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IBM Big Data & Analytics: Customer Panel


Next up is a client panel with Verizon, FleetRisk Advisors and UBS AG.

  • Verizon has created a specific Big Data and Analytics R&D group to diversify their portfolio of services, looking for new opportunities in data and analytics to leverage Verizon’s DAILY 12PB of data.
  • FleetRisk Advisors is a business founded on predictive analytics for the trucking industry that rapidly focused on solutions to prevent the problems it was predicting – generating remediation plans.
  • UBS’s e-discovery technology team is focused on delivering data in support of regulatory and other inquiries. In particular supporting (and understanding) unstructured data is critical.

As always I will try and capture the nuggets from the panel without attempting to transcribe it:

  • Examples include personalized recommendations/advertising across many channels, intelligent network management, telematics-driven closed loop mitigation, detecting and preventing accidents, detecting and preventing driver retention, determine risk of regulatory and other events that will cause discovery activity and more.
  • Volume, range and speed of insight offer potential for competitive advantage. Especially real-time decisions/advice.
  • Analytic solution sales process is 90% education, 10% sales – especially when your customers are not analytically sophisticated. Must change how they think, about risk for instance, to show the value of analytic decisions. Customers have to see probabilities not randomness!
  • Everyone thinks they have dirty data (and not enough data) that cannot be used. But it’s often better, cleaner and more useful – just need the right tools to extract insight.
  • Analytics can, and perhaps should, add to an organization’s value proposition.
  • Analytics teams role up in various ways – as a technology group reporting to the CIO, as an analytic business unit reporting to the CEO, and as a specialist group reporting to the chief product officer.
  • Sometimes an analytic solution has a broader audience than you think – other parts of the industry might care (shippers and insurers care about transportation company safety scores for instance).
  • Analytics teams often have lots of connections in a company – privacy, technology, product, customers etc.
  • Operational teams will often second-guess analytics – “I have 20 years experience I don’t need no stinking analytics.” Means change management, and reasons/explanation of analytics, can be critical.



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