Use Case
Executive Summary
Customer data exists in dozens of different systems with inconsistent definitions and fragmented identifiers. Traditional data modeling takes many months before a single line of code is written — and models become outdated before completion. ABLEMENTS transforms data modeling through automated discovery and semantic understanding that creates unified models in weeks, not years.
60%
Faster data preparation
100%
Regulatory traceability
Zero
Production data access required
The Challenge
Problem Statement
Banking data is fragmented across dozens of systems with inconsistent definitions, multiple identifiers for the same entities, and significant lags for consolidated reporting. Every strategic priority — AI, analytics, compliance — depends on solving data fragmentation.
Manual Modeling Takes Forever
Traditional data modeling requires many months of stakeholder interviews, requirements documentation, and entity relationship design. By completion, business requirements have changed and models are already outdated.
Bottom-Up Integration Never Ends
Connecting systems one at a time reveals new data quality issues, semantic differences, and reconciliation challenges with each integration. Projects expand indefinitely as dependencies emerge.
Top-Down Models Disconnect from Reality
Enterprise architecture teams create elegant, theoretically perfect models that are disconnected from operational reality. Implementing them requires changing every system — an impossible transformation.
AI and Analytics Blocked
Many analytics projects fail due to data availability. Most AI/ML initiatives blocked by data fragmentation. Significant annual spend on data reconciliation.
Our Solution
Solution Overview
ABLEMENTS provides automated data model construction through semantic understanding that creates unified models grounded in actual operational patterns — not theoretical ideals disconnected from reality.
Automated Data Discovery
Data Context Comprehension wizard discovers data structures across all systems automatically through analysis of schemas, APIs, interfaces, and data dictionaries — in days, not months.
Service-Based Model Construction
Service and Data Model Creation wizard builds Universal Service Models (USM) and Universal Data Models (UDM) by analyzing how systems actually use data — creating models that reflect operational reality.
Multi-Dimensional Domain Filtering
Data organization through multi-dimensional taxonomies enables precise navigation. Framework filters and BYOM dimensions accommodate organization-specific structures with semantic search understanding context.
Industry Context
Data fragmentation has become a critical blocker for banking transformation as AI and analytics initiatives depend on unified data intelligence. Organizations face:
Dozens of systems containing customer data in large banks
Significant lag for consolidated reporting
Many analytics projects fail due to data availability
Significant annual spend on data reconciliation
Most AI/ML initiatives blocked by data fragmentation
Featured Modules
Data Module
primary
Provides comprehensive data model construction through automated discovery, semantic understanding, and unified model creation.
Data Context Comprehension for automated discovery
Service and Data Model Creation for USM/UDM construction
Data Model Mapping for semantic harmonization
Data Catalogue for unified access
Systems Module
supporting
Provides system context essential for understanding data sources and integration patterns.
Architecture Module
supporting
Enables data architecture planning and governance framework alignment.
Implementation Workflow
Data Model Construction Process
1
Comprehensive Data Discovery
Data Context Comprehension wizard scans all systems, discovering schemas, relationships, and quality characteristics. Complete data landscape mapped automatically in days.
2
Universal Model Creation
Service and Data Model Creation wizard builds USM and UDM based on actual system usage. Models reflect operational reality while providing standardization needed for unified access.
3
Semantic Mapping and Validation
Data Model Mapping wizard creates cross-system mappings automatically through semantic understanding. Business experts validate and refine edge cases.
4
Model Deployment and Access
Unified data models deployed to Data Catalogue. Teams gain immediate access through natural language queries. Analytics and AI initiatives unblocked.
5
Continuous Enhancement
Models evolve with business changes. New systems integrate seamlessly. Data quality improves through continuous monitoring and governance.
Technical Implementation
Data Module: From Silos to Intelligence
The Data Module provides comprehensive data model construction through semantic analysis and automated modeling technologies that work with schemas and metadata — never accessing production data.
Business Value
60% faster data preparation
100% regulatory traceability
Real-time data quality assurance
Quantified Outcomes
60%
Faster data preparation