big info by oracle Essay

A great Oracle White Paper

Summer 2012

Financial Services Data Management:

Big Info Technology in Financial Services

Big Data Technology in Financial Companies

Introduction: Big Data economic Services....................................... 1 What is Generating Big Data Technology Re-homing in Financial Providers? 3 Customer Insight........................................................................... a few Regulatory Environment................................................................ 3 Explosive Data Expansion.................................................................. 4 Technology Implications................................................................ some The Big Data Technology Continuum................................................ 5 Managing Unstructured Info.......................................................... 6 Managing Semi-Structured Data..................................................... 7 Handling Structured Info.............................................................. 7 Adding New Dimensions to the Decomposition Framework........... almost eight Mapping Oracle Products to Big Info Technology Requirements...... 9 The Oracle Data source 11g: Further than Relational Systems....... 10 Hadoop and the Oracle Big Info Appliance................................. 10 Business Intelligence and Dynamic Info Discovery.......... 14 Why Manufactured Systems Subject................................................ 12 Providing Real-Time Decisions................................................... 14 Oracle Platform Alternatives for Big Info use Instances........................... 14 Big Data System for Risk, Reporting and Analytics.................... 16 Platform to get Data-Driven Buyer Insight and Product Development. 16 Platform for Security, Fraud and Investigations........................... 18 Why Oracle..................................................................................... 19

Advantages: Big Data in Financial Services

The Financial Services Industry is usually amongst the the majority of data powered of industrial sectors. The regulating environment that commercial financial institutions and insurance companies operate within requires these types of institutions to maintain and analyze many years of transaction data, and the pervasiveness of electronic trading has meant that Capital Markets firms equally generate and act upon billions of marketplace related messages every day. In most cases, financial services companies have relied on relational technologies along with business intelligence tools to handle this ever-increasing data and analytics burden. It is however significantly clear that even though such systems will always play an integral role, fresh technologies –many of them created in response to the data stats challenges initially faced in e-commerce, google search and other industries – possess a transformative role in enterprise info management. Look at a problem faced by every top-tier global bank: In answer to new regulations, banking institutions need to have a ‘horizontal view' of risk within their trading arms. Offering this view requires financial institutions to incorporate data by different control capture devices, each with their own data schemas, right into a central database for positions counter-party details and trades. It's not uncommon for classic ETL based approaches to take several days to get, transform, purify and combine such data. Regulatory pressure however dictates that this complete process be done many times every single day. Moreover, various risk scenarios need to be controlled, and it's not unusual for the simulations themselves to generate terabytes of additional data every day. The battle outlined is not just one of absolute data volumes but as well of data variety, and the timeliness in which this sort of varied data needs to be aggregated and analyzed. Now consider an opportunity which includes largely continued to be unexploited: Because data powered as finance companies are, analysts estimate that somewhere between 80...



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