How can you make data lineage and metadata management practices scalable?

You first need to have a clear understanding of where the lineage related metadata or other sorts of metadata are coming from. Defining the purpose of the data lineage is the next essential task: - business lineage and/ or technical lineage - what is necessary versus the extra or 'nice-to-have' elements for your specific use. Lastly, it's important to establish a strong metadata architecture. While data and information architecture often receive attention, metadata architecture - from an infrastructure and information perspective - is equally critical. Simplifying and achieving fitness for purpose within this metadata abundance determines the scalability of your data lineage and metadata management practices.

Technology has certainly come to the party in recent years, and has simplified, centralised, standardised, controlled and enhanced the way we manage data lineage and metadata management today. Leveraging technologies like @Alation that offer automated metadata discovery, centralised cataloguing, and integration with various data sources certainly takes the pain out of 'manual efforts'. Not to mention the over-lay of 'workflow' and 'stewardship' that truly enables the Data Governance function and subsequently allows for scalability.

To achieve truly successful automation, you need to create two lineages: 1. Logical: first, you must record data feeds, transformations and definitions as you believe are correct. The analysis could apply to current and future applications or systems (note that this is usually what most companies and professionals call "lineage"). 2. Physical: Then, you need to find a way to extract the "actual" lineage from the code base without any human intervention or opinion. Few companies have this capability, but it is arguably far more critical than the logical one. Having these two frameworks allows you to discover if there are differences between what you think that data is and what the data actually is, enabling you to find and fix any gaps.

1. Automation reduces the manual effort required and ensures consistency in metadata management. 2. Define common data dictionaries, naming conventions, and classifications that are consistently applied to all data assets. 3. Choose a scalable metadata management system that can handle the growing volume of metadata entries and provide efficient search and retrieval capabilities 4. Leverage scalable infrastructure components, such as serverless computing, to dynamically scale resources based on demand. 5. Use metadata tags to categorize and organize metadata entries for efficient retrieval. 6. Implement interactive lineage diagrams that allow users to explore and navigate through the metadata graph

1. Why might manual data lineage and metadata documentation be problematic? Manual data lineage and metadata documentation can be prone to errors, inconsistencies, and delays due to human factors and the challenges of keeping pace with changes in data sources, pipelines, and applications. 2. What is the suggested solution to overcome issues with manual documentation? To address these challenges, leveraging tools and techniques that can automatically scan, extract, and map data lineage and metadata across the data ecosystem is recommended. This approach helps save time, resources, and ensures up-to-date and accurate documentation.

To scale data lineage and metadata management practices, we can integrate them with data catalog and governance tools. By establishing a unified metadata repository, consolidating data source details and transformations. Connecting this repository with a comprehensive data catalog provides a centralized view of metadata. Integrating data lineage tools within the catalog allows for seamless tracing of data origin and transformations, enhancing scalability. Utilizing metadata tagging and classification ensures efficient data discovery, making metadata more accessible. Regular updates and expansion of the integrated system accommodates new data sources and governance requirements, maintaining scalability in a changing data landscape.

We can scale by monitoring and having rules established on sensitive , regulatory and business critical data’s rather going with all data sets ( larger size).

A lot of times it is often considered that mapping data lineage would suffice for effective data lineage metadata practices. But what is truly the requirement is to enable traceability of data ownership, traceability of data changes and traceability of data lineage. This determines potential risks in access controls, data degradation and inconsistencies. Without data and access traceability, just mapping data lineage does not allow full advantage.

It become very important to regularly monitor and do audit and quality checks to identify and rectify any discrepancies and inconsistencies. This is essential for maintaining the accuracy and reliability of metadata. I feel by combining automation, standardization, collaboration and governance, organizations can create scalable data lineage and metadata management practices that effectively support the data ecosystems.

The most important aspect for metadata, cataloging, data lineage is that it fits within business process and workflow. Governance can not be parallel or segregated from the engineering workflow or other processes that may use, manage or enrich the metadata and lineage. Automation is one aspect, but if there are no key use cases driving its use, you will end up with a lot of content and detail that is not providing value and hard to navigate. For example - focus on how the tool can help diagnose DQ issues, or how an engineer can use it for impact analysis to table changes. Engineering-led, or problem-solving led metadata and data lineage approaches therefore drive towards a more sustainable implementation.

Creating data lineage is a costly exercise. Typically, emphasis has been on capturing lineage. However, value arises when lineage is consumed, not merely created. We often think of lineage as diagrams for humans to visualise, to trace data issues and dependencies. Yet, this use case doesn't offer enough value. The future lies in using metadata and lineage to support automated, machine-to-machine data management. For example, when data tagged as sensitive in a catalogue is moved between jurisdictions, as shown by lineage, we would automatically restrict access to that data and initiate a review process.

Implement version control for metadata changes. This ensures that historical lineage and metadata information are preserved and can be traced back when needed.

Harness the potential of cloud-based platforms, embrace a modular approach (progressively enhance), employ machine learning and AI whenever feasible, establish well-defined governance policies, roles, and responsibilities - recognizing that scaling data lineage and metadata management extends beyond technology.

When we talk about technical and operational metadata, I believe the automation is the key for scaling. The biggest challenge is scaling the business metadata. There are already some AI solutions to help on that, like PII classification as example, but getting the real understanding of a business concept and its business rules continues to be a challenge.