What are the best ways to capture and maintain data lineage and provenance?

Data lineage is starting to get it's place in practice, mostly due to compliance. But if we want to be transparent about the use of data, data provenance have the star role. Fortunately organisations are starting to be aware that the context and history of data has it's impact on the data recorded and thus in 'colouring' the noted facts. Input is done by humans with human sensibilities, views, priorities, etc. So when deciding what to record it is always with in the context of the human recording it. Or choosing not to record something. Knowing the timeframe and context of the data input has massive impact on the usefulness in the current context. And sometimes needs to be corrected before use in AI to match today's views and context.

IMHO, Data lineage and data provenance both are crucial in data management, they operate in different but complementary roles. Data lineage refers to the life cycle of data, tracing its journey from origin to destination. This includes understanding how data transforms and moves across systems, which is vital for impact analysis and troubleshooting. Data provenance, however, focuses on the origin and historical context of data, including who created or modified it and how. Critical practices include maintaining detailed audit trails, implementing rigorous documentation standards, and embedding these processes within a broader data governance framework.

Absolutely, maintaining data lineage and provenance is crucial for data integrity and governance. A best practice is to use automated tools that track data transformations and lineage. These tools should ideally integrate with your existing data ecosystem. Additionally, adopting a standardized approach for documentation and metadata management helps in keeping track of data sources, transformations, and dependencies. Regular audits and reviews of the data lineage process also ensure ongoing accuracy and compliance.

Check out the posts about Data contracts by @Chad Sanderson to document and manage Data lineage These built-in machine/human readable files can be used to alert users (systems/people) when something changes "upstream".

I recommend creating automated pipelines to track data flows from source systems through various transformations into target systems. Document business logic and code in data workflows. Log processing details like scripts executed, parameters used, execution times etc. Store metadata in a centralised catalogue. Enable data scientists and analysts to easily view data context - origin, changes made, ownership. This boosts confidence in analytic outputs. Regularly audit and certify critical data pipelines. As volume and variety of data increases, leveraging machine learning to automatically capture lineage and provenance will be key. The goal should be comprehensive visibility and control over data flows to power accurate, ethical decisions.

Documenting this lineage builds trust and accountability. I advise creating data dictionaries mapping fields to sources, keeping audit trails of ETL/ELT processes, and logging queries and reports. Provenance reveals the context behind data, enabling informed usage. To enable this, implement metadata standards like Dublin Core, PREMIS, and W3C PROV. Appoint data stewards to maintain policies and procedures. Integrate lineage into model development workflows to detect drift. Look into automated tools to capture lineage without manual logging. Ultimately, whether for compliance, governance or analytics, rich metadata ensures quality results. With some diligence in architectural planning and execution, you can reap these benefits.

While run time analysis and log tracking helps to find the lineage. Ideal preference should be design time lineage like with dbt-core.

Data lineage and provenance offer a myriad of advantages, extending beyond facilitating impact analysis and issue resolution. They serve as powerful tools for pinpointing bottlenecks, inefficiencies, and redundancies within existing processes. Moreover, these insights are instrumental in promoting collaboration among diverse teams engaged in shared datasets, enhancing overall operational efficiency and cohesion.

Data lineage should be in place prior to production. When done at this time by the person who created it, it is fresh in minds and is more likely to be accurate. Additionally, if there is an issue in production, there is documentation to review right away. Too often, data lineage is forgotten. Creating it after-the-fact is a huge job that no one wants. Going through program code to try to create data lineage is time-consuming. Using tools to create data lineage is error-prone. A hybrid approach is often best at that point if you have to do it, but it should be there prior to production so you don't get yourself into that dilemma in the first place.

Manual documentation provides helpful context but doesn't scale. Automated lineage tracing is efficient but can miss nuances. A balanced approach combines the strengths of both. Start by manually documenting high-value datasets and key transformation logic. Then implement automated scanning to fill traceability gaps at scale. Open source tools like Amundsen provide a good starting point. The key is striking the right manual vs automated balance for your use case. Don't over-engineer a complex end-to-end solution upfront. Iterate based on impact and risk. Target critical datasets first. Expand scope over time. Adopt agile principles rather than a traditional waterfall strategy. This reduces risk and complexity while still meeting objectives.

Capturing data lineage and provenance is just the first step. Maintaining accuracy over time is critical and challenging. Implement lightweight monitoring on key systems tracking high level lineage changes like data source additions/removals. Use simple automation like scripts scanning system metadata. Institutionalise lineage maintenance through policies, not just technologies. Drive cross-team collaboration making it a shared responsibility. Shift documentation mindset from static reports to living artefacts. Start with quick wins for critical data and grow from there. Making incremental progress is better than being perfect. The tools and technologies will evolve over time. Focus first on building the discipline.

Start with business goals. Anchor on real use cases like analytics, governance and privacy. This drives priorities for capturing lineage. Leverage organisational data standards as foundations for additional metadata. Consistent conventions accelerate automation. Metadata tools require governance for sustainability. Embed stewardship responsibilities across technical and data teams. Automate lineage harvesting but manually curate for accuracy. Blend technology with human oversight for quality. Institutionalise as a practice, not a one-off project. Build through iterations. Learn and improve. Treat lineage as a living asset. Use agile team rituals to keep it evergreen as systems change.

Here are my best ways to achieve this: 1. Implement data lineage tools. 2. Integrate with ETL processes 3. Utilize metadata management 4. Automate data lineage collection 5. Document manual processes 6. Version control for data and artifacts 7. Data provenance standards 8. Regular audits and reviews 9. Training and awareness 10. Integration with data governance framework 11. Data lineage as part of data quality 12. Leverage data catalogs 13. Proactive change management By implementing my practices, organizations can effectively track the history and lifecycle of their data, enhancing transparency, accountability, and trust in their data assets.

Provenance is more important to give a new way of obtaining program to satisfy the clients to take a certain programming pages.