In our mid-sized company, we use Dataiku as the central hub for all our machine learning and advanced analytics projects. Before this, our data workflows were a mess of isolated Python scripts and disconnected spreadsheets. Now, we use it to build end-to-end pipelines, specifically focusing on customer churn prediction models and automated inventory forecasting. It acts as the bridge where our data engineering, data science, and business analytics teams actually collaborate on the same exact projects without stepping on each other's toes.As a senior data scientist, the biggest win for me is the flexibility between visual tools and actual coding. I do not want to be boxed into a pure drag-and-drop interface, and Dataiku gets that. I can dive into a code recipe and write custom Python or SQL when I need to do something highly complex, while our business analysts can use the visual recipes to handle basic data joining and cleaning. This hybrid approach saves me countless hours of doing repetitive data prep. I also really appreciate how straightforward it makes model deployment. Pushing a model into production used to require a massive handoff meeting with IT, but now we can package and deploy our models with just a few clicks, making our iteration cycles much faster.

Automation, data, AI, cataloging, governance

I like how Dataiku helps us identify some data quality issues and correct them before presentation. It's really important because, without data quality, the data is almost worthless, and you can't really make any insight out of it if it's not good quality. I also found the setup process to be very easy. We integrated with our intra authentication system, set up connections to Snowflake, and then started working on it.