I had the pleasure to chair the TINtech session in London on February 4 covering “Data Excellence: Driving Competitive Advantage in Specialty Insurance.” Our panel consisted of Bent Isachsen, Chief Operating Officer, Hamilton, and Ben Ormerod, CDO, Hampden Risk Partners.
The session explored the findings of Datos’ data maturity study conducted in the UK insurance market, focusing on how organisations are leveraging data to drive competitive advantage. The study, which surveyed 43 carriers, identified significant gaps between top performers and the rest of the market, particularly in information utilization and data architecture. The discussion examined how data maturity correlates with better operational performance, improved decision-making and the ability to deploy AI effectively.
The panellists discussed their respective approaches to addressing data silos, governance challenges and improving data quality. It was emphasised that while technology plays a role, the primary challenge lies in fostering a culture that values data and promotes ownership across the organisation. Strategies such as business glossaries, data working groups and automated quality control mechanisms were highlighted as key enablers of better data management. The role of external partnerships, including the use of insurtech solutions, was also discussed as a means to accelerate progress.
A critical point raised was that successful data strategies require strong leadership and a clear business case. While organisations recognise the need for data-driven decision-making, many struggle to demonstrate tangible return on investment. The conversation underscored the importance of incremental change, where proof-of-concept initiatives help validate the value of data-driven insights before full-scale implementation.
The session concluded with a call to action for companies to assess their own data maturity, benchmark against industry peers and identify priority areas for investment. The importance of aligning data strategy with business objectives was reiterated, ensuring that efforts to improve data quality, governance and utilisation translate into measurable business impact.
Key Takeaways
- Data culture and governance are critical to success. A lack of standardized data definitions and fragmented ownership creates inefficiencies. Establishing a strong data culture, embedding governance frameworks and fostering collaboration across departments are essential for improving data quality and accessibility.
- Incremental progress is more effective than large-scale transformation. Organizations should focus on targeted proof-of-concept initiatives to demonstrate value before scaling. Aligning data investments with business outcomes helps secure leadership buy-in and ensures resources are directed to high-impact areas.
- AI and analytics depend on high-quality, structured data. The potential of AI and predictive analytics is limited by the quality of underlying data. Addressing legacy system constraints and ensuring data is properly structured and governed are foundational steps before advanced technologies can be deployed effectively.
If you are part of the insurance industry in the US, Canada or UK and want to talk more about how we can help you assess your data maturity, please reach out to me at [email protected].
