AI talent matching as a service: the infrastructure gap holding consultants back
Talent matching consultants spend more time building pipelines than matching candidates. Configurable matching infrastructure changes the math.
From string comparisons to contextual reasoning: how AI transformed data matching
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.
Best data matching tools in 2026: a feature-by-feature comparison
An in-depth comparison of 12 data matching tools — from AI-powered platforms to open-source libraries — covering features, matching approaches, deployment models, and what actually matters when choosing one.
The CPG translation tax: why demand planning breaks at the data seams
Most CPG forecasting failures aren't analytics problems — they're harmonization problems. Internal SKUs, syndicated codes, and retailer GTINs speak different languages, and the cost of translating between them is quietly destroying forecast accuracy.
How to consolidate customer records from two companies after a merger
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.
How to match patient records across hospital systems without a universal ID
A practical guide to patient record matching — handling nicknames, maiden names, transposed digits, and HIPAA constraints to build a reliable Master Patient Index.
Entity resolution explained: turning messy records into clean data
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.