What is Operational Metadata?

For a thorough understanding of data, we must know about metadata and various types of metadata. Metadata is structured data that tells about other data. It is like an organized and structured book index that tells us about the book’s content.

Operational metadata is one crucial type of metadata that contains information regarding the system’s operational data. Let’s explore the details of operational metadata.

What is Operational Metadata? 

Metadata that tells about the actual data in the system, including items such as when and who created, modified, or updated the data; how the data was cleaned, aggregated, or filtered; and how the data quality is, is called Operational data. Operational metadata is used by the operational staff of an organization’s information system to get end-to-end data visibility for operational purposes.

Operational Metadata plays a crucial role in data management by providing context and additional information on the data being processed, stored, transformed, or filtered. It captures the whole data journey, its processes, and interactions and provides information such as:

• When data was generated or modified?
• Who accessed the data and when?
• What changes were made?
• How is data traversed to different systems?
• Execution logs
• Error messages

Examples of Operational Metadata

Operational Metadata refers to the data that includes operations and operational reports along with management information. Some examples of Operational Metadata are as follows:

1. System Configuration: Operational metadata tracks all system configuration-related information. It stores information regarding system management, such as system setting instructions and various hardware, software, and network components.
2. Audit Trails: Operational metadata tracks records of user activities, such as database files accessed, read, modified, or deleted. Details are traced to a granular level, like how many rows are read, written, or referenced. It also tracks user identities.
3. Access Logs: Provide information about access permissions to users related to a database or data table, such as which users got read-only permissions or read and write permissions. Some users are denied all permissions for security reasons.
4. Performance Metrics: All data related to system performance, such as system usage, resource utilization, response time, timestamp, transaction counts, and operational reports, is stored.
5. Error Messages: When a job run fails or if there are warnings, error messages are generated, which are tracked by operational metadata along with troubleshooting information.
6. Scheduling Information: Operational metadata traces how tasks are scheduled automatically, including frequency, execution time of various tasks, and any task dependencies.
7. Execution logs: These logs provide information about various processes running within a system, such as start and end times, execution status (success or failure), and any error messages encountered.

Why is Operational Metadata Crucial for Effective Data Management? 

Operational metadata plays a key role in data management by providing information about data sources, transformations, and execution. It supports data quality, optimization, compliance, and governance. It can help businesses to grow with effective data management in the following ways:

• Data Quality Improvement: Operational Metadata helps improve data quality through validation, cleansing, transformation, and filtering, resulting in data accuracy and consistency.
• Efficiency Improvement: Operational metadata can help organizations improve the efficiency of their processes by reducing errors, automating all repetitive tasks, and implementing process optimization.
• New Potential Opportunities: Operational metadata can help organizations identify patterns, trends, and results that drive future potential opportunities. 
• Monitor and Optimize Performance: Operational metadata can provide insights and visuals into data usage patterns, which can help with performance monitoring and optimization. 
• Ensure Data Compliance and Governance: Operational metadata can be used to create audit trails and foster a culture of accountability. 

Components of Operational Metadata

Operational metadata contains various components containing information on data processes and lineage, which are essential for accurately and integrityly understanding the entire data lifecycle. The details of components for operational metadata are:

1. Data Processing Information

This provides metadata about data processing information, like how data jobs are executed, such as start and end times, job status, and any errors. It’s necessary for troubleshooting and monitoring the performance of the system.

2. Data Lineage

Operational metadata gives a complete perspective of data lineage, demonstrating the complete flow of data from source to destination. It also saves a complete track of data processing, transformation, and migration. This aids in understanding the analysis of data, the impact of changes in data, and various dependencies.

3. Performance Metrics

Operational metadata includes different performance metrics like throughput and latency. These indicators are critical for evaluating the effectiveness of data processes and systems.

4. Resource Utilization Information

Operational Metadata stores information on the use of resources such as CPU, memory, and I/O operations during data processing is stored. This improves capacity planning and optimization.

How to Collect Operational Metadata?

The most interesting part is learning the procedure for collecting, leveraging, and automating operational metadata. Follow the below steps to collect operational metadata. 

1. Evaluate your Current Metadata

The first step is to evaluate current data sources, processes, systems, and operational metadata. Identify the operational metadata types and any gaps, inconsistencies, and mistakes.

2. Define Metadata Requirements

In the next step, define the operational metadata requirements in detail by engaging all stakeholders in the most relevant and valuable manner to your organization. This could include information about data lineage, quality, structure, transformations, etc.

3. Choose the Right Tools and Technologies

Many tools and technologies, such as AWS Data Catalog and Apache Atlas, are available to create operational metadata. You can choose a tool that aligns with your organization’s requirements and supports metadata collection, storage, automation, and integration with your existing data systems.

4. Develop a Strategy

It is now time to develop a strategy for collecting operational metadata from various data sources, ETL processes, and data pipelines. Ensure that the operational metadata collected is correct and without errors or missing components.

5. Implement Automation

Data systems need to be configured to automatically generate, store, and update metadata from data workflows. Tools with built-in automation features can also automate the operational metadata creation and storage process.

6. Integration

Operational metadata is integrated with data catalog and data lineage tools to provide users with a complete picture of their data assets. This improves discoverability, traceability, and general understanding of the data landscape.

7. Establish Metadata Governance Policies

Lastly, establish governance policies and practices to ensure your operational metadata’s quality, consistency, and accuracy. This could contain recommendations for metadata naming conventions, data quality checks, and lineage documentation. 

Following the above steps, data engineers can easily collect and store operational metadata from various data sources, pipelines, and ETL processes. After collecting operational metadata, they should keep monitoring the operational metadata management process and adjust the metadata as per requirement.

Use Cases of Operational Metadata

Below are the real-world use cases demonstrating the impact of operational metadata on business success.

• Operational metadata is essential in ETL processes, auditing, monitoring, and data pipelines to derive insights for business growth and opportunities.
• Operational metadata is also essential in capturing run-time information, which includes usage statistics, last load, and log reports. This information is used to evaluate system performance and prepare in advance for any errors or problems.
• Operational metadata is also collected and stored for data quality checks and validation, which is necessary to help users assess the reliability and quality of the data, which in turn helps them make more informed decisions.

Challenges in Operational Metadata Management

• Data Ownership and Stewardship: In big organizations, there are different kinds of data and many operational metadata to maintain, with many teams dealing with operational metadata. It is necessary to set KPIs for data ownership and supervision that clarify the responsibilities of every team in an organization and also track accountability.
• Documentation Quality: Document quality directly impacts data usability in an organization. Poor quality or missing documentation leads to inefficient and confused processes. Implementing KPIs to increase documentation coverage is critical for rapid data asset discovery and utilization.
• Data Governance: Secured data and proper access conditionals are needed for effective data management. Data security is a major challenge in this era of big data. Various data governance and compliance policies are implemented to protect sensitive data while allowing for collaboration and usage of data assets.
• Data Tiering: Identifying the most valuable and least valuable assets in an organization is important. For this, a data tiering system is implemented. The most valuable data is identified as Tier 1 data, and the organization focuses on improving its data quality and descriptions. Low-tier data is reviewed periodically for potential decluttering.

Feedback loops are used for continuous improvements to mitigate challenges. Weekly or periodic feedback reports provide insights into system progress and help recognize the team’s contribution to operational metadata management. They also ensure that all KPIs and governance policies are implemented and followed properly by every team of the organization.

Best Practices for Managing Operational Metadata

Operational metadata management is an essential component of modern data ecosystems. To ensure metadata is correct, accessible, and actionable, below best practices need to be followed:

• Centralize Metadata: The best practice is to create a centralized store for operational metadata to ensure a single source of truth. This facilitates access and administration across several systems and teams.
• Automate Ingestion: Automated tools and workflows ingest operational metadata from several sources. This reduces manual errors and automatically keeps metadata up to date.
• Define Standards: Defining metadata standards explicitly, such as naming conventions, formats, and taxonomies, is a practice. This improves the consistency and interoperability of metadata across teams and its usage.
• Implement Access Controls: In every organization, role-based access controls are implemented for data and metadata. These controls limit who can view, modify, or delete operational metadata. Practicing access control for operational metadata ensures data security and compliance.
• Monitor and Audit: Once the operational metadata is generated, it is critical to monitor it regularly for changes and anomalies. The best practice is implementing automated auditing to track access and modifications, which is crucial for security, governance, and compliance.
• Collaboration and Training: Encourage collaboration among data stakeholders by utilizing shared metadata platforms to improve understanding of data assets. Users are also provided with training to understand operation metadata and its effective usage.
• Leverage Metadata for Insights: It is the best practice among data engineers to leverage operational metadata to provide insights into data lineage, quality, and consumption trends. This can help you handle your data more effectively and make better organizational growth and smooth functioning decisions.

Conclusion

Understanding operational metadata, its components, and challenges will make it easy to create, collect, store, and use operational metadata in data management. Every system faces challenges, so it is necessary to implement KPIs to mitigate them. By following the best practices for operational metadata, business value can be derived from data assets with quality, integrity, consistency, and governance.

Frequently Asked Questions

1. What is the difference between technical metadata and operational metadata?

Operational metadata is the metadata of data related to operational purposes. It is the metadata of the actual data of the stem like who and when create the data or modify it. Technical metadata contains information about the data model means the details of the structure of the databases and data types used in it. 

2. What is the purpose of operational metadata?

Operational Metadata is important for a successful ETL process. It informs about the reliability of data and about the data lineage which specifies where and how information was created, where it flows, and how it is modified as it moves from system to system inside the organization.

3. What is the difference between business and operational metadata?

Business metadata describes the context, rules, valid formats, source system names, and definitions of a business’s data. While operational metadata provides details about various operations done with data, and how and when data was created, filtered, or transformed.

4. How is operational metadata generated?

Many tools are used to collect generated metadata like Data Catalog, Apache Atlas, and various Metadata management platforms.