When we talk about the fintech industry, we often refer to the great user experience, better value for money, and wide range of innovative products that it provides to customers.
These great offerings of fintech derive from the creativity of startup founders and of the people they attract to work for their companies, who together have changed the financial world as we know it. These innovators see everything as possible and let their imaginations run wild when devising ways to enhance our everyday digital interactions. As a result of their dynamism, some fintech start-ups have achieved enormous success.
Traditional banks are catching up with fintech companies by offering fintech-like experiences and innovative products. Perhaps these banks feel inspired or threatened by what they see in the fintech industry. Regardless of their motivations, they are increasingly willing to step up to the plate and compete with fintech offerings. What fintech and many banks — at least the ones that want to be around in the future — have in common is the clear understanding that the use of cutting-edge technology is essential to remain competitive.
Thus, banks and fintech companies are deploying new technology to fulfill their implied promise to customers “Come to us; it’s better here!”. This is how artificial intelligence (AI), machine learning, deep learning, big data, and data science have entered the finance industry. To use this science and technology and provide a competitive service and user experience, financial institutions must obtain as much data as possible.
This requires financial institutions to take into account regulatory constraints on data use. A further challenge is that, while companies might be good at collecting data, they may have difficulties uncovering actionable insights that could help them to provide better banking offerings and experiences to customers. It certainly does not help that larger and older financial institutions have to deal with legacy systems, data silos, and unstructured data. And then there is the big bad wolf of cyber security, which leads many executives to hold back when it comes to developing new strategies for the collection and use of sensitive customer data.
In 2006, the mathematician, Clive Humby, stated that
Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.
Many of us would agree with Humby’s analogy. Nonetheless, companies have difficulty managing the vast amount of data available in our technologically connected world. This data is stored in different formats and many places, making it very challenging to take full advantage of its power and potential. Worst of all, this data is growing exponentially due to the digital footprint that customers leave on all the online services they use.
The problem, simply put, is that we have too much data.
This is where data fabric can help companies. The value of the data fabric is that it enables better insights/intelligence because it provides access to more data from an extensive pool of distributed data sources (both on premises or in public clouds than businesses are ordinarily using) and identifies relationships between data to provide richer context. The data fabric also does ensure proper data governance which is very important when scaling the use of data for analytics and AI.
This ensures proper data governance and ultimately allows companies to implement a winning technology strategy. In the process, a data fabric architecture allows companies to take full advantage of a hybrid and a mix of multiple clouds, allowing portability in both data storage and applications. For instance, IBM’s definition of hybrid cloud is inclusive of on-prem data stores and applications, not necessarily just on-prem private cloud.
Despite the challenges posed by an excess of data, many players in the financial industry are moving in the right direction, seeking to overcome the obstacles that come with increased implementation of AI and customer demands for personalization. Data fabric addresses many of the issues faced by companies in their attempts to strengthen and update their technology strategies. Furthermore, it helps companies to comply with strict privacy regulations (e.g., the California Consumer Privacy Act (CCPA) in the US and the General Data Protection Regulation (GDPR) in Europe) when using customer data.
In financial services, tech-savvy customers long for personalized offerings that take into account both their financial history and their present financial situation to provide solutions based on their current requirements and anticipate their future needs. To meet this demand, companies need as many data points as possible to make sense of the vast amount of data collected from their customers and provide them with informed and comprehensive solutions.
In other words, if a company knows its customers better than they might know themselves and designs solutions on the basis of this knowledge, there are likely to be benefits for both the customer and the company.
Our personal lives take many turns, and many would want financial institutions to take their complex pasts and presents into account to provide them with a “humanized” banking experience, regardless of the ever-growing use of technology needed to achieve this.
Faster extraction of insights is a value of a data fabric, in addition, the embedded governance capability ensures that appropriate data security is maintained — this is especially critical in the FSS industry. Also, the fact that data does not have to be moved and can stay secure at its source per the comment above is important for highly regulated industries such as Financial Services.
If a company is not up to speed with the better, richer insights that can be gained from the right implementation of a data fabric architecture, it will likely lose out to its competitors, as customers may move to new banking providers whose financial solutions appear better matched to their current life situation and which seamlessly adapt to their future needs.
The race is on — and becoming increasingly tense — within the crowded space of new financial competitors, which range from fintech start-ups and banks to non-financial companies that are taking advantage of the embedded-finance trend.
For the future winners in the financial industry, the right data fabric strategy is not an option but a must.
