So far I have been focusing on Big Data technology within the Financial Services context. Now, let’s turn our attention to the domain itself and the actual use cases within the Financial Services industry.

As much as Big Data is about data volume and variety, it is velocity which is frequently the most challenging aspect, especially in the context of our industry. Velocity and value should really be considered together for reasons I’ll explain later.

Variety refers to various degrees of structure (or lack thereof) within the source data . And while much attention has been given to loosely-structured web data - whether sourced from the web itself (social media, etc.) or from web server logs - unstructured data within the firm's firewall is sometimes even more critical to financial institutions.

As I mentioned previously, the ability to gain a holistic view of exposures and positions (requiring rapid, timely, and aggregated access to large amounts of financial data that are growing exponentially) is becoming paramount to financial institutions worldwide. The challenge that many firms are facing right now is how to keep up with the sheer volumes of data that are involved.

Systemic risk was at the heart of the financial crisis of 2008, and is again on everyone’s mind as the current sovereign debt crisis unfolds. Regulatory and industry efforts are, therefore, focusing on getting a more accurate view of risk exposures across asset classes, lines of business and firms in order to better predict and manage systemic interplays.