As a major disruptor and innovator in the fashion and retail industry, H&M relies on data as the core for everything they do. With stores opening up globally at a rapid pace, they needed to improve supply chain and forecasting operations to streamline costs and maximize revenues. But their on-premise Hadoop system crippled their ability to ingest and analyze data generated by millions of customers needed to power predictive models. Understanding they had reached their scalability ceiling, H&M moved to the Databricks Lakehouse Platform to simplify infrastructure management, enable performant data pipelines at scale, and simplify the machine learning lifecycle — allowing them to make data-driven decisions that accelerate business growth.
In order to improve supply chain efficiencies, they chose to utilize data and AI to improve decisioning and operations. However, their legacy Hadoop based architecture was inefficient and wasn’t able to scale to meet their rapid business requirements.
Databricks provides H&M with a Unified Data Analytics Platform on Azure that has fostered a scalable and collaborative environment across data science and engineering, allowing data engineers and scientists to focus on the entire data lifecycle instead of managing clusters, to train and operationalize models rapidly with the goal of accelerating supply chain decisions for the business.
At H&M, even a 0.1% improvement in accuracy of a single model has a huge impact on the business. With Databricks, H&M is making data more accessible for each and every decision maker, making business grow faster and more relevant.
Databricks is the core of our data business, it’s the place we go for insights.”
– Errol Koolmeister, Head of AI Technology and Architecture, H&M
Technical Talk at Spark + AI Summit EU 2019