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Banking verified Verified Outcome TRL 9

Federated Data Mesh Implementation

domain Client: The largest bank in the United States handshake Provider: AWS / Internal schedule Deploy: Q4 2021 (Maturity)
96 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: Very High

Executive Summary

ANALYST: COI RESEARCH

Moving away from a bottlenecked central data lake, the bank implemented a 'Data Mesh' architecture. This decentralized model treats data as a product owned by domain teams (e.g., Credit Card, Mortgage), while enforcing federated governance standards for interoperability and security. It enables domains to publish certified data products for enterprise consumption.

rate_review Analyst Verdict

"The definitive enterprise case study for Data Mesh. By shifting governance from a central gatekeeper to a federated responsibility, the entity solved the 'data swamp' issue. It balances the agility of decentralized ownership with the control of enterprise standards."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

The central data lake became a bottleneck. Ingesting data took months due to central team capacity, and data quality was poor because the central engineers didn't understand the domain-specific data (e.g., nuances of mortgage rates). Innovation stalled as data consumers couldn't find trusted datasets.

psychology The Solution

The bank reorganized around data domains. Each domain is responsible for the quality, documentation, and lifecycle of its own data products. A central 'governance layer' enforces policy-as-code (access controls, PII tagging) automatically when a data product is published to the catalog.

settings_suggest Technical & Deployment Specs

Integrations
AWS Lake Formation, Internal Catalog
Deployment Model
Public Cloud
Data Classification
Financial / PII
Estimated TCO / ROI
High (Organizational Change)
POC Summary (2019-01-01 to 2020-01-01)

"Pilot with Consumer & Community Banking (CCB)."

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Domain Silos Medium Strong federated interoperability standards.
Skill Gaps High Upskilling domain teams to handle data engineering tasks.

trending_up Impact Trajectory

Audited value realization curve

Creation of >100 governed data products Verified Outcome
Primary KPIReduction in data discovery time
Audit CycleSignificant increase in data reuse across LOBs

policy Compliance & Gov

  • Standards: BCBS 239
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Financial / PII

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for The largest bank in the United States without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-856
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-856

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