Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

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

DZone's Guide to

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

Despiteincreasing data requirements, most firms still use aging proprietary infrastructures that lack the scale and flexibility to meet the data requirements of tomorrow.

· Big Data Zone
Free Resource

Learn best practices according to DataOps. Download the free O'Reilly eBook on building a modern Big Data platform.

Financial services institutions are faced with a number of high-pressure demands, whether it be from regulators, investors, customers, or internal business users. These demands require that firms create, monitor, and provide access to vast amounts of data that 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.

Key Trends

Let's look at the key trends in fast data that can help you be competitive.

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 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 and 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 the 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

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 (for example, 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.

Find the perfect platform for a scalable self-service model to manage Big Data workloads in the Cloud. Download the free O'Reilly eBook to learn more.

Topics:
big data ,fast data ,financial

Published at DZone with permission of John Piekos, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}