Financial institutions are still grappling with understanding the concept of big data and how it can be applied in their organizations, according to new research by Celent that will be released tomorrow. Celent surveyed IT executives at 33 financial institutions - including banks and insurers, large and small - for a study commissioned by Microsoft on where institutions stand on implementing big data projects. Bank Systems & Technology got an exclusive first look at the research before its publication tomorrow by Celent.
Few of the institutions in the study had much experience with big data projects. Only 24% of the banks in the study had implemented a big data solution, with roughly an equal percentage in the pilot phase of its first big data initiative. Despite this lack of hands-on experience, most of the executives surveyed (60%) said that big data and analytics are a significant competitive advantage for financial institutions. And 90% of the respondents said they think that successful big data initiatives will define the winners in financial services in the future.
The biggest barrier preventing organizations from pursuing big data initiatives, according to the study, was lack of analytical talent, which was cited by half of the respondents as a major impediment. Other barriers cited by the executives included data privacy (43%), lack of business sponsorship (39%) and lack of a clearly defined business case (39%).
Much of the data in the organizations represented in the study exists in independent silos, making it difficult to know where to start in applying big data and anaylitcs. “Few of the respondents knew where the big data function resided in their institution,” Colin Kerr, the industry solution manager for Microsoft’s worldwide banking team, said of the research. “Some people decided to be early movers in this area, but others are still grappling with where to begin.”
The research showed that financial institutions also have a number of different ways of defining big data. When asked how their organization defines big data, the responses varied widely. The most popular definitions centered around using semi-structured and unstructured data or using predictive analytics and modeling. But others simply defined it as handling a greater volume of data than the organization could currently analyze or real-time data analysis.
This lack of an easy definition makes things even more murky for banks that are trying to figure out how to use big data. “It’s about how do you want to grow your business, or defend your business? It’s about recognizing how do I maximize the use of data in my organization so that each business group can derive benefit?” Microsoft’s Colin Kerr says. Many institutions are still struggling to answer those questions, he adds.
Although few of the institutions in the study had implemented big data initiatives, those that had projects that had been up and running for more than a year had seen positive results. The study found that 70% of the initiatives that had been running that long had met or exceeded business expectations. Most of the banks that have big data projects in progress are larger institutions, with community banks lagging well behind in this area.
The two areas that banks said they were most interested in applying big data were risk and fraud monitoring (95%), and product and service marketing (75%). Kerr noted that there are examples of banks combining these two areas in one data-driven project. For instance, banks can use big data to analyze their credit scoring for credit card applicants. If a bank wants to lower its acceptance score by 10 points, they can use analysis to determine the impact on their default rates on the risk side, and target customers and examine pricing on the marketing side, Kerr said.
But to successfully deliver big data initiatives banks are going to have to change their culture and embrace a data-driven business model, the report suggested. Executives and staff have to understand the growing importance of data and the opportunities it provides, the report noted, while taking better care in managing the flow of information through the organization to overcome silos. This begins with the collection of the data all the way through its storage, use and, eventually, its deletion.