How Hedvig Scaled Self-Service Analytics and Maintained 2-Minute Claims with Google Looker
Hedvig, a Swedish insurtech known for its 2-minute claim turnaround, deployed Looker, BigQuery, and dbt to establish a unified semantic layer across underwriting, pricing, and sales. Self-service analytics freed the data team from ad hoc reporting, avoided headcount expansion, and enabled data scientists to focus on predictive pricing models while maintaining the operational speed the insurer is known for.
Impact
2 minutes
Claim turnaround time maintained
significant
Headcount expansion avoided
material shift
Data team time reallocation
Challenge
As Hedvig scaled, inconsistent metric definitions across marketing, underwriting, and pricing teams created actuarial and compliance risk — while ad hoc reporting demands consumed the data team's capacity, blocking high-value work like predictive pricing model development.
Solution
Hedvig deployed Google Looker as a unified semantic layer over BigQuery and dbt, establishing single trusted definitions for all key metrics and enabling self-service analytics across business teams — freeing data scientists from reporting work and avoiding the headcount expansion that would otherwise have been required.
Tools & Technologies
What Leaders Say
“We needed an easier way for data to tell the same story everywhere. Looker's semantic model, and the capability for a single place to define metrics, was the key factor in choosing Looker, supporting the need for trusted metrics.”
“We managed to spend less time on serving other units and more time on high-value tasks like building models. If we hadn't had Looker, then we would have needed to be a bigger team.”
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Full Story
Hedvig is a Swedish insurance technology company built for tech-savvy customers, offering a mobile-first experience defined by its 2-minute claim turnaround — from filing to payment. That operational promise requires more than product speed; it demands that every team across the business reads from the same data. As Hedvig scaled from startup into a growing insurer, inconsistent metric definitions became a structural risk. Marketing, underwriting, and pricing teams were working from siloed data views, and the same business event — a sale, a claim, a renewal — meant different things depending on who was reporting it. In insurance, that kind of ambiguity isn't just inefficient, it's a compliance and actuarial liability.
The data team became a bottleneck. Ad hoc reporting requests consumed the bandwidth of data scientists who should have been building pricing models and risk tools. The team needed a way to give business users reliable, self-service access to trusted metrics without becoming a permanent support desk for dashboard requests.
Hedvig deployed Google Looker as its semantic layer, backed by BigQuery as the data warehouse and dbt for transformations. The combination created a single, governed definition for every key metric across the organization. Business teams in claims, underwriting, and sales could now build their own dashboards using Looker's interface, with confidence that their numbers matched those in every other department. Looker's LookML Liquid templating enabled a sophisticated capability: the same dashboard could dynamically switch between live operational data and frozen historical snapshots, giving actuaries and business managers the precise views they needed without maintaining duplicate reports. CI/CD integration in Looker caught modeling errors before they reached production.
The impact was measurable at both the team and business level. The data team shifted from reactive reporting to proactive model development — specifically predictive pricing models that directly support Hedvig's underwriting accuracy. Headcount expansion that would otherwise have been required to absorb reporting demand was avoided. The 2-minute claim turnaround remained intact, now supported by instant access to reliable operational data rather than manually compiled reports.
Hedvig is now refactoring its data models for an AI-driven future, making them more self-explainable and accessible to language model agents. The semantic layer built on Looker is positioned as the foundation for conversational analytics — enabling natural language queries about profitability and operational performance from non-technical staff. The same infrastructure that eliminated data silos in 2024 is being readied for the next generation of AI-augmented insurance operations.
