The
Overview
A financial services organization had no idea which data assets were actually valuable for AI. Wishtree performed comprehensive discovery and roadmap development across the entire enterprise.
Problem
Statement
The client had a data visibility crisis. Teams did not know what data existed across the enterprise, or which assets were valuable for AI.
Highlights
24
Redundant silos identified
Lakehouse architecture blueprint
Cloud-native data strategy
50%
Faster AI model deployment
8
Business units aligned
Data governance foundation
Agentic AI refers to autonomous, goal-driven software agents that act with
limited human input to optimize specific goals like pricing, forecast demand,
and detect fraud in real time.
About Client
A large financial services organization with data scattered across 8 business units – retail banking, wealth management, insurance, lending, and more. Each unit operated independently, with its own data warehouses, definitions, and tools.
- No enterprise-wide visibility into existing data assets
- 24 redundant data silos identified once discovery began
- Data definitions varied across units
- AI initiatives took months to launch because data discovery and preparation started from zero each time
- Governance was nonexistent
- Conducted comprehensive data discovery across all 8 business units
- Analyzed usage patterns, business value, and duplication
- Identified 24 redundant data silos
- Designed a unified cloud-native lakehouse architecture blueprint that brings all enterprise data together
- Established common data definitions and governance frameworks across business units
- Built a data roadmap with clear phases: foundation, consolidation, AI enablement, and advanced analytics
- Enabled self-service access to trusted data for AI teams, cutting model deployment time in half
- Discovery phase used AI-assisted data profiling to automatically identify duplicate datasets and inconsistent definitions.
- The lakehouse architecture is designed for AI – supporting structured and unstructured data, real-time streaming, and ML model training.
- Data pipelines were architected to serve both BI and AI workloads, eliminating the need for separate copies.
- Governance frameworks include automated data quality monitoring and lineage tracking.
Core Features
Enterprise data discovery
Redundancy elimination
Common data definitions
AI/ML enablement layer
Phased roadmap
Impact
- 24 redundant data silos identified and eliminated
- Unified lakehouse architecture blueprint delivered
- 50% faster deployment of AI models in Phase 2
- Data visibility achieved
- Governance established
Why Wishtree
Wishtree specializes in data strategy for financial services organizations where complexity and regulation demand rigorous, scalable approaches.
For this client, we:
- Eliminated 24 redundant silos through enterprise-wide discovery
- Delivered a unified lakehouse blueprint for cloud-native data
- Enabled 50% faster AI model deployment in Phase 2
- Aligned 8 business units around common data governance