Building a North Star Data First Enterprise Architecture for a Global Insurance Firm

About the Company.

A leading US-based insurance firm with over $7B in annual revenue and multiple lines of business. The company operated on decades old mainframe systems and a fragmented data landscape that limited enterprise wide visibility. To support its transformation toward data driven operations and AI-enable decision making, the organization needed a modern data foundation aligned with future enterprise architecture principles.

Solution Summary

Algoscale partnered with the client to rationalize and modernize enterprise business applications, establish a data-first foundation, and build a unified Data Hub to serve as the single source for analytics and operations. The architecture was designed to enable data democratization, AI readiness, and API-led interoperability across business units.

Customer Challenges.

The client faced significant challenges from fragmented contract management and lack of automation.

Legacy Infrastructure

Several core systems operated on-prem mainframes (AS400 era), limiting scalability and integration capabilities.

Fragmented Data Ecosystem

The traditional data warehouse lacked cross enterprise information, creating data silos across business functions.

Operational Inefficiency

High volume of applications and data complexity made it difficult for customers and teams to identify appropriate products or insights.

Lack of a North Star Architecture

No defined target state or unified data strategy for IT, data, and AI teams to align toward modernization goals.

Algoscale Solution.

Algoscale designed and implemented a Data-First Enterprise Architecture that modernized legacy systems and established a scalable foundation for future analytics and AI initiatives. Key solution components included:

Enterprise Rationalization & Modernization

Conducted a full application inventory, identifying redundant legacy systems and prioritizing modernization targets.

Data Hub Development

Architected a cloud native Data Hub as the core data backbone, integrating disparate data sources into a unified governed environment.

API-Driven Integration

Established API-first design principles to ensure interoperability between business applications and enterprise data systems.

Metadata & Master Data Management

Designed and proposed metadata and MDM capabilities within the Data Hub to create enterprise wide standardized definitions,lineage tracking and consistent data governance.

Data Governance & Standardization

Created a standardized metadata layer and decision matrices to support consistent data interpretation and architecture decisions.

Cross Functional Alignment

Brought together IT, Data, and AI teams under a single North Star Framework, ensuring unified data strategy execution.

AI Agent & Digital Workforce Framework

Proposed an AI+ Human collaboration model and AI-powered digital workforce to automate workflows like claims processing.

AI-Ready Infrastructure

Designed the architecture to support AI models, analytics agents, and data science workloads using modern cloud technologies.

Algoscale Differentiators.

Rapid Development Expertise – Delivered a production-ready cross-platform app in one month.

Ability to bridge IT, data engineering, and AI strategy under a unified blueprint.

Deep experience in API-first, cloud-native architectures using Azure and Databricks.

Focus on data governance, lineage, and interoperability to accelerate transformation.

Delivered a future ready blueprint enabling AI-driven automation and decision intelligence.

Deep expertise in data engineering, AI strategy, and understanding of enterprise legacy systems.

Deep Data/AI expertise paired with hands-on knowledge of mainframe and legacy enterprise systems.

Values Delivered.

Through this engagement, Algoscale delivered measurable improvements:

Strategic Alignment

Unified the IT, Data and AI organizations under a single enterprise data vision.

Operational Modernization

Reduced application rationalization and data migration timelines by 30% through automated mapping and standardized pipelines.

Decision Enablement

Introduced standardized data and architecture decision matrices and accelerating transformation initiatives.

AI Readiness

Enabled activation of AI models and autonomous agents by a well defined data hub architecture.

Scalability & Future Growth

Progressed toward a modular data ecosystem capable of supporting enterprise wide analytics and automation.

Powered by Arcastra’s™ Custom Agent – a backend automation agent that orchestrates ingestion, transformation, and governance across complex enterprise data stacks. In this case, the agent seamlessly integrates Salesforce, Redshift, and Tableau with real-time monitoring, audit trails, and governed access- enabling downstream analytics agents to deliver high-accuracy, low-latency insights.