Session Outline
Have you ever wondered how a global fashion retail company organizes and leverages its massive (and often messy) data from various sales channels, multiple markets across the globe, complex supply chains, customer and loyalty programs (including PII data), and many other systems? In this talk, we will explore best practices implemented within H&M’s data division, including layered data products, standardized integration patterns, data discovery and data governance. These strategies are designed to enhance business consumption and maximize the potential of data. Bingwen will also share his personal insights from his experience as a Machine Learning Tech Lead and Data Engineering Manager, along with his thoughts on transitioning between these roles.
Key Takeaways
- Learn about the vast data landscape of a global fashion retail company
- Explore best practices for managing data and unlocking its full potential
- Personal reflection on the differences and connections between Machine Learning Engineer and Data Engineer, as well as Individual Contributor and Engineering Manager
