Validating Match Rules Before Go-Live, Whatever Platform Runs Them

Last updated 2026-08-07.

Match-rule validation asks one question regardless of where the rules run: are these rules precise enough to trust at go-live? Whether the reader authors match rules inside Informatica MDM, Reltio, or Semarchy, or anywhere else, that question does not change, and neither does the answer to it. The way to answer it is to score the rules against a labeled truth set and compute precision, recall, and F1, using the Match Rule Truth Engine. Where and how those rules get authored is a product-specific detail this page makes no claim about. The validation question and the validation method are not product-specific.

That holds regardless of how a given implementation is structured: a single-domain rollout, a phased multi-domain program, a greenfield build, or a migration off an older MDM tool. In every case, at some point a match rule produces an output, a decision that two records are or are not the same entity, and that output either matches reality or it doesn't. Scoring that output against a labeled truth set is the same exercise whether the rule that produced it lives in a proprietary match engine, a rules-based platform, or a custom script, because the exercise is about the output, not about the code or configuration that generated it.

This page names Informatica, Reltio, and Semarchy because a reader validating match rules before go-live is probably running one of them, not because of any specific claim about what any of the three does internally. Nothing on this page describes a match or merge feature, a scoring method, a UI, a version, or a roadmap item for Informatica, Reltio, or Semarchy. No source available to this page documents that, and guessing about a named vendor's product would be worse than saying nothing about it. What this page does assert is platform-agnostic: whatever process inside whatever platform produces a match decision, an independent labeled truth set and a precision, recall, and F1 score against it are what tell you whether that decision was right.

That distinction, between what this page asserts and what it deliberately does not, is worth stating twice because comparison pages are often read as implicit capability claims even when they don't intend to be. This page names three platforms in its title because that is the search a reader validating match rules is actually running, not to rank them, contrast their feature sets, or suggest one produces better match decisions than another. If a reader wants a specific, checked fact about what any of the three platforms actually does, that fact needs its own named source before it is safe to state, and none exists here.

The scoring method itself does not reference any platform. Precision is the share of pairs the match rules called a match that are actually the same entity. Recall is the share of true matches the rules found. F1 balances the two into one number, though it can hide which of the two a given rule set is actually weak on. That arithmetic, worked through with a full example, is covered on the precision, recall, and F1 method page linked below; it is not repeated here because it does not change based on which platform produced the match decision being scored.

What does change by implementation, though not by platform vendor, is the truth key: which fields carry duplicate signal, how known duplicates are tagged to a Master ID, and how duplicate types are classified for the domain in question. That method is also platform-agnostic and is covered separately on the truth-key how-to linked below. Between the two pages, the full method, building the truth key and scoring against it, is available without reference to any specific platform's internals at any point.

Stating that explicitly is not a hedge, it is the actual scope of the claim. The claim is about a scoring procedure: take a set of match decisions, compare each one to a label set independently of those decisions, and count where the two agree and where they do not. That procedure has no dependency on how the decisions were produced. It runs the same way on decisions from a commercial match engine, from a hand-written rule, or from a script somebody wrote in an afternoon, because it never inspects the producer at any point. Anything narrower, any statement about how a particular named product arrives at a match decision, would need a source this page does not have and does not claim to have.

Definitional boundary. This page states that validation is platform-agnostic. It does not cover how to configure match or merge rules inside Informatica MDM's, Reltio's, or Semarchy's own interface, and it does not imply a certified connector, integration, or partnership with any of the three.

A labeled truth set, delivered as a Match-Ready Sample, works the same way no matter which platform ran the rules being scored: it is a set of record pairs marked match or no-match, built and labeled independently of any platform's own output, using truth keys defined for the reader's domain. Requesting one is not a step in an Informatica, Reltio, or Semarchy partnership, integration, or certification process. Generate-Data.com has no partnership, endorsement, connector, or affiliate relationship with any of the three, and nothing on this page implies otherwise.

Practically, that means the sample arrives as a file, a set of labeled record pairs and the truth key that defines them, not as a plugin or a platform-specific export format. What the reader does with it, load it into whatever environment already runs the match rules under test, is entirely up to the reader and the platform they are already using. The independence is the point: a truth set that had to be built for one specific platform would stop being an independent check on that platform's own output the moment it depended on that platform to exist.