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Operational Data Store for Financial Services Management

by Paresh Bramhane
Operational Data Store for Financial Services Management

The banking and financial services industry has been known to underinvest when it comes to digital systems and innovations, so it’s no surprise that, although financial systems aren’t obsolete, they aren’t cutting edge. As data becomes a core part of business systems, the industry is playing catch up because currently available solutions simply can’t handle current financial and business demands. Even countries like India have developed real-time digital payment platforms like the Unified Payments Interface (UPI) to cement their position in the online payment business.

Technological innovations in data analytics have helped companies open up revenue opportunities through analyzing spending patterns, credit information, and even social media to gain deeper insight on customer behavior and sentiment. The financial services industry takes a significant piece of the pie when it comes to big data, with 22% usage, followed closely by technology and telecommunications, at 16% and 14%, respectively. For most organizations, the main focus of big data analytics are improved risk management, enhanced customer experiences, and more targeted marketing. 

Why the Financial Services Industry Falls Behind

The data generated today comes from both structured and unstructured sources, making their management complex, especially when relying on legacy data systems. Appropriate processes should be established and powerful technologies employed to ensure that the true potential of data is unleashed and transformed into insights that are useful to the business. The technology is available; unfortunately, there are several hurdles that the banking and financial services industry must overcome to ensure that their data strategy is successful.

  • Data security
    Banking and financial services handle large amounts of sensitive data like credit card numbers, account information, and transaction histories. As such, data governance is vital so that risks can be mitigated. The proper set of tools will help ensure that data is protected and anomalies and other suspicious suspicious activity are detected as soon as they occur.
  • Data quality
    Because data comes from various disparate sources, they don’t always match and can be challenging to process and analyze. The appropriate data management solutions will ensure that data gathered is accurate and usable.
  • Regulatory requirements
    Unlike other industries, the banking and financial services industry faces more stringent regulatory requirements when it comes to data governance like the Fundamental Review of the Trading Book (FRTB). As such, it demands accelerated reporting and a scalable and cost-effective risk management policy, which also helps in providing improved metrics and valuable insights.

Benefits of an Operational Data Store in Finance

As access to the latest technologies becomes easier and quicker, consumers demand instant results even from the financial services industry. The maintenance costs of legacy systems balloon while the manpower that can efficiently manage these systems continue to dwindle, making it a challenge to keep up with current data management standards without “breaking the bank.”

Fortunately, banks and financial institutions can make use of an operational data store to store pertinent operational data closer to the user for quick and seamless access. It’s a near-real-time system that helps make gold copy data sets available and easily accessible to users. An ODS benefits financial institutions because, although they handle both static and dynamic data, it retains the relational links to these datasets even if they come from different domains. Pre-related data elements make it easier to control and simplify what could be extremely complex, and time-consuming, processes. With a modern ODS in place, financial institutions can also set event triggers so they are alerted of data breaches and any changes to stored data. As an intermediary to the data warehouse, the ODS keeps the most recent version of data to help in performing business intelligence (BI) tasks like order tracking, customer monitoring, and even logistics management. 

Banks and financial institutions often rely on multiple applications or systems, and bringing the data together from these disparate sources can be very challenging. With an ODS, however, this is solved by the provision of a single repository for current data where it can also be consolidated without sacrificing system speed and performance. Because customer data always changes, it’s also vital for financial systems to be integrated so that there’s a continuous flow of data between them. The ODS architecture facilitates this and allows for the creation of business rules that will trigger a system action whenever there are changes in the data within any of the integrated systems.

Mastering Big Data in Finance

Forward-thinking banking and financial institutions have shown that the adoption of modern data solutions provides some benefits in the long run. Figuring out data is figuring out what problems your business has and how you can solve them. However, regardless of whether you want to enhance the overall customer experience, improve business processes, or optimize operations, there are a few key steps that must be taken. First and foremost is understanding what your organization needs and defining a data strategy. The root of this strategy should always be your business goal, and the strategy should encompass all departments within the organization. you should focus on where your data is taking you instead of wasting resources on temporary fixes and short-term solutions. The data needs of every business will vary, but a modern platform like an ODS will help any business scale by collecting and analyzing the right data while processing it in near-real-time.

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