Case Study: Bigtincan achieves 4x faster time-to-market for ML models with Databricks

Empowering sales teams to create meaningful customer engagements

Bigtincan, a sales enablement platform, was generating large volumes of content and customer interaction data across three AI-enabled platforms, but those sources were siloed with different reporting structures. That fragmentation — combined with a distributed, growing product and data science team — made it hard to produce timely, consolidated insights and build scalable ML solutions to personalize customer engagements.

Bigtincan implemented Databricks on AWS with Delta Lake, MLflow and interactive notebooks, reorganized its data teams, and centralized ETL and analytics to accelerate collaboration and reporting. The unified platform enabled rapid development of recommendation engines (e.g., a Promotion Recommender built in a week and the “Bigtincan Genie” virtual assistant), doubled reporting speed, sped ML time-to-market by 4x and boosted customer adoption by as much as 27%.

Bigtincan

George Ye

Senior Product Manager, Data Science Reporting