Fast Data and Big Data
Big Data is useful for understanding what happened in the past and developing predictive models of what should happen in the future given similar situations. These models are then deployed into real-time streaming analytics engines which process live data feeds of Fast Data. Processing the live data feeds with streaming analytics provides a live view of the current situation. Putting Fast Data into the predictive models provides the most accurate predictions, and indeed provides the ability to predict the near future. Implemeting automated responses to those predictions provides the cabability to positively influense situaitons which can provide significant business value.
Moving Ahead with Fast Data
In today's competitive market, banks need to be closer to their customers and understand them better so they can deliver more relevant and timely services, thereby offering value and earning customer satisfaction. Progressive Digital Enterprises are leveraging Fast Data approaches to improve both customer experiences and to optimize their business operations. Using Fast Data, they can provide customers with individually tailored responses. With Fast Data, they can make better decisions and take more insightful action. Significant competitive advantage over other traditional companies can be obtained by using Fast Data in situations where value increases with shorter response timeframes, and in many situations Fast Data can be the difference needed to gain or retain a customer.
 McKinsey & Company, March 2015 http://www.mckinsey.com/insights/financial_services/capitalizing_on_asias_digital-banking_boom
2 IDC iView, December 2012 http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf
 IDC Financial Insights Asia Pacific, 2015 http://www.ap.afscongress.com/img/PDF/big-data.pdf
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