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Ten Big Data Ideas To Shape 2012

By SiliconIndia   |   Thursday, 28 June 2012, 11:22 Hrs
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Bangalore: Data volumes have been growing steadily upwards and are set to grow by 800% within the next five years. Naturally, Big Data is at the heart of many discussions in organizations all around the world. After the crippling effects of the recession, Dodd-Frank act was passed along with many other regulations that push firms for more transparency in their data and operations. So financial institutions have been busily organizing their data to effectively analyze risk and comply with any regulations.

“Traditional technologies such as relational database management systems make it challenging, if not impossible, to process growing volumes of data and make it accessible, actionable and flexible to changing needs in terms of queries and analytics,” stated Neil Palmer, partner of SunGard’s consulting services’ advanced technology business.

Consequently, big data solutions that support evolving business and regulatory requirements by maintaining an ecosystem of large data sets will become invaluable in months or years from now, he added.

Big Data is a fundamental trend driving investments in enterprise analytics, and analytics is at the crux of innovation in today’s financial industry.
“Business analytics applied to relationship pricing, capital management, compliance, corporate performance, trade execution, security, fraud management and other disciplines is the core innovation platform to improving decision making,” said Michael Versace, research director of worldwide risk and big data industry leader at IDC Financial Insights.

He notes that analytics and the ability to effectively exploit big data and advanced modeling will distinguish those that thrive in uncertain and uneven markets from those that fail.

Take a look at 10 trends that will shape the financial industry and all Big Data initiatives in 2012, according to SunGard.

1. Historical data:
Larger market data sets containing historical data for longer time periods will be required to feed predictive models, forecasts and trading impacts throughout the day.

2. Need for transparency:
New regulatory requirements are emphasizing governance and risk reporting, driving the need for transparent analysis across global organizations.

3. Enterprise Risk Management:
Financial institutions are setting up their enterprise risk management frameworks, which rely on master data management strategies to foster enterprise transparency and executive oversight of risk.

4. More Data, More Channels:
Companies offering financial services are looking to use large amounts of consumer data across multiple service delivery channels (branch, Web, mobile) to support new predictive analysis models in discovering consumer behavior patterns and gain an edge in the market.

5. Emerging Markets:
In post-emergent markets like Brazil, China and India, economic and business growth opportunities are booming compared to Europe and America since significant investments are being made in local and cloud-based data infrastructures.

6. Utilizing the potential of Data
Growth in big data storage and processing frameworks will allow financial services firms to fully utilize the value of data in their operations departments. This will help reduce the cost of doing business and discover new entrepreneurial opportunities.

7. Extract, Transform, Load
Population of centralized data warehouse systems will necessitate traditional ETL (extract, transform, load) techniques to be re-engineered with big data frameworks so that they can handle enormous volumes of data.

8. Predictive Analytics
Predictive credit risk models that tap into a large quantity of data to observe the history of payment behavior are being adopted in consumer and commercial collections practices to help prioritize collection activities. This is done by differentiating those who tend to delay or avoid payment from those who are regular with their payments.

9. Mobility
Mobile applications and internet-connected devices such as tablets and Smartphone put pressure on technology infrastructures and networks to consume, index and integrate structured and unstructured data from multiple sources.

10. Algorithms
Big data initiatives have increased the need for algorithms to process data, to identify challenges around data security and access control, and minimize impact on existing systems.

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