Most AI recruiting consultants match candidates with a mix of spreadsheets, Python scripts, and API calls. Here's how to move from fragile one-off workflows to repeatable matching operations.
Master data management (MDM) is the practice of creating and maintaining a single, accurate view of your core business entities. Here's what it involves, why it fails, and what modern tools change.
Number of matches is not a quality metric. Learn how to measure precision, recall, and F1 score for data matching — including practical sampling methods when you don't have labeled data.
A systematic approach to comparing two datasets — finding overlapping records, identifying gaps, detecting conflicting values, and building a comparison report.
From pandas merge to fuzzywuzzy to recordlinkage — a practical guide to CSV matching in Python, plus a framework for deciding when custom code stops making sense.
Excel's Remove Duplicates misses most real-world duplicates. Learn field-by-field fuzzy deduplication, threshold tuning, and strategies for CSV files with 100K+ rows.
VLOOKUP and INDEX-MATCH break when data is messy. Learn how to merge two spreadsheets with fuzzy matching, AI-powered joins, and practical validation steps.
A practical decision guide for selecting matching algorithms based on data type, quality, and scale — from simple name matching to multi-field entity resolution.
80% of matching improvement comes from basic data cleaning. Here are the specific steps — with before and after examples — that transform garbage-in matches into reliable results.
Entity resolution is the process of determining when two records refer to the same real-world entity. Here's what it is, why it's hard, and what happens when you get it wrong.
A step-by-step walkthrough of your first matching job in Match Data Studio — from upload to download.