Fuzzy Data Matching: Building Reliable Matches Across Disconnected Systems
Fuzzy data matching links records across disconnected systems by scoring similarity rather than requiring identical strings. Learn the five-stage pipeline, key algorithms, and how to scale to millions of records.
Entity Resolution for Regulated Teams: What to Look For and Why It Matters
Entity resolution for regulated industries must match records accurately and produce a full audit trail. Learn the six capabilities compliance-grade platforms must have — and how to evaluate them.
Address Data Cleansing: A Complete Technical Guide for Data Teams
Address data cleansing: parse, standardise, CASS-verify, and deduplicate postal records at scale. Stop 10–15% annual decay. Free trial, no contract.
Deterministic vs. Probabilistic Matching: When to Choose Each
Deterministic matching uses exact rules; probabilistic matching uses weighted similarity scores. Learn which method fits your data, when to combine both, and how to configure thresholds correctly.
Levenshtein Fuzzy Matching & Edit Distance | Match Data Pro
Levenshtein fuzzy matching scores record similarity by counting character edits between strings. Normalisation, thresholds, and scale to millions of records explained. Free trial, no contract.
How to Connect AI and LLM Workflows to a Clean Data Foundation
Connecting LLM workflows to dirty data corrupts AI outputs. Learn the six-stage data quality pipeline — profiling, cleansing, deduplication, entity resolution — that gives AI a clean foundation.
Data Ladder Alternative: How Match Data Pro Compares in 2026
Looking for a Data Ladder alternative? Compare DataMatch Enterprise and Match Data Pro on entity resolution, global address verification, real-time matching, deployment and published pricing.
Build vs. Buy Entity Resolution Software: What’s the Better Option?
For most data teams, buying entity resolution software beats building it. A custom build costs $500k-$1.5M over three years. Here is how to make the right call.
Explainable Entity Resolution: How to Understand and Audit Every Match Decision
Explainable entity resolution shows exactly why two records were matched or rejected — field-by-field scores, algorithm weights, and a full audit trail. Here is how to build it.
Why AI Hallucinates: The Data Quality Problem Behind Bad AI Outputs
AI hallucinations are primarily a data quality problem. Duplicates, unstandardised fields, and missing values cause models to produce confident wrong outputs. Here is how to fix the data before it reaches the model.