What Is Metadata Sharing & How To Implement It in 2026?

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by Emily Winks, Data governance expert, Atlan.Last Updated on: January 06th, 2026 | 16 min read

Quick answer: What is metadata sharing?

Metadata sharing is the exchange of descriptive information about data assets between systems, teams, and organizations. It enables stakeholders to discover, understand, and trust data across different platforms. Organizations implement metadata sharing to break down silos and establish common understanding.

Key characteristics of effective metadata sharing:

  • Bidirectional flow: Metadata updates sync automatically between connected systems.
  • Open standards: FAIR principles and common schemas enable interoperability.
  • Real-time sync: Changes propagate immediately across platforms and tools.
  • Context preservation: Business meaning travels with technical specifications.
  • Automated governance: Policies and tags cascade through lineage connections.

Below: how metadata sharing works, why organizations need it, implementation approaches, Gartner’s perspective on interoperability, how modern platforms enable sharing.

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How does metadata sharing work?

To share metadata, connect disparate tools and platforms in your data ecosystem through common protocols and standards. This facilitates metadata sharing across systems, making it available to the right users and driving data discovery, understanding, trust, and use.

Let’s see how to enable an ecosystem that supports metadata sharing across systems.

What are the core components of metadata sharing systems?


The core components of metadata sharing systems are:

  1. Metadata repository: Centralized storage that captures technical, business, and operational metadata from connected sources. Modern repositories use open formats like Apache Iceberg to ensure metadata remains accessible rather than locked in proprietary systems.
  2. Integration layer: APIs and connectors that enable bidirectional metadata flow between the repository and source systems. Deep integrations with data warehouses, transformation tools, and BI platforms create continuous feedback loops.
  3. Standards and schemas: Common vocabularies and formats that ensure different systems can interpret shared metadata consistently. Organizations leverage standards like Dublin Core, W3C DCAT, and domain-specific ontologies.

What are the three primary metadata sharing mechanisms?


  1. API-based exchange: REST APIs enable programmatic metadata transfer between systems with authentication and version control.
  2. Event-driven sync: Real-time event streams propagate metadata changes immediately when assets are created, modified, or deleted.
  3. Batch export/import: Scheduled bulk transfers for systems requiring periodic synchronization rather than continuous updates.

Modern platforms combine all three mechanisms to match different system capabilities and organizational requirements.

Why does metadata sharing matter for modern data teams?

The objective of good data management is to drive information discovery, understanding, innovation, and decision-making based on trustworthy data. Organizations lose productivity and trust in data when metadata remains siloed in disconnected systems.

According to research published in Scientific Data, data delivers long-term value only when it can be accurately found, reused, and cited over time by all stakeholders, both human and machine. That requirement becomes critical as data platforms increasingly power automation, analytics, and AI.

Humans intuitively understand semantics (the meaning and intent behind data), but machines don’t. Without shared metadata, systems lack the contextual signals needed to interpret, govern, and safely use data.

“A machine may be capable of determining the data-type of a discovered digital object, but can’t parse it due to it being in an unknown format; or it may be capable of processing the contained data, but can’t determine the licensing requirements related to the retrieval and/or use of that data.” - Why FAIR principles matter for better data management and stewardship

The FAIR principles (Findable, Accessible, Interoperable, and Reusable) address this gap by ensuring sufficient context for both people and machines to discover, understand, and reuse data assets effectively. Metadata sharing is foundational to making FAIR principles actionable at scale.

Without shared metadata, data remains locked in silos, undermining governance, analytics, and AI initiatives and ultimately impacting business outcomes.

What is the business impact of metadata silos?


Without shared metadata, teams face predictable challenges:

  • Repeated work: Data engineers rebuild lineage documentation that already exists in transformation tools.
  • Inconsistent definitions: Marketing and finance use different calculations for the same revenue metric.
  • Delayed decisions: Analysts spend 80% of time finding and understanding data instead of generating insights.
  • Compliance risk: Privacy classifications documented in one system don’t propagate to downstream consumers.
  • Broken AI and automation workflows: Machines lack shared semantic context. AI models train on poorly classified or misunderstood data, leading to biased outputs, failed governance checks, and non-explainable decisions.

How does metadata sharing deliver value?


Organizations that implement effective metadata sharing achieve measurable outcomes:

  • Faster discovery, reuse and time to insight: Shared context makes data easy to find, understand, and confidently reuse across teams. The time-to-insight is also faster as teams can locate trusted data and align on consistent definitions.
  • Improved collaboration: When metadata flows between systems, business users in BI tools see the same ownership and quality information that data engineers maintain in data catalogs, eliminating context gaps.
  • Scalable governance and reduced compliance burden: Classifications, policies, and access rules propagate automatically across tools and assets. Tide, a UK digital bank, reduced GDPR compliance work from 50 days to 5 hours by automating PII tagging and propagation.
  • AI and automation readiness: Shared metadata gives machines consistent context. AI, agents, and automated workflows can understand data meaning, trace lineage, and enforce policies, enabling trustworthy, explainable automation at scale.

What approaches can you use to implement metadata sharing?

Organizations choose metadata sharing strategies based on their technical architecture, governance maturity, and business requirements. The three most common approaches are as follows.

1. Centralized metadata hub approach


A single platform aggregates metadata from all connected systems and serves as the authoritative source.

Key characteristics:

Best for: Organizations seeking unified governance and comprehensive visibility across complex data estates.

Trade-offs: Requires initial investment in hub platform and integration development. Success depends on achieving bidirectional sync rather than one-way ingestion.

2. Federated metadata mesh


Multiple domain-specific metadata systems maintain their own metadata and share selectively through peer-to-peer connections.

Key characteristics:

  • Domain teams control metadata for their data products.
  • Shared schemas and standards enable cross-domain discovery.
  • Metadata flows directly between relevant systems without central aggregation.
  • Each domain publishes metadata contracts describing their data products.

Best for: Organizations with decentralized data mesh architectures where domains require autonomy.

Trade-offs: Requires strong governance standards and mature domain teams. Complexity increases with the number of domains and connections.

3. Hybrid orchestration model


Combines central governance with federated execution, balancing control and autonomy.

Key characteristics:

Best for: Enterprises balancing compliance requirements with domain agility.

Trade-offs: Most complex to implement but offers greatest flexibility for large organizations.

What are the technical standards necessary for metadata sharing?

Effective metadata sharing depends on common standards that different systems can interpret consistently. Research published by NIAID emphasizes that standardized formats and schemas make it clear which metadata components are present and enable interoperability.

FAIR principles as foundation


The FAIR principles provide the conceptual framework for shareable metadata:

  • Findable: Metadata must be assigned globally unique identifiers and registered in searchable resources. Systems can discover relevant metadata through standardized query mechanisms.
  • Accessible: Metadata must be retrievable using open, standardized protocols even when underlying data has restricted access. Authentication and authorization procedures protect sensitive content while enabling metadata discovery.
  • Interoperable: Metadata must use formal, shared vocabularies and include qualified references to related metadata. This enables systems to understand relationships and combine metadata from multiple sources.
  • Reusable: Metadata must provide rich descriptions with clear usage licenses and detailed provenance. Well-described metadata can be replicated or combined across different contexts.

Common metadata standards and schemas


Standard

Purpose

Use cases

Dublin Core

General-purpose descriptive metadata

Cross-domain resource description

W3C DCAT

Data catalog vocabulary

Publishing dataset information

ISO 19115

Geographic information metadata

Geospatial data documentation

OpenLineage

Data lineage standardization

Tracking data transformations

Apache Atlas

Enterprise metadata model

Technical metadata in big data ecosystems

Organizations often combine multiple standards to address different metadata needs across their ecosystem.

APIs and protocols for metadata exchange


Modern metadata sharing relies on RESTful APIs with JSON or GraphQL payloads. Key technical requirements include:

  1. Authentication and authorization: OAuth 2.0 or API keys control access to metadata resources.
  2. Versioning: Semantic versioning tracks metadata schema changes over time.
  3. Event notifications: Webhooks or message queues propagate metadata changes in real-time.
  4. Batch operations: Bulk import/export endpoints handle large-scale metadata transfers.
  5. Query capabilities: Filtering, sorting, and pagination enable efficient metadata discovery.

Atlan’s open API architecture exemplifies these principles, enabling programmatic metadata access and automation.

Gartner perspective on metadata sharing: Metadata interoperability is mandatory

Gartner’s research highlights that metadata sharing and interoperability have evolved from nice-to-have features to fundamental requirements for modern data platforms.

Gartner highlights a market shift toward openness


According to Gartner’s Market Guide for Metadata Management Solutions, there is significant push toward open APIs and interoperability standards to overcome isolated, proprietary metadata repositories.

Organizations seek solutions that can share metadata omnidirectionally—exporting their own and importing “foreign” metadata—to enable orchestration between platforms.

Key finding: “There is a lack of common metadata standards, which makes metadata sharing and interoperability a major challenge across multiple metadata management solutions in the market.”

Implication: Vendors who promote open standards (such as OpenMetadata and OpenLineage) and provide open metadata exchange across different applications and environments become major differentiators in the metadata market.

Metadata orchestration and “metadata anywhere”


Gartner describes metadata orchestration as implementing an “anywhere” approach where metadata flows effortlessly across an organization’s entire data ecosystem rather than remaining trapped in standalone catalogs.

Critical capabilities identified:

  • Bidirectional and embedded metadata delivery within existing workflows.
  • Real-time data monitoring through continuous metadata collection and analysis.
  • Automation and augmentation using AI and machine learning.
  • Open APIs and knowledge graphs for comprehensive metadata repositories.

Organizations implementing these capabilities transform metadata from static documentation into dynamic intelligence that drives automated governance and quality monitoring.

Evolution from passive to active metadata


Gartner distinguishes between traditional passive metadata management and modern active metadata approaches:

Passive metadata: Documents data assets through manual curation. Users must visit separate catalog systems to find information. Metadata quickly becomes stale without continuous maintenance.

Active metadata: Continuously analyzes metadata to determine alignment between designed systems and actual operational experience. Generates alerts and recommendations that trigger automated workflows. Metadata flows bidirectionally between connected tools rather than sitting in isolated repositories.

The shift to active metadata enables the real-time sharing and synchronization that modern data operations require.

How does Atlan enable metadata sharing at scale?

Modern data platforms require metadata to flow seamlessly between every tool in the stack. Atlan’s architecture treats metadata sharing as a core capability rather than an afterthought.

Bidirectional metadata sync with source systems


Atlan implements bidirectional metadata synchronization with data warehouses, BI platforms, and transformation tools. When users update descriptions or classifications in Atlan, those changes propagate back to source systems automatically.

How it works:

  • Native integrations with Snowflake, Databricks, and other platforms enable two-way sync.
  • Tag management keeps policies aligned between Atlan and Unity Catalog or Snowflake.
  • Business context enriched in Atlan becomes available in data warehouses.
  • Technical metadata from source systems updates continuously in Atlan.

This eliminates the traditional problem where catalog metadata becomes disconnected from operational systems.

Metadata lakehouse for open interoperability


Atlan’s Metadata Lakehouse stores all metadata types in an open, queryable format using Apache Iceberg. This architecture ensures metadata remains accessible to any system or application that needs it.

Key advantages:

  • Technical, business, operational, and social metadata unified in one repository.
  • SQL access enables teams to analyze metadata coverage, ownership gaps, and usage patterns.
  • Open format prevents vendor lock-in and enables future extensibility.
  • Native versioning tracks metadata changes over time for compliance and auditing.

Organizations can connect their own analytics tools directly to the metadata lakehouse to build custom reporting and automation.

Embedded collaboration for metadata sharing


Rather than requiring users to context-switch into a separate catalog, Atlan surfaces metadata directly in the tools teams already use.

Integration points:

  • Browser extensions bring Atlan’s companion bar into BI tools and data warehouses.
  • Slack and Microsoft Teams integrations enable metadata discovery and updates without leaving communication platforms.
  • GitHub and GitLab integrations show lineage impact analysis in pull requests.
  • Jira connections allow teams to create data quality issues directly from Atlan.

This embedded approach ensures metadata context travels with users throughout their workflows.

Real customer outcomes from metadata sharing


Organizations using Atlan’s metadata sharing capabilities achieve measurable results:

Mastercard unified context across 100M+ assets and thousands of metadata systems. Data scientists previously spending 80% of time finding and understanding data now spend only 20% on data wrangling, achieving 75% reduction in time spent on non-value activities.

Nasdaq leverages Atlan’s active metadata capabilities to embed data context directly into business intelligence tools and collaboration platforms. By making metadata flow to where work happens, they’ve accelerated data democratization and governance adoption across their global organization.

Real stories from real customers: Metadata sharing in practice

From manual compliance to automated privacy: How Tide achieved GDPR readiness

“Tide, a UK digital bank serving nearly 500,000 small business customers, needed to strengthen GDPR compliance as they scaled rapidly. Their original process for identifying and tagging personally identifiable information would have required 50 days of manual effort—half a day per schema across 100 schemas—carrying high risk of human error and inconsistency. After implementing Atlan, Tide's data and legal teams collaborated to define personally identifiable information standards and documented them in Atlan as their source of truth. Using Atlan's Playbooks feature, they automated the identification, tagging, and classification of personal data across their entire data estate. What would have taken 50 days of manual work was accomplished in just 5 hours. The team now maintains continuous compliance monitoring and can respond to data subject requests with confidence. We said: Okay, our source of truth for personal data is Atlan. We were blessed by Legal. Everyone, from now on, can start to understand personal data.”

Michal Szymanski, Data Governance Manager

Tide

🎧 Listen to podcast: How Tide achieved GDPR readiness

How Nasdaq Uses Active Metadata to Embed Context Into Daily Workflows

“Nasdaq leverages Atlan’s active metadata capabilities to embed data context directly into business intelligence tools and collaboration platforms. By making metadata flow to where work happens, rather than requiring users to visit a separate catalog, they’ve accelerated data democratization and governance adoption across their global organization. Active metadata allows us to push context into every tool our teams use, from Tableau to Slack. That embedded collaboration drives adoption in ways a standalone catalog never could.”

Data Platform Team

Nasdaq

🎧 Listen to podcast: How Nasdaq cut data discovery time by one-third with Atlan

Ready to enable metadata sharing for your enterprise?

Metadata sharing has evolved from optional feature to operational necessity as organizations scale their data operations and prepare for AI adoption. The ability to exchange metadata bidirectionally between systems determines whether governance accelerates or impedes business outcomes.

Organizations succeed with metadata sharing when they embrace open standards, implement bidirectional sync, and embed metadata into daily workflows rather than isolating it in separate catalogs. The shift from passive documentation to active metadata orchestration enables automation at scale.

Modern platforms like Atlan demonstrate that metadata sharing requires more than APIs and connectors. True interoperability demands unified metadata repositories, continuous synchronization, and embedded collaboration that meets users where they work.

Atlan’s bidirectional metadata sync and open architecture enable metadata sharing across your entire data ecosystem. Teams using these approaches report dramatic reductions in time spent finding and understanding data, faster compliance cycles, and improved trust in data-driven decisions.

Book a Personalized Demo →

FAQs about metadata sharing

1. What is the difference between metadata sharing and data sharing?


Data sharing involves transferring actual datasets between systems or organizations.

Metadata sharing exchanges only the descriptive information about those datasets including schemas, ownership, quality metrics, and business definitions. Organizations can share metadata to enable discovery and understanding even when the underlying data has restricted access.

2. How do FAIR principles relate to metadata sharing?


The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide the framework for effective metadata sharing. They emphasize that metadata must use standardized formats and vocabularies to enable different systems to exchange and interpret information consistently.

FAIR compliance ensures metadata can travel between systems while preserving meaning.

3. What is bidirectional metadata sync and why does it matter?


Bidirectional metadata sync means changes flow both into and out of connected systems automatically.

When a user updates a description in one tool, that change propagates to all connected platforms. This prevents metadata from becoming stale in source systems and eliminates manual synchronization work.

4. Can metadata sharing work with legacy systems?


Yes, though integration depth varies. Modern platforms can extract metadata from legacy databases and tools through JDBC connections and APIs. However, bidirectional features may be limited for older systems.

Organizations typically adopt hybrid approaches using active metadata for modern stacks while maintaining separate processes for legacy environments.

5. How does metadata sharing support AI and machine learning initiatives?


AI models require rich context to generate accurate recommendations and predictions. Metadata sharing ensures semantic information, quality signals, and business definitions flow to where AI systems consume data. This enables AI to understand what data means, assess its reliability, and generate contextually appropriate outputs.

6. What are common standards for metadata exchange?


Organizations commonly use Dublin Core for general metadata, W3C DCAT for data catalogs, OpenLineage for data lineage, and domain-specific standards like ISO 19115 for geospatial data. APIs typically use REST with JSON payloads, OAuth 2.0 for authentication, and event-driven architectures for real-time propagation.

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