A guide to data team structures with 6 examples

The evolution of data teams in businesses reflects a shift from fragmented data handling by various departments to the formation of dedicated, centralized, or distributed data teams that manage and derive value from data as a core business asset. Companies like Tourlane, Auto Trader, and Peak Labs exemplify different approaches to structuring data teams, each with unique strategies to balance centralized data management with empowering other departments. Tourlane uses a centralized model to democratize data insights, while Auto Trader balances empowerment with maintaining focus on core projects. Peak Labs faced challenges with context switching due to high demand from other teams but addressed this by implementing structured communication channels. Other companies, such as PEBMED, transitioned from centralized to distributed models, while Animoto adopted a hybrid approach with data ambassadors. Omio operates multiple federated data teams to handle scaling demands without overwhelming a single team. Snowplow aids these varied team structures by offering tools for unified data collection, high-quality data assurance, and flexible data management, supporting businesses regardless of their chosen data team structure.