Background
The Utilities company provides energy solutions to commercial, industrial, and retail customers. For large Commercial and Industrial (C&I) customers with complex energy needs, The Utilities company utilises a specialised pricing tool. This system generates customised pricing models and quotes for C&I customers based on
factors like usage data, spot prices, and predefined pricing parameters. The tool was built using legacy technologies. It ingested data from various sources like their CRM system and meter data to generate pricing models. However, this tool was being depreciated and end of service life.
Challenges
- The existing tool used outdated technologies that needed modernisation
- It did not provide the flexibility to quickly adapt to changing business requirements
- Data ingestion from source systems was limited to daily batches, affecting pricing timeliness
- Lack of scalability to handle growing data volumes and complexity
- The inability to easily implement pricing model testing and simulations
Solutions
- SISU deployed a Squad-as-a-Service under an Agile methodology to address the following needs.
- User-Friendly Interface: A ReactJS-based front end tailored for business users.
- Microservices Architecture: Decomposed core functions into microservices for enhanced efficiency.
- Advanced Data Management: Leveraged Kafka for messaging, Databricks for pricing models, and integrated with CRM systems.
- Robust Data Storage: Utilized S3 for storage, DynamoDB for caching, and Delta Lake for intermediate storage.
- Cutting-Edge Technologies: Employed Python and R for dynamic pricing models
Results
- A modern, cloud-native architecture replacing the legacy system.
- Enhanced data ingestion for timely pricing.
- Flexible, notebook-driven pricing logic adjustments.
- Scalability for increasing data volumes.
- Streamlined pricing model testing for future needs.
- Significantly reduced risk and accelerated market readiness.
