Financial Services and Insurance Edition
Oracle Corp
May 2012 Stay Connected: TwitterFacebookYoutube    TwitterFacebookLinkedIn Youtube
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How to Profit from Big Data in Financial Services

The financial services industry faces some of the biggest challenges of any sector when it comes to mastering big data—the large volumes of high-velocity, varied data coming from disparate external and internal sources. Deriving business insights from this data is key to gaining market share, assessing risk, and developing new products. But this is possible only if financial institutions have the tools and processes in place to integrate, filter, and analyze fragmented data gathered from a variety of sources. We spoke to Amir Halfon, senior director of technology for Oracle Financial Services, about the challenges.

Q: Much of the buzz around big data is about capturing intelligence from social networking sites. Is this where financial services firms should focus their efforts?
A: I would argue that the real power of big data comes from the combination of external and internal data. It’s true that the initial focus is around the wealth of information available on social media and the greater Web. But we also have to look at how this information relates to internal data. If I can tie what I know about customer activities to my core banking system, I can adjust my product offerings to get a leg up on the competition, and also more accurately evaluate the customer’s credit worthiness based on his behavior.

Q: Can financial organization rely on traditional BI tools to slice and dice this information or does big data require a new approach?
A: The value of big data analytics comes from the integration of newer technologies such as Hadoop and R with traditional BI tools and data warehouses. Rather than having to choose between them, financial institutions are finding that their business needs require the full scope of these technologies. And integrating them to create a holistic platform is where Oracle provides true thought leadership, building on the innovation of its engineered machines.

Oracle recently introduced Oracle Big Data Appliance, an engineered solution optimized to run big data workloads using Hadoop, Oracle NoSQL Database, and R. It features an InifiniBand backplane for high-speed connectivity within the cluster, as well as to other engineered machines such as Oracle Exadata, a high-performance data warehousing and processing platform. It also offers Oracle Big Data Connectors to facilitate moving the results of Hadoop-based analysis into the data warehouse, where it can be combined with structured, transactional information to provide deep business insights.

Oracle Exalytics is another engineered solution that is designed to run BI analytics at the speed of thought, without waiting for data to be pulled from the warehouse. It does so by dynamically caching data into an in-memory database, which is controlled by the BI server. Since the latter has knowledge of users’ interactions through dashboards and workflows, it uses intelligent algorithms to prefetch the required data into memory.

Oracle Exalytics, Oracle Exadata, and Oracle Big Data Appliance are all connected using their InifiniBand fabric to expedite data movement between them and enhance their combined capabilities to process big data workloads, providing a truly unified platform.

Q: What advice do you have for financial institutions as they develop their big data strategies?
A: Focus on the use cases that will deliver the biggest business value the most quickly. Then create your architecture and roadmap based on those use cases.

Read more of Halfon’s analysis of big data for financial services.

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