The insurance industry continues to evolve at pace, with data playing an increasingly central role in all aspects of the value chain, especially . Owen Greenwood, our Client Partner for Insurance, shares four key data trends shaping the sector this year – and what insurers need to prioritise to stay ahead.

1. Centralising Data for a Unified Customer View

Bringing together disparate data sources into a centralised platform is a top priority for insurers in 2025. With access to an ever-expanding volume of data – such as telematics in cars to IoT data for commercial buildings—offering rich insights into risk and supporting product development, risk assessment pricing, claims and customer experience. However, fragmented data across multiple systems can limit the ability to extract meaningful value.

A centralised data platform enables insurers to gain a more complete picture of their customers, covering everything from their policies and claims history to behavioural insights. For customers, this importantly improves underwriting accuracy, but also enhances customer experience through more personalised offerings.

Use Case: Covéa Insurance transformed its data platform with Dufrain, consolidating disparate sources into a structured data architecture. By introducing Bronze, Silver, and Gold data layers within Databricks, Covéa improved accessibility, ensuring business users could extract insights without relying on data engineers. This centralised approach enabled faster decision-making and better customer insights.

Watch our video about IoT here

2. Elevating Data Quality as a Business Asset

With centralisation comes the challenge of robust data management, governance and data quality. Duplicate records, inconsistencies, and unstructured data scattered across legacy systems create significant barriers to effective data use.
For example, a single customer might appear as ‘James’ in one system and ‘Jim’ in another, leading to fragmented records and inaccurate reporting. Ensuring high-quality data through robust management, governance, cleansing, and harmonisation processes is becoming a business imperative. In fact, some insurers are finding that data quality now plays a role in their overall company valuation, underscoring its importance as a key business asset.

Use Case: Howden Group faced challenges with inconsistent and siloed data across its operations. Dufrain helped design and implement a unified data lake using Azure and Databricks Delta Lake. This significantly improved data management, governance and quality, reduced inconsistencies, and enabled self-serve reporting, empowering teams to make accurate, data-driven decisions.

3. Driving Business Value from Data

Once data is centralised and of high quality, the next challenge is unlocking its business value. Many insurers are grappling with how best to monetise their data, whether through improved risk modelling, cross-selling opportunities, or market-derived income (MDI). However, measuring the tangible impact of data-driven initiatives remains a sticking point.
The industry is shifting focus from ‘just having data’ to generating real, measurable business outcomes. This means identifying the right insights, operationalising them effectively, and ensuring alignment between data strategy and overall business objectives.

Use Case: Covéa’s proof of concept for a written loss ratios dashboard revolutionised performance tracking. Previously reliant on manually compiled Excel reports, the business now updates crucial performance metrics daily, allowing them to optimise pricing strategies and identify profitable business segments in real-time.

4. AI Adoption: Evolution Over Revolution

Artificial Intelligence (AI) continues to dominate industry conversations, but for insurers, it’s not about jumping on the bandwagon – it’s about ensuring AI delivers true business value. The rapid evolution of AI tools such as Large Language Models (LLMs) and AI-powered underwriting raises new opportunities and challenges.

Despite the buzz and FOMO, many insurers remain focused on foundational data strategies. AI adoption is still in its exploratory phase, with businesses seeking clarity on its practical applications and return on investment. Insurers are increasingly aware that without strong data foundations, AI implementation will be limited in its effectiveness.

Use case: Leading insurance client focused on raising its data maturity to unlock AI-driven capabilities. While AI adoption remains a future priority, the foundational work in data structuring and process optimisation has positioned them to leverage AI tools effectively when the business is ready to do so.

Final Thoughts

While AI and advanced analytics are capturing headlines, the real focus for insurers in 2025 is getting the basics right: centralising data, ensuring quality, and extracting measurable value. Those who master these fundamentals will be best positioned to leverage AI effectively and gain a competitive edge in an increasingly data-driven market.

If you’re keen to explore how these trends can impact your team, connect with Owen by contacting us here. We’re here to help you stay ahead.