The 2025–2026 Digital Transformation Innovation Practice Awards were recently announced at the 2026 Digital Transformation and Strategy Forum hosted by the China Academy of Information and Communications Technology (CAICT).
Under the theme “AI-Driven Transformation, Digital Innovation for the Future,” the forum addresses enterprise digital transformation, AI innovation, and the integration of emerging digital technologies. CAICT evaluated submitted projects through its Enterprise Digital Development Co-Construction Platform (EDCC), selecting innovative practices that demonstrate significant business value and provide reference models for digital transformation across industries.
The “Digital Governance for Financial Technology R&D and Test Environments” project, jointly implemented by a leading commercial bank and CanWay, was selected as a 2025–2026 Digital Transformation Innovation Practice.
This recognition reflects the bank’s continuous investment in financial technology and digital operations, while also affirming CanWay’s capabilities in integrated R&D, intelligent operations, and IT resource operations management.
By integrating R&D management, resource management, operation management and analysis, the project established a centralized capacity management platform for R&D and test environments. The platform enables integrated resource governance, intelligent capacity analysis, granular cost management, and environment lifecycle management.
This transformation has shifted environment management from manual, experience-based processes to data-driven and intelligent operations, providing a scalable model for the modernization of R&D and Test environment management in the financial sector.
1. Project Background: Challenges in Managing Financial Technology R&D and Test Environments
1.1 Client Background
The bank continuously committed to financial technology innovation and digital capabilities building, accelerating the integration of technology with business operations. As digital transformation progresses, increasing investment in technology and infrastructure has placed greater demands on the management of R&D and test environments, resource efficiency, and technology operations.
As critical infrastructure for financial technology innovation, R&D and Test environments support application development, system validation, business innovation, and technological advancement. The efficiency of resource management directly affects development productivity, business responsiveness, and system stability.
1.2 Key Challenges
As financial technology initiatives have expanded, R&D and test environments have become increasingly complex, involving a broader range of resource types, workloads, and management requirements. Resource utilization, environment delivery, and capacity planning directly affect development velocity and system reliability.
Before the project, the bank faced these major challenges:
（1）Fragmented Resource Data and Lack of Centralized Governance
R&D and Test environments included Multiple types of cloud, physical servers, network devices, IaaS, PaaS, containers, and other resources.
Data was distributed across multiple systems, resulting in information silos and making it difficult to establish a unified resource view.
（2）Limited Capacity Visibility and Low Resource Utilization
Resource utilization and capacity status were difficult to monitor in real time. Overloaded and underutilized resources coexisted, making accurate capacity planning and dynamic resource allocation difficult.
（3）Poor Cost Management
R&D and test resources span procurement, asset management, contracts, and business applications. Because resource and cost data were not effectively correlated, it was difficult to accurately assess resource investment across different business scenarios or identify optimization opportunities.
To improve development efficiency, optimize resource utilization, and control costs, the bank required a centralized platform capable of unified resource management, accurate capacity analysis, and value-oriented resource operations.
2. Solution: A Digital Governance Platform for R&D and Test Environments
To address these challenges, the bank partnered with CanWay to establish a capacity management platform based on an integrated R&D.
The solution focuses on four core areas:
Unified Data Governance
Intelligent Resource Operations
Precise Cost Management
Environment Lifecycle Management
A unified data foundation connects R&D, resource, IT operations, and data, enabling centralized resource management, intelligent capacity analysis, cost optimization, and automated environment management.
This approach shifts R&D and test environment operations from traditional resource administration to data-driven intelligent operations.
2.1 Overall Architecture: Five Layers for Unified Governance
The platform adopts a layered architecture with clearly defined responsibilities and coordinated operations. It consists of five layers:
（1）Infrastructure Resource Layer
Provide data from the various infrastructure resources supporting R&D and test environments.
（2）Data Collection & Governance Layer
Collect data from multiple sources and perform standardized processing and unified governance so as to build a unified data foundation.
（3）Core Capability Layer
Provides resource management, capacity analysis, cost analysis, and operational support capabilities.
（4）Business Application Layer
Provides applications for capacity management, operational analytics, asset management, and other R&D and test environment use cases.
（5.）User Access Layer
Supports the requirements of management, operations teams, development teams, and other user groups.
Standardized interfaces connect all five layers, enabling data exchange and capability orchestration while supporting scalability, compatibility, and continuous evolution.
2.2 Core Capabilities: Building a Resource, Cost and Efficiency management Close-Loop
Across the full lifecycle of R&D and test environment management, the platform delivers four core capabilities: Unified Data Governance、 Intelligent Capacity Operations、 Asset & Cost Management、 Test Environment Lifecycle Management.
Together, these capabilities shift resource management from fragmented administration to centralized governance, and from experience-based decision-making to data-driven intelligence.
（1）Unified Data Governance
To address fragmented resource sources, inconsistent data standards, and isolated system information, the platform establishes a centralized data governance framework.
Its multi-protocol integration capabilities connect 13 core data sources, enabling centralized collection and standardized processing of resource, business, capacity, workload, asset, and contract data.
Through data collection, cleansing, transformation, and correlation analysis, the platform creates a unified resource view and provides comprehensive visibility into the R&D and test environment.
This establishes a reliable data foundation for capacity analysis, resource allocation, and cost optimization.
（2）Intelligent Capacity Operations
Built on the unified data foundation, the platform continuously collects resource performance metrics and analyzes capacity and workload across multiple dimensions.
Its resource analysis models support: Capacity monitoring, Capacity alerts, Capacity trend forecasting, Workload analysis, Optimization recommendations.
This enables operations teams to identify overloaded and underutilized resources in a timely manner and proactively optimize resource allocation and scheduling.
Visual dashboards and automated analysis reports provide management with real-time visibility into:
Resource utilization by business teams and projects
Capacity and workload across R&D and test resources
Overall resource operations
Capacity trends and optimization opportunities
The platform also combines historical utilization data with business growth trends to forecast future resource demand, enabling teams to plan infrastructure investments in advance, avoid capacity shortages, and reduce unnecessary expenditure caused by idle resources.
(3) Asset & Cost Management
The platform connects resource utilization data with procurement and contract data to support full asset lifecycle management, including asset modeling, onboarding, change management, and status tracking.
It also incorporates FinOps principles to provide granular resource cost allocation by business line and project, together with cost trend monitoring and optimization recommendations.
This shifts resource management from merely tracking cost consumption to value-driven operations.
(4) Test Environment Lifecycle Management
The platform standardizes and automates the entire lifecycle of test environments, covering request, provisioning, operations, adjustment, and release.
Once a development team submits an environment request, predefined templates enable rapid environment provisioning. During operations, resources can be automatically scaled according to workload without manual intervention.
Environment delivery is therefore transformed from manual requests and setup into a standardized, automated process, significantly improving testing efficiency.
3. Results: Optimizing Resources and Modernizing Operations
The capacity management platform has enabled the bank to establish unified resource governance, improve operational efficiency, strengthen cost control, and enhance data-driven decision-making.
R&D and test environment management has shifted from manual administration to data-driven intelligent operations.
3.1 Stronger Data Governance
The platform successfully centralized 13 core data sources and established a standardized data governance framework, eliminating data barriers across resources, business operations, and assets.
The R&D and test environment now benefits from centralized data aggregation and management, with data completeness exceeding 99%, significantly improving resource visibility and operational transparency.
3.2 Significant Operations Efficiency Improvement
Through automated data collection, intelligent analysis, and visual operational dashboards, the efficiency of resource capacity statistics and workload analysis has improved by more than 80%.
Management teams can monitor resource status in real time and make faster resource allocation and operational decisions, substantially reducing manual reporting and repetitive work.
3.3 Higher Resource Utilization
Through capacity forecasting, workload analysis, and dynamic resource allocation, the platform accurately identifies available and underutilized resources.
Resource utilization in key R&D scenarios has increased by more than 20%, while annual costs associated with idle resources have been reduced by more than USD 100k. The solution also helps reduce unnecessary investment in additional hardware.
3.4 FinOps in Practice
By linking resource and contract data, the project established a unified cost accounting framework and introduced a value-oriented approach to managing R&D and test resources.
Continuous optimization of resource allocation provides a data-driven foundation for long-term resource planning and investment decisions.
3.5 A Scalable Industry Reference
In addition to addressing the bank’s resource management challenges, the project has established a practical methodology for capacity management that can be adapted by other financial institutions.
It provides a scalable reference model for the digital management and operations of financial technology infrastructure.
4. Outlook: Advancing R&D Operations Integration
Going forward, the bank will continue to enhance its R&D and test environment operations by strengthening intelligent resource forecasting, automated scaling, operational analytics, and FinOps capabilities.
Building on the data assets and operational knowledge accumulated through the platform, the bank will further integrate R&D and IT operations, improve resource allocation efficiency and responsiveness to innovation initiatives, and provide a more stable, efficient, and intelligent technology foundation for financial technology innovation.
As AI, big data, and other technologies continue to evolve, enterprise infrastructure operations are progressing toward greater intelligence and automation.
CanWay will continue to advance integrated R&D and IT operations by combining DevOps capabilities for R&D and delivery management with intelligent operations capabilities, while exploring new models for intelligent infrastructure operations in financial technology.
