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#IBMInsight Realizing Opportunities through the deployment of predictive analytic models


My second session at IBM Insight is Hamilton Faris Chief Data and Analytics Officer of MetLIfe – a past customer of ours – talking about Predictive Analytics.  MetLife of course is a big insurer with 100M customers in 47 countries. The group Hamilton runs focuses on bringing analytics to the forefront of the decision-making process across the whole organization. Insurance companies have all sorts of insight professionals like actuaries in business units so the group initially started with a focus on operational analytics. It provides services, tools and technology across the range of analytic solutions and has a strong relationship with IT.

Hamilton starts with a key insight – you can’t get value from analytics without activating those insights and that means continuously moving toward Decision Management. It means moving from ad-hoc and one-time use to increasingly automated deployment and execution of analytics to drive ongoing decision management and embedded decision support – whether the decision is automated or not the analytic must be embedded into the systems and processes that people use and that handle transactions.

Three key elements:

  • Business, focused on model development, data requirements, SLAs and KPIs.
  • Technology deliver the data stream, integration, deployment activities and optimization.
  • Activation Enablement means tracking KPIs and SLAs as well as break/fix support.

Deployment is critical for analytics and this means understanding the decision you are trying to influence and how often do you make it, what latency do you need, what kind of answer is required and who is involved? How automated is this going to be and how often might it need to change? All great things to know and part of a decision-centric modeling approach – the kind of thing we capture in a decision model to frame analytic requirements.

As projects move into more complex decision-making the issue of how the analytic fits with the business rules that are also part of the decision. These used to be embedded in legacy systems but increasingly MetLife is carving out decisions and managing the business rules and analytics for those decisions in a more agile, flexible way. Especially when there is value in moving past ad-hoc, one-off execution they find this becomes critical. The ability to take a set of models and get them quickly deployed with automated updates as necessary drives significant incremental value by making them part of the day to day business cadence. These vary from presenting scores to running batch updates to a more real-time scoring and decisioning environment – Decision Management.

Some lessons:

  • In one example they were able to deliver changes in 24 hours and the team gradually extracted legacy business rules from the old system into the decision management system.
  • They focus on making sure that data elements being passed to the model for scoring are as wide as possible so that changes in the model don’t have to be re-integrated each time the model is updated.
  • Some groups want reports or ask for analytics without a decision-making context. Others understand the need to focus on decision-making but need help with the organizational change. Others are more decision-centric and focused on turn-around time and getting the next thing deployed.
  • Takes time to grow this kind of business and being results focused, building a capability that is scalable and repeatable is key as it an ability to find the right opportunities.

It’s interesting how much value they can add even without changing the “core” decision like pricing or underwriting – improving queuing, focusing resources, selecting between alternative approaches can all make a huge difference to operations. As their business partners get more comfortable with the approach they are increasingly also looking for ways to apply it to the “core” business decisions too – taking advantage of the scale and flexibility the team offers to improve the actual actuarial and underwriting decisions for instance.


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