How a database match can confuse two people
Combining two lists sounds simple until two people share the same name and birthday, or one person's name is spelled two different ways. In database record linkage, an overly strict rule misses real matches, while a loose rule merges two strangers into one file. When those merged records are used for credit checks, watchlists, or medical histories, a data-matching rule becomes a high-stakes human problem.
Questions you could explore
- Why can a shared name and date of birth be insufficient to establish a unique identity?
- What safeguards are needed before an automated database match triggers a real-world penalty?
Start reading
- William E. Winkler, US Census BureauOverview of Record Linkage and Current Research Directions
This freely available research paper introduces matching names and addresses across files, including false matches, missed matches, and clerical review. Its statistical model provides background for investigating the human consequences of linkage errors.
- Thomas N. Herzog, Fritz J. Scheuren, and William E. Winkler / SpringerData Quality and Record Linkage Techniques
Explains how false matches and missed links occur when combining large datasets.
The publisher page introduces a specialist book; full access may require a library loan or purchase.
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