Airlines today have ecosystems that depend on many systems of record, from booking engines to baggage transportation, loyalty programs, flight ops and more. Airlines that want to improve the customer service experience can leverage cloud native components including AI/ML, and data and analytics services on a cloud native platform like Google.
The data stored by airlines is complex, and services often do not adapt to changes due to rigid schemas and longer implementation cycles within analytics and operations systems. In addition, building these systems on-premise does not allow for scaling when there is a need to add new domains and features such as personalization, baggage sorting, etc.
In addition, airlines seek to personalize and improve customer experience by proactively acknowledging service changes and disruptions by interacting with the customer on their preferred communication channel (e.g., mobile, email). To accomplish this, multiple providers are required to build separate operational email systems, notification systems, and campaign and marketing systems capable of delivering a personalized customer experience.
Airlines looking to modernize those systems can do so by implementing a data platform architecture that will help relieve and eventually replace these on-premises data platform loads. In return, they will be rewarded with cost savings and an agile environment. Flight, passenger, and loyalty are examples of a few key areas where we have a need to separate storage and compute and pave the way for Data as a Service (DaaS) platforms.
The following reference architecture diagram addresses the challenges of replacing existing data systems with managed services and integrating multiple Google Cloud services like artificial intelligence and machine learning. The use of managed databases, serverless and fully managed services for microservices and events, provides elasticity and resilience. These cloud platforms thereby enable airlines to avoid expensive on-premise operational databases, service-oriented architecture infrastructure, and message-oriented middleware systems.
Reference Architecture Overview
The following diagram depicts Infogain's reference architecture for airlines operations:
Reference Architecture Features
In conclusion we can say that this data platform reference architecture can be used as the foundation layer for airline operations. It can be used to build features like customer lifetime value, segmentation, offers and personalization with artificial intelligence and machine learning services.