Data Provenance

Captures the complete end-to-end journey of data from its point of origin through every transformation, aggregation, and movement. This creates a directed acyclic graph (DAG) of data operations.

• Records who created or modified the asset
• Tracks what transformations were applied (e.g., normalization, deduplication)
• Logs when each operation occurred with precise timestamps
• Stores where data resided across systems and storage layers

Example: A financial report cell can be traced back through an ETL pipeline to the raw transactional database row ingested three months prior.

Ensures data has not been tampered with by generating cryptographic hashes at each processing stage. Any alteration to the data or its provenance record produces a hash mismatch, immediately flagging corruption.

• Uses SHA-256 or BLAKE3 for content-addressable storage
• Enables verifiable compute where outputs can be independently validated
• Supports tamper-evident audit logs for regulatory compliance
• Integrates with digital signatures to authenticate the creator's identity

This is critical for high-stakes domains like pharmaceutical research where data integrity directly impacts patient safety.

Links every data point to its original authoritative source, whether that is a human annotator, a sensor, an external API, or a specific document. This enables precise citation in generated answers.

• Assigns persistent identifiers (e.g., DOIs, UUIDs) to source entities
• Captures confidence scores and uncertainty from the origin
• Distinguishes between primary sources (raw data) and secondary sources (derived data)
• Enables micro-attribution where individual cells in a table can cite different sources

This granularity allows a GraphRAG system to say 'This fact came from paragraph 3 of document X' rather than citing an entire database.

Maintains a bitemporal history of data, tracking both the valid time (when a fact was true in the real world) and the transaction time (when it was recorded in the system). This enables point-in-time querying.

• Supports time travel queries to view the graph state at any historical moment
• Enables rollback to previous versions if corrupted data is ingested
• Tracks schema evolution alongside data changes
• Facilitates slowly changing dimension (SCD) management for data warehousing

Example: Querying a knowledge graph 'as of Q3 2023' to reproduce a regulatory filing exactly as it was submitted.

Captures not just the raw data but the semantic context in which it was generated, including the schema, ontology, and business rules active at the time of creation. This prevents meaning from being lost during transformation.

• Records active ontology versions and namespace mappings
• Preserves measurement units, locale settings, and encoding formats
• Documents assumptions and constraints applied during data generation
• Links to data contracts and service-level agreements (SLAs) in effect

Without this, a temperature reading of '100' is ambiguous—provenance clarifies whether it is Fahrenheit or Celsius, and which sensor model captured it.

Provenance graphs enable statistical profiling of data pipelines to automatically detect deviations from expected behavior. Sudden changes in volume, schema, or value distributions trigger alerts.

• Monitors freshness (is data arriving on schedule?)
• Tracks distribution shifts (has the statistical profile of the data changed?)
• Detects lineage gaps (missing links in the transformation chain)
• Identifies unauthorized access or modification attempts

This transforms provenance from a passive audit log into an active data observability system that prevents bad data from poisoning downstream models.