Understanding Different Types of Metadata

Metadata provides essential context, meaning, and structure to raw data. Just as a library catalog helps readers understand the details of a book (like its title, author, and publisher), a data catalog uses metadata to provide the background information that allows users to understand, manage, and utilize data effectively.

In this article, we will explore why metadata is important, the different metadata categories, and how metadata is used.

What is Metadata?

Metadata is essentially data about data. It provides information about other data assets, helping describe, manage, and organize them. While the primary data holds the actual content (like a document, image, or dataset), metadata gives context and meaning to that content, making it easier to search, manage, and understand.

Why is Metadata Important?

Metadata serves as a guide to understanding, navigating, and managing data, adding significant value to data-driven processes. Here are some of the key reasons why metadata is important:

• Organization: It helps keep data organized and easy to find, especially in large datasets or repositories.
• Data Management: Metadata enables efficient management of data, such as tracking file versions, ensuring proper access, and facilitating backups.
• Searchability: Metadata enhances the ability to search for and retrieve data quickly based on keywords, dates, or other descriptive criteria.
• Interoperability: Metadata ensures that data can be understood and used correctly across different systems and platforms, improving data sharing.

5 Types of Metadata

Metadata can be categorized into several types, each serving different purposes. Let’s take a look at the main metadata types.

1. Structural Metadata

This type of metadata describes the organization and structure of data, including relationships between different components of data or how data is organized in a system. It’s often used to track data lineage. Examples include:

• Information about how a document is divided into chapters or sections.
• Descriptions of data formats (such as tables, columns, and rows in a database).
• Metadata about a digital file’s parts (e.g., a multimedia file might have metadata for audio and video streams).

Structural metadata helps with navigating and understanding how data is organized or how different parts of a resource are interrelated.

2. Descriptive Metadata

This type describes the content and attributes of the data, making it easier to discover and identify. Examples include:

• The title, author, keywords, abstract, and subject for a document.
• The tags, descriptions, and captions for images, videos, and other media.

This type of metadata helps users find and understand the content of a dataset, file, or resource through search and discovery processes.

3. Administrative Metadata

This type of metadata describes the actions taken to preserve the data and ensure its long-term accessibility, integrity, and authenticity. Examples include:

• Information about backup processes, format migrations, and preservation actions.
• Integrity checks like hash values or checksums.

This type of metadata helps maintain the longevity and reliability of digital resources over time, ensuring that they remain accessible and usable in the future.

4. Legal Metadata

Legal metadata provides information on the management of resources, including technical details, permissions, and rights. Examples include:

• File creation date, file format, and file size.
• Access control (who can view, edit, or distribute the data).
• Information related to rights management, such as licensing or copyright information.

This type of metadata facilitates the management, preservation, and access control of data or resources.

5. Definitional Metadata

Definitional metadata refers to metadata that provides essential information about the meaning, context, or definition of the data itself, helping users understand what the data represents and how to interpret it. It’s a specific type of metadata that focuses on the conceptual and semantic aspects of the data, often used to ensure consistency and clarity when sharing or analyzing data. Examples include:

• The field definition in a database.
• The column definition in a spreadsheet.
• The units of measurement for data fields.

This metadata type ensures that everyone interprets data in the same way, especially in large datasets or across different systems.

Examples of Metadata

Let’s look at some of the common examples of metadata for images, websites, and documents.

• For an image: Resolution, file size, format (JPEG, PNG), creation date, location (GPS coordinates if available), and camera model.
• For a website: Title, description, keywords, author, last modified date, and HTML tags.
• For a document: Title, author, file type, date created, and word count.

How is Metadata Used?

Metadata is used to provide information about data, making it easier to find, organize, and understand. It essentially describes the structure, characteristics, and context of a piece of data, which can be crucial for managing and interpreting large datasets. Here are some common ways metadata is used.

Data Organization

Metadata such as file size, creation date, author, and format help categorize files efficiently. In databases, metadata describes the structure of tables, columns, relationships, and constraints, helping software or users understand how to query the data and what information is available.

Data Management and Governance

Metadata can track the origin and transformations of data as it moves through different systems. This is important for ensuring data accuracy and reliability, especially in complex workflows.

Metadata also helps manage access permissions and ensures that sensitive data is protected. For example, metadata can indicate who has permission to access, modify, or share certain pieces of data.

Search, Discovery, and Retrieval

Metadata allows for more accurate and efficient searches. For example, the tags and descriptions on an image or video file provide context that enables more precise search results. In websites or digital libraries, metadata like keywords, author information, and categories help users find relevant content more easily. For example, when you search for a document on your computer, the operating system uses metadata to locate files based on attributes like file type or last modified date.

Organizing metadata in a knowledge graph can help improve searchability by providing context and links between related data.

Data Interchange and Standards

In systems that share data, metadata ensures that data can be correctly interpreted across platforms. For instance, in the case of a CSV file, metadata might specify the delimiter used or the format of dates, allowing different systems to properly import and process the data.

Metadata can be used to enforce standards in data collection, such as specifying the units of measurement, language, or other relevant attributes, ensuring consistency in datasets.

Content and Media Management

For images, audio, video, and other digital content, metadata includes details like resolution, camera settings, file type, author, copyright, and much more. This helps in organizing, editing, and even automating processes, such as automatic tagging or categorization.

Metadata in music files (like MP3s) includes information like the song title, artist, album, genre, and track number, which helps users organize and search their media libraries.

Improving User Experience

On platforms like social media or e-commerce sites, metadata can be used to recommend content or products based on user preferences and behavior. For instance, metadata about a user’s past activity might be used to suggest similar articles or products.

Metadata can be used to adjust the display of content, such as showing the release date for movies or showing a document’s last updated date.

Use the Actian Data Intelligence Platform to Manage Metadata

If an organization needs help managing and leveraging metadata for better data discovery, look no further than the Actian Data Intelligence Platform. The platform provides an active metadata management solution to power data and analytics use cases.

Our platform collects, inventories, and shares metadata throughout the organization and across all of its data sources. It leverages intelligent search to map complex relationships between data assets, users, processes, and other metadata elements. Intelligent search includes natural language processing and semantic analysis for more relevant search results.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.