Data Engineering

Enterprise Data Warehouse for a Large Insurance Group

Enterprise Data Warehouse for a Large Insurance Group

An enterprise data warehouse project that unified more than ten internal data sources into a single analytical model, supporting management reporting, BI, and self-service work by finance and controlling teams.

Pregled

A single analytical foundation for management reporting and BI

This project delivered an Enterprise Data Warehouse for a large insurance group. Data from more than ten internal systems and sources was collected into one unified model suitable for management reporting, BI, and corporate analytics.

The core value was turning fragmented operational data into a consistent analytical environment that business users could trust and use across the organization.

How the warehouse was built

The work focused on integrating diverse source systems and shaping them into a coherent analytical structure that supported enterprise reporting needs.

  • Integration of 10+ data sources into one warehouse environment.
  • Support for tables, files, databases, and ERP/accounting systems.
  • Creation of a unified analytical model for management use.
  • Preparation of data for reporting and BI consumption.

ETL, semantic layer, and business reporting

SQL-based ETL, transformation batches, and data preparation pipelines were used to populate the warehouse. On top of the core model, a semantic layer and corporate reporting environment were configured for business-facing analytics.

  • SQL-based ETL for data extraction and transformation.
  • Batch processing pipelines for structured loading flows.
  • Semantic layer setup for analytical consumption.
  • Corporate reporting in BI tools for management visibility.
Stack

Data engineering, BI, and enterprise reporting infrastructure

The project sat at the intersection of data engineering, reporting infrastructure, and business enablement for finance and controlling teams.

Osnovni stack

MS SQL Server DTS Oracle Business Objects ETL Data Warehouse Semantic Layer Corporate BI

The technology stack supported extraction, transformation, warehouse modeling, semantic reporting, and enterprise BI delivery.

Organizational impact

Single source of truth Cleaner reporting Consistent model Better analytics Self-service BI Finance enablement Controlling support Data transparency

The company gained a unified source of management data, more transparent reporting, and the ability to build analytics on top of a consistent model rather than disconnected sources.

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