Zero-Based Data Lineage using the Power of Graph RAG 

In today’s data-driven world, financial institutions operate with vast and complex ecosystems of data pipelines. Data lineage—the ability to track and trace data from origin to consumption—has traditionally been an incremental process, often built on historical assumptions rather than a continuous evaluation of necessity and efficiency. This is where Zero-Based Data Lineage (ZBDL) emerges as a paradigm shift.

What is Zero-Based Data Lineage (ZBDL)?

ZBDL applies the Zero-Based Budgeting (ZBB) concepts to data lineage, challenging organizations to justify every data movement, transformation, and storage decision from scratch. Instead of presuming all current data pipelines are essential, ZBDL compels companies to re-examine their data architecture from a cost-conscious and business-value-driven perspective.

For large financial institutions, particularly those that have operations in retail banking, corporate banking, risk management, and regulatory compliance, ZBDL assists in:

• Removing duplicate data pipelines.
• Lower storage and computational expenses.
• Enhance data governance and security.
• Comply with GDPR, BCBS 239, FCA, and PRA regulations.
• Support better decision-making through correct, justified, and high-value data.

However, applying ZBDL to highly complex, interdependent enterprise ecosystems can be daunting. Conventional lineage tracking techniques rely on manual efforts, fragmented metadata management, and static documentation, all of which fall short of today’s AI-powered automation and constant change.

This is where Generative AI, specifically the Graph Retrieval-Augmented Generation (Graph RAG) framework, can revolutionize how enterprises extract intelligence from their existing data ecosystem to drive ZBDL implementation.

Graph RAG: The Key to AI-Driven ZBDL

Graph Retrieval-Augmented Generation (Graph RAG) is an advanced AI framework that enhances generative AI models by integrating structured graph-based knowledge retrieval into their reasoning process. Unlike standard Retrieval-Augmented Generation (RAG), which fetches documents from vector databases, Graph RAG builds a knowledge graph of interconnected data flows, metadata, and lineage relationships, enabling more precise and contextual AI reasoning.

For enterprises managing thousands of interconnected data pipelines, ETL processes, and governance rules, Graph RAG provides a self-learning AI-driven knowledge base that enables intelligent, automated ZBDL evaluation.

How an Enterprise Can Leverage Graph RAG to Enable ZBDL

Enterprises can harness Graph RAG and Generative AI to build a dynamic, self-optimising data lineage framework. Here’s how:

1. Building a Knowledge Graph of Data Lineage

Traditional Challenge:

Enterprises have siloed data pipelines across departments, with multiple teams managing lineage manually through static documentation, spreadsheets, and metadata repositories.

Graph RAG Solution:

• Ingest existing data lineage metadata from sources like Collibra, Informatica, Azure Purview, and data lakes.
• Construct a Graph Database (Neo4j, ArangoDB, or AWS Neptune) that captures:
  • Data Sources & Destinations (e.g., core banking systems, risk models, regulatory reporting).
  • Transformation Logic (ETL, SQL queries, Python scripts).
  • Business & Regulatory Dependencies (which processes use which data, for what purpose).
  • Anomaly & Redundancy Detection (identify duplicated or unused flows).
• AI models trained on graph embeddings can now query lineage relationships in a context-aware, intelligent manner.

2. AI-Driven Justification of Data Pipelines

Traditional Challenge:

Data teams lack a real-time, automated way to assess which data flows are business-critical and obsolete. Manual validation takes months.

Graph RAG Solution:

• Generative AI models prompted to use the graph to answer:
  • “Which datasets power critical risk models?”
  • “What is the regulatory justification for storing X dataset?”
  • “Are there redundant ETL jobs consuming unnecessary compute resources?”
• AI-powered scoring mechanisms can assign a “ZBDL score” to each pipeline based on:
  • Business Relevance: Is this dataset actively used in decision-making?
  • Regulatory Necessity: Is this data required for compliance?
  • Operational Efficiency: Can this pipeline be optimized or merged?
• AI suggestions can be integrated into governance dashboards (Power BI, Tableau, Looker) for real-time insights.

3. Cost and Performance Optimization with AI Recommendations

Traditional Challenge:

IT teams overprovision cloud storage, databases, and ETL jobs, leading to excessive costs and performance bottlenecks.

Graph RAG Solution:

• AI can simulate cost-saving scenarios by predicting the impact of removing redundant or inefficient pipelines.
• Example:
  • AI detects that two different teams maintain separate but overlapping data extracts for risk reporting.
  • It suggests merging them into a single standardized source, cutting down computing costs by 40%.
• AI-powered observability can detect anomalies in data lineage flow, alerting teams before failures impact operations.

4. Automating Governance and Compliance Audits

Traditional Challenge:

Regulators require detailed audit trails of data usage, transformations, and access controls, but manual audits are error-prone and costly.

Graph RAG Solution:

• AI continuously monitors, validates, and logs lineage changes, ensuring:
  • Every transformation is documented.
  • Every regulatory-relevant dataset is justified.
  • Access permissions are aligned with security policies.
• AI can generate instant compliance reports for audits instead of months-long manual preparation.

The Future of AI-Driven ZBDL in Financial Services

By integrating Graph RAG and Generative AI into ZBDL, financial enterprises can achieve: 

✅ Real-time, AI-powered data lineage tracking

✅ Cost-efficient, optimized data architecture

✅ Stronger regulatory compliance with automated governance

✅ Self-healing data pipelines with intelligent anomaly detection

✅ Proactive identification of obsolete or redundant data flows

In a world where data is an enterprise’s most valuable asset, ZBDL powered by Graph RAG AI is the key to unlocking a truly optimized, compliant, cost-effective data ecosystem.

By transforming how data lineage is managed, financial institutions can move from reactive, manual governance to a proactive, AI-driven data strategy—future-proofing their operations in an increasingly complex regulatory and competitive environment.