Royal London operated multiple siloed data sources across pensions, savings and operations. Reporting was manual, inconsistent and difficult to trust. Leaders across Finance, Operations and Risk struggled to get timely insight, affecting forecasting, regulatory reporting, fraud monitoring and customer performance analytics. The Board wanted to shift the business toward evidence-led decision making, but lacked a unified data platform, governance model or clear ownership.

Define a group-wide data strategy, build a scalable BI platform, and create the governance and operating model needed to make data reliable, secure and usable. Success required aligning competing stakeholder priorities, proving ROI, and changing the culture from spreadsheets to trusted, self-serve insight.

• Created an enterprise data strategy endorsed by the CFO, CRO, COO and CIO, aligning investment to regulatory needs, customer outcomes, and operational efficiencies.

• Selected Snowflake and Azure as core platforms after cost-benefit analysis, demonstrating total cost of ownership, scalability, security and long-term extensibility.

• Built a unified BI layer for 5,000+ internal users, integrating customer, operational and financial datasets into governed, high-quality data models with access controls.

• Implemented data governance standards covering quality, lineage, auditability, retention and role-based access, supported by automated quality checks and alerting.

• Worked with Finance, Marketing, Operations and Compliance to replace static spreadsheets with self-serve dashboards, real-time insights and drill-down reporting.

• Introduced a business-led analytics operating model where domain owners prioritised use cases, ensuring adoption was pulled by the business, not pushed by IT.

• Established a quarterly Data Council with C-suite leaders to track outcomes, ROI and policy decisions, ensuring transparency and momentum.

• Enabled insight-driven decisions for 5,000+ business users, replacing manual spreadsheet reporting with real-time dashboards and governed access.

• Improved forecast accuracy, risk detection and operational planning using live data feeds instead of static reports.

• Reduced reporting cycle times from weeks to hours, cutting manual effort and operational bottlenecks.

• Created a trusted “single source of truth” used by Finance, Risk, Operations, Customer and Board Reporting.

• Secured continued investment in analytics due to measurable business value, paving the path for advanced AI/ML use cases and predictive modelling.

• BI & Analytics
• Digital Enablement

• Financial Services