Deep into big data

By Divina Paredes, CIO New Zealand |  Big Data, Analytics, big data

In government, the data is around tax fraud/collections, criminal justice, pension portfolio risk, child support areas and delinquences, and in manufacturing they are around predictive asset failure and inventory allocation optimisation. In a lot of organisations, the issue is around customer analytics which include segmentation, acquisition and churn. He says many organisations approach the situation from a technology level. "We need infrastructure to support it, we need to put data in one place and then we will figure out what to do with it." He likens it to a "build it and they will come, a Field of Dreams situation."He suggests taking a different approach. "You have got to focus on the business problem, what am I trying to solve?"What is the actual problem and work backwards," he says. "What is the information that I can potentially use to solve that problem? How do I get that? "Part of the big data challenge [is] if you go down the traditional approach of, 'I am going to structure my data in a certain way,' you are already making assumptions on what data is important rather than letting the data speak for itself. "High performance analytics is saying, why can I not have all the data there and let the data tell me what is important?""You can use analytics to go across all the data, it might pick up trends or pieces of information that you would have excluded from previously."His key advice to CIOs? "Treat data and information as an asset to your company, understand that you need to have an enterprise approach to analytics.""Consider how analytics is used in the organisation and help drive those business outcomes rather than just providing infrastructure support. "There are operational applications that are integrated with the front end. IT's role is to understand how that integration can work and make sure it delivers on that business value."HSBC is a good example to reinforce the increasing importance of IT in these analytical environments, he says.HSBC used analytics to understand the losses due to fraud. A typical fraud process, he says, is detected after the fact, and there is a need to refine the detection models. For instance, if someone in Paraguay is buying a car using a stolen credit card, the owner is called after four hours and has to cancel the card. HSBC is now using SAS fraud management to protect all credit card transactions in real time. The solution runs at the point of transaction to decide whether that transaction is potentially fraudulent and runs analytical processes quickly. The shift is from fraud detection to fraud prevention, says Foster.The result is significantly lower incident of fraud across tens of millions of debit and credit card accounts, and improved detection rates and significant reduction in false positives. In HSBC it is important to run analytical processes quickly and in an environment that is highly available. IT in this case plays a critical role, he says.

Originally published on CIO New Zealand |  Click here to read the original story.
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