Article Synopsis :
The insurance industry has long collected mountains of data and has yet to tap the full power of that information. This report from West Monroe Partners, based on a survey of 122 insurance executives, explores the whys and hows.
Failure to extract advanced analytics’ full benefits: Just 11% of respondents ‘strongly agree’ their organizations are fully realizing the benefits of advanced analytics. 46% ‘somewhat agree.’
Data’s opportunity as a business accelerator: About a quarter (27%) named ‘improved customer experience’ as the greatest potential benefit of advanced analytics. 21% said ‘reduced claims costs’ and 14% chose ‘increased sales.’
Major worries about data quality: Nearly two-thirds of respondents said data quality and accuracy is the greatest challenge associated with advanced analytics. The greatest risk? About half chose inaccurate data.
Current focus on claims modeling and reduction: 51% of respondents said they already use advanced analytics in claims modelling, 42% use analytics in actuarial model testing.
Lack of investment in disruptive data sources: 76% of respondents said they’re not investing in disruptive data sources, though about half said they’re considering investments in secondary sources augmenting their own data.
The body of the report is organized in seven parts, as follows, with hard survey data infographics and insightful recommendations:
- The Big Picture
- The Potential
- The Hurdles
- The Implementation
- The Owner
- The Budget
- The Disruption
Is your organization struggling to take advantage of advanced analytics? The report issues four recommendations:
Step 1: Start with a Specific Business Problem
Focus on a specific business problem – with the goal of demonstrating advanced analytics’ value in ways that can be broadcast across the company. One example would be to use consumer information (perhaps based on engagements to be married, home sales, etc.) to identify suitable products to recommend to targeted individuals at their time of need or triggering life event. Measure and share results.
Step 2: Make Sure Data is Clean and Accurate
It’s difficult, if not impossible, to derive much value from bad data. And then there’s the matter of convincing internal stakeholders that the data is accurate and will stay accurate. Companies should measure data quality issues through an integrated exception reporting process, documenting abnormalities, ideally managed by a data governance committee. Without knowing where the problems lie, steps cannot be taken to fix them.
Step 3: Attack Data Segmentation Problems
These issues typically stem from difficulty in unifying data from an acquired company or dealing with disparate legacy systems within the organization. Regarding acquisitions, you need a formalized process to integrate data from an acquired company on the first day post-acquisition. Options for integrating information from disparate legacy systems include using lean analytics or data warehouses to aggregate data for analysis.
Step 4: Focus on the Possible and Results
The biggest mistake companies make is trying to move forward without any sort of analytics strategy. To be effective, the analytics strategy must be aligned to organizational and market objectives, and include components that are measurable against business processes.
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Digital Insurer's CommentsThis report’s two conclusions are spot on:
- Too often, organizations get excited about the potential of advanced analytics and turn to their technology experts. Though technology knowhow is required, creating value from data must be driven by business strategy leaders and the C-suite.
- The rapid rise of cloud computing is a game changer. Analytics solutions can be realized more quickly and cheaply in cloud environments. AI and cognitive computing are, likewise, more accessible via cloud tools (e.g., AWS SageMaker) to improve everyday decision-making.
Harnessing data assets and advanced analytics is not only important in and of itself, it’s also the first step to prepare for coming megatrends.
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