Guest Analysis – Bank On It: Why Financial Institutions are Slow to Adopt Big Data and Why They Should
Rick Delgado
July 23, 2014 – Big data is transforming the way that businesses conduct their operations. Indeed, it is the very definition of a revolution, one that many experts predicted years ago. Yet even though big data has made major inroads into businesses from healthcare to retail, other fields have seen a noticeable lack of big data adoption. One such area is the banking industry. Though banks and other financial institutions have generally been slow to take advantage of big data, they do recognize its potential. In one study, about 60% of North American financial institutions think big data analytics would give them a significant advantage over their competition. It’s only a matter of time before banks implement big data solutions.
Obstacles to Adoption
Banks may know they need to use more big data, but they’ve been very reluctant to make the change. Research shows that more than half of banks are only exploring and experimenting with big data adoption. There are many reasons financial institutions have been slow to take the leap. For one thing, banks are used to dealing with the more traditional structured data. Big data, on the other hand, is unstructured and requires new technologies and processes if institutions are looking to organize, store, and retrieve data in order to use it. Many banks also have organizational problems that prevent them from making the most of big data since divisions and departments are usually organized into silos where data is not used for the entirety of the institution. Banks also tend to have a lack of people who have the skill set necessary to manage and properly analyze data for the benefit of the business.
Available Solutions
Luckily there are a number of solutions that can address many of these obstacles. New technologies exist today that can help to manage all that new data being generated through internet activity, social media interactions, emails, and financial transactions. Parallel-processing hardware makes understanding and synthesizing data much easier than before, while data-processing software like Hadoop can help organizations analyze all the new big data being collected. The improvement of flash storage can enable financial institutions to store and retrieve data at a more manageable rate (and if you’re asking, “What is flash storage?” find out more here). Along with new technology, there are now lots of experts that can get the most out of big data. Data scientists are growing in number and can extract meaning from big data sets. Usually the skills required for gathering and analyzing big data require teams of people up to the task, so as long as banks are willing to put together the right people, they’ll soon see why big data can be so useful.
Benefits of Big Data
For those banks that implement the solutions to big data adoption, the benefits will be worth all the time and money invested in doing so. One major advantage banks stand to gain from big data is improved customer segmentation. Banks have long divided customers based on a number of factors, but big data takes the practice and makes it much more refined. Instead of separating people based on simple demographics, big data allows them to create segments based on expectations, financial needs and preferences, life events, etc. From this, banks can develop new ways to interact with customers and address what they need. This can lead to an improved customer relationship along with giving customers a better experience and instilling company loyalty. With personalized offerings and bundles, banks may even be able to cut costs while growing their customer base. Better customer segmentation also leads to more effective marketing campaigns and improved product development. Predictive analytics through big data can even give banks a glimpse into the future to prepare for customer trends and financial fluctuations in the marketplace.
Implementation Examples
While the obstacles to big data implementation are considerable, many financial institutions have already made strides in adopting it. US Bank, for example, took the big data it was collecting on customers and integrated it with call centers, providing them more reliable and relevant leads. Other major banks like Discover, Bank of America, and Wells Fargo have worked at making big data a more prevalent part of their organizations. Bank of America noticed the organizational problems that impeded big data adoption and changed their structure to allow for more effective use of big data while also showing a better understanding for customers. These are just a few of the examples of banks using big data to present a better product. As big data capabilities become more available to banks of all sizes, all financial institutions will show progress in meeting the demands of higher expectations.
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