Real-Time Data Analytics and Machine Learning Accelerate Business Growth for TripActions

TripActions, a corporate travel management organization, was facing a significant challenge with its existing infrastructure and data storage solution. The increasing volume of historical indexed data was straining the company’s infrastructure and primary storage solution, slowing down its performance and causing an ever-increasing cost of ownership. The historical data was never cleaned while analytical data was stored across various databases in different formats, creating multiple data silos and making data unavailable for analytics and machine learning. The company’s existing data solution was failing in terms of analytic capacity and scalability, which increased operational costs, slowed down onboarding of new clients, and stifled business growth. The initial architecture and data solution were based on Amazon Elasticsearch, which proved to be inefficient and expensive when data volumes increased. Data was schemaless, and there was no mechanism to join data from different databases. Partial data in Amazon S3 was stored in JSON format and synced with one-day lag, with no partitioning, which delayed TripActions’ reaction to issues or changes in data.

TripActions is a corporate travel management organization that helps control costs of business travel and incentivize employees via easily accessible business travel opportunities. The company was looking to design and build a new data streaming solution to accommodate the increasing amount of historical indexed data that was overstraining the company’s infrastructure and primary storage solution. The company’s existing data solution was failing in terms of analytic capacity and scalability, which increased operational costs, slowed down onboarding of new clients, and stifled business growth.