What is Metadata Management?
“By 2021, organizations will spend twice as much effort in managing metadata compared with 2018 to assess the value and risks associated with the data and its use.”
*Gartner, The State of Metadata Management
The Definition of Metadata Management
Metadata provides context to your data. To trust your data’s context, you must understand it. Knowing the who, what, when, where, and why of your data means knowing your metadata, otherwise known as metadata management.
With the arrival of Big Data and the various accompanying regulations, data leaders must look further into their data through metadata. Metadata is created whenever data is:
- Created
- Added to
- Deleted from
- Updated
- Acquired.
For example, metadata in an Excel spreadsheet includes the date of creation, the name, the associated authors, the file size, etc. In addition, metadata could also include titles and comments made in the document.
In the past, a form of metadata management would be to look up a book’s call number in a catalog to find its location in a library. Today, metadata management is used in software solutions to comply with data regulations, set up data governance, as well as understand the data’s value. Thus, this discipline becomes essential for enterprises.
Types of Metadata
Metadata can be categorized in several ways depending on its purpose and how it is used. Understanding these metadata types helps organizations manage data more effectively and apply the right controls, tools, and standards.
Descriptive Metadata
Descriptive metadata provides information that helps identify and discover data. It includes titles, descriptions, keywords, authors, and tags, making data easier to search, understand, and reuse.
Structural Metadata
Structural metadata explains how data is organized and how different components relate to one another. Examples include schemas, table relationships, file formats, and hierarchies that define how pieces of data fit together.
Administrative Metadata
Administrative metadata supports data management and operations. It often includes:
- Rights metadata (ownership, licensing, access permissions)
- Lifecycle metadata (creation dates, retention rules, archival status)
Technical Metadata
Technical metadata describes the technical characteristics of data, such as data types, field lengths, indexes, system configurations, and storage locations. It is essential for developers, engineers, and system integrations.
Operational Metadata
Operational metadata captures information generated during data processing and usage. This includes job logs, processing times, error rates, and data refresh schedules, helping teams monitor performance and reliability.
Business Metadata
Business metadata adds business context to data. It includes business definitions, KPIs, calculations, and usage guidelines that help non-technical users interpret and apply data correctly.
Lineage and Provenance Metadata
This type of metadata tracks where data comes from, how it changes, and where it is used. Lineage and provenance metadata improve transparency, trust, and impact analysis when changes occur.
Security and Privacy Metadata
Security-related metadata identifies sensitivity levels, classifications (e.g., confidential, public), and privacy attributes such as personal or regulated data. It supports compliance, risk management, and access control.
Together, these metadata types provide a complete picture of data—what it is, how it is structured, how it is used, and how it should be governed.
Why Should You Implement a Metadata Management Strategy
Metadata management gives your organization clarity and control over its data ecosystem. By consistently defining, cataloging, and governing metadata, teams can quickly understand what data exists, where it comes from, and how it should be used. This shared understanding reduces confusion, speeds up analytics and reporting, and improves trust in data-driven decisions. When users can easily discover and interpret data assets, productivity increases and the risk of misusing or misinterpreting data drops significantly.
Beyond efficiency, metadata management strengthens data governance, compliance, and scalability. Clear lineage, ownership, and quality indicators make it easier to meet regulatory requirements, protect sensitive information, and enforce data standards. As data volumes and sources grow, well-managed metadata acts as a foundation that supports integration, automation, and advanced analytics initiatives. In short, implementing metadata management turns data from a fragmented resource into a strategic asset that can reliably support long-term business goals.
Benefits of Metadata Management:
- A better understanding of the meaning of enterprise’s data assets.
- More communication on a data’s semantics via a data catalog.
- Data leaders are more efficient, leading to faster project delivery.
- The use of data dictionaries and business glossaries allow the identification of synergies and the verification of coherent information.
- Reinforcement of data documentation (deletions, archives, quality, etc.).
- Generate audit and information tracks (risk and security for compliance).
Metadata Management Best Practices
Below, we’ve compiled a short list of best practices to implement when creating your organization’s metadata management strategy.
Establish Clear Standards and Taxonomies
Define consistent naming conventions, data definitions, and classification schemes. Standardized taxonomies reduce ambiguity, improve interoperability, and make metadata easier to understand and reuse across teams and systems.
Assign Ownership and Governance
Designate metadata owners and stewards responsible for accuracy, updates, and approvals. A governance framework helps resolve conflicts, enforce standards, and ensure accountability over time.
Automate Metadata Capture
Leverage tools and integrations to automatically collect technical and operational metadata. Automation minimizes manual effort, reduces errors, and keeps metadata synchronized with source systems.
Ensure Quality Through Validation and Audits
Apply validation rules to check completeness, accuracy, and consistency. Conduct regular audits to identify gaps, outdated entries, or noncompliance with standards.
Improve Discoverability and Lineage
Enable robust search, tagging, and lineage tracking so users can easily find data and understand its origins, transformations, and usage. This builds trust and supports better decision-making.
Document Policies and Train Teams
Maintain clear documentation of metadata policies, processes, and roles. Provide ongoing training so teams understand how to create, maintain, and use metadata effectively as systems evolve.
Manage Your Metadata With a Metadata Management Platform
With the Actian Data Intelligence Platform, transform your metadata into exploitable knowledge! Our metadata management platform automatically curates and updates your information from your storage systems. It becomes a unique, up-to-date source of knowledge for any data explorer in the enterprise.
Ready to see how the platform can transform your business’ metadata management practices? Get a personalized demonstration today.
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