Mitsubishi Electric: Accelerating analytics delivery by 60% & scaling GenAI with Dataiku

As Mitsubishi Electric built Serendie, its digital ecosystem for data-driven value creation, analytics remained fragmented and slow due to manual workflows, disconnected tools and inconsistent insight-sharing across expert and non-expert users, with a need to integrate with Snowflake. Dataiku became foundational to Serendie, consolidating data ingestion, preparation, modeling, visualization and reporting. This cut workload from data prep to reporting by 60%, visualization time by 80% versus Python, and system-documentation creation time by 40%; specific projects included a 2-week railway electricity/regenerative-braking analysis for decarbonization, a 20-day thermal energy optimization across multiple buildings using AutoML, and a GenAI RAG system for failure-response history. Shoma Fukuhara of the DX Innovation Center said 'everything from data analysis to report creation and application development can be completed on one platform with Dataiku.'

Dataiku is a low-code/no-code AI platform whose Prompt Studios and LLM Mesh let teams prototype and A/B-test candidate LLM approaches before committing to a production workflow.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.