Scalable, secure architecture
Modular components
Automated workflows
Validated data workflows
The challenge
Enhancing customer journey tracking and contact center operations
An insurer sought to track customer journeys across various insurance functions and optimize contact center operations. The existing relational data structure captured partial customer history. The company data was too big to be transformed or extracted using traditional tools. Manual intervention was required for data generation, and standardized data pipelines were needed to create efficiencies.
Key challenges
Relational data structure couldn't capture complete customer journeys
Need for advanced tools for large data processing
Manual data generation, validation, and loading
Lack of standard data pipelines
Need for improved security and governance
The solution
Optimized data architecture and automation
Data layer development
Ingested data with Airflow
Integrated raw data in Spark
Optimized storage
Data automation
Scheduled ingestion and validation
Removed manual processing
Automated data generation
Implementation approach
1
Data lake creation
Centralized data lake
Ongoing refinements
Scalable architecture
2
Data transformation
Automated data ingestion
Merged into key-value format
Tracked customer journeys
3
Security and governance
Secured data access
Ensured data integrity
Complied with security protocols
The impact
Strategic benefits realized
Scalable and robust architecture
Standardized development
Facilitated stepwise updates
Secure data handling
Efficient data processes
Efficiency-focused component design
Automated workflows
Improved execution efficiency using Airflow
Enhanced customer insights
Tracked customer journeys
Utilized key-value structure
Easier access to actionable insights
Looking ahead
Enhanced data personalization
Use analytics for smart customer behavior insights
Scalability expansion
Extend the architecture for growing data volumes
AI-driven decision making
Use AI and ML for real-time data-driven decisions
