History
Before the rise of the inexpensive server, massive mainframe computers were used to maintain name and address data so that the mail could be properly routed to its destination. The mainframes used business rules to correct common misspellings and typographical errors in name and address data, as well as to track customers who had moved, died, gone to prison, married, divorced, or experienced other life-changing events. Government agencies began to make postal data available to a few service companies to cross-reference customer data with the National Change of Address registry (NCOA). This technology saved large companies millions of dollars compared to manually correcting customer data. Large companies saved on postage, as bills and direct marketing materials made their way to the intended customer more accurately. Initially sold as a service, data quality moved inside the walls of corporations, as low-cost and powerful server technology became available.
Companies with an emphasis on marketing often focus their quality efforts on name and address information, but data quality is recognized as an important property of all types of data. Principles of data quality can be applied to supply chain data, transactional data, and nearly every other category of data found in the enterprise. For example, making supply chain data conform to a certain standard has value to an organization by: 1) avoiding overstocking of similar but slightly different stock; 2) improving the understanding of vendor purchases to negotiate volume discounts; and 3) avoiding logistics costs in stocking and shipping parts across a large organization.
While name and address data has a clear standard as defined by local postal authorities, other types of data have few recognized standards. There is a movement in the industry today to standardize certain non-address data. The non-profit group GS1 is among the groups spearheading this movement.
For companies with significant research efforts, data quality can include developing protocols for research methods, reducing measurement error, bounds checking of the data, cross tabulation, modeling and outlier detection, verifying data integrity, etc.
Read more about this topic: Data Quality
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