big data by oracle

Topics: Business intelligence, Data management, Data warehouse Pages: 23 (8910 words) Published: October 11, 2014
An Oracle White Paper
June 2012

Financial Services Data Management:
Big Data Technology in Financial Services

Big Data Technology in Financial Services

Introduction: Big Data in Financial Services ....................................... 1 What is Driving Big Data Technology Adoption in Financial Services?3 Customer Insight ........................................................................... 3 Regulatory Environment ................................................................ 3 Explosive Data Growth .................................................................. 4 Technology Implications ................................................................ 4 The Big Data Technology Continuum ................................................ 5 Handling Unstructured Data .......................................................... 6 Handling Semi-Structured Data ..................................................... 7 Handling Structured Data .............................................................. 7 Adding New Dimensions to the Decomposition Framework ........... 8 Mapping Oracle Products to Big Data Technology Requirements...... 9 The Oracle Database 11g: Beyond Relational Technologies ....... 10 Hadoop and the Oracle Big Data Appliance................................. 11 Business Intelligence and Dynamic Information Discovery .......... 11 Why Engineered Systems Matter ................................................ 12 Delivering Real-Time Decisions ................................................... 14 Oracle Platform Solutions for Big Data use Cases ........................... 14 Big Data Platform for Risk, Reporting and Analytics .................... 14 Platform for Data-Driven Customer Insight and Product Innovation.16 Platform for Security, Fraud and Investigations ........................... 18 Why Oracle ..................................................................................... 19

Introduction: Big Data in Financial Services
The Financial Services Industry is amongst the most data driven of industries. The regulatory environment that commercial banks and insurance companies operate within requires these institutions to store and analyze many years of transaction data, and the pervasiveness of electronic trading has meant that Capital Markets firms both generate and act upon hundreds of millions of market related messages every day. For the most part, financial services firms have relied on relational technologies coupled with business intelligence tools to handle this ever-increasing data and analytics burden. It is however increasingly clear that while such technologies will continue to play an integral role, new technologies –many of them developed in response to the data analytics challenges first faced in e-commerce, internet search and other industries – have a transformative role in enterprise data management. Consider a problem faced by every top-tier global bank: In response to new regulations, banks need to have a ‘horizontal view’ of risk within their trading arms. Providing this view requires banks to integrate data from different trade capture systems, each with their own data schemas, into a central repository for positions counter-party information and trades. It’s not uncommon for traditional ETL based approaches to take several days to extract, transform, cleanse and integrate such data. Regulatory pressure however dictates that this entire process be done many times every day. Moreover, various risk scenarios need to be simulated, and it’s not uncommon for the simulations themselves to generate terabytes of additional data every day. The challenge outlined is not only one of sheer data volumes but also of data variety, and the timeliness in which such varied data needs to be aggregated and analyzed. Now consider an opportunity that has largely remained unexploited: As data driven as financial services companies are, analysts estimate that somewhere between 80...
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