Bank Data Warehouse Modernization & Analytics Platform Transformation
Unified Data Sources + Scalable Analytics Foundation for Faster Insights & Future Credit Scoring
Data Load Times Reduced to <2 Hours
Integrated 1,000+ Tables & Views from Multiple Systems
6 Business-Specific Datamarts + OLAP Cubes
Introduction
As part of the strategic evolution from DataSphere to Simov Labs, this case showcases a comprehensive Data Warehouse implementation that transformed a leading bank's fragmented reporting platform into a unified, scalable analytics foundation—enabling faster insights, reduced load times, and readiness for advanced models like credit scoring.
Business Challenge
One of the country's top banks relied entirely on its core banking system for all reporting and analytics, causing slow response times, limited access to critical information, and inefficiencies across business areas. Data sources were fragmented (core banking, CRM, ERP, accounting, marketing campaigns, loans, collections, delinquency management, plus Excel budgets), lacking integration and optimization for analysis. This hindered decision-making, scalability, and the ability to implement future advanced analytics such as credit scoring models.
Implemented Solution
- 1Led the transformation using a two-layer Data Warehouse architecture to unify disparate sources.
- 2Applied the Kimball Methodology, including Bus Matrix analysis to map all required metrics and dimensions for daily business questions.
- 3Integrated over 1,000 tables and views from core banking, CRM, ERP, accounting, marketing, loans, collections, delinquency, and Excel budgets.
- 4Developed 6 targeted datamarts for key business areas and 23 ETL processes using Python and Java, reducing data load times to under two hours.
- 5Implemented multidimensional models and OLAP technologies for data cubes and interactive dashboards.
- 6Delivered a comprehensive knowledge transfer plan to IT, BI, and commercial teams for autonomous management and scaling.
Obtained Results
Reduced data load times dramatically to less than two hours, enabling near-real-time access and faster responses to business requests.
Achieved centralized, consolidated visibility across all major data sources for improved accuracy and decision-making.
Enabled interactive dashboards, OLAP cubes, and business-specific datamarts for self-service analytics across departments.
Built a scalable foundation ready for advanced analytics, including future implementation of credit scoring models without major rework.
Empowered internal teams through knowledge transfer, boosting confidence, autonomy, and long-term platform management capabilities.
Evolution in Simov Labs
This foundational Data Warehouse project is now fully integrated into our portfolio at Simov Labs. We evolve these architectures into modern, cloud-native implementations (Snowflake, Databricks, BigQuery) with production-ready AI/ML integration, automated ETL pipelines, real-time data ingestion, advanced governance (DAMA/TDWI-aligned), and explicit ROI measurement.
Our current approach remains business-first, with radical honesty in assessing fit and opportunities, and a focus on long-term partnership—ensuring data platforms deliver sustainable scalability, faster insights, cultural adoption, and competitive advantage in banking and financial services. No promises. Just results.
Ready to modernize your data warehouse for unified insights, scalability, and AI-ready analytics?
Book a free 30-minute consultation. We'll discuss your data platform goals and where a modern warehouse can deliver the most impact.