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This report is based on a survey of findings, exploring how insurance companies are using data, identifying where the opportunities to leverage analytics are and the key challenges in transforming data and analytics capabilities
Working with Guidewire, The Insurance Network surveyed its audience to understand how companies are using data within the organisation, where they see the opportunities to leverage analytics, and the key challenges they face in transforming their data and analytics capabilities. To discuss the issues highlighted the two companies co-hosted an online panel discussion on 20th October 2021 entitled “Harnessing the power of data and analytics in insurance”. The panel featured Steve Jolly, Group Chief Information Officer at Tysers, Jason Cripps, Global Head Intelligent Automation & Operational Excellence at Zurich Insurance, Saki Thethy, Head of Data and Market Relationships at Miller Insurance Services, Yuanyuan Liu, Head of Statistical Machine Learning at AIG and Roger Arnemann, General Manager, Guidewire Analytics.
This report seeks to summarise the findings of the survey, and highlight the key themes and issues raised during the panel discussion.
Roger Arnemann at Guidewire Analytics set the scene for the discussion on the journey to becoming a data-driven organisation by arguing that the journey should always start with a ‘why’, a clear business objective. Companies now see that the “go for big data” approach - buying, collating and analysing seas of data - is “a hammer looking for a solution."
Some people say ‘we have to be data driven. Here's all the data we could buy. Where do we use it?’ I think the better way is to say ‘where can we improve in the organization? What issues are challenging us? What opportunities would we like to grasp that we can't grasp yet? And then how do we unlock those?’ And very often you'll find data analytics are those unlocks.
The first question in the survey sought to identify those business objectives, and indicated that achieving better insights and improving decision making are the ‘why’ for many, with more than half (53%) choosing “Improving / enhancing MI to enable better business decisions” as their primary driver for data and analytics, beating both “Increasing operational efficiency” (32%) and “Responding to changing customer expectations” (11%).
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