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Guidewire’s Data Guru Mike Byam on How Insurers are Using Internal and Third-Party Data

Mike Byam knows data. He spent 22 years with The Hartford, most recently as Chief Data Officer.  At the start of his career, he was a software engineer for Pratt & Whitney. Today, fresh on the job as VP of Data Strategy and Strategic Programs for Guidewire (he’s been in his current role two months), he’s charged with spreading the word about Guidewire’s current and future capabilities in helping its customers curate, integrate, and understand both their internal and third-party data.

So, when he stresses the importance of data to the growth and success of insurers, he knows what he’s talking about.

“Insurance is a data game,” he says. “It’s always been there, but companies are starting to realize that new technology allows them to use that data to more accurately define risk and drive efficiency.”

Not Just for Actuaries

Traditionally, internally stored data – the reams of information buried in policies and claims -- was the exclusive realm of the insurance actuary, Byam says. But today, everyone in an insurance organization is using data -- from data scientists with models to the marketing team – although everyone is using data differently.

Some carriers already have sophisticated data teams and tools, while others may not have as many resources as the big carriers. Guidewire is positioned to help both ends of the spectrum by providing ways to manage, analyze, and integrate data insights back into the systems.

“Since I’ve joined the Guidewire team, it’s become clear to me that our carrier base is interested in learning how to use data across the entire insurance value chain, from customer experience to underwriting to claims,” he says. “The consistent problem is either the challenge of accessing data in old legacy systems, or how to get value by integrating the insights in their operational systems.”

Guidewire is addressing this with its Data and Analytics product family, including Explore for Claims & Policy, a real-time analytic solution; Predictive Analytics, a machine learning platform that turns data into insights for claims and profitability; and DataHub, an operational data store that conforms multiple data sources using proven P&C data models.

Guidewire added new technical capabilities through several key acquisitions – in 2016 with Eagle Eye, which became Predictive Analytics, and in 2017 with Cyence, which applies data science and risk analytics to insurance data, enabling property/casualty insurers to underwrite “21st century risks,” including cyber, reputation, and new forms of business interruption. 

And since so many other insurtechs are tackling the data issue, Guidewire may well continue such acquisitions, or add new partners to its data ecosystem, Byam says. “Our goal is not for Guidewire to be the ‘center of the universe,’ but to provide our carriers easy access to their information, along with a suite of tools to easily analyze and integrate insights to get value from data.”

The Role of the Cloud

At the recent Guidewire Connections 2019 conference, the role of the cloud in future operations was front and center – and the cloud will inevitably play a major part in the ability to store and access data.

Currently, Guidewire is building an end-to-end data platform in the cloud, a P&C insurance-specific data repository and factory that continuously collects data from internal and external sources and provides analytical insights across the value chain. The Guidewire Data Platform will make insurance smarter by enabling insurers to leverage up-to-the-minute information to make smarter, faster decisions.

“As we go forward into the cloud, it’s essential to use data curation to bring other sources of third-party information together with data from the legacy system…Our goal is to simplify as much as we can out of the core,” Byam says. “Old mainframe legacy systems are limiting and expensive. We aim to provide insurers a modern core system that allows them to more easily access internal data and also embed analytics to get more value from that data.”

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