How Persistent helped design a graph-led enterprise metadata intelligence platform to improve governed discovery, lineage transparency and AI-ready data operations
150+
Systems of record onboarded
1.8T
Data elements harmonized
80%
Development effort saved
60–80%
Metadata discovery effort reduction potential
A leading financial services institution needed to strengthen enterprise data governance, metadata discoverability and regulatory traceability across a complex data ecosystem. Metadata was spread across platforms, tools, domains and business processes, making it difficult for users to find trusted data, understand lineage, validate controls and connect business context with technical assets.
Persistent helped the institution move beyond traditional metadata management by designing a Neo4j-powered enterprise metadata intelligence platform. The solution created a governed, graph-based intelligence layer that connected applications, databases, schemas, tables, physical data elements, business data elements, glossary terms, controls, classifications, ownership and lineage relationships into one navigable experience.
The engagement built on Persistent’s prior metadata work with the institution, including GLIMMER, which enabled metadata-based onboarding of 150+ systems of record, harmonized 1.8 trillion data elements and helped save 80% of development effort through automation tools for data extract and load.
When Metadata Fragmentation Becomes a Governance Risk
For large financial institutions, metadata is not just technical documentation. It is the foundation for data governance, regulatory traceability, lineage transparency, encryption visibility, data quality and enterprise reuse. When metadata remains fragmented, business and technology teams lose confidence in where data comes from, how it is classified, who owns it, which controls apply and what downstream impact a change may create.
The institution’s metadata landscape had become increasingly distributed across systems such as Dataplex, Purview, Ab Initio, ServiceNow, data quality tools, encryption and classification systems and governance repositories. Each system held part of the picture, but none offered a unified intelligence layer that could connect business meaning with technical lineage and control context.
The business impact was significant. Data stewards needed to validate, approve and enrich metadata. Business owners needed visibility into glossaries, business data elements and KPIs. Technical owners needed lineage, controls and classification views. Executives needed portfolio-level visibility into compliance posture across data-at-rest and data-in-motion encryption.
The cost of inaction was clear. Without a connected metadata model, teams would continue to spend significant effort manually tracing information across systems, relying on SMEs for context, duplicating enrichment work and struggling to deliver consistent regulatory visibility. The enterprise needed a platform that could support governed discovery, lineage transparency and AI-ready data operations at scale.
A Neo4j Model for Enterprise Metadata Intelligence
Persistent shifted the approach from static metadata documentation to relationship-aware enterprise data intelligence. The proposed architecture preserved existing metadata investments while using Neo4j to create a governed intelligence layer above them. This allowed metadata to be searched, traversed and consumed through business context rather than isolated technical records.
Unified metadata repository and canonical model Persistent designed a unified metadata architecture that harvested information from source platforms, data quality tools, reference data systems and encryption or classification platforms. The harvested metadata was standardized into a canonical model and enriched with business, technical, lineage, ontology and control context.
Neo4j for connected lineage and relationship intelligence Neo4j powered the knowledge graph and lineage layer, connecting business data elements, physical data elements, applications, schemas, tables, controls, ontology mappings and governance relationships. This enabled users to move from keyword-based search to contextual discovery and downstream impact analysis.
Persona-driven portal and information marketplace Persistent designed an information marketplace so business owners, technical owners, data stewards, general users and executives could interact with metadata through role-specific views, dashboards, discovery experiences, lineage exploration, compliance reporting, access requests and approval workflows.
Agentic AI for curation and conversational discovery Persistent proposed an agentic AI layer to support metadata curation, enrichment, classification and conversational discovery. By combining graph intelligence with AI-led automation, the platform created a stronger foundation for trusted, explainable and operationally useful metadata across the enterprise.
Enterprise Metadata Intelligence Built for Governed Discovery
The documented value from related metadata initiatives provides strong evidence of Persistent’s ability to deliver impact at enterprise scale. GLIMMER enabled the onboarding of 150+ systems of record, harmonized 1.8 trillion data elements and saved 80% of development effort through automation tools for data extract and load.
For the Neo4j-powered metadata repository, portal and marketplace, the expected uplift can be positioned around reduced manual discovery effort, faster lineage analysis, improved domain onboarding and stronger compliance visibility, subject to final client and SME validation.
With Persistent, the institution would be positioned to:
- Reduce metadata discovery effort by an estimated 60–80% by replacing fragmented searches across tools with contextual discovery through a Neo4j-powered marketplace.
- Accelerate lineage analysis by an estimated 70–90% through graph traversal across applications, databases, schemas, tables, physical data elements and business data elements.
- Improve onboarding of new metadata domains by an estimated 40–60% through reusable harvesting adapters, canonical model mapping and Persistent iAURA accelerators.
- Extend the 80% development effort reduction potential demonstrated through GLIMMER’s automation-led data extract and load capabilities.
- Improve compliance visibility through executive dashboards for encryption, classification, governance and remediation lifecycle reporting.
From Metadata Management to AI-Ready Data Intelligence
Persistent helped define a future-ready metadata intelligence platform that moves the institution beyond fragmented metadata repositories and into a connected model of business meaning, technical lineage, controls, classifications, ownership and policy context.
By placing Neo4j at the center of the metadata architecture, the proposed platform gives business and technology teams a stronger foundation for governed discovery, regulatory traceability and AI-led data operations. For the institution, the transformation is not only about improving metadata visibility. It is about building an enterprise intelligence capability where trusted metadata becomes essential to context, automation and decision-making.
