What is Metadata?

What is Metadata?

Metadata provides information about data. 

It’s a bit abstract, but we see this in action in our everyday lives. For example, every item in a grocery store has an expiration date, manufacturer information, and a nutrition label that provides details on calories, ingredients, and portion sizes. Each of these pieces provides context for the food item, enabling you to make informed decisions based on that information, such as allergies, expiration dates, and dietary requirements. Similarly, metadata provides context, enabling us to better understand data that may not be otherwise apparent.

Why is Metadata Important?

It provides information that helps consumers find, use, and understand the quality and origin of the data. Metadata also demonstrates how data is related to provide context for users.

In today’s data-driven business landscape, organizations face rapidly increasing data volumes from various sources. Valuable insights often stay hidden, underused, or misinterpreted. Teams spend countless hours searching for the correct datasets, doubting data quality, or duplicating analyses already available within the organization. Metadata changes raw data into strategic assets, enabling better decision-making across the enterprise.

Types of Metadata

The most common forms of metadata can be classified into the following categories:

• Business Metadata provides business context and meaning to data in terms non-technical users can understand. It includes business definitions, glossary terms, business rules, data ownership, and descriptions of the business purpose behind data collection. It enables self-service analytics by making data discoverable and understandable
• Technical Metadata includes technical specifications and properties of data, including database schemas, data types, file formats, data structures, field lengths, indexes, data relationships, and system information about where and how data is stored. It provides the foundation for data integration, quality assessment, and system compatibility.
• Operational Metadata has information about how data systems operate day-to-day, including job schedules, processing logs, ETL run times, error logs, system performance metrics, data freshness, when data was loaded or updated, and who accessed what data when. It enables proactive problem detection and helps prioritize which issues to fix first based on business impact.
• Governance Metadata includes information about data policies, standards, compliance requirements, access permissions, data stewardship roles, retention policies, privacy classifications, and regulatory alignment (GDPR, HIPAA, etc.). It’s critical for avoiding compliance violations, managing risk, and maintaining customer trust.
• Quality Metadata has information about data accuracy, completeness, consistency, timeliness, validity, and reliability. Includes quality scores, data profiling results, anomaly detection, and quality rule definitions. It helps users assess whether data is trustworthy enough for their intended use, and prevents bad decisions based on poor-quality data by flagging issues before they cause problems.
• Administrative Metadata includes management information about data assets, including ownership details, creation and modification dates, version history, file formats, usage rights, preservation plans, and lifecycle status (active, archived, retired). It supports data asset management and governance by tracking who owns what, when it was created, and how it should be managed over time, and ensures proper data lifecycle management and supports audit trails for compliance.

How it’s Used

Metadata fulfills multiple functions. Cataloging the data is important as it informs users of its quality, completeness, provenance, and authoritativeness. Images can have associated metadata, including digital signatures, creation dates, geographic locations, size, and color depth. Exchangeable Image File (EXIF) data is a standards-based metadata embedded within the image file.

A data lakehouse captures metadata that increases the value of datasets by documenting data quality and the relationships between data assets. A data lakehouse provides metadata. Database management systems maintain it in system catalogs that record the number of records in a table, the cardinality of the data fields, high-water marks and low-water marks, the selectivity of indexes, and the clustering of data to indexes.

Modern web-based applications use application programming interfaces (APIs) to access third-party tools and pass data using metadata-rich datatypes such as JSON and XML. Traditional applications passed data between them, but it was not self-describing, and you could not interrogate them to learn what data they were expecting, as you can with a modern web service. As applications become more modular for easier app development, their numbers will increase, necessitating better self-documentation of their functions and data requirements. 

Applications

Metadata is used in every industry, some examples include:

• In life sciences, metadata is essential for ensuring research integrity and regulatory compliance throughout the drug development lifecycle. It’s used to track clinical trial databases, lab equipment information, and output, enabling interoperability and replicability. Operational metadata can be used to monitor dates for lab sample processing, the batch of regents used,  and other variables critical for validating experimental results. At every stage, metadata facilitates the research and development process.
• In manufacturing, metadata orchestrates the complex relationships between production systems, supply chains, and quality control across global facilities. Technical and operational metadata capture machine run times and maintenance schedules to optimize production and predict equipment failures. Governance and quality metadata ensure product traceability from raw materials through finished goods.
• In financial services, metadata provides the foundation for risk management, regulatory compliance, and customer trust. Technical and operational metadata document transaction lineages and processing times, ensuring auditors can validate financial reporting while business users access near real-time information for critical decisions. Governance and quality metadata enforce access controls, maintain audit trails for regulations, and validate that account balances reconcile while flagging suspicious transactions.
• In transportation and logistics, metadata enables the real-time visibility and coordination that modern supply chains demand. Technical and operational metadata capture shipment status and driver hours-of-service logs, providing dispatchers the insights needed to reroute deliveries around disruptions. Governance and quality metadata help enforce DOT regulations and customs requirements while monitoring delivery accuracy.
• In energy and utilities, metadata supports reliable service delivery while navigating the transition to renewable sources and smart grid technologies. Technical and operational metadata integrate data from smart meters, SCADA systems, and weather forecasts to help grid operators balance supply and demand in real-time. Governance and quality metadata ensure compliance with NERC reliability standards and EPA emissions reporting while validating meter readings and detecting anomalies.

Benefits of Metadata

The need for metadata is growing primarily due to the following benefits:

• It increases the usefulness of existing data sources.
• It makes data useful by documenting its quality and utility.
• It includes labels that enable data to be found using search engines.
• It promotes data governance by documenting the presence or absence of data owners.
• As data volumes and sources grow, it becomes increasingly valuable.
• The use of data is a best practice of data management that benefits the data owner and business partners who share data. Data sharing success is dependent on good metadata. Data that is not well-documented is likely to be unused or untrusted.
• It is a foundational pillar of advanced data models such as data warehouses, data lakes, and data mesh.
• It supports the records discovery process for compliance audits.
• The visibility gained by using it to document an organization’s data assets is the first step in streamlining data use, so duplicate data can be reviewed, merged, or removed.

Metadata & AI

Metadata is essential for artificial intelligence (AI) and machine learning (ML) systems because it provides the critical context, structure, and quality indicators that enable AI systems to process, interpret, and generate trustworthy results. Without high-quality metadata, AI models are prone to inaccuracies, biases, and a lack of explainability.

Metadata is important for:

• Context and Meaning: Raw data is meaningless to an AI without descriptive information (metadata) about its source, creation data, format, and relationships. This context is vital for AI to understand the data’s purpose and make accurate interpretations.
• Data Quality and Accuracy: Metadata is key to managing data quality by helping identify anomalies, errors, and missing values in the training data. Models trained on data with robust metadata achieve higher accuracy and reliability in real-world applications.
• Discoverability and Efficiency: Comprehensive metadata makes large datasets easier to find, organize, and retrieve for specific AI projects. This streamlines the data preparation and feature selection processes, which often consume a large portion of AI development time. 
• Compute Resourcing: Metadata helps teams identify redundant or low-impact data, allowing models to focus on the most relevant information. This leads to more efficient use of computational resources.

Actian Data Intelligence

Effective metadata management has become essential for organizations seeking to maximize data value while ensuring governance and compliance. Actian Data Intelligence Platform delivers comprehensive capabilities that transform how enterprises discover, trust, and utilize their data. 

Accelerating Discovery and Building Trust

The platform’s federated knowledge graph architecture creates a unified source of truth for enterprise metadata, enabling Google-like search capabilities across all data assets. With over 75 automated connectors, organizations can harvest metadata from diverse sources without manual intervention.

Supporting AI and Compliance

As AI initiatives accelerate, the platform’s knowledge graph enables AI systems to discover and access data across organizational boundaries while maintaining governance and control. Rich semantic context fuels generative AI with high-quality business definitions, while smart lineage supports AI model governance by providing visibility into data sources and transformations. 

Ready to transform your data assets into your competitive advantage? Please take a look at our product tour or schedule a demo to learn more.