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Fast Data in Financial Services: Key Trends to Maintain a Competitive Edge

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Finserv: Key Trends for Fast Data

Financial services institutions are faced with a number of high-pressure demands, whether it be from regulators, investors, customers, and internal business users. These demands require that firms create, monitor, and provide access to vast amounts of data which must be immediately accessible, correct, and stored for various lengths of time. Data is a lifeblood as much as a currency.

Yet, despite these increasing data requirements, most firms still use aging, largely proprietary infrastructures, which lack the scale and flexibility to meet the data requirements of today and tomorrow. Fraud, increased competitiveness, new regulations, and more uncertainty mean financial services institutions need to use innovative technologies like fast data to become and remain industry leaders.

On Wednesday, August 23rd, we’ll dig into this very subject at our webinar, Fast Data in FinServ – Key Trends to Maintain a Competitive Edge, hosted in conjunction with BobsGuideRegister here and continue reading on for more details on the topics we’ll cover in the webinar. Check best platform for data science first.

Table Of Contents

Choosing the ‘Right’ Technology for Financial Services

In the past decade, the rise of NoSQL has changed the options for enterprise architects and developers in financial services. Unfortunately, many NoSQL offerings, which offer a more flexible approach to scale out, flexible schema and data types, fail on support for scalable transaction support when working with shared, finite resources: credit balances or trade verification, risk management, fraud detection and management, and customer interaction and personalization. These applications directly affect an institution’s revenue stream. Institutions require tight, predictable latencies for physical transactions, such as approval of credit card swipes — in the range of sub 20ms — so performance and scalability are non-negotiable requirements.

On the other hand, NewSQL offerings like Volt Active Data offer the best solution available for ingesting, analyzing and acting on the massive volumes of real-time data streaming from trading, fraud detection and bid & offer management systems. These solutions combine accuracy, scalability and manageable TCO, even for cutting edge scenarios such as managing trading operations, detecting credit card fraud in real-time, and managing quality of service for many millions of users based in multiple data centers simultaneously.

Moving from Near Real-Time to Real Real-Time Data Processing

What is real time?

For some enterprise architects and business users, it’s a fairly crude measure: a day, a week, or a month faster than they have now. But in a financial services institution, that’s not good enough. Improving the speed of a near real-time batch process from 12 hours to two hours may seem like a huge leap, but when your job is fraud detection/prevention, reconciling trades or balancing performance and risk, you need immediate, correct results — millisecond responses with predictable low latency. Fast data ensures your ability to move from near real-time to real real-time, which means accurate results – instantly.

Achieving a Single Source of Truth

The move away from legacy database solutions offered users an appealing array of options: horizontal scalability, the ability to use unstructured data and simplified data models, freedom from onerous or unpredictable licensing costs, and access to the innovative world of open source software. But the downsides can be considerable: lack of a standardized, well-known query language; myriad data models to accommodate different data formats, e.g. document, graph, and columnar; lack of immediately-consistent data; and lack of enterprise-grade support are just a few. NewSQL databases offer the best of both worlds — a consistent approach to data processing, ensuring data is a correct, single source of truth; the horizontal scalability of NoSQL, without the lack of immediately-consistent data; expressive queries with standard SQL; and the speed of in-memory.

As the industry shifts towards offering customers a more personalized, omnichannel experience, master data management can provide financial institutions with a single source of truth about their customers and how they interact with their institutions. This creates more opportunity for banks to cross-sell and upsell by sending the right message to the right person at the right time, while serving as a source of important information for identity management.

Learn More

To learn more about the role of fast data in financial services, register now for our webinar and join us on Wednesday, August 23rd at 10 AM EDT or download our FinServ business justification kit.

Volt Active Data Blog Staff