What are the best practices for tracking data lineage in BI data quality assessment?

I agree. Lineage criteria should be part of any analytic project. The criteria should be defined before the project starts. Ideally, that is how it should be. Unfortunately, in real life, most analytic projects are developed on different timeline. Now, the complexity arises not from the new project but from aligning to existing ones, how you integrate their definitions. In addition, analytic projects are dynamic and always evolving. In this sense, you need a sophisticated strategy to keep up the lineage, its scope, criteria, monitoring, processing and governance, not to mention the tool to simplify everything (including documentation).

These practices ensure efficient tracking of data lineage in BI data quality assessment, maintaining the reliability and accuracy of BI systems. 1. Document all data sources and their metadata. 2. Automate data lineage tracking. 3. Use specialized tools for visualization. 4. Include transformation logic in lineage metadata. 5. Track data quality metrics at each stage. 6. Implement version control for lineage metadata. 7. Regularly validate lineage accuracy. 8. Foster collaboration between teams.

In BI data quality assessment, tracking data lineage effectively involves meticulously documenting all data sources and the transformations they undergo. Start by creating comprehensive metadata for each data element, including its origin, format, and any changes applied during processing. Utilize data lineage tools to automate the tracking of data flow across systems and transformations, ensuring transparency and traceability. Regular audits and updates to this documentation are crucial as data pipelines evolve. This approach not only aids in identifying and rectifying quality issues but also enhances trust in the data used for business intelligence.

their no single tool that does all the intricate requirements of an end to end data lineage. In 2019, i encountered a very nice tool specialized in data lineage. The tool is called Octopai. It works very well on existing MS SQL Server and able to build visualization and documentaion. It uses advance ML algorithm to extract from SSIS and Stored procedures the relationships of databases, tables, views. Pretty impressive, however, during the time, it cannot accommodate our big data assets (i.e., cloudera, aws, etc). The quest for alternative tool becomes a life long struggle.

among many items stated here, establishing the definition and imposing a governance is the key. This will allow you to propagate the implementation of upcoming projects, sustain the existing, and tie them up altogether. There is no single tool that can do the lineage altogether. However, you can augment this defficiency by defining the policies and enforcing them to follow a certain baseline for any analytic projects that will make the data lineage less cumbersome.

Best practices for tracking data lineage include using automated tools to map data flow from source to report, ensuring transparency. Document transformations and dependencies to identify data origins and processing steps clearly. Integrate lineage tracking into your BI platform for real-time insight into data quality issues. Regularly review and update lineage documentation to reflect changes. Communicate insights effectively through visualizations, making it easier for stakeholders to understand data paths, dependencies, and impact of changes on reports.