Risk Data Management and BI at the Bank of Montreal

With dramatic growth in both the scale and velocity of data in the financial services sector, traditional business intelligence (BI), risk analytics, and database tools for extracting actionable intelligence in an efficient and timely manner have had to improve to keep pace.

Across the financial services industry,  traditional data and BI architecture supporting market data, risk data, risk analytics and trading systems has been challenged to provide real-time access to large data sets, while maintaining audit trails, what-if scenarios, and the integration of advanced analytics. The databases in question have seen the introduction of columnar, document-oriented, massively parallel file systems.