Metadata management is the process of handling each type of metadata captured by a data platform during its lifecycle. This “data about data” comes in various forms, formats, and serves a range of purposes. One key distinction lies in its origin. Some of the most valuable metadata is created by dedicated personnel, such as data stewards or metadata curators. Often critical for governance and compliance, this type of metadata requires a structured management framework to ensure it remains accurate and up-to-date.
Other types of metadata are automatically generated by tools and processes within the data platform. This operational metadata can be vast. An effective metadata management framework must focus on efficient search, classification, and archival to handle this high volume of information.
This article explores various metadata management strategies for different types of metadata, helping you navigate this complex landscape and maximize the value of your data.
Metadata Management Framework
While metadata provides invaluable context and insights, it can quickly become outdated and irrelevant if not managed effectively. Organizations operating data platforms over many years often accumulate vast amounts of metadata. As platforms evolve and business processes change, some datasets become obsolete, rendering associated metadata unusable. Imagine users trying to access documentation for a dataset that no longer exists – it’s a waste of time and a source of frustration.
To avoid such scenarios, a robust metadata management framework is essential. This framework should provide tailored strategies for handling different types of metadata, considering their criticality and how quickly they become outdated.
A comprehensive metadata management framework addresses each stage of the metadata lifecycle:
- Metadata Collection: This involves capturing metadata, either through automated tools or manual entry and curation by designated personnel.
- Metadata Storage: Metadata needs a dedicated storage solution, such as a data catalog for dataset documentation or a log management platform for operational logs.
- Metadata Maintenance: Keeping metadata up-to-date is crucial. This includes regular reviews for manually curated metadata and archival or deletion strategies for automatically generated metadata like log files.
Categories of Metadata
Metadata can be broadly categorized based on the stage of the data platform’s lifecycle where it’s generated and used. Metadata created in later stages often describes earlier stages and typically won’t exist until those earlier stages are complete.
Here’s a breakdown of the key metadata categories:
- Technical Metadata: This describes the core structure of datasets, including table schemas, data transformation rules, and data quality rules. While this information can reside within source code, many teams opt for dedicated tools to centralize and manage it effectively.
- Operational Metadata: Automatically generated by data processing code, this category primarily comprises log files. These logs provide a detailed history of executed processes, error messages, and status entries like data quality validation results.
- Governance and Compliance Metadata: This category focuses on information critical for data governance and regulatory compliance. It’s often manually curated by dedicated personnel, even when initially captured automatically (e.g., identifying columns containing sensitive data like phone numbers).
- Runtime Metadata: Generated as a result of running the data platform, this category includes information like data lineage, temporary files created during processing, and even metadata related to data consumers, such as configurations of machine learning models.
Each of these categories requires careful lifecycle management. Every data platform typically contains some form of metadata within each category, highlighting the need for comprehensive strategies to ensure metadata remains valuable and relevant. The following infographic summarizes various types of metadata and their metadata management strategies.
Technical Metadata Management
Technical metadata defines the structure and rules of your data, ensuring consistency and quality. Managing it effectively is crucial for a reliable data platform.
Table Schema Metadata
Table schemas act as blueprints for database tables, outlining columns, data types, constraints, and relationships. For example, a “customer” schema defines columns like “customer_id,” “first_name,” and “email,” along with their data types and any constraints.
- Capture: Automatically extracted by the database during table creation/modification and often stored as DDL scripts.
- Storage: Primarily resides within the database itself but can be replicated in data catalogs for centralized access.
- Maintenance: Automatically updated when table structures change and deleted when tables are dropped. Data catalogs should be kept synchronized.
Data Transformation Rules Metadata
These rules define the logic for how data is extracted, transformed, and loaded in data pipelines. An example is converting “date_of_birth” to “age” and categorizing it into age groups.
- Capture: Defined by data engineers in pipeline code or ETL tools.
- Storage: Embedded within pipeline code or managed in ETL platforms and version control systems.
- Maintenance: Updated by engineers as data requirements or business rules change. Version control is essential for tracking modifications.
Data Quality Rules Metadata
Data quality rules ensure data meets defined standards. For example, a rule might require a valid email format or prevent future dates in an “order_date” field.
- Capture: Defined by data stewards or automatically generated by rule mining engines.
- Storage: Formalized in data quality platforms or as data contracts, often integrated with data catalogs.
- Maintenance: Continuously reviewed and updated by data stewards. Automated monitoring helps identify violations and trigger updates.
Operational Metadata Management
Operational metadata provides a detailed record of events and activities within your data platform. This information is essential for troubleshooting, performance monitoring, and ensuring smooth operations.
Error Logs
Error logs capture information about system failures, data processing errors, and anomalies. For example, they might record a database connection failure or a data type mismatch in an ETL job.
- Capture: Automatically generated by system components, databases, and data pipelines when errors occur.
- Storage: Stored in flat files or dedicated log management platforms with features like filtering and alerting.
- Maintenance: Managed using data retention policies to balance debugging needs with storage costs.
Data Processing Logs
Data processing logs provide a record of all data processing activities, capturing details like timestamps, job durations, and data volumes processed. For example, a log might track the start and end times of an ETL job and the number of records processed.
- Capture: Generated during ETL jobs, data ingestion, and custom data processing scripts.
- Storage: Stored in flat files, database tables, or centralized log management platforms.
- Maintenance: Strategically retained for performance monitoring and troubleshooting. Older logs may be deleted to manage storage.
Data Quality Results
Data quality results capture metrics and scores related to data validity, accuracy, completeness, and consistency. For example, they might include the percentage of valid email addresses or the number of duplicate records.
- Capture: Generated by automated data quality monitoring tools or custom validation logic.
- Storage: Stored in dedicated data quality monitoring databases or custom tables.
- Maintenance: Regularly reviewed by data stewards to identify issues and track improvements. Often preserved as an audit trail.
Governance and Compliance Metadata Management
This type of metadata focuses on information critical for data governance and regulatory compliance. It ensures data is handled responsibly, ethically, and in accordance with legal and organizational policies.
Data Sensitivity Metadata
Data sensitivity metadata classifies data according to its confidentiality level (e.g., public, confidential, restricted). It helps organizations protect sensitive information and comply with data privacy regulations. For instance, it might label data containing personally identifiable information (PII) as “restricted” to ensure proper access controls and handling.
- Capture: Can be manually assigned by data stewards or automatically detected using tools that identify sensitive data like PII.
- Storage: Typically managed within governance and compliance databases, often integrated with data catalogs to provide context and control access.
- Maintenance: Regularly reviewed and updated by data governance and compliance teams to align with evolving regulations and business policies.
Data Access and Usage Metadata
This metadata tracks how data is accessed, including who accessed it, when, and for what purpose. This information is crucial for auditing, security monitoring, and demonstrating compliance with data usage policies. For example, it might log when a user queries a specific customer database, what data they accessed, and the time of access.
- Capture: Captured by database systems, applications, and access control mechanisms that track user activity and data interactions.
- Storage: Stored in database audit logs or centralized logging platforms that aggregate access information from various sources.
- Maintenance: Analyzed by security teams and data stewards to detect anomalies, enforce access controls, and demonstrate compliance.
Community Comments Metadata
Community comments provide a way for data users to contribute annotations, explanations, and context to data assets. This collaborative knowledge sharing improves data understanding and findability. For example, a data analyst might add a comment to a dataset explaining a specific data anomaly or providing context on how the data was collected.
- Capture: Manually entered into data catalogs by data users, data stewards, and other stakeholders.
- Storage: Managed within data catalog systems that support version control and user contribution tracking.
- Maintenance: Data community members and designated curators review and validate comments to ensure quality and accuracy.
Runtime Metadata Management
Runtime metadata is generated as a result of running the data platform. It captures information about data lineage, temporary files, and data consumption patterns, providing valuable insights into data flows and usage.
Data Lineage Metadata
Data lineage tracks the origin, transformations, and movement of data throughout its lifecycle. It maps the complete journey of data, from its source to its final destination, including all intermediate steps and transformations. For example, lineage might show that a customer’s address was initially sourced from a CRM system, then cleansed and enriched with geographic data before being loaded into a data warehouse.
- Capture: Automatically generated during data transformation processes or extracted by analyzing data processing scripts and queries.
- Storage: Often stored in dedicated data lineage platforms that use graph databases to represent the complex relationships between data assets.
- Maintenance: While often automatically generated, lineage information requires periodic validation and updates to ensure accuracy and completeness. Outdated mappings should be removed, and new data sources and transformations should be incorporated.
Temporary Files
Temporary files are intermediate data files created during data processing. They are often used to store intermediate results, facilitate data transfers, or optimize pipeline execution. For example, a temporary file might hold a subset of data extracted from a database before being transformed and loaded into a data warehouse.
- Capture: Created by custom data processing code or ETL tools to support specific processing steps.
- Storage: Typically stored in local file systems, cloud storage buckets, or temporary database tables.
- Maintenance: Requires a well-defined cleanup process to prevent the accumulation of obsolete temporary files, which can consume storage space and impact system performance.
ML Models Metadata
Metadata related to machine learning (ML) models captures information about model configurations, training data, and performance metrics. This metadata is essential for model reproducibility, governance, and monitoring. For example, it might include hyperparameter settings, training data sources, model accuracy scores, and deployment details.
- Capture: Generated during model development, training, and deployment processes.
- Storage: Stored in specialized model registries or ML model governance platforms that provide versioning, access control, and lineage tracking for models.
- Maintenance: Data scientists and ML engineers are responsible for managing model versions and ensuring metadata accuracy. Automated publishing pipelines (CI/CD) can streamline the process of deploying models and updating associated metadata.
Enterprise Metadata Management
Managing metadata at an enterprise level requires a strategic approach that goes beyond individual projects or departments. It involves establishing governance standards and selecting appropriate metadata repositories to ensure consistency, accessibility, and compliance across the organization.
Many organizations, particularly those in highly regulated industries like healthcare and finance, are legally obligated to adhere to strict data governance requirements. Even non-regulated organizations may face audits or legal scrutiny, making robust metadata management essential.
A key aspect of enterprise metadata management is selecting preferred enterprise-level repositories for each type of metadata. These repositories should be used consistently across all departments to ensure standardization and avoid data silos.
Here are some common enterprise-level metadata repositories:
- Source Code Repositories: Tools like Git are ideal for storing technical metadata, including table schemas, transformation rules, and data quality rules. These repositories offer security, versioning, and collaboration features, allowing for controlled changes and seamless integration with CI/CD pipelines for automated deployments.
- Enterprise Log Management Platforms: Platforms like Logstash are essential for aggregating and managing operational metadata, such as error logs and data processing logs. These platforms can be configured to enforce data retention policies, segregate logs based on criticality, and ensure long-term storage and backup for audit and compliance purposes.
- Data Quality Platforms: Dedicated data quality platforms provide a centralized repository for storing data quality rules, validation results, and derived KPI metrics. These platforms enable organizations to monitor data health, track improvements, and demonstrate compliance with data quality standards.
- Specialized Tools: For other metadata types, specialized tools may be necessary. This includes enterprise-grade data lineage solutions for tracking data flows and model registries for managing machine learning models. These tools provide advanced capabilities for visualizing, analyzing, and governing specific types of metadata.
By adopting a standardized approach to enterprise metadata management, organizations can ensure data quality, facilitate data discovery, streamline compliance efforts, and unlock the full value of their data assets.
Data quality best practices - a step-by-step guide to improve data quality
- Learn the best practices in starting and scaling data quality
- Learn how to find and manage data quality issues
Data Quality Metadata Management
Data quality metadata plays a crucial role in assessing and ensuring the fitness of data for its intended use. By capturing information about data quality rules, validation results, and overall data health, organizations can make informed decisions, build trust in their data assets, and drive continuous improvement.
The journey of data quality metadata typically begins with defining data quality rules. These rules, derived from stakeholder requirements and expectations, are essentially tests that verify data against desired standards. They are often stored within dedicated data quality platforms (e.g., DQOps) as a list of validation checks, allowing for easy maintenance and updates.
Once these rules are applied to data, the data quality platform generates detailed audit entries capturing the results of each validation check. These entries typically include information such as the table and column tested, the timestamp of the validation, and the health status (e.g., pass/fail, number of violations). This granular information provides a comprehensive view of data quality at a specific point in time.
Finally, summarized data quality health results are often pushed to data catalogs, providing users with an easily accessible overview of data quality. This typically includes a health status for each table and column, allowing users to quickly assess the trustworthiness and reliability of data assets during data discovery.
What is the DQOps Data Quality Operations Center
DQOps is a data observability platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and managing the data quality incident workflow.
DQOps is a platform that combines the functionality of a data quality platform to perform the data quality assessment of data assets. It is also a complete data observability platform that can monitor data and measure data quality metrics at table level to measure its health scores with data quality KPIs.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time. This is a great resource to learn about data quality.
