Big Data Governance for the Finance Sector

SAN DIEGO - July 27, 2016 - PRLog -- As increasing numbers of companies in the finance sector begin leveraging the benefits of Big Data, data governance -- managing the availability, usability, integrity and security of that data -- is emerging as a primary focus for CXOs. The speed and agility by which banks, brokerage firms and other financial enterprises can integrate data governance with big data platforms could, in fact, determine who becomes the industry leaders and who the laggards. A 2014 survey by the Aite Group, for example, found that 41 per cent of respondents (including broker-dealers, asset managers and hedge funds) currently lack a big data solution. Knowing which aspects of data governance are overhyped and which will change the face of risk management, operational efficiency and customer experience is key to determining an overall roadmap for big data adoption, as well as the tools that will keep such an operation functioning at an optimal level.

As with any enterprise, sound data governance for financial firms begins with developing a governing body or council, a set of well-delineated procedures and a plan of execution to managing both internal operations and client-facing activities. From there, data governance strategies tend to diverge depending on the size of the company. Big banks typically focus governance efforts on long-term priorities like mitigating risk, adhering to regulatory compliance and improving customer experience, while smaller firms have the added advantage of architectural agility and can bypass, for example, legacy software by using cloud-based services to spearhead short-term growth and strategic projects.

One area where data governance can play an important role for firms of any size is in creating "know your customer" strategies for preventing money laundering. Anti-money laundering (AML) compliance programs require that data be accessible and accurate in order to identify customers and their potential for risk, as well as for keying in on expected transactions and providing data for suspicious activity reports. Related data -- when cleansed and made appropriately accessible -- will also facilitate the coming era of machine learning, which will make possible real-time analysis of fraud potential and provide for immediate alerts.

A second priority for big data governance platforms is risk and regulatory data management. Regulatory risk and compliance standards are increasingly complex and statistical in nature -- meaning their outcomes are highly dependent on the quality of the data used for measurement. Compounding this is the pressure on firms to save on costs and improve margins while also adhering to these regulatory demands. Yet firms driven by robust data governance strategies are naturally in a better position to aggregate risk and pave the way for predictive analytics. Models built on strategic data governance can be used to report risk results to regulatory boards and external agencies to both demonstrate a firm's financial stability and enhance customer confidence.

A data governance strategy that provides real-time access to accurate portfolio and risk data can also help traders react much more quickly to market events,  Even unstructured data that comes from market rumours, social media and other events can prove to be valuable. By contrast, a firm's accounting branch might require role-based access to a separate data repository to perform reconciliations, check on corporate actions, etc. and might not need such data in real-time. A sound data governance strategy will take such variances into account.

Using modern data governance tools for enterprise recording and reporting can also help facilitate the industry-wide move toward transparency in broker and trade compliance. The advent of  detailed operational due diligence questionnaires used by institutional investors has intensified the need to manage and process enormous amounts of data, but often data thought leadership or operational infrastructure within these firms is lacking. The most successful strategies employ extensive audit logs and activity/permissions records that carefully track who is accessing or editing a file, and when. Of course, this also requires resilient and secure storage capability paired with an archiving platform that can be maintained offsite and yet still allow for persistent access to everything from block data to database records and emails.

"Data governance is not a one-size-fits-all endeavor, but instead must customizable, flexible and agile, allowing for varying degrees of access to as much data as possible, in an organized fashion.," says Ajay Sarkar, CEO of RoundWorld Solutions. "Data governance, when approached in this way, becomes a key driver of business values, be it revenue increase, cost savings or adherence to regulatory requirements. Successful CXOs understand that data is crucial to running a successful business, and that growth and customer-centric activities sit atop the list of corporate strategies linked to big data.

"Our Big Data 360-degree tool is designed to help CXOs in the financial sector to evaluate data -- in a meaningful way -- for accuracy, timeliness, relatability and completeness, while simultaneously simplifying risk management, increasing operational efficiency and enhancing customer experience. We achieve this through our template-driven tool, which can be used to establish controls for data quality and data integrity, build strong information architecture and also manage metadata.

"The right data, when governed and made available to the right stakeholder at the right time, can provide unparalleled insight into business operations, procedural analytics and predictive analytics," Sarkar continues. "It can make a measurable difference in the success of firms both large and small, and indeed might prove to be the asset that separates the wheat from the chaff in terms of marketplace competition."

This article was provided by:
Tiffany Fox
Public Information Officer
RoundWorld Solutions

http://roundworldsolutions.com