Case Study: Mitsubishi Electric accelerates analytics delivery by 60% and scales GenAI with Dataiku
Key results
The challenge
Mitsubishi Electric's analytics were fragmented, slow, and difficult to scale across teams, with manual workflows, disconnected tools, and inconsistent sharing of insights that limited decision-making and enterprise-scale AI. For its Serendie data platform, the company needed an environment that could support both data experts and non-experts while integrating directly with Snowflake and other data pools.
The solution
Mitsubishi Electric adopted Dataiku for data prep, modeling, visualization, and app building, consolidating ingestion, preparation, modeling, visualization, and reporting into one platform alongside Snowflake. Cross-functional teams collaborate in shared Dataiku flows, and the DX Innovation Center built a retrieval-augmented generation system for failure-response history using Dataiku's GenAI features.
“By linking the data stored in Snowflake with Dataiku, we are creating a system that allows a wide range of people at our company to easily use data and AI.”
SKSusumu KosekiDX Innovation Center, Mitsubishi Electric
The results, in context
Mitsubishi Electric reports a 60% reduction in workload from data prep to reporting, an 80% reduction in time needed for visualizations versus Python, and a 40% reduction in time creating system documentation. A full year of thermal energy analysis and reporting was completed in 20 days, and railway decarbonization insights were delivered in two weeks.