Data matching evolved from rigid rules to machine learning to neural embeddings to LLMs. Each generation solved problems the previous one couldn't. Here's how the technology progressed, what each approach actually does, and why modern systems layer all of them.
ChatGPT, Gemini, Claude, and other LLMs can absolutely do fuzzy matching. They're just not built for it. Here's what works, what doesn't, and when you need a dedicated matching tool.
Deterministic matching compares exact values. Probabilistic matching uses statistics, embeddings, and LLMs to find likely matches. Here's how each works, where each fails, and how combining them produces faster, cheaper, more accurate results.
LLMs like Gemini, ChatGPT, and Claude can read PDFs, understand tables, extract text from images, and interpret graphs. Here's how multimodal AI enables granular PDF document classification — and where it still needs help.
SQL JOINs and pandas merges fail on color variants, promotional naming, translated descriptions, and spec formatting differences. AI embeddings and LLMs understand that 'Midnight' means black and 'Violet' means purple. Here's why traditional tools hit a ceiling and how hybrid pipelines break through it.
Thousands of images sitting in folders with meaningless filenames. AI image categorization extracts structured labels, categories, and descriptions — turning visual assets into matchable data.
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.
PDFs contain structured information trapped in unstructured format. AI extraction turns invoices, contracts, reports, and spec sheets into matchable data rows — no manual data entry required.
Product matching accuracy depends on attribute richness. Sparse product data produces weak matches. Here's how to annotate product catalogs — manually and with AI — to make matching reliable.
Scraped marketplace data is full of duplicate listings — different sellers, different titles, same underlying product. AI-powered deduplication collapses these into canonical records for reliable analytics and catalog management.
How to handle the N x M explosion in record matching — blocking strategies, pre-filter cascades, batch processing, and fault tolerance for large datasets.
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.
Matching slows down fast at scale. Learn how blocking keys reduce comparisons by orders of magnitude, how to choose effective keys, and how multi-pass blocking recovers missed pairs.
Duplicate mailers waste money and annoy recipients. Learn how to match across mailing lists, standardize addresses for deliverability, and build a clean send file.
After an M&A deal, two CRMs become one. Learn how to match, deduplicate, and merge customer records while handling subsidiaries, DBAs, and conflicting data.
Catch migration errors before go-live. Learn how to match source and target records, check for data loss, detect transformation errors, and build a migration validation report.
Transaction reconciliation involves matching records across systems with amount differences, date offsets, and description mismatches. A step-by-step guide for finance teams.
Duplicate candidate records waste recruiter time and create poor candidate experiences. Learn how to build a unified talent pool by matching across sourcing channels.
Duplicate vendor records cause overpayments, missed discounts, and audit findings. Learn how to match vendor records across ERPs and maintain a clean vendor master.
A practical guide to patient record matching — handling nicknames, maiden names, transposed digits, and HIPAA constraints to build a reliable Master Patient Index.
A systematic approach to comparing two datasets — finding overlapping records, identifying gaps, detecting conflicting values, and building a comparison report.
Real estate portals and brokerages ingesting data from multiple MLS feeds routinely encounter the same property listed twice with conflicting details. AI-powered deduplication keeps inventory counts accurate and analytics trustworthy.
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.
Addresses are the hardest field to match. Abbreviations, unit numbers, directionals, and international formats make exact matching useless. Here's how to handle them.
These five common record matching errors don't throw exceptions or show warnings. They just quietly produce bad results. Here's how to identify and fix each one.
A practical breakdown of six fuzzy matching algorithms — how they work, where they excel, and when to combine them for record matching across messy datasets.
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.