The TDWI Checklist Report on Product Data Quality points out the most pressing technology requirements for improving the quality of product data:
1. Support product data's diverse and complex standards. Standardization is the most common data quality technique, regardless of the data type.
2. Enhance product data with additional information. Even when product data is clean and standardized, its value can be raised by filling in vacant fields and appending data.
3. Classify products as you process and improve records. Classification is critical to business processes that depend on accurate matches and comparisons of diverse-but-equivalent products.
4. Extract product attributes from textual product descriptions. Parsing textual descriptions of products can locate missing product attributes.
5. Increase the analytic effectiveness of product data. Properly processed product data will support analytics and reporting for material identification, spend analytics, complete views, supplier ranking, and more.
6. Don't overlook requirements common to all data domains. Product data needs the basic data quality functions that other data domains require.
7. Evaluate tools that satisfy product data's special quality requirements. Product data has special attributes and uses that to make some of its technology requirements unique.
This article was written by Valerie Valentine, senior editor for Information Management. firstname.lastname@example.org