Why is lineage important for AI?

AI depends on data quality. Lineage helps: trace issues to sources, verify training data provenance, demonstrate compliance (AI governance), and understand model inputs.

How do I implement data lineage?

Automated: tools like dbt generate lineage from code. Manual: document flows in data catalogs. Combination is common. Start with critical data paths and expand.

What is the difference between lineage and metadata?

Metadata describes data (schema, types, statistics). Lineage describes data journey (where from, what transformations). Lineage is a type of operational metadata. Both support data governance.

How granular should lineage be?

Depends on needs. Column-level for sensitive data, compliance. Table-level for general understanding. System-level for high-level architecture. More granularity = more effort and storage.

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Data Lineage

Tracking the origin, movement, and transformation of data throughout its lifecycle in an organisation.

In-Depth Explanation

Data lineage tracks where data comes from, how it moves through systems, and what transformations are applied. It provides a complete audit trail of data from source to consumption.

What lineage tracks:

Origin: Where data was created/sourced
Movement: How data flows between systems
Transformation: Changes applied to data
Consumption: Where and how data is used
Timing: When changes occurred

Lineage benefits:

Impact analysis (what breaks if X changes?)
Root cause analysis (where did bad data come from?)
Compliance and audit (prove data handling)
Documentation (understand data flows)
Trust (confidence in data sources)

Lineage implementation:

Column-level lineage (most detailed)
Table-level lineage (relationships)
System-level lineage (data flows)

Tools:

dbt (built-in lineage)
Apache Atlas
Data catalog platforms (Alation, Collibra)
Cloud-native (AWS Glue, GCP Dataplex)

Business Context

Lineage answers "can I trust this data?" by showing its complete journey. Essential for compliance, debugging, and data governance.

How Clever Ops Uses This

We implement data lineage for Australian businesses needing audit trails, compliance documentation, and confidence in their AI training data.

Example Use Case

"Tracing a suspicious ML model prediction back through the data pipeline to identify a data quality issue in a source system."

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