Types of Metadata in Data Governance: A Complete Guide

The challenge in modern data governance isn’t data volume, it’s visibility. Without structured metadata, organizations can’t track where data lives, how it moves, or who owns it. Descriptive, structural, and administrative metadata work together to solve that, transforming raw data into an auditable, compliant, and actionable asset. With OvalEdge, enterprises achieve that balance, simplifying discovery, enforcing compliance, and accelerating insight.

As organizations grow and collect more data from various sources, the complexity of managing it increases. Data is no longer confined to a single system or department. It spans across multiple platforms, systems, and even geographies. 

The increasing volume and variety of data make it more challenging to track, secure, and govern effectively, placing significant pressure on data governance frameworks.

According to the 2023 Gartner press release, 45% of organizations experienced third-party-related business interruptions during the past two years, highlighting the impact of inadequate data governance frameworks in managing third-party risks.

This underscores the growing challenge of ensuring data is protected across external systems and platforms, where governance is often less controlled.

Without a robust governance framework, businesses face numerous challenges. Data becomes fragmented, inconsistent, and difficult to access. Regulatory compliance can become a significant risk, and sensitive information might be exposed due to inadequate security measures. 

This lack of control over data can lead to inefficiency, costly errors, and even legal penalties. Furthermore, it can reduce trust within the organization, as teams struggle to find reliable, accurate data when they need it most.

Metadata is a crucial part of any data governance strategy, as it provides context and structure to the data itself. Metadata alone cannot solve governance challenges if it is not properly organized, categorized, and aligned with governance policies.

In this blog, we will explore how different types of metadata play a crucial role in data governance. 

What is metadata?

Metadata is crucial for effective data governance. It helps organize, manage, and ensure data accessibility while maintaining compliance. The three main types of metadata include descriptive, structural, and administrative, each serving distinct roles. 

Descriptive metadata aids in identifying and finding resources, structural metadata defines data relationships and organization, and administrative metadata governs data retention, access, and security. Proper use of metadata types streamlines governance processes, improves data quality, and ensures regulatory compliance, making metadata an essential element in data management strategies.

Types of metadata

Metadata can be categorized into several types, each with its own specific purpose. The three most commonly used types of metadata are descriptive, structural, and administrative metadata. 

Each type contributes in different ways to enhancing the usability, searchability, and management of data, especially in digital content and databases.

Descriptive metadata

Descriptive metadata is primarily concerned with providing detailed information about a resource to facilitate its discovery and identification. It allows both humans and machines to easily search for, locate, and retrieve data based on its key characteristics. 

This type of metadata is commonly used in cataloging systems, content libraries, and search engines to make resources more discoverable.

The goal of descriptive metadata is to make resources findable and understandable. It contains attributes like titles, authors, keywords, summaries, and abstracts, which help users determine whether a particular resource is relevant to their needs.

Key elements of descriptive metadata:

• Title: The name of the resource, which serves as the first point of interaction for users and search engines.

• Author: The creator or contributor to the resource, often used to establish credibility and context.

• Keywords: Relevant search terms or phrases that help users find the resource based on search queries.

• Description/Abstract: A summary that provides an overview of the resource’s content.

For example, a retail enterprise catalogs data assets from its sales, customer, and marketing databases as follows: 

• Asset Name: Customer_Orders_Fact

• Business Description: Contains daily transactional records of customer purchases, including order ID, customer ID, product ID, quantity, and sales amount.

• Owner: Sales Analytics Team

• Tags: revenue, transactions, retail data, order history

• Glossary Terms: “Customer,” “Order,” “Sales Amount” (linked to enterprise business glossary)

• Source System: Salesforce → Snowflake

This descriptive metadata enables analysts and business users to quickly understand what the dataset represents without reading through its schema.

This information improves data discoverability across departments, ensuring users searching for “customer sales” or “revenue analytics” can find this dataset immediately.

It also supports data literacy initiatives, helping non-technical stakeholders interpret business definitions consistently across the organization.

Structural metadata

While descriptive metadata helps in identifying resources, structural metadata provides information on how the data is organized, both internally and in relation to other data. This type of metadata is essential for understanding how data is structured and how its components interconnect.

Structural metadata defines the logical or physical structure of a resource, allowing users and systems to navigate, access, and coherently interpret data. For example, it can describe the chapters of a book, the layout of a database, or the sequence of pages in a document.

Key elements of structural metadata:

• Data Relationships: The way different pieces of data are related or linked together.

• Content Organization: The order in which elements or sections appear within a resource (e.g., chapters in a book, sections in an article).

• Sequence: The ordering of data, which is essential for proper navigation.

For example, a financial institution manages relationships between multiple databases in its data warehouse.

• Table Name: Account_Master
• Primary Key: Account_ID
• Foreign Key Relationships:
  • Transaction_History → Account_ID
  • Customer_Profile → Account_ID
• Schema Hierarchy: Finance_DB > Core_Banking_Schema > Account_Master

Column Sequence: Account_ID, Account_Type, Customer_ID, Balance, Created_Date

This structural metadata defines how different datasets are connected and how data flows across systems. 

This information powers data lineage visualization, showing how an “Account_ID” moves from raw ingestion in the core banking database to the reporting dashboard.

By maintaining accurate structural metadata, data engineers and governance teams can trace dependencies, assess impact before schema changes, and ensure smooth integration between upstream and downstream systems.

Administrative metadata

Administrative metadata is focused on managing the data throughout its lifecycle, ensuring compliance, security, and proper governance. This type of metadata is crucial for data retention, access control, and archival purposes.

Administrative metadata helps manage data and ensures that resources are properly handled from creation to eventual archiving or deletion. This metadata often includes information like access rights, file formats, and retention periods, and is particularly important for legal, regulatory, and organizational compliance.

Key elements of administrative metadata:

• Creation Date: When the data was created or the resource was produced.

• File Format: The format in which the data is stored (e.g., PDF, DOCX, JPG).

• Access Rights: Information about who can access or modify the data (e.g., public, restricted, internal).

• Retention Period: The duration for which the data should be retained before it’s archived or deleted.

• Version History: Records of changes made to the resource, including updates and revisions

For example, consider a company’s internal policy document:

• Creation Date: January 1, 2022

• Access Rights: Restricted to employees only

• File Format: PDF

• Retention Period: Archive after 5 years

This type of metadata is important for maintaining control over the data, ensuring that it is compliant with legal regulations (e.g., GDPR, HIPAA), and that it can be easily retrieved when needed. 

For example, in industries like healthcare or finance, administrative metadata ensures that sensitive data is kept secure and accessible only to authorized individuals.

When all three types of metadata are implemented effectively, they create a robust framework for managing data, driving efficiency, and improving content visibility across systems and search engines.

How to choose & apply metadata types

Choosing and applying the right metadata types is critical for ensuring that data is effectively organized, searchable, and usable in various contexts. 

Metadata is the backbone of data management and governance. When used correctly, it helps businesses and organizations streamline data retrieval, improve SEO rankings, ensure compliance, and maintain security. 

It’s essential to tailor the metadata used according to the nature of the data, the specific use case, and the overall governance objectives of your organization.

Step 1: Identify use-case & governance objectives

Before you dive into choosing which metadata types to implement, it’s crucial to clearly define why you need metadata in the first place. Different use cases and governance objectives will prioritize different types of metadata. Without understanding these objectives, it becomes challenging to determine which metadata types will be the most beneficial.

The objective of this first step is to ensure that the metadata serves its intended purpose, whether that’s to improve searchability, data security, regulatory compliance, or content management. Each of these use cases has distinct metadata requirements.

For example, a large insurance company is rolling out a data governance program to improve visibility into its data ecosystem. Before selecting which metadata types to focus on, the data governance team defines its core objectives based on organizational priorities.

Let’s understand this implementation with different use cases: 

Use case 1: Data discoverability and business enablement

Objective: Help analysts and business users quickly locate reliable data for reporting and analytics.

Metadata Priority: Descriptive Metadata

Example Application:

• The team enriches all critical data assets (claims, policies, customer records) with business glossary terms, owner details, and usage context.

• Descriptive metadata (title, description, tags, and linked glossary terms) allows business users to search for “policy renewal rate” and instantly find trusted datasets.

Analysts spend less time searching for data and more time generating insights, improving efficiency and trust in analytics.

Use Case 2: Compliance and risk management

Objective: Ensure data handling aligns with GDPR and HIPAA compliance requirements.

Metadata Priority: Administrative Metadata

Example Application:

• Compliance officers configure administrative metadata such as retention periods, data classification (PII, PHI), and access permissions.

• OvalEdge automatically flags datasets without assigned stewards or retention policies and triggers review workflows.

The organization gains visibility into where sensitive data resides and ensures consistent enforcement of data privacy rules.

Use Case 3: Data lineage and impact analysis

Objective: Understand how data moves across systems to reduce errors during schema changes or migrations.

Metadata Priority: Structural Metadata

Example Application: Structural metadata captures primary and foreign key dependencies, allowing lineage visualization from source to report.

When a source column changes, OvalEdge’s impact analysis identifies affected dashboards and ETL jobs, reducing downtime and avoiding costly reporting errors.

Use Case 4: Access control and data security

Objective: Protect sensitive customer data while enabling appropriate access.

Metadata Priority: Administrative + Descriptive Metadata

Example application:

• Access rights metadata defines which roles can view or modify datasets.

• Descriptive tags such as “confidential” or “restricted” are automatically applied to datasets containing PII or financial data.

Governance teams maintain strict access policies while providing transparency for auditors through OvalEdge’s metadata-driven controls.

By clearly identifying governance objectives before implementation, the organization ensures each metadata type serves a purpose:

• Descriptive → Improves discoverability and understanding

• Structural → Enables lineage and impact analysis

• Administrative → Ensures compliance and security

This clarity not only guides the configuration but also helps define KPIs for metadata success, such as search success rate, compliance coverage, and lineage completeness.

Step 2: Map metadata types to data assets

Once you've identified the use case, the next step is to assess your data assets and determine which metadata types should be applied. This step ensures that the metadata you implement is tailored to the nature of the data and its specific role in your organization.

The goal is to apply the right type of metadata to each data asset, ensuring that each data asset is discoverable, usable, and compliant with any governance regulations.

Considerations:

Structured data: If your data is structured (such as relational databases), structural metadata becomes paramount. It defines the organization and relationships between data elements, ensuring that data is organized in a way that makes sense for both humans and machines.

For example, for a financial institution using a database, structural metadata might include:

• Primary keys: Identifiers for records (e.g., Customer ID).

• Foreign keys: Links between related tables (e.g., linking customer information to transaction data).

Documents or media: If your data consists of documents or media files (like PDFs, Word files, images, or videos), descriptive and administrative metadata are typically more relevant. 

Descriptive metadata helps users quickly identify resources, while administrative metadata controls how long the data should be stored and who has access to it.
For example, for an image in a digital asset management system:

Descriptive metadata might include the title, creator, and description (e.g., "Sunset over the ocean," photographer name, location).

Administrative metadata would include access rights and retention period (e.g., "Only accessible by marketing team," "Retain for 5 years").

Web content: For websites or digital content, combining descriptive, structural, and administrative metadata is essential. Descriptive metadata helps optimize content for SEO, structural metadata organizes how content is displayed or navigated, and administrative metadata manages user access and content lifespan.

For example, a blog post on a website:

• Descriptive metadata: Title, author, and keywords.
• Structural metadata: Relationship between the post and other related content (e.g., linking to previous and next blog posts).
• Administrative metadata: When the post was published, when it was last updated, and who can edit it.

By mapping the appropriate metadata to each data asset, you help ensure that data is both manageable and compliant with organizational standards.

Step 3: Implement with examples and templates

After determining the right metadata types for each asset, the next step is implementation. One of the most effective ways to ensure consistency and accuracy in metadata usage is through the use of standardized templates.

Standardized templates help automate the metadata capture process, ensuring that metadata is consistently recorded for every asset. This reduces the chance of errors and saves time by eliminating the need for manual entry of basic metadata elements.

Templates for metadata Collection:

Descriptive metadata template:

• Title: [Enter Title]

• Author: [Enter Author]

• Keywords: [Enter Relevant Keywords]

• Summary/Description: [Enter Description]

Structural metadata template:

• Parent ID: [Enter Unique Identifier for Related Resources]

• Relationship Type: [Enter Type of Relationship (e.g., child, sibling)]

• Sequence: [Enter the Order of Elements (e.g., page order)]

Administrative metadata template:

• Owner: [Enter the Data Owner]

• Retention Period: [Enter the Retention Period (e.g., 5 years)]

• Access Rights: [Enter Access Control Details (e.g., restricted to HR department)]

By using predefined templates, you ensure that key metadata elements are consistently captured, improving the quality of your metadata and streamlining the process. Templates also reduce human error and ensure that crucial information is not overlooked.

Step 4: Measure & maintain metadata quality

The last step in choosing and applying metadata types is ensuring that the metadata remains relevant, accurate, and high-quality over time. Metadata quality should be regularly monitored to avoid issues such as outdated information, incorrect classifications, or inconsistent formatting.

Regular measurement and maintenance of metadata quality help ensure that data remains discoverable, usable, and compliant with regulations, as well as relevant for evolving business needs.

How to measure & maintain metadata quality:

• Completeness: Check if all required metadata fields are filled out. Incomplete metadata can hinder the discoverability and proper management of resources.

• Accuracy: Ensure that the metadata accurately reflects the content or data. Inaccurate metadata can lead to errors in data retrieval and decision-making.

• Relevance: As the data evolves, so should its metadata. Regularly update metadata to reflect changes in content or business processes.

Tools like OvalEdge’s Data Catalog can help automate metadata quality monitoring and provide valuable insights into inconsistencies or gaps in metadata records. With OvalEdge, you can easily connect to every data source, capture active and extended metadata, and enrich it with context and collaboration. 

It not only automates tasks like lineage generation, PII detection, and auto-classification, but it also provides tailored, role-specific views, ensuring that metadata remains both relevant and actionable across your organization. These tools can track changes, enhance compliance, and ensure governance policies are met, streamlining your data management efforts.

The process of choosing and applying metadata types requires a structured, thoughtful approach to ensure that metadata serves its intended purpose. 

By taking the time to align metadata practices with your organizational needs, you ensure that metadata continues to provide value over time, making data management smoother and more effective. 

Whether you are optimizing a website for search engines, ensuring regulatory compliance, or improving internal data management, metadata is a crucial tool for success.

Challenges for metadata management

Organizations often face significant challenges when managing metadata, which can undermine its effectiveness and hinder the benefits it provides. 

These challenges, if not addressed properly, can lead to inefficiencies, errors, security risks, and even regulatory non-compliance.

Lack of standardization

One of the most persistent challenges in metadata management is the lack of standardization across systems, departments, or platforms. In many organizations, different teams may develop their own metadata schemas without coordinating with others. 

This inconsistency can create significant hurdles when trying to integrate data across multiple sources, especially when those sources are spread across different departments, systems, or geographic regions. Inconsistent metadata formats not only complicate data retrieval but also impede meaningful data analysis and decision-making.

Standardizing metadata formats and naming conventions across an organization is crucial to resolving this issue. Developing a centralized metadata management framework, aligned with industry standards, ensures consistency in how metadata is structured and stored.

Implementing data catalogs or metadata registries is another useful practice, as these tools can help maintain a single source of truth for metadata standards and streamline integration efforts.

For example, in a large multinational organization, different departments might use different formats to label customer data. The marketing department may use “customer_name,” while sales might use “client_name.” 

When this data is combined for reporting or analysis, mismatched metadata makes it difficult to merge the data effectively. By implementing a standardized format like “full_name” across departments, the organization ensures that customer data from different teams can be aggregated seamlessly, improving accuracy and reducing integration issues.

Data quality

Poor-quality metadata is another major challenge that can degrade the value of data management systems. This includes metadata that is incomplete, outdated, or inaccurate. When metadata is incorrect or missing key elements (such as author names, timestamps, or access rights), it can lead to confusion, misinterpretation, and even errors in data analysis.

Inaccurate metadata also increases the risk of compliance violations, especially in industries that require strict adherence to regulatory standards.

To maintain high-quality metadata, organizations must implement robust data validation processes and automated tools that ensure metadata is accurate, up-to-date, and comprehensive. 

Regular audits and automated checks can identify gaps or inaccuracies in metadata, and metadata management tools can help maintain consistency across systems. Additionally, defining clear metadata ownership within the organization ensures that there are designated individuals or teams responsible for keeping metadata up to date and accurate.

Data integration

Integrating metadata from various sources (such as databases, cloud platforms, and third-party services) is often a complex and resource-intensive task. One of the key hurdles in this process is dealing with mismatched identifiers, taxonomies, or incompatible data formats. 

When metadata is stored in different systems with different standards or structures, it becomes difficult to create a unified view of data. This is especially problematic when organizations attempt to merge data for cross-platform reporting or analytics.

The solution to this challenge lies in establishing a data integration strategy that includes metadata mapping and data transformation processes. Organizations should standardize the way metadata is handled across different platforms and ensure that metadata can be easily transformed into compatible formats for integration. 

Metadata-driven integration platforms can facilitate this process by automatically mapping and transforming metadata between different systems, reducing manual intervention and errors.

Data governance

Effective metadata governance is essential for ensuring that data is handled appropriately throughout its lifecycle. Without clear policies, roles, and responsibilities in place, metadata can quickly become fragmented or neglected, leading to security risks and compliance issues. 

Data governance failures can result in unauthorized access to sensitive data, a lack of accountability, and difficulties in enforcing retention and archiving policies.

Organizations must establish a comprehensive data governance framework that clearly defines the roles, responsibilities, and policies for metadata management. This framework should outline processes for managing metadata access, retention, and security, and ensure that there is accountability for maintaining metadata accuracy and compliance. 

Regular audits and monitoring tools can help enforce governance policies and ensure that metadata remains consistent and secure.

By addressing these challenges through proper planning, robust tools, and effective strategies, organizations can ensure that their metadata management efforts are effective, secure, and compliant with regulations.

Conclusion

Data governance is complex, but it doesn’t have to be overwhelming. As organizations continue to manage vast amounts of data spread across multiple platforms, the challenges of staying organized and compliant grow exponentially. 

It’s no longer just about storing data. It’s about knowing what data you have, where it resides, who can access it, and how it should be used. With the growing regulatory landscape, such as GDPR, HIPAA, and CCPA, the stakes have never been higher.

Without a clear and structured approach to data management, companies risk running into major problems. 

• How do you ensure that the right people have access to the right data? 
• How can you be confident that your data meets regulatory standards without spending countless hours on audits?

According to 2024 Gartner Insights, by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks.

This highlights the significant cost of poor governance because businesses are not only wasting resources but also missing out on the transformative potential of their data. 

Without effective metadata practices, businesses often find themselves drowning in disorganized data that can’t be trusted. Data quality suffers, compliance becomes a moving target, and inefficient processes create bottlenecks. 

Without proper metadata:

• How do you know which datasets are secure? 

• How do you track data lineage? 

• How can you ensure data is used ethically and responsibly? 

These are all critical pain points that organizations face without good metadata management.

• What if you could automatically track every change made to a dataset? 

• What if you could easily categorize and prioritize your data for compliance and security? 

Proper metadata management answers these questions and puts companies in control.

Ready to take control of your data governance journey?

With OvalEdge, you can ensure your data is trusted, discoverable, and AI-ready. From seamless deployment to comprehensive metadata management, our platform is designed to grow with your business needs.

Book a demo today and see how OvalEdge can simplify your data governance, enhance compliance, and accelerate decision-making.

FAQs

1. What are the three main types of metadata?

The three core types of metadata are:

• Descriptive Metadata: This type of metadata helps to identify and discover resources, typically used for cataloging and searching. Examples include titles, keywords, authors, and descriptions.
• Structural Metadata: Defines the organization and relationships between data elements. For example, it can describe the structure of a database or the relationship between chapters in a book.
• Administrative Metadata: Manages data throughout its lifecycle, covering aspects like access rights, retention periods, and file formats. This type of metadata is crucial for governance, compliance, and data security.

2. Why is metadata important?

Metadata is vital for organizing and managing data. It provides context, making it easier to find, access, and interpret data. Without metadata, finding relevant data or content would be much more difficult, especially in large, complex data environments.

3. What is an example of descriptive metadata?

An example of descriptive metadata for a web page could include:

• Title: "How to Optimize Your Website for SEO"

• Author: "John Doe"

• Keywords: "SEO, website optimization, search engine visibility"

• Description: "This article explains effective SEO strategies to improve your website’s ranking and visibility on search engines."

This metadata helps search engines index the content correctly and improves its visibility to relevant users.

4. How can organizations improve metadata quality?

Organizations can improve metadata quality by implementing standardized templates, automating metadata capture, and conducting regular audits. Additionally, ensuring that metadata is consistent, accurate, and up to date will enhance its effectiveness. 

5. Is metadata part of data governance?

Yes, metadata plays a critical role in data governance. It is essential for managing data access, ensuring compliance with legal and regulatory standards, tracking data lineage, and enforcing retention policies. Effective metadata management supports data stewardship by making data more secure, traceable, and manageable throughout its lifecycle.

6. How does metadata help with data security?

Metadata is crucial for enforcing data security protocols. It can define access rights, specify user roles, and ensure that only authorized individuals can access sensitive data. Administrative metadata can also ensure that sensitive data is retained or deleted according to regulatory requirements, helping prevent unauthorized access and ensuring compliance with data privacy laws like GDPR and HIPAA.