One of the biggest problems finance houses today face is running credit checks on a client without causing way too much unnecessary complication for said client. Despite the technological advances happening on a day-to-day basis, the financial industry continues to lag behind slowing to incorporate new technology in their business practices. So, what is the solution?
This is where big data comes in. Big data and financial services are natural match – after all, with nothing to manufacture and no physical product to sell, data is the bedrock the industry is built on. Moreover, big data analytics offer more actionable insights, real-time experience, and solutions which save costs and improve products and services. And then there is smart data, the younger and more skillful brother of big data. But before we delve into that, let’s set the stage with some groundwork, concerning terms and definitions.
Big data has been a well-known ‘entity’ for quite some time now and has been commonly used for over a decade. It describes large volumes of data, be it either structured or unstructured that inundates a business on a day-to-day basis. Conversely, smart data describes data that has valid, well-defined, meaningful information that can expedite information processing.
“Smart data describes data that has valid, well-defined, meaningful information that can expedite information processing.”
Big data is defined by four key elements (or properties if you will) – data volume, data velocity, data veracity and data value. The volume and velocity aspects refer to the data generation process: how to capture and store the data. Veracity and value deal with the quality and usefulness of the data. The thing is, as the name suggests, the amount of data referred to is indeed huge and not all of it is valuable; a lot of it just ‘noise’ – information or metadata having low or no real value for the enterprise. Smart data filters out the noise and retains the valuable data, which can be effectively used by the company to solve business problems. Analyzing data qualitatively enables one to not only become data-driven, it also creates opportunities to become creatively-driven while weeding out the noise for a more logical approach.
And herein lies the biggest issue many companies face: quantity vs quality. Data needs to be rigorously analyzed and assessed for its veracity and functionality. What is the variation, ease and extractability of it? Is it embedded in a mass of other irrelevant information? Collection and usage of big data is only meaningful when it is used to optimize and automate solutions and solve problems. We need to shift the focus from just collecting vast amounts of all possible data to contextualizing the one we have collected, within its own specific area. This is why big data tends to get more useless and proves to be burdensome as a whole.
Smart data on the other hand only becomes… well, just smarter. Fact is, data that has been appropriately sorted and structured can be usable long past the expiration date of typical data. It presents businesses with the ability to reach back into the archives and identify trends, look for anomalies and project patterns going forward. This capacity is only possible if data is approached intelligently and, obviously, there must be intention and vision when collecting, collating, and utilizing it.
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