Caroline Hermon is Head of Fraud Prevention Solution Sales. She has a particular focus on exploring best practices to help organisations protect revenue by increasing their effectiveness in use of data, and ensure that they get fast, accurate analytical results. She is keen to ensure that her customers can manage their fraud prevention process and protect their customers and employees so that analytics delivers rapid value. Away from work yoga, swimming and travel keep me out of trouble!
We like to think of analytics as a logical discipline, where investment decisions are consistently rational and predictable. Not so. Customer organisations are driven by all the usual complex and sometimes contradictory forces. Business users want analytics to help them make good customer facing decisions as quickly as possible, while
I’ve had several meetings lately on data management, and especially integration, where the ability to explore alternatives has been critical. And the findings from our internet of things (IoT) early adopters survey confirms that the ecosystem nature of data sources in IoT deployments means we need to expand the traditional
Data monetization, at its simplest, is the process of turning data into bottom-line value for a company -- often through improving efficiency and/or customer experience, and building customer loyalty as a result. This may sound simple, but in practice, it’s anything but. Good data, advanced analytics and real-time decision making
While the growth of big data is an issue that preoccupies both the public and private sector, it’s also high on the UK government’s legislative agenda. All eyes are currently on the authorities, as we wait with bated breath to see what’s next in the quest to manage data escalation.
