In my April 2009 blog post Data Quality Whitepapers are Worthless, I called for data quality whitepapers worth reading.
This post is now the third entry in an ongoing series about data quality whitepapers that I have read and can endorse as worthy.
Matching Technology Improves Data Quality
Steve Sarsfield recently published Matching Technology Improves Data Quality, a worthy data quality whitepaper, which is a primer on the elementary principles, basic theories, and strategies of record matching.
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This free whitepaper is available for download from Talend (requires registration by providing your full contact information).
The whitepaper describes the nuances of deterministic and probabilistic matching and the algorithms used to identify the relationships among records. It covers the processes to employ in conjunction with matching technology to transform raw data into powerful information that drives success in enterprise applications, including customer relationship management (CRM), data warehousing, and master data management (MDM).
Steve Sarsfield is the Talend Data Quality Product Marketing Manager, and author of the book The Data Governance Imperative and the popular blog Data Governance and Data Quality Insider.
Whitepaper Excerpts
Excerpts from Matching Technology Improves Data Quality:
- “Matching plays an important role in achieving a single view of customers, parts, transactions and almost any type of data.”
- “Since data doesn’t always tell us the relationship between two data elements, matching technology lets us define rules for items that might be related.”
- “Nearly all experts agree that standardization is absolutely necessary before matching. The standardization process improves matching results, even when implemented along with very simple matching algorithms. However, in combination with advanced matching techniques, standardization can improve information quality even more.”
- “There are two common types of matching technology on the market today, deterministic and probabilistic.”
- “Deterministic or rules-based matching is where records are compared using fuzzy algorithms.”
- “Probabilistic matching is where records are compared using statistical analysis and advanced algorithms.”
- “Data quality solutions often offer both types of matching, since one is not necessarily superior to the other.”
- “Organizations often evoke a multi-match strategy, where matching is analyzed from various angles.”
- “Matching is vital to providing data that is fit-for-use in enterprise applications.”
Submit Worthy Data Quality Whitepapers
If you have or know other worthy data quality whitepapers, then please submit them via the Data Quality Symposium.
