About the client
Leading Wholesale Logistics Firm
- Creating a unified advanced analytics platform that empowers agents with intelligent carrier search and an all-encompassing booking dashboard.
- Harness internal, historical, and external data sources like DAT and Truckstop, presents several potential challenges.
- Integrating diverse data sources and ensuring data quality, addressing scalability concerns.
- Developing and maintaining a robust machine learning model for carrier matching and rate prescription.
- Safeguarding data security and compliance, training users effectively, and fostering user adoption.
- Maximizing profitability and facilitating data-driven execution.
- Started by conducting a comprehensive data assessment and cleansing process to ensure data integrity.
- Implement data integration and ETL (Extract, Transform, Load) processes to harmonize diverse data sources.
- Concerns on scalability were addressed by designing the platform for modular and cloud-native architecture.
- Developing and maintaining the ML model involving a dedicated data science team.
- Monitoring data security and compliance through strict access controls, encryption, and ongoing audits.
- Optimize cloud infrastructure using best practices and cost-effective services.
- Effective user training and change management strategies to drive seamless adoption.
- Continuous iterations for regularly refining the platform to meet evolving business needs and data-driven execution goals.
- Advanced analytics platform to provide agents with intelligent carrier searching capability and a comprehensive booking dashboard, leveraging internal, historical, and 3rd Party data (DAT, Truckstop) on AWS.
- ML-based scoring model to rank and prioritize carrier matches and prescribe booking rates to maximize profitability and efficient data-driven execution.