The Challenge: A Data Infrastructure at Its Limits
A leading microfinance institution was operating on legacy ERP systems that had long outgrown the business’s needs. Data consolidation was slow, fragmented, and heavily reliant on manual processes. Generating consistent reports required significant analyst effort — time that should have been spent on decision-making, not data wrangling.
When the decision was made to migrate to a modern, cloud-hosted ERP, the opportunity was clear: don’t migrate the old architecture’s limitations into a new environment. Build right, from the ground up.
The stakes were high. The new Data Warehouse had to be designed and developed in parallel with the ERP implementation itself — meaning data models and business requirements were evolving throughout the project. This was not a straightforward rebuild. It was a moving target.
The Discovery: Bringing in IDS Consulting
IDS Consulting was engaged to design and build the new Data Warehouse end-to-end — from ingestion layer to reporting. The mandate was clear: deliver a modern, scalable platform that would serve the institution’s needs today, while laying the infrastructure for tomorrow’s cloud migration.
The Solution: An Automated, Future-Ready DWH
The foundation of IDS’s approach was a metadata-driven automation framework — a deliberate architectural choice that would prove critical given the project’s inherent complexity.
Rather than manually coding database tables, stored procedures, and objects one by one, the framework generates them automatically from metadata definitions. Add a new data source or entity, and the framework creates every required structure — consistently, predictably, and at speed. This wasn’t just an efficiency gain; it was a resilience strategy.
The full solution delivered by IDS:
- A layered DWH architecture (Delta → Stage → Data Mart). cleanly separating data ingestion, transformation, and consumption. Easier to develop, maintain, and extend over time.
- ETL pipelines via SSIS. loading data from the new cloud-hosted ERP (PostgreSQL) and additional operational systems and external partners into SQL Server.
- A dedicated Data Mart. structured for business reporting — serving management, finance, risk, and portfolio analysis teams.
- Power BI reports. giving business users fast, consolidated access to key metrics — replacing slow, manual reporting cycles.
- Historical data migration. ensuring continuity in reporting and analysis from the moment the new system goes live.
Rollout was phased and coordinated with the ERP go-live: first the DWH infrastructure through to Stage, then the Data Mart, then full Power BI reporting — each layer validated before the next was opened. No big-bang transitions, no surprises.
The Challenge in Execution: Building on Moving Ground
The most demanding aspect of this engagement was not the technology. It was the timing.
Building a Data Warehouse while the source ERP is itself being implemented means the ground never stops shifting. Data models evolve. Business requirements get refined mid-project. Structures defined in one quarter look different three months later.
For most DWH projects, this degree of change would be a serious delivery risk. Here, it was manageable — because of the automation framework. When requirements changed, adapting the DWH meant updating metadata and re-running the framework, not rewriting hundreds of lines of code. The standardized approach absorbed the turbulence that would otherwise have derailed timelines and inflated costs.
The Results: A Platform Built to Last
With delivery completing in late 2026, the institution will have:
- One unified data platform. a single source of truth replacing fragmented, siloed legacy systems.
- Modern Power BI reporting. accessible, fast, and maintainable across management, finance, and risk teams.
- Eliminated manual reporting bottlenecks. freeing analyst capacity for higher-value work.
- Historical continuity. key historical data migrated so analysis and trend reporting don’t start at zero.
- A cloud-ready architecture. designed from the ground up for future migration with minimal rework.
- A scalable foundation. built to handle growing data volumes without structural changes.