For insurance providers, the integrity of data flowing in from healthcare providers and other sources is critical for training reliable automated underwriting, claims processing, and risk modelling systems. However, there are concerning scenarios where if upstream healthcare AI systems become compromised by data poisoning attacks, that corruption could potentially cascade through the entire AI pipeline [...]
The advent of Hadoop for processing Big Data many years ago solved the problem of needing to scale hardware to specialized and impractical dimensions, both from the specification as well as cost points of view. Hadoop distributes processing and storage to smaller units of hardware and allows new hardware to be added as required. These [...]
Telematics is the application of a combination of technologies to gather data from automobiles and transmit it to a receiving organization for various purposes. It could also be two-way communication, and this again depends on the purpose. The use of telematics is an example of an end point technology that forms one part of the [...]
much interest. Why is it growing? In almost every financial transaction there is an element of trust involved. Before a seller hands over a good or service in exchange for money they need to be trust that the buyer has enough money to pay for the purchase, the same money is not simultaneously being committed [...]
Data scientists are a rare breed. It’s not easy to find the perfect blend of skills in information technology, statistics and business. Having said that, more and more are slowly beginning to be emerge after building up their profiles through a mix of experience and training. We’re still a long way off, however, in terms [...]
A couple of decades ago I had the opportunity to work on a business problem that initially seemed rather daunting. The challenge was to optimize the operations of a precision engineering goods manufacturer by moving to a completely different approach to planning. Our company initially used to follow the practices that were derived from the [...]
Business analytics with big data is increasingly being used to help manage risk in a number of areas such as operations, finance, insurance and security. While the broad lifecycle phases of any analytics project remain pretty much the same, the techniques that are used to implement each phase may vary a little depending on [...]
Many companies have succeeded in implementing analytics and using it for various purposes. Some of the better known companies that were early adopters included those like GE, a company that has always been associated with excellence and innovation driven by facts and metrics programs (commonly known these days as a data driven culture). They are [...]
As the adoption of business analytics continues to grow, greater clarity is being gained in understanding what goes into achieving results with it, and how much effort various functions need to contribute to it. In earlier posts I’ve expressed my perspective on howanalytics projects should be run, and what factors make them successful and productive. [...]
As the adoption of predictive analytics in business continues to pick up, there is also a parallel thrust to harness the use of Big Data for analytics purposes. Working with Big Data, of course, means working with huge volumes of varied data. The statistical techniques that are used in analytics are not new, but their [...]
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