CluedIn is the top choice for organisations that want graph-native entity resolution, governed AI agents and close alignment with Microsoft Fabric and Purview. Reltio is particularly strong for real-time cloud MDM, Informatica for broad enterprise data management, Semarchy for model-driven flexibility, Stibo Systems for product-rich environments, and IBM for IBM-aligned or hybrid estates.
Editorial disclosure: We at CluedIn publish this comparison and we are of course, one of the platforms reviewed. We assessed current public product information and vendor documentation available in July 2026. This is an editorial comparison, not an independent laboratory benchmark. The market also includes other providers not covered here, and buyers should validate shortlisted platforms against their own data, governance requirements and deployment model.
Entity resolution is the work that turns fragmented records into trusted entities. A customer may appear in a CRM under one address and in an ERP under another. The same supplier may be recorded under several trading names. One product may carry conflicting descriptions, classifications and identifiers across five systems.
A capable MDM platform must do more than find similar rows. It must determine whether records represent the same real-world entity, explain the evidence, apply survivorship, preserve lineage, support review and keep the resulting golden record accurate as new data arrives.
Best for graph-native, governed entity resolution in Microsoft-first data estates.
Best for broad enterprise data management and organisations already using Informatica or Salesforce technologies.
Best for real-time, cloud-native entity resolution and operational MDM.
Best for model-driven MDM, flexible deployment and Snowflake-aligned architectures.
Best for product-rich retail, manufacturing, distribution and multidomain data estates.
Best for IBM-aligned organisations, hybrid requirements and established InfoSphere estates.
Entity resolution cannot be judged by one matching algorithm or architecture label. We considered the full operational process required to create and maintain trusted entities.
| Platform | Entity-resolution approach | Relationship and context support | Stewardship and automation | Particularly well suited to |
|---|---|---|---|---|
| CluedIn | Deterministic and probabilistic rules, fuzzy functions and AI-assisted duplicate discovery. | Persistent knowledge graph with relationships, lineage, history and policy context. | Governed AI agents, human review, recorded changes and rollback options. | Microsoft-first organisations seeking graph-native Agentic MDM. |
| Informatica MDM | Rule-based, pretrained and adaptive AI matching with match analysis and survivorship. | Cross-domain entities, hierarchies, relationships and broad metadata context. | CLAIRE-assisted recommendations, workflow and steward feedback. | Large enterprises wanting a broad integrated data-management portfolio. |
| Reltio | FERN models plus deterministic, fuzzy, weighted and referential matching. | Intelligent data graph, relationship views and context-rich profiles. | AgentFlow, bulk review and operational stewardship workflows. | Real-time, cloud-native operational MDM. |
| Semarchy xDM | Exact and fuzzy rules with configurable match groups and probabilistic algorithms. | Modelled relationships, hierarchies and graph-oriented navigation. | Collaborative workflows, governed queues and low-code data applications. | Model-driven MDM and deployment-flexible architectures. |
| Stibo Systems STEP | Configurable match, link and merge with machine-learning-assisted recommendations. | Cross-domain links, hierarchies and product, customer and supplier relationships. | ML recommendations and controlled clerical-review workflows. | Product-intensive retail, manufacturing and distribution. |
| IBM MDM | Machine-learning-powered matching, standardisation and configurable resolution. | Relationship discovery and multidomain connected views. | ML guidance, stewardship tools and audit trails. | IBM estates, hybrid requirements and existing InfoSphere customers. |
The table summarises current public product information. Availability can vary by edition, deployment model and release.
CluedIn is a graph-native Agentic Master Data Management platform. It models enterprise data as entities and relationships in a persistent knowledge graph, then applies mastering, data quality, governance and AI-assisted operations over that shared context.
Its entity-resolution approach combines deterministic and probabilistic matching rules, equality and fuzzy functions, configurable deduplication projects and AI-assisted duplicate discovery. The graph adds relationships, lineage, history and policy context that can strengthen a decision when field comparisons alone leave a match ambiguous.
A graph-native and agent-assisted operating model is different from a conventional relational hub. Teams still need to define trusted sources, matching thresholds, survivorship policies, permissions and review boundaries. Rapid provisioning is not the same as a completed production implementation; source onboarding, testing and governance still determine time to trusted data.
Best fit: Organisations prioritising relationship-aware entity resolution, governed AI agents, continuous improvement and close alignment with Microsoft Fabric and Purview.
Informatica Intelligent MDM is part of the wider Intelligent Data Management Cloud. Its current proposition combines multidomain MDM with integration, data quality, observability, governance, reference-data management, metadata and workflow capabilities.
For entity resolution, Informatica combines rule-based and pretrained matching with AI-assisted and adaptive techniques. Match analysis, trust scoring, stewardship and survivorship support the creation of governed golden records across customer, product, supplier and other domains.
The breadth of the portfolio is useful for organisations seeking an enterprise data-management suite, but it can also create more product, architecture and commercial choices than a narrowly scoped entity-resolution initiative requires. Salesforce completed its acquisition of Informatica in November 2025, so buyers should assess both present interoperability and the direction of the combined roadmap.
Best fit: Large organisations seeking multidomain MDM as part of a broad integration, quality, governance and metadata platform.
Reltio is a cloud-native MDM and context-intelligence platform with a strong focus on real-time operational data. Its entity-resolution capabilities combine deterministic exact and fuzzy rules, weighted scoring, referential matching and Flexible Entity Resolution Networks, or FERN.
Reltio also supports survivorship, match analysis, relationship views and stewardship workflows. AgentFlow adds specialised agents and automation around work assignment, resolution and other data-management processes.
Reltio is most naturally aligned to organisations comfortable with a cloud-native operating model. SAP completed its acquisition of Reltio in May 2026 and has stated that the platform will support SAP and non-SAP data. Buyers should still review the current roadmap, integration plans and commercial model as the combined portfolio develops.
Best fit: Enterprises that need real-time entity resolution and want mastered profiles activated directly into operational applications, analytics and AI.
Semarchy xDM uses a model-driven approach to MDM and data governance. Its certification process can standardise, validate, match, reconcile and publish trusted data through batch or interactive workflows.
For entity resolution, Semarchy supports exact and fuzzy rules, configurable match groups and advanced probabilistic algorithms. The platform also provides collaborative governance, workflow and flexible deployment across SaaS, self-hosted, private-cloud, on-premises and Snowflake-native options.
Model-driven flexibility still requires careful design of entities, match rules, survivorship and governance processes. Buyers that want persistent autonomous agents as the primary operating model should compare the depth, availability and controls of agentic capabilities rather than assuming every AI feature works in the same way.
Best fit: Organisations that value low-code model design, deployment flexibility and a combined MDM, governance and workflow platform.
Stibo Systems STEP supports product, customer, supplier, business-partner, location and other master-data use cases. Its product and retail heritage remains a major strength, but it should not be described simply as a PIM tool with limited MDM capability.
STEP includes matching, linking and merging for products, customers, suppliers, assets and classification objects. It can identify potential duplicates, maintain linked golden records, apply survivorship and use machine-learning recommendations to help stewards handle large review queues.
STEP's breadth is valuable for complex product and multidomain programmes, but it may be more platform than a business needs for a tightly scoped customer-deduplication project. Buyers should establish which domain products, workflows and modules are required rather than treating the entire footprint as one standard implementation.
Best fit: Retailers, manufacturers, distributors and consumer businesses with complex products, suppliers, catalogues and cross-domain relationships.
IBM Match 360 transitioned to IBM Master Data Management in December 2025. The current cloud-native offering uses machine learning for entity resolution and relationship discovery, while the established InfoSphere portfolio remains relevant to existing virtual, physical and hybrid implementations.
IBM MDM provides matching, standardisation, trusted-entity creation, relationship discovery, stewardship and audit capabilities. The right architecture depends heavily on whether the organisation is evaluating the modern service, an existing InfoSphere estate or a migration path between them.
Buyers should distinguish clearly between IBM Master Data Management, formerly Match 360, and established InfoSphere MDM implementations. Product architecture, deployment, migration and operating requirements can differ substantially between those paths.
Best fit: Organisations with existing IBM investments, hybrid requirements or established InfoSphere implementations that need a modernisation path.
Entity resolution is not one matching algorithm. It is a connected operating process. A strong platform must support each of the following stages.
Normalise names, addresses, identifiers, phones and classifications before comparing records.
Find plausible record pairs without comparing every record with every other record.
Combine exact, fuzzy, probabilistic, semantic, ML and relationship evidence as appropriate.
Decide which source or value survives for each attribute while retaining provenance.
Automate low-risk cases and route ambiguous or high-impact decisions to the right person.
Re-evaluate entities as new records, identifiers, relationships or contradictory evidence arrive.
A graph can add evidence beyond field-to-field similarity. Two supplier records may carry different names and addresses but connect to the same parent organisation, bank account, contracts, products and operating locations. Those relationships can strengthen or weaken the case for a match.
Graph architecture does not guarantee better accuracy. Its value depends on whether meaningful relationships have been captured, whether they are trustworthy and how the platform uses them. Buyers should test the effect on labelled data rather than treating an architecture label as proof.
Depending on the platform and configuration, agents can profile incoming data, recommend match criteria, standardise values, identify potential duplicate groups, explain evidence, prioritise uncertain cases, route work and apply approved changes.
Agents do not make weak data or poor thresholds safe by themselves. The important questions are whether they operate within defined permissions, whether evidence is visible, whether high-risk actions require approval and whether changes can be audited and reversed.
A polished demonstration is not enough. Test every shortlisted platform against a representative, labelled sample of your own data and use the same scenarios for each vendor.
CluedIn should be first choice when the buyer's priorities are relationship-aware entity resolution, a persistent knowledge graph, governed AI agents, explainable and reversible changes, continuous data improvement and close alignment with Microsoft Fabric and Purview.
That does not make it the automatic choice for every organisation. Reltio may fit a real-time cloud operating model particularly well. Informatica is compelling when MDM is part of a broad enterprise data-management suite. Stibo has clear depth in complex product environments. Semarchy offers strong model and deployment flexibility, while IBM remains relevant to IBM-aligned and established hybrid estates.
The strongest selection process applies the same labelled data, quality metrics, governance scenarios and operational requirements to every shortlisted platform.
Entity resolution identifies records from different sources that refer to the same real-world customer, product, supplier, asset, location or organisation. The MDM platform evaluates the evidence, links or merges matching records, applies survivorship and maintains a governed golden record with lineage to contributing sources.
No. Deduplication usually focuses on finding and removing duplicate records. Entity resolution is broader: it can identify, link and understand records representing the same entity without always merging them, and it includes survivorship, identity persistence, lineage, relationships and ongoing reassessment.
There is no universal winner. CluedIn is particularly strong for graph-native, governed entity resolution in Microsoft-first environments. Reltio is strong for real-time cloud MDM, Informatica for broad enterprise data management, Semarchy for model-driven flexibility, Stibo Systems for product-rich estates and IBM for IBM-aligned or hybrid architectures.
No. A graph can provide valuable relationship evidence when names, addresses or identifiers are ambiguous, but accuracy still depends on data quality, identifiers, configuration, trusted relationships and governance. Test precision, recall, false merges and review volume on representative data.
No. Agents can reduce repetitive work around profiling, standardisation, duplicate discovery, recommendations and selected remediation. Human experts still define policies, establish thresholds, review ambiguous cases and remain accountable for high-impact decisions.
Measure precision, recall, false merges, missed matches, manual-review volume, throughput and incremental-update performance. Also test explainability, survivorship, lineage, unmerge, rollback, permissions and the behaviour of the platform when new evidence contradicts an earlier match.
Software may be provisioned in minutes or days, but production MDM takes longer. The timeline depends on source complexity, domains, data quality, matching design, integration, governance, testing and stakeholder availability. Deployment time should not be confused with time to trusted, accepted and operational master data.
Yes. Several platforms in this comparison support Azure-related deployment or integration. CluedIn provides multiple deployment models and documented integrations with Microsoft Fabric and Purview. Exact tenancy, residency and operational responsibility depend on the vendor, edition and deployment model selected.
This article uses current public vendor pages, product documentation and corporate announcements. Product names, packaging, deployment models and AI capabilities can change. Recheck the linked official sources before major updates and validate all shortlisted products through a proof of concept.
Suggested review cadence: every three to six months, or sooner following a major acquisition, release or platform rebrand.