If you enter the wrong country as a criterion during a search or monitoring process, the implications depend on how the algorithm processes the mismatch. Here’s what happens based on the latest adopted algorithm:
- Impact on Matching:
- The algorithm uses a Holistic Matching approach, where all data points, including names, dates, and locations, are considered in parallel.
- If the wrong country is provided, it might:
- Exclude relevant records from the correct country.
- Focus on irrelevant records from the incorrect country, reducing match precision.
- Weighting and Scoring:
- The Metascore combines scores from all matched data points, with user-defined weights determining the importance of each field.
- If the country field is given high weight and the input country is incorrect, the final Metascore may drop, potentially hiding true matches.
- Potential Consequences:
- False Negatives: A correct match may not appear because the country mismatch lowers the overall score below the threshold.
- False Positives: Results from the wrong country may appear, increasing the time spent reviewing irrelevant matches.
- Mitigation:
- The algorithm’s fuzzy matching and semantic capabilities attempt to compensate for errors in one field by leveraging other matching criteria like names or dates. However, the effectiveness depends on the quality and completeness of the remaining data points.
- Key Takeaway:
- Providing accurate country information is crucial for optimal matching, especially if the country field is given high weight in the scoring process.
- If the country information is uncertain, using broader search parameters (e.g., omitting the country criterion) may improve the likelihood of identifying relevant matches, albeit at the cost of precision.