Ruchi Chauhan & Mukesh Pandey, TCS Financial Solutions

More businesses today are riding the Big Data analytics bandwagon with the objectives of converting insights—gleaned from huge piles of data–into genuine business advantage.

In the retail banking space, unstructured data collected from a broad range of social media sources has resulted in advanced customer profiling and in-depth analytics that in turn are helping enhance customer loyalty and experiences. However, in capital markets so far, firms have traditionally dealt with structured data sets from limited and pre-defined sources.

Big data strategies have now begun to impact a select few areas in capital markets firms over the recent years, including sentiment analysis for trading, risk analytics, and market surveillance. Data management is now a strategic function within most financial institutions, and regulatory, customer and internal drivers have resulted in firms re-evaluating data related to trading, risk management and operations.

Big Data in the Context of Capital Markets

Capital markets areas typically generate large volumes of data—be it through trading, transactions or operations. However, computing efficiencies and cost constraints limited the management of such data in the past. Today, advanced computing powers coupled with new technologies like Hadoop, Spark, and others have made it possible to have integrated views of data. Regulatory changes, advanced trading strategies, tighter risk management and compliance, complex processing and stricter timelines for reporting are fast paving the way for the adoption of Big Data.

Typically, data strategies can be applied to a whole range of functions, ranging from front-office trading to back-office processing, surveillance, reference data and support. Many firms today are focused on data-driven initiatives, and are looking to discover unique ways in which data can address prevailing problems or give them a competitive advantage.

Regulatory mandates demand that firms eliminate silos, and this means combining isolated data sets with heterogeneous assets, products and such. In many ways, such strategies are analytical tasks. Audit trails for data underlying risk analytics or pricing of trades are necessary for investors and regulators. The need for transparency in financial markets means that data must be stored and analyzed in a comprehensive manner, while also keeping costs of managing it low.

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