Traceability
The degree to which data lineage is available.
- •Increased trust, transparency, and confidence in data by understanding its origin, journey, and transformations.
- •Easier and faster root cause analysis for data errors, anomalies, or unexpected analytical results.
- •Improved auditability and demonstrable compliance with data governance policies and regulatory requirements.
- •Enhanced ability to manage data changes effectively and assess their downstream impact.
- •Key enabler for explainable AI (XAI), understanding model behavior, and debugging AI systems.
- •Lack of trust in data if its provenance and processing history are unknown (black box data).
- •Difficulty and significant effort in identifying and resolving data quality issues at their source.
- •Challenges in meeting audit and regulatory requirements for data provenance and process transparency.
- •Inability to accurately assess the impact of changes in source systems on downstream reports, analytics, and AI models.
- •Difficult or impossible to explain AI model outputs or biases if data lineage is unclear or unavailable.
Story
A grade (1-10)
Logistics: Data lineage tools show that the vessel ETA originated from the shipping line's EDI feed, was then updated based on AIS tracking data, and subsequently adjusted by the port agent, with timestamps and user IDs for each change.
Finance: An auditable data trail exists for all financial reporting, allowing any number to be traced back through aggregation and transformation steps to its source general ledger entries.
Data Analytics: A data catalog provides complete lineage for all datasets, showing their origin, transformations, dependencies, and usage in models and reports.
Logistics: Business users are unsure where the 'estimated time of arrival' (ETA) data for a vessel originated, what transformations it underwent, or if it was manually adjusted.
Finance: A key figure in a regulatory report cannot be easily traced back to its source transactions or calculation logic, making audits difficult.
Data Analytics: A machine learning model's predictions cannot be explained because the lineage of the training data (sources, preprocessing steps) is not documented.
Unique Identifier Non-Reuse Policy improves 54
Batch/Lot Number Traceability improves 54
Material Safety Data Sheet (MSDS) Linkage improves 54
Timestamp Immutability Rule improves 54