2016 Big Data Analytics Conference

Participate in an overview of how to approach a “big data” problem. Attendees will be grouped into teams, given a data set, a computer, and a data problem to solve. Each team will have a different tool set to work with. We’ll conclude with presentations of the results and shared learning. Limited to 40 attendees. Additional registration fee applies.

Jennifer Golbeck, Ph.D.

Director of the Social Intelligence Lab, University of Maryland

Increasingly companies are seeking to leverage their social media strategy to maximize customer engagement.  However, there is a desire to balance the need for the insight and the desire to respect customer privacy. Dr. Jennifer Golbeck, a world leader in social media research, tracks the rise of social networks and data analytics, how new computational techniques are revealing hidden traits of millions of people online, and how this impacts the future of business. Part creepy and part surprise, this opening keynote address looks at how scientists and companies are leveraging big social data to develop new insights into customers and what they want.

Join us as we hear from a group of analytics leaders discuss some of today’s most critical aspects of advanced analytics. Topics discussed will include the emergence of cognitive computing, the role of analytics in strategic and tactical decision making, the challenges of advancing analytics initiatives, and the threat of legislation to the fair use of data. In addition, when registering for the conference, attendees will be able to suggest questions for our industry experts.

Companies are actively addressing the consumer and seller experience within life insurance, specifically when it relates to the significant time and effort it takes to issue a policy.  New data sources and data science hold promise for making instant or near instant decisions, eliminating the intrusive, costly and time consuming requirements traditionally utilized.  This session will discuss the latest underwriting advancements being developed and used in the industry, how good the science is, and the regulatory and operational barriers that must be overcome to make automated underwriting a reality.

Voya Financial is using predictive modeling to deliver targeted messaging that drives thousands of positive customer actions every year. Using data from millions of customers, Voya’s analytics engine helps to identify the best call to action for each individual, and then delivers that message by phone, web or email. In this session, you will hear: 

• How predictive modeling can be used to deliver targeted messaging that works
• How you can measure success, and use measurement to drive continuous improvement Lessons learned about getting started, building a team, and maintaining momentum

 What does it mean to capture experience? In this talk, Shelly Blake-Plock, CEO of Yet Analytics will describe how through a blend of the science of semantics and the application of tracking technologies to human performance metrics, a new branch of technology has developed which will have significant impact upon the way that individuals and businesses understand and leverage the data of experience. Focusing on how the experience of employees effects the outcomes of business, we’ll examine how the data of experience is collected and made sense of; and how a better understanding of employee experiences may boost the capabilities of an organization. Surveying the current landscape of data technologies, we’ll also look into the future and consider the consequences of what today’s decisions about experience-based data models may ultimately have both for business and society with regards to privacy, communications, and emergent forms of artificial intelligence.

What do you do with limited data? How can you use analytics to tackle business issues – simple and complex, small and large, core and non-core? Drawing on experience from implementing analytics solutions at carriers, Nirav Dagli, CEO of Spinnaker Analytics, will share lessons and best practices that drive insights through the use of predictive analytics spanning M&A, distribution, and operations. You are encouraged to submit questions ahead of time.

Big Data requires a mental shift: what we lack in cleanliness or completeness, we make up for in volume and variety.  In many respects, partial data can tell us more than seemingly complete data.  This paradigm shift lies at the heart of understanding how Big Data processing works and how Big Data yields business value.

As companies seek out talent for their analytics initiatives, academia is rapidly evolving their curriculum to produce analysts with the requisite skill sets. Forward thinking companies are helping to shape this curricula by partnering with academia. Come and learn how one company has partnered with multiple universities to foster a pipeline for well-trained analysts.

A follow-up to Session 1.1 and a discussion of advanced analytics and its role in underwriting

Predictive models provide insight that can be used to transform the insurance business. The challenge is, how do you communicate the results of very sophisticated tools and gain understanding from an audience that finds the topic complex and confusing.

• Learn tools and techniques to explain the results of predictive models
• Hear examples of how to present to different end-users of predictive models, both internal and external to your organization

A follow up to Session 2.2 and other approaches to handling unstructured data

Tomorrow for some is today for others! One re-insurer has grasped the future and its potential by creating powerful B2B2C solutions that are driven by emerging technologies such as smart analytics and the Internet of Things (IoT). These solutions allow data to facilitate services that meet the needs of clients and, ultimately, the end consumer. While some view these trends as potentially disruptive, Swiss Re will share their vision of how this evolution can be leveraged in an enterprise-transforming manner.

An increasing amount of data, affordable computational power and unprecedented access to a large number of algorithms to extract information from the data have increased the importance of analytics organizations like never before. The combination of the internet and a growing philosophy of sharing have led to open source implementation outpacing proprietary implementations. This session will provide a practical guide to adopting open source algorithms in an analytics organization. Using a publicly available dataset, attendees will learn how open source can be used to solve a business problem - end to end: from data exploration to model development and deployment.

A discussion of partnering with academia, crowdsourcing, and other novel approaches to complementing your analytics staff.

The life insurance industry is ripe for disruption. To help manage this disruption, Data Science comes to the rescue. But the journey is not without challenges. This session will walk through the various places where Data Science can be embedded, discussing challenges and mitigation, and offering some practical solutions and best practices. The audience for this session is practitioners of Data Science. The session can get very technical.

The advances we have seen in recent years in data-focused tools and technology have opened doors to creating up to the minute views of the drivers of business performance and profitability.  Companies can not only combine a multitude of data sources into a single view of current business activity and financial health but can also employ these data sources along with advanced statistical modeling techniques to create customer, agent and business quality/value scores.  This session will discuss the types of new data and indicators being included in dashboard development and then present a template for a Management Dashboard for insurance products including key indicators of business performance developed using predictive analytics techniques.

Share your data visualization practices and get your questions answered

Data Scientists and Business Analysts have historically gone around IT to get their jobs done because of platform and tool limitations, as well as IT's black and white view of "applications". These users would pull data out of certified repositories and create copies of data on their desktops to blend with other data and make meaningful discoveries.  They spend significant time on non-value add tasks and increase information risk. Through Governed Data Discovery, we can enable the business and reduce information risk.

The utilization of data analytics for social media and next-generation data is disrupting the traditional insurance process, changing the way carriers market to and engage with customers, assess risk, and process claims.  Traditional data sources, such as credit scores and contributory databases, no longer provide insurers with the whole story, particularly for millennials and individuals who typically fall into the “unscorable” category. Next-generation data targets this information gap, changing the way insurers conduct business and solving one of today's biggest industry challenges.

The benefit of an ongoing, two-way dialog with highly engaged consumers is full of allure, but insurance marketers need to determine how to effectively measure ROI in this new model, which spans across online and offline marketing channels. In this session, we’ll discuss what you need to know to shape your omni-channel marketing program and clearly understand its success. And, we’ll answer some key questions:

• Do you have a 360-degree view of your customer, effectively tying on-line with off-line consumer attributes to engagement strategy? 
• Is your digital marketing helping to improve your KPI’s
• How well do your digital marketing channels work with other channels? Which audiences are responding? Can you correctly measure and attribute their behaviors? 
• Are valuable marketing dollars being wasted due to redundancy across channels?

Do you still have questions that have not been answered?  If so, join us at the LIMRA Analytics Town Hall where you will have a last chance to pose questions, discuss analytics strategies, and participate in a “Q&A hackathon”!

 The frequency of the big data capture from mobile devices exposes the regularity of specific human behavior and represents a new frontier for Life Analytics, which hopefully can be used to adjust actuarial assumptions.    

•  The potential & speed bumps of innovation: [brief] presentations by Lexis Nexis, John Hancock, etc. 
• Introducing mobile device &  telematics data into the mix
•  Customer acquisition – potential and limits of prospect targeting 
•  Underwriting – advancing “traditional” UW using predictive analytics  and combining mobile data with current applicant data
•  4 Steps to Dealing with telematics Big Data - meaningful summaries of  data from mobile devices 
•  Data Privacy & Security: state and federal & regulatory environment 

Most salespeople don’t have access to data or the tools to analyze it, manufacturers do. While many companies began their analytics work building lead generation and cross-sell programs, the types and sophistication of analytics being developed to support distribution continue to increase. Come to this session to hear how companies can leverage advanced analytics to support distribution.

Sridhar Iyengar, IBM TJ Watson Research Center

IBM Distinguished Engineer, Cognitive Computing Research

 Take a journey with IBM to see how advances in cognitive computing can help:   

•  An investment banking expert narrow mergers and acquisition targets by using cognitive assistants to leverage techniques like 'Bayesian Smart Swaps' 
•  A financial sentiment aggregator derive investment sentiment and outlook by using a cognitive engine to consume investment reports 
•  A compliance officer extract requirements and obligations from industry regulatory documents and map them to controls through a cognitive text analytics engine 
•  A financial service company better tailor products and services to individual customers through analysis of psycholinguistic features from written text and images 

We will close with a demonstration of how Watson technology can provide timely and relevant guidance and recommendations to a financial advisor by combining traditional portfolio data with a stream of social, news, investment reports and other unstructured data.