With data volumes set to grow 800% in the next five years, big data is – or should be – at the heart of every IT discussion on Wall Street.
[Read: Five Things To Know About Big Data to learn more.]
And as the Dodd-Frank Act and other new regulations push firms to be more transparent with their data and operations, financial institutions have been busily consolidating data traditionally managed in silos so that they can more efficiently analyze risk and comply with regulatory demands.
“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,” says Neil Palmer, partner of SunGard’s consulting services’ advanced technology business.
As a result, 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 adds. Michael Versace, research director of worldwide risk and big data industry leader at IDC Financial Insights, confirms that Big Data is a crucial trend driving investments in enterprise analytics, and analytics is at the heart 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,” he argues.
He notes that analytics and the ability to efficiently and effectively exploit big data and advanced modeling, in memory and real-time decision-making across channels and operations will distinguish those that “thrive in uncertain and uneven markets from those that fumble.
With that said, here are the 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 over longer time periods and increased granularity will be required to feed predictive models, forecasts and trading impacts throughout the day.
2. Governance and risk reporting:
New regulatory and compliance requirements are placing greater emphasis on governance and risk reporting, driving the need for deeper and more transparent analysis across global organizations.
3. Enterprise Risk Management:
Financial institutions are ramping up their enterprise risk management frameworks, which rely on master data management strategies to help improve enterprise transparency, auditability and executive oversight of risk.
4. More Data, More Channels:
Financial services companies are looking to leverage 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 increase conversion rates.
5. Emerging Markets:
In post-emergent markets like Brazil, China and India, economic and business growth opportunities are outpacing Europe and America as significant investments are made in local and cloud-based data infrastructures.
6. Unlocking the Value of Data
Advances in big data storage and processing frameworks will help financial services firms unlock the value of data in their operations departments in order to help reduce the cost of doing business and discover new arbitrage opportunities.
7. Extract, Transform, Load
Population of centralized data warehouse systems will require traditional ETL (extract, transform, load) processes to be re-engineered with big data frameworks to handle growing volumes of information.
8. Predictive Analytics
Predictive credit risk models that tap into large amounts of data consisting of historical payment behavior are being adopted in consumer and commercial collections practices to help prioritize collections activities by determining the propensity for delinquency or payment.
Mobile applications and internet-connected devices such as tablets and smartphone are creating greater pressure on the ability of technology infrastructures and networks to consume, index and integrate structured and unstructured data from a variety of sources.
Big data initiatives are driving increased demand for algorithms to process data, as well as emphasizing challenges around data security and access control, and minimizing impact on existing systems.