Solution Based on A-Module
Situation at the Start of the Project
The bank is a universal commercial bank serving more than 10 million retail and corporate clients. As part of its digital transformation strategy, the bank sought to modernize its approach to data management.
Obtaining up-to-date data had become a time-consuming process due to internal bureaucracy and the bank’s dependency on the IT department. Each new analytical request required the involvement of developers and data engineers for validation and testing, which often took weeks and diverted IT teams from strategic infrastructure tasks.
The core challenge was the gap between the speed of business and IT capabilities. The bank needed a secure, flexible tool that would allow business users to access and process data independently — without overloading technical teams or compromising corporate security.
Task
Business goals:
- Reduce response time to market changes;
- Decrease dependency on IT for data requests;
- Improve transparency and agility in analytics;
- Create a flexible tool for independent data analysis.
IT goals:
- Maintain infrastructure security and manageability
- Ensure secure access to data from multiple sources
- Automate resource management and eliminate idle server time
- Reduce operational costs and IT workload.
Solution
To address these challenges, the vendor proposed A-Module – a data management and KPI calculation ecosystem developed by RunRule Solutions. The project was implemented jointly with the bank’s internal team over several months, from design to full production rollout. A-Module became a central platform for analytics, automation, and collaboration between business and IT.
The system provided:
Secure Data Access
A-Module was integrated with corporate data sources — DWH, CRM, APIs, and internal analytical services — creating a unified access layer with user permissions and safeguards that prevent data corruption.
Process and Communication Automation
Built-in integrations with SMS, email, and task trackers allow the platform to automatically notify employees about KPI results and assign follow-up tasks, ensuring a closed feedback loop.
Self-Service Analytics with “Cubes”
Business analysts can build calculation functions in Python — reusable low-code “blocks”, or “cubes.”
Using these cubes, users can assemble complete KPI pipelines without IT involvement, enabling rapid testing of new hypotheses and faster implementation of business metrics.
Automated Resource Management
All computations are performed in isolated containers that launch, execute, and shut down automatically.
This ensures optimal resource utilization, reduces infrastructure load, and prevents downtime.
Results
For Business:
- Market reaction time reduced from weeks to hours
- Decisions are made based on real-time, accurate data
- New metrics can be created and tested without IT involvement
- Direct influence on process management in real time
- Measurable cost savings and operational efficiency gains.
For IT:
- Significant reduction in operational workload and repetitive requests
- Secure, automated data and resource management
- Lower infrastructure maintenance costs
- Ability to focus on strategic, value-added initiatives.
Scale and Performance
- 900+ active users today, with a roadmap to thousands;
- 100,000+ calculations completed within 30 minutes;
- Millions of records processed (expected to reach billions);
- 25 deviation types analyzed automatically (up from 5 at launch).
Economic and Strategic Impact
- Direct cost reduction in infrastructure and maintenance;
- Enhanced transparency and control over analytics;
- Development of in-house data-driven expertise.