The financial services industry information technology world is abuzz with talk of Big Data. Attend any technology conference, pick up any industry publication ... and there it is. While many think that Big Data is simply the buzzword du jour, evidence is mounting that its impact is immediate and here to stay.

In August, Oracle released findings from a study in which it asked 333 C-level executives from the financial services industry and 10 other industries to weigh in on how well they were managing and capitalizing on the deluge of data - structured, and increasingly unstructured - pouring into their enterprises.

The results were quite revealing and pointed to a direct impact on the bottom line. Financial services executives said, on average, that their organizations were unable to capitalize on an additional 12% of revenue because they cannot fully leverage the information they collect. (For a $1 billion enterprise, that would translate to $120 million).

The bottom line is: Big Data = big opportunity - that is, if financial services organizations can put their data to work when and where they need it. The study revealed that they are eager to improve and are focused on this issue, with 97% of overall study respondents saying that they need to make a change to improve information optimization over the next two years.

Today, financial services executives are clearly not where they want to be when it comes to managing the massive volume and variety of data coming into their enterprises today. Roughly a quarter of financial services executives gave their organization a D or F when asked how well they were putting their data to work for them.

The top three struggles in the study were: getting timely data to business managers, not having the right IT foundations in place and the fact that existing systems are not designed to meet the industry's needs.

These challenges are compounded by the fact that financial institutions still work within silos. To get a better grip on all the data held by organizations and deliver timely data to business managers, banks and financial organizations need applications and systems that pull data from the same sources. This will also provide them with a complete view of their customers. The study found, however, that information is hard at work in some areas. Financial services executives say they're making the best use of their data in the areas of regulatory compliance (38%), customer service (34%) and sales/marketing (31%).

Big Data is about re-evaluating the potential value of business data and working to build an infrastructure that taps into all of an enterprise's data assets - looking for some of the previously unknown relationships between products, customers, and suppliers and channels. What that really means is Big Data can be characterized by a couple of new requirements.

First is the need to embrace new types of data and incorporate it into the information architecture. Often this is semistructured data, not the traditional rows-and-columns relational data.

More sophisticated data analysis is also critical. Financial services organizations want to be able to do new types of statistical analysis - such as sentiment analysis and predictive analytics - not over a small sample of gigabytes of data, but over terabytes, potentially even petabytes, and spanning new channels, such as social media. Not all these technologies are new. For example, organizations have done text mining in the past, but they've used it in very specialized ways.

So the multimillion-dollar question is, "How can organizations get in a position to effectively harness the power of Big Data and put it to work to improve their bottom line?" We've identified five steps:

1. Think big but start small. Big Data is a challenge, but by tackling the issue in smaller pieces, banks and other financial institutions increase their chances of success. For example, commercial and retail banks might want to improve their loan risk analytics and profiling capabilities, while a capital markets firm may want to focus on rogue trading based on transaction and accounting records.

2. Identify functional use cases where the return on investment for integrating data silos is justifiable. One example is bringing together finance and risk data for regulatory reporting. In fact, the study shows that 41% of financial services respondents say the alignment of risk and finance is an area in which they can benefit most from better analytical capabilities. Another example is bringing together various customer data profiles - internal and external (such as social media) - to create propensity models for targeted offers.

3. Identify critical internal data sources and build a comprehensive data model that is capable of handing these functional use cases.

4. Evaluate your organization's current analytics and reporting capabilities to see if they can handle data streams from a variety of sources and generate reports and dashboards within acceptable time frames. Then determine what legacy systems can be leveraged.

5. Embark on a well-defined, well-scoped and time bound proof-of-concept.

Big Data represents big potential for today's FSIs, including an opportunity to drive differentiation and accelerate time to market in an environment in which institutions are increasingly challenged on both fronts. The race is on, and with the right processes, strategies and systems for handling data, firms can effectively prepare for the data deluge.

 

S. Ramakrishnan is group vice president and general manager, Oracle Financial Services Analytical Applications.