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Data Lineage & Provenance

Overview

Data Lineage describes the life cycle of data, tracking its origin, movement, transformation, and usage across systems.
Provenance provides additional context on who created or modified data, and under what conditions.

Together, they enable transparency, impact analysis, regulatory compliance, and data quality monitoring within Nissan North America (NNA).

Purpose

  • Understand where data comes from, how it flows, and how it is used across the enterprise.
  • Enable impact analysis when making changes to data, systems, or processes.
  • Support regulatory compliance and auditing by providing traceable data records.
  • Improve data quality management by identifying transformation or integration issues.

Key Components

Component Description
Source Systems Original systems or feeds where data originates.
Transformation Processes ETL/ELT pipelines, data enrichment, aggregation, or cleansing steps.
Target Systems Destination systems, data warehouses, analytics platforms, or reports.
Data Owners & Stewards Individuals responsible for data at each stage of the lineage.
Timestamp & Provenance When and by whom data was created, modified, or moved.
Dependencies Upstream and downstream systems, processes, and data assets.

Lineage & Provenance Benefits

  1. Impact Analysis: Understand how changes affect downstream systems and reports.
  2. Regulatory Compliance: Demonstrate traceability for audits and reporting requirements.
  3. Data Quality Improvement: Identify points where errors or inconsistencies occur.
  4. Operational Transparency: Enable stakeholders to track the flow of critical data.
  5. Risk Mitigation: Reduce errors, duplication, or misuse of sensitive data.

Lineage Capture Process

  1. Inventory Data Assets: Identify datasets, tables, and critical data flows.
  2. Document Sources & Transformations: Capture how data is created, processed, and transformed.
  3. Track Lineage to Targets: Map flow to all systems, reports, dashboards, or external feeds.
  4. Assign Ownership & Stewardship: Ensure accountability at each stage.
  5. Maintain Provenance Metadata: Record timestamps, responsible users, and system changes.
  6. Review & Update: Regularly validate lineage and provenance with operational teams.

Visualization Approaches

  • Graph-based lineage diagrams to visualize flow from source to target.
  • Tabular lineage metadata capturing source, transformation, target, owner, and timestamps.
  • Automated lineage capture tools for real-time updates.

Roles & Responsibilities

Role Responsibility
Data Owner Validates lineage and approves any modifications.
Data Steward Monitors lineage integrity and updates documentation.
IT / Data Engineering Implements technical lineage capture and integrates with governance tools.
Data Consumers Leverage lineage information to validate reports and analytics.

Tools & Technologies

  • Metadata management platforms (Collibra, Alation, Informatica EDC)
  • ETL/ELT orchestration tools with lineage tracking (e.g., Talend, Informatica, Apache NiFi)
  • Data warehouses and BI tools supporting lineage visualization
  • Automated lineage extraction scripts for large or complex datasets

Visual Representation

flowchart TD
    A[Source Systems] --> B[ETL/Transformation Processes]
    B --> C[Data Warehouse / Analytics]
    C --> D[Reports / Dashboards / Data Products]
    B --> E[Provenance Metadata]
    C --> E