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.
Big Data vs. Smart Data
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.
Quality over Quantity
Koeck points out that having lots of data is not enough. Data needs to be rigorously analyzed and assessed for its regularity and uniformity. What is the variation, ease, and extractability of it? Is it embedded in a mass of other irrelevant information?
Collection and exploitation of data is only meaningful when it is used to optimize and automate solutions and solve problems, also known as data-driven decision-making. The focus should not just be to collect vast amounts of all possible data, but also contextualize it within its own specific area.
Big data tends to get more useless as it grows.
But smart data only becomes more intelligent. In fact data that has been appropriately sorted and structured can be usable long beyond the shelf life of typical data. Businesses have 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. There must be intention and vision when collecting, collating, and utilizing it.
Why the Smart Data Affects Privacy
The affect smart data is having on the financial industry is enormous. Creamfinance works in the short term loan industry, one that has been plagued by predatory companies and outdated business practices.
It is a good thing that smart data can help in that industry because, despite the reputation of the companies that have defined it, nearly half of Americans would have trouble finding $400 to pay for an emergency.
When Creamfinance was getting started and began looking for funding, they found that not only did it take too long, but lenders were quick to deny them a business loan based on irrelevant data. Koeck wanted to create a company that helped people get small loans faster in as hassle-free a process as possible.
The fintech company uses smart data to provide loans to consumers in a convenient and expedited manner, minimizing consumer effort and maximizing risk management. Using smart data, they constantly refine algorithms and analyze their data sets to measure whether it is appropriate to lend.
“We’ve found that focusing on smart data is the most accurate way of measuring creditthat translates into fast product delivery to make personal finance accessible to everyone,” says Koeck.