The Challenge
The organisation faced persistent data quality issues across critical operational datasets.
Inconsistent standards, manual processes and limited orchestration resulted in unreliable pipelines, poor visibility and reduced confidence in reporting and decision-making.
At the same time, cloud-native tooling was not yet production-ready — creating a risk of rework or misalignment with long-term strategy.
The challenge was to improve data quality, automation and reliability quickly, while maintaining flexibility for future cloud adoption.
The Solution
Agile embedded a senior team to work alongside internal stakeholders, applying Agile delivery practices to accelerate progress and ensure alignment.
We designed and implemented a hybrid data quality architecture using Informatica Data Quality (IDQ) on AWS, orchestrated via Informatica Cloud.
Key elements included:
- Integration with MySQL and Oracle data sources
- Automated data profiling, quality measurement and cleansing pipelines
- API-driven orchestration to trigger and monitor processes
- Automated testing using Postman, Newman and JavaScript
- A scalable architecture aligned to future cloud-native services
This approach delivered immediate improvements while establishing a clear pathway to full cloud adoption.
Break up these sections with testimonial quotes or other highlights that reinforce your case study narrative.
The Results
- Improved data quality visibility, consistency and trust
- Reduced manual processes through API-driven automation
- Increased reliability and confidence in production workloads
- Strengthened operational resilience
- Delivered measurable value within a short timeframe
Impact/Outcome
- Established a secure, future-ready data foundation
- Enabled seamless transition to cloud-native data quality services
- Avoided costly re-engineering of core pipelines
- Accelerated progress toward a modern, cloud-aligned data estate
