Published 2026-02-25
Clean random rows are not production mess
Libraries like Faker are good at filling forms and bootstrapping demos. They invent plausible names, emails, and addresses. What they do not invent is the mess matching and scoring systems see in production: exact duplicates, near-duplicates with typos, shared household phones, and field-level drift across source systems.
If your regression suite only feeds tidy, unique rows into a matcher or quality pipeline, the suite will pass while production still fails. You are testing the happy path of data generation, not the failure modes of the system under test.
What serious evaluation needs
Matching and quality tests need controlled duplicate rates, fuzzy variants, and ground-truth labels you can score against. You need to know which records should merge, which should stay apart, and how confidence should move when a middle initial disappears or an address line changes.
That means the generator has to treat input quality as a first-class configuration: exact duplicate injection, fuzzy duplicate injection, and realistic field variation per attribute. Generic faker libraries leave that work to you, usually as brittle post-processing scripts.
Where generate-data.com fits
generate-data.com is built for teams evaluating matching systems and related intelligent pipelines. You model a schema, set quality characteristics per field, and inject exact and fuzzy duplicates at configurable rates so datasets look like the production inputs your systems will encounter.
If you are stress-testing matchers, building QA regression sets, or validating review workflows, start with test data that encodes the problem. Then compare notes with how we approach AI and technology research in the lab.
Related: generate-data and how we approach AI and technology research.