Smart Data: 3 Reasons it’s the Future of FinTech

Despite these advances, the industry continues to lag behind, slow to incorporate new technology into their business practices.

But there are signals that some of the larger players are beginning to explore methods for renovating their archaic processes. And if there seems to be a tone of urgency, it is because they have seen what the fintech industry is capable of and how quickly it has grown.

But how does an industry as old as currency itself change its architecture to incorporate not just technology, but smart technology? The answer: smart data.

Big data is a well-known term and has been commonly used for over a decade. It refers to the large volumes of data–both structured and 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.

I recently spoke to an authority on smart data, Patrick Koeck, Chief Officer at Creamfinance, to learn more about this shift in aggregation.

There are four key elements that define big data: 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. In reality, not all of this data is valuable and functionally is 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.

“Big data is a good thing to have, but it is a blunt tool, lacking precision. Smart data cuts to the heart of the issue faster, allowing executives to peel back the layers of extraneous or distracting information and look directly at the important issues,” says Koeck.

Koeck points out that having lots of data is not enough.