Data strategy for insurance
The insurance industry is ripe for advanced analytics, AI and ML. However, before you jump right in, your organization needs an enterprise data strategy to ensure long-term success. These foundational basics – and pitfalls to avoid – will start you off on the right foot.
How to build the right foundation for analytics and machine learning
Analytics and machine learning technologies are revolutionizing the insurance industry. Rapid fraud detection, improved self-service, better claims handling and precise customer targeting are just some of the possibilities. Before you jump headfirst into an insurance analytics project, however, you need to take a step back and develop an enterprise data strategy for insurance that will ensure long-term success across the entire organization.
Here are the basics to help get you started – and some pitfalls to avoid.
The foundation of data strategy for insurance
Identify your current state.
What are your existing analytics capabilities? In our experience, data infrastructure and analysis are rarely implemented in a tidy, centralized way. Departments and individuals choose to implement their own storage and analytical programs, creating entire systems that exist off the radar. Evaluating the current state and creating a roadmap empowers you to conduct accurate gap analysis and arrange for all data sources to funnel into your final analytics tool.
Define your future state.
A strong ROI depends on a clear and defined goal from the start. For insurance analytics, that means understanding the type of analytics capabilities you need (e.g., real-time analytics, predictive analytics) and the progress you want to make (e.g., more accurate premiums, reduced waste, more personalized policies). Through stakeholder interviews and business requirements, you can determine the exact fix to reduce waste during the implementation process.
Pitfalls to avoid
Even with a solid roadmap, some common mistakes can hinder the end result of your insurance analytics project. Keep these in mind during the planning and implementation phases.
Don’t try to eat the elephant in one bite.
Investing $5 million in an all-encompassing enterprise-wide platform is good in theory. However, that’s a hefty price tag for an untested concept. We recommend our clients start on a more strategic proof of concept that can provide ROI in months rather than years.
Maximize your data quality.
Your insights are only as good as your data. Even with a well-constructed data hub, your findings cannot turn low-quality data into gems. Data quality management within your business provides a framework for better outcomes by identifying old or unreliable data. But your team needs to take it to the next level, acting with care to input accurate and timely data that your internal system can use for analysis.
Align analytics with your strategic goals.
Alignment with your strategic goals is a must for any insurance analytics project. There needs to be consensus among all necessary stakeholders – business divisions, IT and top business executives – or each group will pull the project in different directions. This challenge is avoidable if the right stakeholders and users are included in planning the future state of your analytics program.
Integrate analytics with your whole business.
Incompatible systems result in significant waste in any organization. If an analytics system cannot access the range of data sources it needs to evaluate, then your findings will fall short. During one project, our client wanted to launch a claims system and assumed it would be a simple integration of a few systems. When we conducted our audit, we found that 25 disparate source systems existed. Taking the time up front to run these types of audits prevents headaches down the road when you can’t analyze a key component of a given problem.
If you have any questions or are looking for additional guidance on analytics, machine learning or data strategy for insurance, Ollion’s insurance data and analytics team is happy to help. Feel free to contact us here.