September 02, 2011

For most banks, the conversation about Big Data goes something like this: After explaining what the trend means, IT departments focus on the technical challenges of analysis and reporting of very large amounts of data in an acceptable period of time. This time period is, of course, dictated by the needs of business users who depend on data analysis and reporting to support compliance, risk management and other strategic decision functions.

But the focus on analysis and reporting tools is only half of the picture. Enterprises have to gather information from a myriad of systems, standardize it and then actually process it (run real applications on it), before it is ready for analysis. In other words, banks must somehow wrangle standardized data into applications that run at a granular level that reflect the ever more detailed needs of the business before meaningful analysis and reporting can happen.

Sound complicated? It can be. But a clear understanding of how to best prepare the data and how to make it actionable in real-world applications and business processes helps pave the way for Big Data in even the most complex financial services organization.

Understanding the Drivers for Big Data

Preparing enterprise data and putting it to use in enterprise applications demands a set of capabilities and systems architecture that organizations must either adopt or adapt. This is a key point: Many banks do an excellent job of finding data, but they don't always have the ability to deal with it effectively. This is not a criticism of an IT organization's strengths, but rather an affirmation that the complex web of disparate systems, geographies and functions within a financial services firm makes it challenging to understand and deal with Big Data.

In our experience, the firms that succeed in turning Big Data into a valuable asset rather than a liability adopt solutions that can:

Standardize. In an age where information is stored on many different systems, some of which do not "talk"to one another, technology solutions for Big Data must integrate different technologies, data formats and coding structures including exception management, error reporting and audit trails.

Provide detailed processing to deliver on business needs. Banks need sophisticated technologies that can perform business transaction-level logic such as cost allocation, revenue distribution and micropayments.

Work fast, change fast and stay within budget. The best solutions are capable of adjusting these business processes rapidly and with minimal cost.

Turning Big Data Into a Competitive Advantage

Businesses are innovating products and services at an ever-increasing rate and then bundling them together to stay ahead of the competition. For banks, that means creating mobile applications and transaction capabilities, offering online access to new products and creating a holistic view of the customer by integrating information related to customers' investment behavior, savings strategies and interaction preferences. Some customers want only online access, while others need a personal touch. Banks are increasingly looking to technology to enable their understanding of how to delight and retain each customer with a bundle of services while keeping those service costs in check.