Articles

7 Microsoft Fabric data management use cases worth designing for

CluedIn — Wed, 21 Oct 2026 10:14:34 GMT

A useful thing happens once an organisation gets serious about Microsoft Fabric. The conversation gradually stops being about moving data into it. That job obviously still matters. Fabric gives teams Data Factory, engineering workloads, warehouses, real-time processing, analytics and OneLake underneath them. Increasingly, it also gives data owners a central place to discover and govern the Fabric estate through the OneLake catalog.

CluedIn 2026.03: Making Agentic Data Management Practical

CluedIn — Tue, 13 Oct 2026 13:25:11 GMT

Anyone who has spent time implementing MDM knows how much work happens around the edges of the actual problem.

There is the data to connect, obviously. Then there is the mapping, the modelling, the rules, the relationships, the validation, the scheduling, the checking and the steady accumulation of small decisions that somebody has to make before the platform starts doing anything useful.

None of that work is particularly surprising. Much of it is unavoidable. But an uncomfortable amount of it has traditionally existed because software needed people to tell it, step by step, what to do.

CluedIn 2026.03 removes more of that burden.

CluedIn 2026.03

More of the platform can now understand the 'work' around the data, not simply process the data itself.

That shows up in several places across this release: how new data is onboarded, how long-running work is organised, how enrichment decisions are made, how relationships are explored and how much of the platform can be operated without somebody constantly sitting in front of it.

Give CluedIn the data. It can now do considerably more with the arrival.

The AI Agent work in this release is probably the clearest example of where CluedIn is heading. When new files or ingestion endpoints appear, agents can inspect what has arrived and begin putting together an onboarding plan. That can cover mappings, Business Domains, validation rules, relationships, AI Jobs, icons and other configuration that would normally be assembled manually during implementation.

It gets more interesting when the data stops being tidy...

Multiple files can belong to the same Business Domain. Different files can describe different domains but contain relationships between them. Several source systems can be onboarded at the same time. An early mapping choice can look reasonable until another file provides context that changes the picture.

The agent experience has been built to deal with that kind of messiness. It can work across the wider onboarding task, revisit earlier decisions and build a plan that reflects what it finds as it goes.

The plan is visible before it runs. A user can inspect it, amend it and decide what should happen next. The automation is doing more of the preparation, but the implementation has not disappeared into a black box.

Job Monitoring and Job Detail in CluedIn

CluedIn Job Monitoring

CluedIn Job Detail

This also makes the idea of a headless MDM or data quality implementation considerably less theoretical.

There will always be people who want to work directly in the interface, and there are plenty of decisions where that remains exactly the right thing to do. But the interface no longer needs to be the starting point for every piece of work.

A greater proportion of the implementation can now be driven by intent, conversation and agent-led planning. The platform works out more of the steps underneath, while the user concentrates on whether the result makes sense.

That is a subtle shift, but an important one. MDM has accumulated decades of operational ceremony. We are steadily stripping away the parts that exist only because the software once needed them.

The aim is not to remove people from data management. It is to stop wasting their time on work the platform is increasingly capable of carrying itself.

Enrichment is rarely as certain as the workflow makes it look

Ask an enrichment provider to identify a company, address or organisation and sometimes there is one obvious answer. Sometimes there are three. That ambiguity is part of working with real enterprise data. Pretending it does not exist only pushes the decision somewhere else in the process.

Enrichers in CluedIn can now return multiple candidate matches for a Golden Record. Those candidates can be compared together, approved or rejected, or automatically selected through rules and AI where that is appropriate.

There is also a testing step before enrichment is committed. You can evaluate what the change will do to Golden Records first, which makes it possible to automate more without asking users to accept more uncertainty.

Multiple candidates

The platform can surface uncertainty instead of quietly burying it.

Candidates can be reviewed side by side, rejected, approved or selected automatically where confidence and policy allow.

Test before applying

Automation is far more useful when you can see the consequences first.

Enrichment changes can be evaluated against Golden Records before they are committed.

Then there is all the work that should happen without somebody watching it

Reprocessing rules. Running bulk operations. Reprocessing records. Retention jobs. Scheduled maintenance. These are necessary parts of running a serious data platform, but nobody buys MDM because they want to spend their day supervising background processes.

The new Jobs and Monitoring framework gives that work a proper home. Custom Jobs can be created with or without schedules. Their progress can be followed through the UI, completed work can be reviewed later and running jobs can be cancelled when required. System Jobs now sit inside the same framework rather than behaving like a separate layer of platform machinery.

This is one of those improvements that becomes more valuable as automation increases. The more work you hand to the platform, the more important it becomes to know exactly what is running, what happened and where something needs attention.

Enricher Matches

A business is not a collection of disconnected records

This sounds obvious until you look at the way enterprise software has traditionally presented data.

Customers are viewed in one place, products in another, suppliers somewhere else. Relationships exist, but the interface often makes you hunt for them. When the data estate gets large enough, the picture disappears behind the records.

The improved Global Data Model and Ontology Viewer makes that picture easier to see. Business Domains can be viewed according to record volume, filtered by data source and displayed even where relationships have not yet been established.

That matters for people trying to understand the estate, but it also matters for the agents working across it. Context is increasingly part of the product itself: which data belongs together, how records relate, what came from where and what those relationships mean to the business.

Global Data Model / Ontology Viewer

There is also a lot in this release that is deliberately less visible.

The platform has moved to .NET 10. Elasticsearch has been upgraded from 7.17.29 to 9.5.1 and RabbitMQ from 3.7 to 3.13.7. Those changes bring the usual security and platform improvements, but the release also contains a substantial amount of work aimed at making everyday use faster.

Mapping previews are significantly quicker. Large dataset views have been optimised. Dataset mappings, ingestion queries and relationship queries have all received performance work. Duplicate detection is faster, Golden Record searching has been improved and several expensive background operations have been reduced or removed.

Data source processing idle memory usage has dropped from roughly 1.5 GB to around 650 MB. That is not the kind of feature anyone puts on a buying committee slide, but it is exactly the kind of engineering work a mature platform needs.

.NET 10

Core platform upgrade

9.5.1

Elasticsearch

3.13.7

RabbitMQ

~60%

Lower data source processing idle memory

We are gradually changing the amount of MDM that feels like operating MDM

That is probably the simplest way to describe the direction behind 2026.03.

CluedIn still gives data teams the controls, workflows, rules and interfaces they need. Those things are not going away. What is changing is how often a person needs to initiate every task, assemble every sequence and supervise every piece of routine work.

Some work can now begin because data arrived. Some can run because a schedule says it should. Some decisions can be prepared by an agent and brought to a person with the context already assembled. Other actions can simply continue in the background, visible and governed without demanding attention.

That is what makes Agentic Data Management interesting to us. Not the presence of AI in the interface, and not another assistant waiting for instructions. The value appears when the platform starts carrying a meaningful share of the operational load.

2026.03 carries more of it than the release before.

Release 2026.03

Less operational friction around the data. More of the work handled inside the platform. The same expectation of control.

The result is a CluedIn platform that is faster, more automated and increasingly capable of turning fragmented enterprise data into trusted records without making people manually orchestrate every step along the way.

Read the full release notes Try CluedIn

Microsoft Fabric, Purview and CluedIn

CluedIn — Thu, 08 Oct 2026 09:56:12 GMT

Consider a spend report with the same supplier listed twice. One purchasing system holds the legal name; another uses a trading name and an older address. Both rows have arrived in Fabric exactly as supplied. The person preparing the report now has to work out whether the business has two suppliers or one.

Microsoft Fabric - Agentic data operations in a Microsoft data estate

CluedIn — Mon, 05 Oct 2026 08:14:05 GMT

Komatsu’s brief for AI agents included a very specific request: understand the company’s abbreviations and data dictionary. The agents also needed to work with Microsoft Fabric and reach people through Microsoft Teams or Jira.

Master Data Management is becoming something bigger

CluedIn — Wed, 30 Sep 2026 09:58:32 GMT

Earlier this year, CluedIn was recognised in two Gartner® Magic Quadrants™: as a Visionary in the 2026 Magic Quadrant for Master Data Management Solutions and as a Niche Player in the 2026 Magic Quadrant for Augmented Data Quality Solutions.

Microsoft MDS to CluedIn: a technical migration guide for SQL Server, Fabric and Purview

CluedIn — Wed, 16 Sep 2026 16:32:20 GMT

MDS remains supported in SQL Server 2022 and earlier releases. Existing installations do not suddenly stop working because SQL Server 2025 exists.

But Microsoft has removed Master Data Services from SQL Server 2025. That changes the question for architects from "How do we upgrade MDS?" to "How do we get the data, rules, hierarchies, integrations and consumers out of MDS without recreating the same technical debt somewhere else?"

What this guide covers

The migration mechanics, the MDS-to-CluedIn concept mapping, where Fabric and Purview fit, and what Microsoft and other third parties have actually published about CluedIn.

The aim is not to produce another generic "modernise your data" article. It is to separate what is technically useful from what is merely marketing shorthand.

First, what does Microsoft actually say?

Microsoft's current position on Master Data Services is straightforward.

Microsoft Learn

"MDS is removed in SQL Server 2025 (17.x)."

Source: Microsoft Learn

Microsoft also confirms that MDS remains supported in SQL Server 2022 and earlier versions.

That distinction is important. There is time to migrate properly. There is considerably less justification for designing new architecture around MDS.

And where does CluedIn appear?

This is where the history becomes useful. Microsoft's Architecture Center repository contains a reference architecture titled:

Historical Microsoft architecture

Migrate master data services to Azure with CluedIn and Azure Purview

View the MicrosoftDocs architecture source: MicrosoftDocs Architecture Center repository

The live Architecture Center has evolved since then, but the original MicrosoftDocs architecture metadata remains publicly visible. That matters because it establishes that the CluedIn/MDS relationship did not appear only after Microsoft removed MDS from SQL Server 2025.

A Microsoft-hosted presentation from 2022, End-to-end Data Estate Lineage with Purview and CluedIn, also includes that same MDS migration architecture in its resource material.

Source: Microsoft-hosted presentation

The Microsoft references did not disappear with the old MDS architecture

The current material has moved on from MDS migration specifically and into the wider MDM architecture. Microsoft Learn currently has a dedicated page titled Microsoft Purview and CluedIn integration for master data management.

Current Microsoft Learn

Microsoft describes the CluedIn architecture as providing a "coherent, consistent, end-to-end Master Data Management (MDM) solution."

Source: Microsoft Learn

That page covers ingestion, data quality, mastering, governance, graph relationships and downstream data delivery.

Microsoft also publishes a current guided project for building a complete master data management and data governance stack using Microsoft Purview and CluedIn.

The training walks through Azure Data Factory, CluedIn ingestion, streaming data back to ADLS, deduplication, cleaning, enrichment and Purview scanning.

Source: Microsoft Learn guided project

The evidence chain

MDS migration architecture → Azure and Purview MDM architecture → current Microsoft MDM training

The technology has moved forward. The architectural role has remained recognisable.

What does an MDS migration actually involve?

This is where migration projects often underestimate the problem. MDS is rarely just a database.

MDS component	What may depend on it
Models	Domain boundaries and organisational ownership
Entities	Master data objects
Members	Business records
Attributes	Data definitions and controlled values
Domain-based attributes	Relationships between entities
Business rules	Validation, defaults and data acceptance
Hierarchies	Operational and reporting structures
Subscription views	Downstream integration contracts
Change tracking	Incremental integration
Excel Add-in	Manual stewardship processes
Security	Who can view and modify domains
Transaction history	Audit and investigation

The dependencies are more important than the tables.

An MDS migration should start by identifying what actually relies on MDS, not by assuming every object deserves to be recreated.

Three technical paths from MDS into CluedIn

There is no requirement to switch MDS off before CluedIn comes online.

Connect CluedIn directly to MDS

CluedIn has a dedicated Microsoft SQL Server MDS integration.

There is a practical technical issue to handle: MDS uses Windows authentication by default, while the CluedIn crawler runtime does not rely on Windows authentication in the same way.

CluedIn therefore documents direct connectivity where the network path permits it, and Azure Relay where an on-premises MDS server does not have a routable path to CluedIn.

On-premises MDS → Azure Relay → CluedIn

Useful when coexistence is required before MDS is retired.

Treat existing SQL outputs as migration contracts

Many mature MDS implementations already publish stable subscription views.

If those views are understood and trusted, they can provide a useful migration surface without forcing the team to reproduce the entire MDS model first.

The caution is obvious: a beautifully simple subscription view can hide years of assumptions and transformation logic underneath it.

Use both

For larger estates, this is often the sensible route.

Connect directly to MDS first to establish parity, identity and downstream publishing. At the same time, begin replacing the dependency on MDS with cleaner ingestion from original source systems or curated upstream datasets.

MDS disappears from the middle only after the replacement path has earned confidence.

Hybrid migration

Phase 1: Source systems → MDS → CluedIn → Consumers

Phase 2: Source systems → CluedIn → Consumers

How MDS concepts map into CluedIn

Some concepts map cleanly. Others should change.

Microsoft MDS	CluedIn approach
Model	Data model scope across Business Domains
Entity	Business Domain
Member	Mastered record
Attribute	Vocabulary Key
Domain-based attribute	Relationship between Business Domains
Business rules	Validation, quality, survivorship and publishing logic
Hierarchies	Graph relationships and controlled classification structures
Validation issues	Quality exceptions and failed checks
Subscription views	Streams and Export Targets
Change tracking	Event-log Streams
Data steward	Data steward / data owner with increased focus on exceptions and policy

Technical mapping: CluedIn MDS migration playbook

Do not copy MDS business rules line for line

An MDS business rule can do several different jobs.

It might validate a value. It might set a default. It might determine whether a record is valid enough to publish. It might trigger something operational.

Years later, all of those behaviours can be buried inside the same rule estate.

The business requirement should survive. The implementation does not always need to.

In CluedIn, validation, data quality, survivorship, workflow and publishing logic can be separated into the controls that actually own those responsibilities. That is more useful than recreating old coupling because "that is how MDS did it."

Subscription views deserve special treatment

In many MDS environments, subscription views are where the real operational dependency lives.

Finance pulls one.
The warehouse loads another.
A CRM integration depends on a third.
Someone has an Excel workbook connected to a fourth.

That is why CluedIn's adoption guidance recommends starting with consumers and contracts rather than attempting to perfect the whole model first. In CluedIn, mastered data is typically distributed using Streams and Export Targets.

Synchronized streams

Maintain a downstream representation of mastered data.

Event-log streams

Emit create, update and delete events for event-driven consumers.

A useful migration test:

Can the replacement platform produce the data this consumer requires, with the same or better reliability, semantics and auditability?

What happens to hierarchies?

Do not flatten them because it makes migration easier. Parent-child structures, product hierarchies, customer ownership structures and organisational relationships often contain genuine business meaning.

CluedIn's graph-native model treats relationships as first-class elements rather than forcing all that context back into denormalised tables.

Supplier → Material → Product → Plant → Region

Relationships can provide context for matching, governance and agentic data operations.

What about the MDS Excel experience?

This deserves attention because many people experience MDS through Excel rather than through architecture diagrams.

CluedIn's technical guidance does not assume every manual maintenance process should simply be reproduced. Modern stewardship may use forms, governed workflows, application interfaces, controlled ingestion patterns or Power Platform experiences depending on the use case.

The steward's role changes too. Instead of becoming the person through whom every correction must pass, stewardship increasingly becomes exception handling, approval, policy definition and oversight.

Where Microsoft Fabric fits

Fabric does not turn into MDS because MDS disappeared. A solved Microsoft Fabric Community discussion from 2025 made the architectural point succinctly: Fabric does not contain a built-in replacement for legacy Master Data Services.

Community source: Microsoft Fabric Community

That is community guidance rather than Microsoft product documentation, but the distinction is technically useful. Fabric is extremely capable at data engineering, analytics and AI workloads. Master Data Management still has to deal with identity, mastering, relationships, survivorship, quality, stewardship and controlled distribution.

Operational systems
↓
CluedIn: identity, mastering, quality, relationships and governed operations
↓
Fabric: data engineering, analytics, AI and consumption

And where does Purview fit?

Purview is equally important, but for a different reason. It is not simply "new MDS". Microsoft's current Learn architecture documents Purview and CluedIn working together across governance, lineage and operational MDM.

Microsoft Purview

Governance, discovery, lineage and understanding of the data estate.

CluedIn

Operational mastering, entity-level quality, matching, relationships, enrichment and governed data operations.

A practical migration sequence

A complicated MDS estate becomes much less frightening when the unit of migration is a consumer contract rather than the entire platform.

1. Inventory the MDS estate

Inventory dependencies, not just objects. Find the consumers, rules, hierarchies, identities and manual processes that still matter.

2. Pick one meaningful domain

Customer, Product or Supplier will usually expose more useful migration issues than a harmless lookup table.

3. Establish connectivity

Use direct MDS integration, Azure Relay or an existing SQL contract depending on the topology.

4. Preserve identity first

Establish business keys and identifiers before polishing every attribute. A clean record with the wrong identity is still the wrong record.

5. Translate the important rules

Separate validation, quality, survivorship and publishing concerns instead of reproducing legacy coupling unnecessarily.

6. Publish early

Create the first Stream and Export Target before declaring the mastering model "finished".

7. Dual run

Publish the new contract alongside the current MDS output. Compare, investigate differences and repeat until the behaviour is understood.

8. Move one consumer

Not the whole enterprise. One consumer. Then repeat.

The 90-day version

CluedIn's adoption playbook proposes a useful shape for proving the migration early.

DAYS 0–30

Prove one domain

Ingest one domain and prove one consumer contract end to end.

DAYS 31–60

Improve mastering

Improve matching and survivorship, then introduce a second consumer.

DAYS 61–90

Dual run and cut over

Reconcile outputs and move the first consumer away from MDS.

So, is CluedIn actually recognised outside CluedIn for this?

Yes. The important thing is to describe the evidence accurately.

Current Microsoft documentation

Microsoft Learn documents a complete CluedIn MDM architecture with Purview.

It also publishes a hands-on guided project using Purview, Azure Data Factory and CluedIn.

Historical Microsoft architecture

Microsoft's Architecture Center repository contains an explicit MDS-to-CluedIn migration architecture.

Microsoft's own Purview and CluedIn webinar also referenced that architecture.

Historical Microsoft Q&A

Microsoft employees previously named CluedIn among third-party MDM options where organisations required alternatives to MDS.

These are historical Q&A responses, not current Microsoft product policy.

Microsoft Q&A example

Microsoft Fabric Community

A solved Fabric Community discussion from 2025 points to CluedIn in the context of replacing legacy MDS.

Community content is useful supporting evidence, but it should not be presented as official Microsoft product documentation.

Independent research

Info-Tech highlights CluedIn's graph architecture, AI integration and strong Microsoft Azure alignment.

Its technology note says CluedIn "may be a good choice for organizations with strategic reliance on Microsoft Azure cloud."

Source: SoftwareReviews / Info-Tech

Independent comparison signal

Gartner Peer Insights maintains a direct comparison page for CluedIn and Microsoft MDS (Legacy).

This is not an analyst endorsement. It is an independent signal that the two products appear in a directly comparable MDM buyer context.

Gartner Peer Insights comparison

What this evidence does not mean

It does not mean Microsoft has designated one universal successor to Master Data Services. It has not. It does not mean every MDS deployment requires an enterprise MDM platform. Some do not. And it does not mean a historical Azure reference architecture should be treated as the current implementation guide for CluedIn in 2026.

What the evidence does establish is a long-standing, documented architectural relationship between CluedIn and Microsoft around MDM and MDS migration.

Current CluedIn documentation should be used for implementation. The older Microsoft material provides provenance.

The migration is finished when MDS is no longer a dependency

Not when all the records have been copied. MDS has not really been retired if:

A nightly job still reads one subscription view
Finance still maintains a hierarchy through an old process
An undocumented Excel workbook still depends on it
Change notifications still originate there
An application still expects an MDS-generated identifier
Nobody knows whether an old business-rule job can safely be switched off

The cleanest migration is not the one that moves the largest number of objects.

It is the one that leaves no unexplained dependencies behind.

Planning the technical move from MDS?

Start with the architecture you actually have.

Bring the important subscription views, the rules nobody wants to touch, the consumer dependencies and the current MDS topology. Then work out what deserves to survive, what needs to change and what can finally be retired.

Explore MDS migration with CluedIn Read the technical migration playbook

Best Microsoft MDS Alternatives 2026

CluedIn — Wed, 16 Sep 2026 09:45:59 GMT

Microsoft removed MDS from SQL Server 2025 while continuing to support it in SQL Server 2022 and earlier releases. If you run MDS today, that leaves you with something more interesting than an upgrade problem.

You now have to decide what kind of Master Data Management you actually want next.

Before making a shortlist

Do not start by asking which platform looks most like MDS.

Start by deciding which parts of MDS are genuinely valuable, which parts are technical baggage, and what your data architecture needs to do over the next five to ten years.

First things first, there is no direct Microsoft successor to MDS

Microsoft MDS Replacement After SQL Server 2025 | CluedIn

CluedIn — Tue, 15 Sep 2026 15:09:30 GMT

Microsoft Master Data Services has been removed from SQL Server 2025. That does not mean existing MDS implementations suddenly stop working. Microsoft continues to support MDS in SQL Server 2022 and earlier versions.

But it does mean organisations relying on MDS now have a clear architecture decision to make: what replaces it, and how do you move without turning years of master data logic into a high-risk migration project?

Quick answer

Is Microsoft MDS discontinued?

Master Data Services is no longer included in SQL Server 2025. Microsoft continues to support MDS in SQL Server 2022 and earlier versions.

So there is no immediate shutdown. But there is now a clear ceiling for organisations planning future SQL Server upgrades or wider data platform modernisation.

What has actually changed?

MDS has traditionally been used to define and manage models, entities, attributes, members, hierarchies, business rules and mastered outputs. Microsoft describes an MDS model as the highest level of organisation for master data, containing entities, attributes, hierarchies and collections. That model has served many organisations well.

The issue is not that MDS suddenly stopped being useful. It is that it no longer has a place in Microsoft's forward SQL Server platform.

The bigger question

Should you simply recreate MDS somewhere else, or use this as an opportunity to modernise Master Data Management properly?

What are the options for organisations still using MDS?

From AI Assistance to AI Action: What’s Coming Next to CluedIn Agentic MDM

CluedIn — Tue, 08 Sep 2026 11:00:20 GMT

CluedIn is introducing a significant set of new capabilities across AI Agents, data enrichment, the Global Data Model and Topology Explorer.

Each enhancement solves a specific product challenge. Together, they support a bigger change in how CluedIn helps enterprises manage data, moving from AI that waits for individual instructions towards AI that can work proactively towards a defined outcome.

From helping people manage data to actively helping manage the data itself.

This does not mean removing people, policies or governance from the process. It means reducing the need for a person to identify, initiate and supervise every individual step.

Give an AI Agent an objective, not just another prompt

The redesigned CluedIn AI Agent experience will support proactive Agents and long-running work that may continue for minutes or hours.

Instead of asking an Agent to complete one isolated task, users will increasingly be able to give it a broader objective, such as:

Improve the quality of this customer domain.
Investigate why these records are failing quality rules.
Enrich incomplete supplier records.
Investigate and resolve suspected duplicates within defined governance rules.

The Agent can then work through multiple steps, use the context available to it, take permitted actions and bring a person back into the process when judgement or approval is required.

Agents will also be able to track the work they perform and the results of their actions. Over time, those outcomes can provide useful context for approaching similar work. Support for Model Context Protocol, or MCP, will also allow CluedIn Agents to interact with external tools and services as part of a wider enterprise AI ecosystem.

Do not ask the AI whether it worked. Measure it.

An AI model can make a change and produce a convincing explanation. That is not proof that the data improved.

CluedIn will combine AI-driven action with independent, deterministic data quality measurement. This makes it possible to assess whether completeness increased, validity improved, duplicate rates fell or the quality of a domain moved in the right direction.

Identify → Reason → Act → Measure → Improve

This creates an objective scorecard for Agent actions. The Agent can reason and act, but CluedIn can independently determine whether the intended result was achieved.

More intelligent enrichment for messy enterprise data

Enterprise enrichment is rarely a simple exchange of one internal record for one external record. Company names vary. Addresses change. Legal entities multiply. Enrichment providers may return historical records, branches, subsidiaries or several plausible matches.

CluedIn is improving enrichment with AI-assisted fuzzy matching, helping identify likely external records even when names, addresses, abbreviations, formatting or identifiers do not align perfectly.

New multi-record matching capabilities will also help CluedIn work with multiple related responses rather than assuming the first result is the right one. The aim is higher enrichment coverage and more accurate matching, while handling the ambiguity that appears in real enterprise data.

The hard part of enrichment is not finding more data. It is knowing which data belongs to whom.

A Global Data Model with more meaning and context

The CluedIn Global Data Model is also evolving with new search, filtering, semantic model capabilities and synchronisation with Microsoft Fabric IQ.

Search and filtering will make larger enterprise models easier to navigate. The bigger strategic change is the growing role of the model as a semantic representation of the business.

AI needs more than access to tables, columns and values. It needs to understand what an entity represents, how it relates to other entities and what those relationships mean. Customers, products, suppliers, assets, locations and legal entities do not exist as isolated records. Together, they form a map of the enterprise.

The Global Data Model can provide this context to CluedIn AI capabilities and, through Fabric IQ synchronisation, help make governed enterprise context available within Microsoft’s wider data and AI ecosystem.

Topology Explorer: from seeing change to understanding it

Enterprise data estates never stand still. Sources, mappings, schemas, relationships and properties change continuously. Finding the change that caused a later issue can mean manually comparing complex technical representations across months of history.

The new CluedIn Topology Explorer is designed to work across thousands of historical versions, with improved ways to explore how the topology and model have evolved.

AI-generated explanations will help users investigate questions such as:

What changed between these versions?
Which entities and relationships were affected?
When was this relationship introduced?
What changed before this issue appeared?

For data architects, engineers, governance teams, administrators and auditors, this changes the question from “show me the topology” to “help me understand what happened”.

One product direction, not four isolated features

These capabilities are connected. The Global Data Model provides semantic context. Data quality and topology help CluedIn observe the state of the data estate. AI Agents reason about what needs to happen. Agents and enrichment capabilities perform governed work. Deterministic metrics measure the result. Previous outcomes can then become context for future work.

The Agentic Data Management loop

Understand → Observe → Reason → Act → Measure → Learn

People remain in control of the policies, permissions, approval points and acceptable level of autonomy. The aim is not blind automation. It is useful, explainable and measurable action within clear enterprise boundaries.

This is the direction CluedIn is building towards: a platform that can increasingly understand the state of enterprise data, identify what needs attention, take governed action and prove whether that action improved the result.

From AI assistance to AI action.

Explore Agentic Master Data Management

See how CluedIn combines AI Agents, graph-native context, data quality and governance to help enterprises build trusted, AI-ready data.

Explore the CluedIn platform

When Should You Merge Two Customer Records?

CluedIn — Tue, 11 Aug 2026 10:51:23 GMT