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The Unique Requirements of Product Data Quality

The majority of data quality software implementations have long been focused on customer data– more so than on other data domains like product data, financial data, asset data, location data, and so on. The focus on customer data helps explain why most data quality software techniques–the most common being data standardization, verification, and matching–were originally designed for customer data, whether built in-house by IT or built into a software vendor's tool. That's great for customer data, but not so good for other data domains. Customer-oriented data quality techniques and tools can be retrofitted to operate on other data domains, but with limited success. There's a need to redesign standard data quality techniques–and design new ones–that address the unique requirements of non-customer data domains.


Philip Russom Senior Manager, TDWI Research, The Data Warehousing Institute Philip Russom is the senior manager of TDWI Research at The Data Warehousing Institute (TDWI), where he oversees many of TDWI's research-oriented publications, services, awards, and events. Before joining TDWI in 2005, Russom was an industry analyst covering BI at Forrester Research, Giga Information Group, and Hurwitz Group. He's also run his own business as an independent industry analyst and BI consultant, and was contributing editor with Intelligent Enterprise and DM Review magazines. Before that, Russom worked in technical and marketing positions for various database vendors.
Silver Creek Systems
23 Jul 2008
01 Mar 2008
14 Page(s)
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