insideAI News Guide to Big Data for Finance

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Introduction to Big Data for Finance

According to the 2014 IDG Enterprise Big Data research report, companies are intensifying their efforts to derive value through big data initiatives with nearly half (49%) of respondents already implementing big data projects or in the process of doing so in the future. Further, organizations are seeing exponential growth in the amount of data managed with an expected increase of 76% within the next 12-18 months. With growth there are opportunities as well as challenges. Among those facing the big data challenge are finance executives, as this extraordinary growth presents a unique opportunity to leverage data assets like never before.  As the 3 V’s of big data: volume, velocity and variety continue to grow, so too does the opportunity for finance sector firms to capitalize on this data for strategic advantage.  Finance professionals are accomplished in collecting, analyzing and benchmarking data, so they are in a unique position to provide a new and critical service—making big data more manageable while condensing vast amounts of information into actionable business insights.

Although many perceive a financial industry mindset that’s wary of change—it is the cautious adoption of big data technologies such as cloud solutions and Hadoop using the open source distribution model that will be transformative in many fundamental ways. Further, financial services firms that use business analytics efficiently and effectually will thrive in the current uncertain and fluctuating markets, while those that do not are likely to founder.  Financial services firms are discovering many innovative and strategic directions by consolidating data traditionally managed in departmental silos in order to more holistically analyze risk exposure, comply with regulatory mandates, and perform enterprise-level analytics. Traditional technologies
such as relational database management systems make it challenging, many times impossible, to process growing volumes of data and make them accessible, actionable and elastic to changing
requirements in terms of advanced analytics. Big data solutions that support evolving business and regulatory requirements by maintaining a network of growing data stores will become invaluable in their ability to be used for multiple purposes and to answer any query months or years from now.

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