Using Similarity Scoring

Problem Statement:

We encountered multiple Data Dictionaries/Glossaries created and used in the organization.

Solution:

Designed and deployed a similarity scoring model to identify and group semantically similar data elements across large-scale datasets—leveraging NLP techniques and fuzzy matching algorithms to detect duplicates, near-matches, and inconsistent naming conventions.

This solution significantly reduced the need for manual review, cutting data rationalization time by over 60%, and enabled faster standardization of metadata across business glossaries, critical data elements (CDEs), and reporting inventories.

The model played a key role in improving data quality, supporting ML-readiness, and enhancing data governance outcomes across the enterprise.