Data Analytics

Big Data and Data Analytics in the Finance Domain

Vamshi Ramarapu

October 4, 2022

Digital representation of a world made of binary data, illustrating big data and data analytics.

Big data is revolutionizing virtually every industry, perhaps none more than financial services. It is giving finance firms the ability to do things they never could before – like roll out new payment systems, deliver data-driven offers and use AI to combat fraud.

Banks, investment firms, stock traders, and others have more data at their disposal than ever before. To generate positive business outcomes, they must master the art of organizing, accessing, and analyzing this vast amount of structured and unstructured data to pull insights out in efficient, timely, and cost-effective ways.

Legacy data management systems are struggling to keep up with the myriad sources and different types of data flowing in at higher velocities. Data platforms operating in the cloud provide a solution to these issues across industries. They also have the power, storage, and scaling capabilities necessary to solve specific data-related challenges that financial services firms face.

Regulatory Requirements

The finance industry, of course, is one of the most tightly regulated of all industries. Many countries require data to stay in their country, which makes it difficult for financial firms to pull reports and perform analytics across geographical boundaries. Firms wanting to look at how a payment instrument performs in one country vs. another, or on a global basis, face challenges accessing and analyzing that data. Modern data management tools enable them to set up data warehouses country by country or region by region. Analytical tools can study the data in stages, with queries getting rerun against different warehouses, all using one platform.

Data Quality

Data quality is critical in financial services because firms generate reports and perform predictive intelligence based on the data they have. Because data comes from disparate sources, quality is often suspect. There might be some data missing or in a different format. Data management tools can preview the data that is collected, and integration tools can translate data from one format to another. Data platforms can fix data quality issues within systems and integrate with other data quality management solutions.

Data Governance

Because financial services firms also deal in sensitive data, they must maintain fine-grained control as to who has access to specific reports. This is especially true for personally identifiable information (PII). Plus, organizations must adhere to data governance rules, as certain types of data can only be “kept” for certain time frames. Using database management tools, financial companies can comply with timelines on transaction and processing data and create governance rules on access and archival data.

Data Silos

Data silos are a significant problem for financial services companies. They often have credit data, customer data, and marketing data in separate warehouses, governed by separate sets of rules. Data integration tools can connect the sets in one warehouse, where departments can run analytics across forms, functions, and geographies. Data management tools provide the capability to connect to different sources and generate reports in one format.

Data Security

As hackers intensify their efforts and broaden their intrusion tactics, financial services firms must respond with tougher security strategies. It is a challenge because every piece of data that gets brought in or shared must be authenticated for each database it connects with. Organizations need to encrypt data warehouses to ensure that data is secure. Integration tools and analytics software also play a key role in providing access to secure data warehouses.

Moving Forward With a Data Strategy

Financial services firms are no stranger to data. They have been collecting and analyzing big backlogs of information for decades. But today’s data requirements dwarf those from previous decades. For those looking to adopt a big data strategy or refine their current tactics, a methodical approach makes the most sense.

Here are some steps they should take:

  • Interview internal and external stakeholders.
  • Evaluate the current state of systems, processes, and skills.
  • Identify a problem space to focus on.
  • Create a roadmap for transformation.
  • Develop a platform for data collection, organization, and analysis.
  • Utilize a cloud data management platform that aligns with your strategy to accelerate this step.

Financial services firms recognize the value data can provide. They are developing new and creative ways to pull insights from data to do a better job connecting with customers and driving efficiencies through their own operations. Taking advantage of tools like the Actian Data Platform can provide the strategic advantage they need in today’s competitive environment.

Vamshi Ramarapu headshot

About Vamshi Ramarapu

Vamshi Ramarapu is the Vice President of Actian Data Platform Engineering, focusing on the development and delivery of the cloud data management platform. He is committed to research and development, scalability, and user experience. With more than 20 years of experience in software development and operations, Vamshi has previously held leadership roles at Mastercard and Visa. He is passionate about innovation in cloud-native development, data engineering, and FinTech.