Recently, Sunline secured a major contract worth ten million RMB to build an enterprise-wide public data mart and metrics system for a national joint-stock commercial bank. The project marks a significant benchmark in applying AI-enabled data engineering and governance to large-scale banking data infrastructure.
The implementation centers on the implementation of DataMind Synan, Sunline’s intelligent business decision-making platform. It represents a milestone in deploying AI technology across critical financial data scenarios, highlighting Sunline's technical capabilities in constructing banking data foundations.
Addressing Structural Data Fragmentation
As banks’ competition shifts from front office operations to data dashboard, the enterprise data warehouse is undergoing a fundamental shift. The challenge is no longer merely storing vast amounts of data but establishing a governed and unified enterprise-level data foundation that both human analysts and autonomous AI agents can reliably interpret. Fragmented data marts, inconsistent metric definitions, and the high operational risk of legacy system migration remain primary bottlenecks in this evolution.
To address the structural challenges, the bank launched this enterprise-wide public data mart and metrics system initiative, guided by its enterprise architecture, to dismantle legacy data silos and resolve cross-domain data fragmentation.
With its proven track records in financial data engineering, Sunline is establishing a unified, shared, and trusted data architectural blueprint that consolidates fragmented assets into a Single Source of Truth. By providing consistent, end-to-end, and traceable data support across corporate banking, retail, financial markets, and finance functions, the modernized foundation will ensure efficient, stable, and governed enterprise data consumption.
Secured, Governed, and AI-Powered Data Engineering
Drawing on its exploration and implementation experience in financial AI, Sunline deploys DataMind Synan across five strategic dimensions in building the new data infrastructure for the bank, bridging traditional data governance with AI-native engineering:
Subject Consolidation and Unified Standards
Built on a standardized three-tier architecture spanning the Source-Aligned Data Area, Public Data Mart, and Domain Data Mart, the project unifies data supply across "10+2" core business domains. This initiative drives the consolidation of over 300 legacy data marts into a cohesive enterprise asset, transitioning the bank's data landscape from fragmented silos to a unified architecture.
Configurable Metrics and Single Source of Truth (SSOT)
DataMind Synan centralizes the management of metric definitions, calculations and publication. With over 1,500 out-of-the-box standard financial metrics, business, technology, and data teams jointly review definitions to ensure reports, dashboards, and analytics use a single measurement standard for decision-making and regulatory reporting.
Standardized and AI-Ready Semantics
Configured metrics automatically generate complete semantic metadata spanning business, technical, and management attributes. By standardizing terminology, business users can access data intuitively while AI agents can "understand" data accurately, driving trusted intelligent querying and attribution analysis.
Embedded Security and Compliance-by-Design
Replacing direct physical table access with Service View abstractions, the architecture enforces dynamic data masking, fine-grained access control, and consolidated data access pathways. Financial-grade compliance and audit requirements are embedded into every layer of the data architecture, ensuring secure data provisioning.
Agentic AI Toolchain for Engineering Efficiency
To address the operational bottlenecks of data warehouse reconstruction, Sunline is deploying a specialized financial AI Agent toolchain built on its proprietary agentic foundation which accelerates the reconstruction of the public data mart across eight critical scenarios: existing script analysis, data lineage analysis, entity modeling design, SQL generation, compliance checking, automated testing, batch migration verification, and business semantic generation.
Applying Financial Data Expertise at Enterprise Scale
As a global leading financial solutions provider, Sunline delivered over 1,800 data projects for more than 400 financial institutions globally, spanning policy banks, large state-owned commercial banks, joint-stock commercial banks, city commercial banks, rural credit institutions and securities firms.
This new project implementation reflects strong customer recognition of Sunline's data product portfolio, technical expertise, and project delivery capabilities for a complex enterprise data initiative. Through this engagement, Sunline will also refine standardized methodologies and develop reusable approaches that inform industry-wide digital and intelligent transformation.
AI readiness begins with rigorous data governance. By embedding a robust semantic layer, unified metric management, and agentic AI engineering tools into the data foundation, banks can transition from reactive data retrieval to proactive, AI-driven knowledge operations. Furthermore, utilizing AI agents for data engineering significantly de-risks the migration process, reducing manual effort while ensuring strict auditability.
As the industry moves toward AI-native banking, Sunline remains committed to providing the governed, AI-ready data foundations that empower financial institutions to activate their data assets securely, efficiently, and at scale.