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Choose the platform that performs best on your own duplicate and conflicting records, then confirm your team can run it. Two jobs need separate judgment: deciding which source records describe the same real-world entity, and deciding which value each attribute should show once those records are grouped. Vendor documentation explains how these features are configured. It cannot tell you which product will be most accurate on your data, so the test has to use your records and your business rules.
Matching and survivorship are two separate decisions
Buyers often treat duplicate detection and conflict resolution as one step. They happen in sequence. Matching comes first and decides which records belong together. Survivorship comes second and decides which value each attribute shows for that group. A platform can link records correctly and still display a stale phone number, or choose sensible values from a cluster that was grouped wrongly. Test both.
| Question | Matching (identity resolution) | Survivorship (conflict resolution) |
|---|---|---|
| What it answers | Do these two records refer to the same entity? | Which value should represent each attribute of the combined entity? |
| Typical inputs | The attributes you select, such as names, addresses, identifiers, and dates | Values from every record already grouped as one entity |
| Typical output | Links, candidate groups, and match decisions | A surviving value per attribute, or several values where the rule aggregates |
| Typical failure | False merges (two entities combined) and missed links (one entity left split) | Correct records are linked, but the wrong value is shown |
| What to test | Labeled pairs: duplicates, near matches, and non-matches | Known conflicts, compared with the value a reviewer judges correct |
The academic survey End-to-End Entity Resolution for Big Data: A Survey (arXiv, 15 May 2019) defines entity resolution as finding descriptions of the same real-world entity. It also names incompleteness, redundancy, inconsistency, and incorrectness as source-quality problems that make this difficult. Those problems will appear in both decisions, so your test data should include them on purpose. The survey is a useful definition of the problem; it does not measure how any product performs.
Criteria for comparing candidates
Run every candidate on the same sample against the same acceptance criteria. The four areas below are the ones to compare.
#1 Best Overall
Identity matching
- Exact and fuzzy comparison options, and whether each can be chosen per attribute.
- Attribute selection and threshold settings, and whether the tool shows why a pair scored as it did.
- Candidate generation: how the platform decides which pairs are compared at all. IBM’s matching documentation names bucketing as one stage. Ask how it limits comparisons and whether it can exclude a true pair before scoring begins.
- Handling of missing or inconsistent values, such as an empty postcode or a transposed date of birth.
Survivorship and provenance
- Per-attribute rules, so a name, a phone number, and an address can each use a different method.
- Source traceability: can a reviewer see which contributing record supplied each surviving value?
- Preservation of contributing values, so a later rule change does not destroy the evidence.
- Correction and reversal: can a wrong merge or a wrong survivor be undone, and what happens in downstream systems?
- Whether the returned value can differ by user role or calling application.
Steward workflow
- A queue for ambiguous pairs, with the ability to accept, reject, or defer each one.
- The ability to create or correct a master record during review.
- Access roles, and a record of who made each decision.
Operating model
- Onboarding effort for each source system you need to connect.
- Batch matching versus ongoing matching, and how long a full run takes at your volume.
- Scale, and the skills needed to tune match rules. The vendor documentation reviewed here confirms configurable workflows but does not establish comparative cost or performance. Those answers must come from vendors and from your own timings.
What the example platforms document
IBM Master Data Management, Reltio Entity Resolution, and Qlik Talend appear here as examples of this category, not as a ranking. Dates matter for these pages: IBM’s matching documentation was retrieved on 4 October 2026; Reltio’s match-rule and survivorship pages were updated on 31 July 2026; Reltio’s Entity Resolution overview was updated on 5 August 2025; and Qlik Talend’s survivorship page was last updated on 24 September 2026. “Not stated” in the table means the feature is not described on the page reviewed, not that the product lacks it.
| Feature | IBM Master Data Management | Reltio Entity Resolution | Qlik Talend |
|---|---|---|---|
| Match configuration | Configuration by entity type; selection of matching attributes; optional record-selection filters; tuneable matching attributes and autolink thresholds; match-result statistics | Attribute-based conditions and thresholds; profiling and data-quality preparation guidance | Grouped duplicate candidates feed a survivor representation; match-rule settings not stated on the reviewed page |
| Matching method | Standardization, bucketing, and comparison stages | Exact or fuzzy matching; ML-based matching alongside custom rules and thresholds | Not stated on the reviewed page |
| Survivorship and values | Not stated on the reviewed matching pages | Merging keeps crosswalk values; survivorship computes operational values by attribute rule, with aggregation and frequency given as examples; caller role can change returned values | A survivor representation is created from grouped candidates; survivorship rules are reviewed in steward campaigns |
| Steward workflow | Not stated on the reviewed pages | Not stated on the reviewed pages | Data-steward campaigns classify cases and merge records into a golden record |
| Change control | Resiliency rules can constrain entity merges and splits as records are added, updated, or deleted | Not stated on the reviewed pages | Not stated on the reviewed page |
| Source scope | Not stated on the reviewed pages | Not stated on the reviewed pages | Sources may come from the same database or from different databases |
IBM Master Data Management
Check whether the entity-type configuration, record-selection filters, and autolink thresholds can be set to match your business definition of an entity. If match-result statistics are available, have reviewers compare each threshold you try with the outcomes it produces. Because IBM documents rules that govern entity changes after records are added, updated, or deleted, include changed and deleted source records in the test, not only an initial load.
Rank #2
Reltio Entity Resolution
Test both halves of the merge-and-survivorship process. After a merge, inspect the crosswalk values that remain and the operational values that survivorship computes from them. Compare exact-only and fuzzy configurations on the same labeled pairs. If the product you are evaluating offers the ML-based matching the overview describes, run it on the same set. Confirm which configuration options and tenant features your edition includes before the test begins.
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Qlik Talend
Run a steward campaign from start to finish with your own reviewers: classify a set of cases, apply a survivorship decision, and merge a cluster into a golden record. Include at least one cluster whose records come from different databases, because source placement affects how the grouped candidates are assembled. Confirm product edition and deployment fit with the vendor before the test.
Design survivorship rules before you test
There is no single correct survivor value for every field. The right rule depends on what the consolidated record is used for, so business owners should define it before any tool is configured. Common options include:
- Frequency: the value that appears on the most source records, such as the address most systems agree on.
- Aggregation: keep several values, such as every known address with its source, when one value would hide a real difference.
- Source authority: take each attribute from the system that owns it, such as billing for payment terms.
- Recency: take the most recently updated value. This only works if source timestamps are reliable.
Frequency needs a caution. If one faulty source is copied into many records, the wrong value can become the most common one. Preserving lineage, so reviewers can see where each surviving value came from, is the protection against this. For each important attribute, write the rule and the answer a reviewer expects it to produce, then check whether each candidate can express that rule without custom code. Custom code is a maintenance cost and belongs in the operating model.
Rank #4
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Running a proof of concept on your own records
- Define the entity and the cost of each error. Write the rule that makes two records the same entity, such as the same person at the same postal address versus the same household. Then decide which error hurts more in your business. A false merge combines two customers’ histories. A missed link leaves duplicates to reconcile. The answer determines which thresholds to favour.
- Profile representative data. Sample from each source system, including records with missing fields, inconsistent formats, conflicting values, and duplicate clusters found in earlier cleanups.
- Build a labeled test set. Include true matches, non-matches, difficult near matches such as two people with the same name at different addresses, and conflicts where each source is authoritative for different attributes. Have two reviewers label the sample independently and record how disagreements were settled. Hold part of the set back from tuning so no configuration is graded on the data used to tune it.
- Fix the configuration for each candidate. Record the matching attributes, the exact or fuzzy setting for each, thresholds, record-selection filters, and survivorship rules. Give every candidate the same time budget for tuning, and log each change.
- Score against the labels, then inspect failures. Check each false merge and each missed link, and classify its cause as data quality, attribute choice, threshold, or candidate generation.
- Test change over time. Add, correct, and delete source records; change one threshold; unmerge a wrong link. Then confirm the surviving values and downstream systems update as expected.
- Compare effort alongside accuracy. Log the hours spent on configuration, source onboarding, and steward review. Obtain current prices, service terms, security details, deployment options, and regional availability directly from each vendor, because the vendor documentation reviewed here does not cover them.
Measuring what the test shows
| Measure | How to calculate it | What it shows |
|---|---|---|
| False merge rate | Wrong merges divided by all merges the tool proposed | How often unrelated entities are combined (1 minus precision) |
| Missed-link rate | True matches left unlinked divided by all true matches in the labeled set | How often one entity stays split (1 minus recall) |
| Survivor accuracy, per attribute | Surviving values that match the reviewer-judged value divided by surviving values tested for that attribute | Whether the rule gives an answer the business accepts |
| Lineage coverage | Surviving values with a traceable contributing source divided by all surviving values | Whether a steward can explain any value |
| Steward effort | Ambiguous pairs reviewed, with minutes per decision timed by your stewards | The ongoing labour the configuration creates |
| Reversal cost | Steps and time to undo a wrong merge, and whether downstream values update | Whether mistakes can be corrected safely |
Set an acceptance threshold for each measure before the test starts. A low false-merge rate is not a success if it comes from a threshold so strict that most true duplicates are missed. These measures are a proposed test design. The vendor documentation reviewed here does not publish them in a uniform way, so any figure a vendor quotes should be reproduced on your data before you rely on it.
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Uncertain pairs need a person to decide them. Decide in advance which matches are applied automatically, which go to a review queue, who may approve a merge, and how a merge is reversed. A platform that matches well but cannot be reviewed or reversed by your stewards will create a backlog of unresolved records after go-live. Test these steps with the stewards who will run them, not only with the engineers who configure the tool.
Best Value
When the test surfaces problems, these branches help narrow down the cause:
- False merges rise after a rule change. Re-run the labeled set with the previous configuration, then change one setting at a time to find the attribute or threshold responsible.
- Known duplicates stay separate. Check whether the pair was excluded before scoring, then check how each attribute handles missing values.
- The survivor is right for one user and wrong for another. Check whether values are returned by caller role or application before changing the rule itself.
- A reversed merge reappears after a source update. Check whether an entity rule re-creates the link, and test the sequence of update, reversal, and reload explicitly.
How to decide between candidates
Drop any candidate that cannot meet the acceptance thresholds you set before testing. Among those that pass, prefer the one your stewards can operate and reverse without vendor help, because accurate matching alone does not keep the consolidated view trustworthy. If no candidate passes, the cause is often the configuration or the source data rather than the category of tool. Repeat steps 2 to 4 of the proof of concept before comparing products again.
Quick Recap
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