Obstacles to Operationalizing Analytics, and Strategies to Overcome Them

Getting models and analytic products "over the line" and implemented or operationalized can be challenging due to factors often outside the direct control of a modeling or analytics team. I’ll share some of the obstacles that we’ve met in our journey maturing as an Enterprise Data and Analytics organization. Some are business and organizational challenges, others are technology related. At Northwestern Mutual, we’ve successfully navigated a number of these challenges and deployed models and other analytic products for mission critical business functions. I’ll talk through some specific challenges such as getting proper buy-in, pursuit of perfection, execution approach, and more. Through some specific examples, I’ll share our approach to overcoming these challenges and getting our analytical products implemented and incorporated into business processes, resulting in revenue gains and expense reductions.

Impacting the Claim Process Through Predictive Modeling

Predictive modeling continues to play an important role in the claim process for Property & Casualty (P&C) insurers and Third Party Administrators (TPAs). This session focuses on the TPA environment utilizing the workers' compensation line of business as a case study into the rationale, implementation, and outcomes that follow the decision to deploy predictive modeling. The speaker will explain the environment of workers' compensation claims handling and the role of the TPA as it relates to assisting employers in managing risk.  With this foundation, the session will move into how predictive modeling can be deployed in the claim process and specifically look at one TPAs efforts to increase efficiency and improve client outcomes.  Participants of the session will walk away with the following information:

• Basics on the P&C TPA environment

• Opportunities for predictive modeling in the claim process

• Implementation challenges and pitfalls to consider regardless of industry

• Overcoming pitfalls

• Realizing outcomes in a dynamic environment

Silicon Valley's Hottest Cross-Sector Applications of Deep Learning

A veteran applying deep learning at the likes of Apple, Samsung, Bosch, GE, and Stanford, Mohammad Shokoohi-Yekta kicks off Mega-PAW 2019 by addressing these Big Questions about deep learning and where it's headed:

• Late-breaking developments applying deep learning in retail, financial services, healthcare, IoT, and autonomous and semi-autonomous vehicles

• Why time series data is The New Big Data and how deep learning leverages this booming, fundamental source of data

• What's coming next and whether deep learning is destined to replace traditional machine learning methods and render them outdated

Pre-Conference workshops from:

Marc Smith
Chief Social Scientist
Connected Action Consulting Group

Brennan Lodge
Data Scientist VP
Goldman Sachs

John Elder Ph.D.
Founder & Chair
Elder Research

Dean Abbott
Co-Founder and Chief Data Scientist
SmarterHQ

James McCaffrey
Senior Scientist Engineer
Microsoft

Post-Conference Workshops from:

John Elder Ph.D.
Founder & Chair
Elder Research

James Casaletto
Senior Solutions Architect
MapR Technologies

Martin Gorner
Developer Relations
Google