16 Weeks Data Governance MVP |
5 Sources Connected and cataloged |
35 Rules Data quality controls |
5 Personas Role-based access groups |
About the Customer
The customer is a leading North American mortgage finance company modernizing how business users, analysts, data scientists, and stewards discover and trust enterprise data.
A critical machine-learning initiative made the need particularly urgent: the organization needed confidence not just in the model, but in the data feeding it.
Customer Challenges
Governance existed, but much of it lived in SharePoint documents maintained manually and separately from the underlying data estate. The organization lacked automated answers to fundamental governance questions:
- Where does this data come from, and what happens to it along the way?
- Can the organization trust the data feeding analytics and machine-learning models?
- Which sources are sanctioned for business use, and who approved them?
- How should business users discover and request access to the data they need?
Rather than launch a multi-year enterprise transformation immediately, the company wanted to prove a practical governance model on one high-value use case and then scale it.
PDI Solution
PDI delivered a seven-phase Data Governance MVP over 16 weeks using Informatica Intelligent Data Management Cloud (IDMC). The MVP centered on the data pipeline supporting a machine-learning model, making trusted data for AI the proving ground for broader governance.
Key Governance Components
- Living data catalog. Five data sources across the warehouse and integration landscape were connected and scanned into Informatica CDGC.
- End-to-end lineage. Data could be traced from source systems through staging and transformation layers to the tables consumed by the ML model.
- Automated quality monitoring. PDI implemented 35 data quality rules spanning completeness, validity, format, range, and distinct-value checks, surfaced through a quality scorecard.
- Sanctioned-source workflow. Custom attributes and approval workflow formally distinguish approved data sources from other assets.
- Data Marketplace. Curated collections with access-request and approval workflows provide a governed front door for business users.
- Persona-based RBAC. Five persona-based access groups align privileges with administrators, stewards, and business consumers.
- Structured SharePoint migration. PDI delivered migration mappings and import templates and proved the approach with a sample glossary domain.
Results
Governance moved from documents to the data platform. Cataloging, lineage, quality monitoring, sanctioning, and access workflows became part of the operating governance capability rather than processes maintained on the side.
Trusted data for AI. The machine-learning use case at the center of the MVP now has profiled, quality-monitored, traceable data behind it, creating a pattern for future analytics and AI initiatives.
Faster, safer access for the business. Data Marketplace and approval workflows replaced ad hoc requests with governed self-service.
Self-sufficiency from day one. Recorded training, persona-based adoption workshops, runbooks, and quick-reference guides equipped the client's team to operate and extend the platform independently.
Delivered within the original 16-week window. Despite an early infrastructure dependency, the engagement stayed within its planned timeframe, with trade-offs handled transparently.
What's Next
PDI delivered a post-MVP roadmap covering enterprise-wide glossary migration, expanded data quality coverage, deeper lineage, Data Marketplace growth with usage analytics, a formalized governance operating model, and automation of scanning and scorecard refresh cycles.
About Pacific Data Integrators
Pacific Data Integrators helps financial-services organizations implement Informatica IDMC across governance, catalog, data quality, MDM, integration, and modernization, with an emphasis on measurable business use cases and operational adoption.
Ready to Move Governance Out of Documents and Into Your Data Platform?
Build a focused governance MVP around a high-value business, analytics, or AI use case and establish the operating pattern before scaling enterprise-wide.