Big Data has emerged in recent months as a potential technology solution to the issue of dealing with vast amounts of data within the enterprise. As in other industries, financial services firms of all kinds are drowning in data, both in terms of the sheer volume of information they generate and / or have to deal with, and in terms of the growing and diverse types of data they confront in those efforts.
As elements of the global financial markets regulatory puzzle are slowly pieced together, the case for enterprise risk emerging in 2011 as the long-awaited ‘killer app’ for enterprise data management is growing.
In line with the current trend towards investment in real-time risk technology, 90% of the respondents to Swift’s recent survey on liquidity risk management are keen to receive more intraday liquidity reports. The survey, which is a follow up to a white paper on the subject of liquidity risk published earlier this year (see our coverage 
Firms should focus on achieving a top down approach to liquidity risk management and concentrate on improving the two areas of intraday liquidity visibility and liquidity forecasting capability, according to a recent Swift white paper. The paper, which is largely targeted at bank treasury and payments transaction business managers, identifies the pitfalls involved tackling the area of liquidity risk management in order to meet the regulatory requirements of the current environment.
A-Team Insight Events combine A-Team's expertise in financial markets IT with thought leadership from world-class technology innovators and practical experience from financial market practitioners. In 2011, a quality constituency will once again gather for these focused events in London and New York City.
This white paper is based on primary research by A-Team interviewing senior IT and Data Strategy managers at tier 1 and tier 2 banks.
Risk management has been accepted as the new imperative for financial institutions of all types and sizes. But for Tier 1 and Tier 2 banks and brokerages, the complexity of their organisations is presenting risk professionals with a thorny data management challenge: How to gather, normalise and synchronise information from a broad array of internal and external sources that provide the data required to drive their risk analytics and management systems?
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