HOW TO RATIONALIZE AND MAKE SENSE OF UNSTRUCTURED BIG DATA?
To smile and smile and be faithful is not enough to woo the Banking Customer anymore. Apart from regular banking transactions, today, what he or she expects is a whole gamut of ancillary services at designated branches to online, mobile, call center’s kiosks and ATMs – with swifter and personalized applications.
Today all these channels are manned and controlled from interconnected tech silos that work in close coordination with centralized data centers to streamline the data inflow and process output basis analytics. Given the sheer volume of sensitive BIG DATA that the banking and financial institutions have historically struggled to control and work around to their advantage, it is obvious that, with consumer banking behavior also increasingly led by technology advances, a profound shift towards portable device based interactions is the obvious trend. Thus it is an extremely practical idea to invest in developing smart personalized apps that aid both banks, at an organizational level, and customers, at a transactional level alike.
The fact that banks and financial institutions are constantly aggregating huge volumes of data is a natural corollary of the booming growth of the sector. Times have changed since the early days when banks first hit upon the idea of better understanding their customers and their needs. Considering the various processes that a customer journeys through to avail any of the services today, the need for tech-accuracy cannot be underlined enough.
The huge data overload from unstructured data can either be neglected or ‘mined’ to derive miraculous results for the CMO. Big data technologies can help bring together data to build more intelligent information that can then be analyzed using rules, predictive and sentimental analysis, etc to provide meaningful information that can be harnessed in weaving a better network with customers. Besides this the pressure for banks from regulators to monitor what the eye cannot capture is building a demand for tools that can collate information across the enterprise in deriving risk indicators and data for better risk management and reporting. Big Data technologies, if implemented in the right way of addressing the business use cases as an aggregator, can be a better solution than just building a traditional data warehouse solution.
The combination of big data and mobility can help provide a new technosphere of solutions that address needs of customers, stakeholders and regulators of banks alike.
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