A mid-sized specialty P&C insurer had invested heavily in systems and reporting tools - yet leadership still lacked consistent, reliable data for decision-making. AIA designed the foundations of a data-driven enterprise.
The organization had articulated a clear strategic objective: become a data-driven enterprise. Despite significant investment in systems and reporting tools, the foundational capabilities required to achieve that vision were missing.
Leadership lacked consistent, enterprise-wide visibility into performance across the business.
Decision-making depended on data that was inconsistent, unstandardized, and difficult to trust.
Analytics capabilities could not scale to support underwriting, pricing, and operations.
Eight business units operated independently, each with its own data, tools, and reporting practices.
The assessment revealed that the obstacles were structural - woven through data, governance, architecture, and organization alike.
Teams were spending significant time simply finding and reconciling data. Management insights arrived late and inconsistent, and leadership operated with limited confidence in the accuracy and completeness of what they saw.
Assess, analyze, design - grounded in what the organization could realistically execute.
Conducted 20+ stakeholder interviews across business units and IT, evaluated data, systems, workflows, and reporting environments, and assessed maturity against industry data management and analytics models.
Identified gaps across data governance, data quality, data architecture, business intelligence, and organizational alignment - benchmarked against industry best practices and maturity models.
Defined the future-state data environment, developed an enterprise data strategy aligned to business objectives, and established a prioritized roadmap over a 3-4 year horizon.
A comprehensive enterprise data transformation framework - sequenced by business priority, resource capacity, and organizational readiness, with defined timelines, cost estimates, and staffing requirements.
An Enterprise Data Governance Committee, a defined stewardship model, and data policies, standards, and accountability structures.
An enterprise data model, data dictionary, and business glossary - with data quality metrics, controls, and metadata management.
An enterprise data warehouse with detailed historical data, standardized integration patterns, and a centralized, scalable platform.
A BI Center of Excellence, enterprise dashboards and reporting standards, self-service analytics, and a foundation for predictive modeling.
Improved decision-making through consistent, reliable data across the enterprise.
Reduced manual effort spent finding and reconciling data across business units.
Greater visibility into performance across all eight business units.
Increased ability to support product development, pricing, underwriting, and customer and distributor insights.
Positioned to close the analytics capability gap and leverage predictive analytics for growth and profitability.
A durable base for future digital and data initiatives - built once, extended over time.
AIA helps insurers assess data maturity, design practical governance, and build roadmaps their teams can actually execute.