Beyond the Big Data Hype – The Real Future of Analytics | By Arun Shastri

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The field of data and analytics is undergoing a major evolution and phrases such as “Big Data”, “predictive analytics”, “data science”, and “machine learning” are becoming commonplace. Much like other disruptive innovations, there has been a healthy dose of hype, but many data-smart companies are transforming their business model with the help of data and analytics—and this trend is likely to accelerate in the coming years.

There are a number of factors that have contributed to this evolution in analytics: (i) explosion in volume and variety of data (new data sources, sophisticated internal systems capturing streams of valuable data, social media, clickstream, etc.), (ii) analytical techniques that range from traditional statistics and econometrics to new methods such as machine learning, (iii) cloud computing and related technology to process a huge volume of structured and unstructured information in a fraction of time, (iv) mobile devices facilitating “just in time” consumption of insights and (v) greater comfort and acceptance of advice from data & machines with emergence of such systems in the consumer space (GPS, Netflix, Amazon).

Increasingly insurance companies have also started to benefit from using new data and analytical techniques to drive business insights. There have already been applications in underwriting and product design, including Progressive’s well-publicized Snapshot device, which tracks driving data to build usage-specific rates. While life insurers have perhaps seen fewer applications so far, opportunities still abound, from using third-party data to improve risk modeling to applying Big Data techniques to model agent and salesperson behavior and sharpen program effectiveness. And as comfort with various data types and techniques grow, the ability to experiment and learn will grow, too.

But barring a few examples, a single set of analytics is rarely going to drive “big insights”. The reality will be more around developing analytic capabilities that allow the organization to rapidly collect data and metrics, perform analysis, and derive insights across a wide range of business problems.  Ultimately a collection of such insights may lead to transforming the business.  In order to effectively do this, organizations need to think through changes in their capabilities:

  • Analytics Roles and Responsibilities
    • How do we enable shift in roles across various analytics producers and consumers—analytics “producers” (e.g., analytics COEs) as consultants and thought-partners to Business; analytics consumers (e.g., brand managers)—as more engaged and interactive in their consumption and use of analytics?
    • What is the right “contract” and governance model between internal department-level analytics teams and analytics shared service teams?
  • Analytics Organizational Capability Building
    • What new skills do we need to acquire (e.g. UX, Data Science)? What should we build ourselves, where do we rely on our partners?
  • Analytics Infrastructure Capability Building
    • What changes are necessary to capture the rapidly changing data landscape?
    • What changes are necessary to support the new analytics?
    • With the blurring line between technology and analytics, how do different cross-functional groups (IT, Analytics CoE, Commercial Ops) work seamlessly and effectively? 
  • Analytics Operations Capability Building
    • How do we find a more efficient solution for regular analytics delivery to free up time and money for undertaking the new challenges? 
  • Analytics Innovation Capability Building
    • How do we develop an agile innovation environment that encourages more experimentation and proofs of concept in a “fast fail” model? 
    • How do we drive change in our stakeholders to make them more comfortable with analytics driven decisions and actions?   

While these are not easy questions, many insurers will answer them in the coming years. And those that do will have robust analytic capabilities that deliver true lasting value. If you are struggling with these questions or want to learn more, please contact Jason Brown or Arun Shastri.