What is CluedIn?

CluedIn is an Agentic Master Data Management platform built for enterprises that need trusted, governed, AI-ready data. It brings together master data management, data quality, entity resolution, governance, enrichment, workflows, and AI agents into one operational data management layer.

What problem does CluedIn solve?

Most enterprises cannot fully trust the data that powers their analytics, AI, reporting, automation, and business processes. The data is often duplicated, incomplete, inconsistent, poorly governed, or spread across too many systems. CluedIn helps resolve this by continuously improving data quality, mastering critical entities, applying governance, and using AI agents to reduce the manual work required to keep enterprise data reliable.

What is Agentic Master Data Management?

Agentic Master Data Management is a modern operating model for MDM where AI agents help monitor, improve, enrich, classify, validate, and maintain master data continuously. Instead of relying only on static rules, batch processes, and manual stewardship, agentic MDM uses governed AI agents that can detect issues, recommend actions, perform approved tasks, and learn from outcomes.

How is CluedIn different from traditional MDM?

Traditional MDM often depends heavily on static matching rules, batch processing, manual stewardship, and centralised data management processes. That model can be slow, expensive, and hard to scale as data estates become more complex.

CluedIn is graph-native and agentic. It uses a persistent knowledge graph to understand relationships, context, lineage, and business meaning, while AI agents help keep master data improving over time.

The simple version

CluedIn helps enterprises move from manually fixing data problems to continuously improving data with governed AI agents, graph context, and human oversight where it matters.

What are CluedIn AI agents?

CluedIn AI agents are governed data management agents that help perform work such as data classification, enrichment, deduplication, validation, rule suggestion, quality improvement, and issue detection. They are designed to work within enterprise controls, including permissions, auditability, explanations, workflows, and human-in-the-loop approval where required.

Does CluedIn replace data stewards?

No. CluedIn is not about removing human judgement from data governance. It is about reducing the repetitive manual work that stops data teams and stewards from focusing on higher-value decisions. Agents can handle routine tasks, surface exceptions, suggest fixes, and keep improving data quality, while people remain in control of policy, approvals, design, and business judgement.

How does CluedIn help make data AI-ready?

AI systems need trusted, governed, complete, contextual data. CluedIn helps prepare that foundation by resolving entities, improving data quality, enriching records, applying business rules, preserving lineage, and creating trusted data products. This gives AI, analytics, copilots, and automation a stronger data foundation to work from.

What does graph-native MDM mean?

Graph-native MDM means CluedIn models enterprise data as entities and relationships, not just isolated rows and tables. This matters because customers, products, suppliers, assets, policies, systems, and business rules are connected. A graph model gives both users and AI agents the context needed to understand relationships, trust levels, lineage, ownership, and downstream impact before action is taken.

What types of data can CluedIn help manage?

CluedIn is commonly used for critical enterprise data domains such as customer, product, supplier, asset, location, finance, operational, and reference data. It is especially useful when data is spread across multiple ERP, CRM, PIM, PLM, finance, analytics, or operational systems and needs to be trusted across the business.

How does CluedIn work with Microsoft Fabric?

CluedIn helps Microsoft Fabric customers bring trusted, governed, enriched, AI-ready data into their analytics and AI workflows. Fabric is powerful for data movement, engineering, analytics, and reporting. CluedIn complements it by improving the quality, context, and reliability of the master data that flows into those environments.

How does CluedIn work with Microsoft Purview?

CluedIn helps operationalise governance by connecting data quality, rules, deduplication, glossaries, vocabularies, streams, lineage, and data products with governance workflows. Used alongside Microsoft Purview, CluedIn helps make governance active and auditable, rather than leaving it as static documentation.

Is CluedIn only for Microsoft environments?

CluedIn is particularly strong for Microsoft-first enterprises, with alignment across Microsoft Fabric, Microsoft Purview, Azure AI Foundry, Azure OpenAI, Power Platform, Azure services, and Microsoft Marketplace routes. However, the core problem CluedIn solves is broader than Microsoft: enterprises need trusted, governed, mastered data across complex data estates.

How does CluedIn support data governance?

CluedIn supports governance through access control, rules, workflows, lineage, audit trails, glossaries, vocabularies, data products, explainability, and human-in-the-loop review. The goal is to make governance part of day-to-day data operations, so policies are not just documented but actively enforced and evidenced.

Can CluedIn help with entity resolution and golden records?

Yes. CluedIn helps identify, match, merge, and maintain trusted profiles for core business entities. This includes deduplication, matching logic, source trust, relationship context, rules, and golden records. The result is cleaner, more reliable data that can be used across analytics, operations, AI, and business processes.

How does CluedIn improve data quality?

CluedIn improves data quality by validating fields, detecting anomalies, standardising values, applying rules, tagging invalid records, identifying duplicates, enriching missing information, monitoring quality signals, and helping users resolve issues through governed workflows and AI-assisted actions.

Is CluedIn safe for production data environments?

CluedIn is designed for enterprise data environments where control, governance, explainability, and auditability matter. AI agents can operate with permissions, workflow controls, human approval, read-only modes, audit trails, explanations, and rollback-oriented operating patterns depending on the task, risk, and configuration.

What makes CluedIn different from data observability tools?

Data observability tools are useful for finding issues, but many still leave teams to fix those issues manually. CluedIn focuses on what happens next: governed remediation, enrichment, deduplication, validation, quality improvement, and operational action. In plain terms, CluedIn helps fix data, not just flag it.

Who uses CluedIn?

CluedIn is used by enterprise data teams, governance teams, data stewards, data architects, CIOs, CDOs, Microsoft data platform leaders, and business domain owners. It is a strong fit for organisations with complex data estates, multiple systems, governance requirements, and pressure to make data usable for AI, analytics, and operations.

Which industries is CluedIn suited to?

CluedIn is suited to industries where master data quality, governance, and AI readiness matter. This includes manufacturing, financial services, insurance, healthcare, energy, utilities, aviation, retail, media, gaming, construction, public sector, and telecommunications.

How has CluedIn helped customers in practice?

Komatsu uses CluedIn as part of a Microsoft data ecosystem to support trusted data for analytics and AI, with AI agents helping improve data quality, matching, enrichment, and governed data operations at enterprise scale.

SEGA has used CluedIn AI agents to improve product data quality, automate classification, enrich missing attributes, and reduce manual tagging across product catalog data.

Do business users need to write code to use CluedIn?

No. CluedIn is designed to help business users participate in data quality and stewardship without relying on engineering teams for every task. Users can define rules, review issues, approve changes, work through workflows, and use natural language interaction in supported areas to express what they need the platform to do.

Can CluedIn work with existing data platforms?

Yes. CluedIn is designed to sit within an enterprise data estate rather than force a full rip-and-replace. It can connect to existing systems, ingest data from multiple sources, apply data quality and mastering logic, and support downstream activation into analytics, AI, operational systems, and Microsoft data platform environments.

How quickly can organisations start seeing value from CluedIn?

The best path is usually incremental. Start with a high-value domain such as customer, product, supplier, or asset data. Baseline quality, apply rules, resolve duplicates, involve the right business owners, and expand once measurable improvement is visible. CluedIn is strongest when organisations focus on practical outcomes rather than trying to boil the ocean.

Is CluedIn available through Microsoft Marketplace?

Yes. CluedIn is available through Microsoft Marketplace routes, making it easier for Microsoft-aligned organisations to evaluate, procure, and deploy CluedIn as part of their broader Microsoft data strategy.

What is the best way to get started with CluedIn?

Start with a specific data domain and a clear business outcome. Good starting points include improving customer data quality, resolving product duplicates, preparing trusted data for Microsoft Fabric, operationalising governance with Purview, or reducing manual stewardship effort. From there, CluedIn can help you prove value, expand coverage, and move toward governed agentic data operations.