Choose a platform by testing two separate jobs on your own data: whether it correctly identifies records for the same entity, and whether it returns the right value for each attribute once those records are linked. A convincing demo is not enough. Compare candidates using labeled examples, agreed business rules, error costs, source lineage, and the effort required to review uncertain matches.
First separate identity matching from conflict resolution
Entity matching asks whether two records describe the same real-world person, organization, product, or other entity. Survivorship asks which value—or set of values—should appear for each attribute after records have been associated. These are related, but they are different decisions: a platform can link the right records and still return an unsuitable phone number, address, or status.
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For example, suppose two customer records share a verified email address but list different mailing addresses. Matching determines whether they belong to one customer. A survivorship rule then decides whether to prefer an address from a designated source, use the most recently verified address, retain multiple addresses, or send the conflict to a steward. The right choice depends on the attribute and how the consolidated view will be used.
Reltio’s documentation explicitly treats merging and survivorship as separate processes: merging retains crosswalk values, while attribute rules determine operational values. Its documentation also notes that the caller’s role can affect returned values. That distinction is important when different users or applications need different views of the same underlying entity.
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Define what a good result means for your data
Before comparing software, decide what counts as the same entity in the domain you are evaluating. A match rule suitable for customer accounts may not be suitable for households, suppliers, or products. Also agree on the relative cost of two kinds of error:
- False merge: records for different entities are combined. This can contaminate a master record and pass incorrect information to downstream systems.
- Missed link: records for the same entity remain separate. This can leave duplicate records and fragmented histories in place.
Those costs may not be equal. In a high-impact domain, a cautious workflow that sends ambiguous pairs for review may be preferable to automatic merging. In another use case, missing a link may be the more serious failure. Set acceptance criteria accordingly rather than treating one overall accuracy score as sufficient.
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What to compare in a platform
Matching logic and data preparation
Check whether the platform can configure the attributes, exact or fuzzy comparisons, and thresholds that your use case needs. Ask how it generates candidate pairs and what happens when key fields are missing, inconsistently formatted, or incorrect. Candidate generation matters because a record pair that is never considered cannot be matched by a later comparison stage.
Data preparation affects these outcomes. IBM’s Master Data Management documentation describes matching stages that include standardization, bucketing, and comparison. Reltio’s match-rule guidance recommends profiling data, choosing relevant attributes, addressing data-quality issues, and balancing exact and fuzzy matching. Ask vendors to demonstrate how their proposed configuration handles the specific inconsistencies in your sample, not just clean records.
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Attribute-level survivorship and provenance
Look for rules that can differ by attribute. A source may be authoritative for legal name but not for current contact details; a frequently occurring value may be useful for one field but not another. Depending on the purpose, a rule may select one value by source priority or frequency, aggregate multiple values such as addresses, or route a conflict for review.
Verify that users can trace a returned value to its contributing source record and understand why that value won. Also test what happens when a merge was wrong: whether staff can correct the master record, reverse or unmerge the association where supported, and propagate the correction safely. IBM documents resiliency rules intended to constrain some entity changes after records are added, updated, or deleted; confirm the available controls for the specific configuration you are considering.
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Stewardship and exception handling
Ask how an uncertain pair reaches a person who can make a decision, what evidence the steward sees, and how a decision is recorded. A useful workflow should let reviewers classify ambiguous cases and correct or create master records without losing the source information needed to audit the result.
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Operating fit
Include the work around the matching algorithm in your evaluation: onboarding sources, integrating results with existing applications, tuning rules, managing access, and staffing review queues. Determine whether you need batch matching, ongoing matching as records change, or both, and test the workflow that corresponds to your actual operating model. Ask vendors about current pricing, service terms, security, deployment options, regional availability, and edition-specific capabilities; these details are not established by the documentation cited here and can vary by offering.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use vendor examples as shortlist candidates, not a ranking
The following documentation-backed examples illustrate different capabilities to examine. They do not establish comparative performance, cost, or a universal best choice.
| Platform example | Documented capabilities relevant to evaluation | What to verify |
|---|---|---|
| IBM Master Data Management | IBM documentation describes matching configuration by entity type, selection of matching attributes, optional record-selection filters, match-result statistics, and tunable matching attributes and autolink thresholds. Its matching-algorithm documentation covers standardization, bucketing, comparison, and resiliency rules. | Test configuration and entity stability against your data changes, including additions, updates, and deletions. Documentation retrieved 2026-10-04. |
| Reltio | Reltio documentation covers attribute-based match conditions and thresholds, exact and fuzzy matching, profiling and data-quality preparation, and separate merge and survivorship processes. Its Entity Resolution overview describes ML-based matching as well as custom rules and thresholds. | Confirm the exact product configuration and tenant availability, then test attribute outcomes, lineage, and any role-dependent returned values. Match-rule and survivorship documentation updated 2026-07-31; Entity Resolution overview updated 2025-08-05. |
| Qlik Talend | Qlik Talend documentation describes creating a survivor representation from grouped duplicate candidates and steward campaigns for reviewing survivorship rules, classifying cases, and merging records into a golden record. It says sources may be in the same or different databases. | Check product edition and deployment fit with the vendor, and test the steward workflow with your own ambiguous pairs. Help page last updated 2026-09-24. |
Run a proof of concept that exposes failure modes
Use the same representative data sample and acceptance criteria for every candidate. Treat the proof of concept as an evaluation of both results and operating effort—not a guided demonstration on vendor-selected examples.
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- Agree on entity identity and error costs. Define what counts as one entity in the selected domain, and decide how costly a false merge is relative to a missed link.
- Profile representative source records. Include missing fields, inconsistent formats, conflicting values, and known duplicate clusters. Incompleteness, redundancy, inconsistency, and incorrectness are recognized challenges in entity resolution.
- Build a labeled test set. Include true matches, non-matches, and difficult near matches. Have reviewers establish the expected answer, including cases where different sources are authoritative for different attributes.
- Configure and record the rules. Capture matching attributes, exact and fuzzy logic, thresholds, and record-selection rules. Apply comparable settings and document intentional differences so the test remains interpretable.
- Measure and inspect outcomes. Compare results with the reviewed labels. Examine false merges, missed links, candidate-generation behavior, returned values for important attributes, source provenance, and the number and difficulty of cases needing steward review. These are proposed evaluation measures, not uniform vendor-reported statistics.
- Test changes over time. Add, correct, and delete source records; change a rule or threshold; and test correction of a mistaken merge and downstream propagation. IBM’s documentation notes that record changes can alter entity composition and describes resiliency settings that can constrain some changes.
- Compare the work required to operate it. Record implementation effort, rule-tuning effort, exception volume, and the skills needed to keep data and steward decisions reliable. Include those findings alongside matching and survivorship results.
Make the decision on evidence from your use case
Choose the candidate that meets your defined error tolerances and produces acceptable attribute values with traceable sources, a workable exception process, and an operating model your team can sustain. If two candidates look similar on an aggregate score, inspect the costly edge cases: the false merge that could alter a customer record, the near match a rule misses, or the conflict that lands in a review queue. No neutral cross-platform accuracy ranking or decision-relevant comparative statistic is established by the cited materials, so a result from your own labeled test set is more useful than an unsupported universal winner.
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