focus on three major tasks: enforcing consistent, compatible data standards; eliminating errors like duplicate or erroneous records; and creating the basis for drawing sensible, meaningful conclusions from a data warehouse. Data standards problems can occur in many areas, ranging from invalid mailing addresses to improperly formatted data, such as a manufacturer part number that omits either a crucial prefix or suffix. Data quality procedures ensure that data is consistently applied to the most stringent specifications of all the applications and integration tools that read the information. "In data quality profiling you identify what your defects are, and how your data compares against your business rules," says Frank Dravis, vice president of information quality at FirstLogic.
These data quality problems are typically what stands in the way of the third component of the data quality discipline, the focus on building a warehouse of comparable information that can be validly cross-referenced. With solid data quality, marketing and service organizations can confidently build consumer household and corporate hierarchy plans, as it will be easier to confidently identify that two or more individuals belong to the same physical location, company or family name, and buying patterns.
Dedicated data quality tools have existed for some time, particularly focused on the problems of data duplication and of invalid or malformatted postal addresses, but only recently have CRM developers incorporated the concept into their application design.
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