Building an Effective Data Warehousing for Financial Sector

TL;DR

This paper details the development of a data warehouse for a financial holding company, enabling quick, versatile access to real-time and projected financial data through OLAP cubes, improving analysis efficiency.

Contribution

It introduces a practical implementation of a multidimensional data warehouse using Microsoft SQL Server tools tailored for financial data analysis.

Findings

OLAP cubes improve performance over static reports.

The system enables real-time access to financial balances.

Automation enhances robustness of financial data analysis.

Abstract

This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a business intelligence system that, in a simple, quick but also versatile way, allows the access to updated, aggregated, real and/or projected information, regarding bank account balances. The established system extracts and processes the operational database information which supports cash management information by using Integration Services and Analysis Services tools from Microsoft SQL Server. The end-user interface is a pivot table, properly arranged to explore the information available by the produced cube. The results have shown that the adoption of online analytical processing cubes offers better performance and provides a more automated and robust process to analyze current and provisional aggregated financial data balances compared to the current process based on static reports built from transactional databases.