Big Data: What Does it Really Mean for the Financial Sector?
Traditionally, the financial sector hasn’t been the most receptive to new technologies. As an industry that thrives by minimizing risk and making carefully calculated business decisions, choosing to handle high value sensitive information with what may simply be the technological flavor of the month is not a decision many rush to make.
Despite these reservations, the UK’s Financial Conduct Authority (FCA) included investments in technology as the top priority of its 2015/16 business plan, a clear sign that the sector needs to be making a more conscious push towards digitization.
Investing in big data
The next step is big data, a technological phenomenon that has stirred significant interest from the banking industry. The idea that data generated during the everyday processes and operations of a business can be used to inform strategies and achieve objectives is an exciting one, particularly in a post-crash economy where banks are faced with constant scrutiny over the detail of risk reporting.
However, there remains a question of what big data truly means for the financial sector. Although the information can yield a competitive advantage for banks, for it to be effective it has to be analyzed effectively.
The majority of the conversation surrounding big data banking to date, has looked at what models and systems are best at making the data accessible for analysts. Yet the question banks should be asking is, “How can this information be actionable for us?”
Although many financial institutions are increasingly using cloud computing to host software platforms and to store data, the analysis itself is still reserved for specially trained individuals—typically data analysts rather than bank managers.
This approach limits the functionality of big data. One of the most valuable characteristics of big data is that it gives banks a real-time insight into multiple data sets. The places that customers regularly use their cards, for example, can be analyzed to highlight opportunities for additional revenue streams by partnering with relevant retailers. This could take the form of targeted customer-cashback offers or even to provide commercial insights to the retailer.
Changing the data analysis game
When choosing a data analytics package, banks should look beyond SQL-based software into the different types of big data analytics for financial services—notably search-based analytics.
Search-based analytic software makes use of natural-language search, the same technology that internet search engines use, for a simple and uncomplicated approach to navigating and inspecting data sets. Cross-referencing becomes an easy process and correlations can be spotted without the need for technical skills. This means people at all levels in the bank can benefit from actionable business insights. Software such as Connexica’s CXAIR, for example, can even draw this data from a wide range of disparate sources, meaning that banks that prefer the traditional bespoke systems can make use of the functionality without the need for migration to a new system.
This is where the true potential for the concept lies. Big data might be a much more buzzword-friendly phrase than “search-based self-service analytics” or words to that effect, but it is limited as a standalone concept. Banks can only truly reap the rewards by setting up an effective means of using it.
Greg Richards is sales and marketing director at Connexica.