With a global reach and ever-expanding scope, the FinTech industry is continuously growing. While already a core sector, the next few years will see financial technology grow from a market cap of $245 billion to $1.5 trillion, cementing this industry’s presence and importance.

Over the past decade, the FinTech sector has had to expand the countries they work with, the currencies they manage, the banking partners, and the financial products they offer. With the sheer diversification that is inherent in a complex field like finance, the continual growth of this field is to be expected. However, as FinTech firms continue to scale, they must understand how to expand their offerings without compromising on the quality of their products and services.

At the core of successful scalability is data analytics, providing a transparent system that allows firms to understand their customers, investment climate, and competitors to a greater degree. Existing in a storm of growth, Fintech firms need a stream of accurate, updated, and rapid analytics, pulling from existing data and real-time events.

As FinTech continues to scale, the industry’s reliance on data has only grown more acute. In this article, we’ll dive into the integral balance of data, data analytics, and data architecture in the financial field.

How Data Analytics Has Revolutionized FinTech

Data analytics is a central part of the financial technology industry, propelling businesses forward with a higher degree of certainty, personalization, and precision. Over time, as the technologies that support the data industry have expanded, leaders in FinTech have been able to incorporate new systems, platforms, and methodologies to refine their offerings.

There are a number of core areas where FinTech makes use of data analytics to drive progress:

The Rise of Data-Driven Insights

Financial analytics is a leading segment of the financial technology field, one that empowers businesses to make informed decisions about their company and its future. Data-driven insights can impact every single choice in a company, steering them toward a more productive and profitable future.

A phenomenal example of leveraging data analytics in the finance field is Grainger, a Fortune 500 company that used data to boost profitability. Grainger leverages profitability analysis to reduce costs and boost transparency. Each Grainger group was conducting its own data analysis.

Instead of taking this segmented approach, they were able to collect data into one comprehensive system. By defeating data silos, Grainger achieved more effective analytics across the board. With this, they were able to locate weak points in their business and scale more effectively.

Direct Personalization

The vast majority of customer-facing financial systems need to directly personalize in order to win new customers and keep current ones satisfied. FinTech firms are able to personalize their financial products and better tailor them to their specific audience.

Instead of producing generic platforms and adapting them to the largest audience possible, Fintech firms can use data analytics to hone in on what the customer is looking for. By using customer data and scaling its collection and processing, brands are able to offer a more personalized experience.

When clients and customers feel that a brand is actively adapting to their desires, they are more likely to continue working with that service. For FinTech brands, this aspect of data forms a core part of their growth strategy. Commanding a larger audience allows more money to flow into the company, further accelerating expansion.

Operational Efficiency

External processes and client-facing services aren’t the only parts of FinTech businesses that data informs. By taking a scaled approach to data, businesses are able to optimize their internal processes to make each department more efficient.

Data can help pinpoint areas in which a business loses momentum, wastes time, or has decreased efficiency. By focusing on these areas and streamlining them over time, businesses are able to reduce costs while boosting internal productivity. When businesses scale their process to encompass every single division of their company, they can achieve much higher levels of efficiency.

Across the board, this approach radically improves internal efficacy across everything from risk management and marketing to sales and predictive growth. Without a clear understanding of the data that underlies every action team members take, businesses cannot transparently trace the internal motions of their company and move to improve them.

In order to improve an area of a business, it must first be measured. Once that data is readily available, positive change will come.

Delivering Data at Scale to FinTech Solutions

Data spans every function within the FinTech sphere. Without a consistent and accurate entry of useful data into the system, firms are unable to provide an innovative and insightful customer experience. As FinTech firms now serve an international selection across several services, a scalable data system is vital.

In order to deliver this continual stream of new information, FinTech uses a series of base technologies like cloud data warehouses to provide the flexibility and scalability when it comes to hosting the data that FinTech solutions need to thrive. When comparing Clickhouse vs Snowflake, two leading data cloud data platforms, the dramatic extent of additional layers of functionality help to streamline almost every area of data performance. From optimized storage to efficient delivery and processing of data, cloud data warehouses form the basis to provide FinTech solutions with its vital stream of data.

Other technologies, like data lakes, data meshes, and even governance policies that financial companies follow, help streamline data use and utility.

By converging these technologies that provide high scalability, data storage, and security measures, FinTech firms are able to deliver their solutions to a global audience. The scale of applications, services, and concurrent processes is only made possible by the effective balance of distinct architectural components.

Final Thoughts

Over the past few decades, FinTech firms have faced rising competition and increased demand from customers. In order to thrive in this challenging climate, FinTech firms offer a numerous range of applications, solutions, and platforms that provide customers with a unique and optimized experience. Yet, behind the scenes of this expanding industry lies a huge quantity of data, held up by the sector’s dependence on data architecture.

Scaling FinTech services to their current size and scope has been facilitated due to the wide range of new and refined data infrastructure technologies that businesses can draw on. Across the world, a complex web of data warehouses, data lakes, governance systems, and connective resources allows FinTech to flourish.

Finance and data analytics are inseparable, both empowering the other to streamline the industry and drive progress forward.