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cloud.google.com·6h ago
Data Science
Towards Data Science·23d ago·5 min read
One Vendor, Four Spellings: How Deterministic Stages Beat Similarity Scores
TL;DR
A Python-based approach effectively deduplicates a 10,000-row supplier list by using deterministic stages instead of relying solely on similarity scores. This method improves accuracy in identifying unique entries despite variations in spelling.
✦ Why It Matters
Implement deterministic stages in your data deduplication processes to enhance accuracy and reliability.
Key Takeaways
- Normalize vendor names to eliminate surface noise.
- Use subdomain collapsing to consolidate records.
- Employ blocking techniques to improve duplicate detection.
- Ensure human review for ambiguous matches to avoid costly errors.
How It Works
The pipeline consists of two main stages: normalization, which standardizes vendor names by converting them to lowercase and removing unnecessary components, and subdomain collapsing, which consolidates records based on registered domains using the Public Suffix List. This approach effectively reduces the number of duplicates before applying similarity scoring.
⚠ The Catch
Despite the efficiency of the pipeline, many ambiguous pairs remained, particularly those with similar names across different country domains, necessitating human intervention for accurate resolution.