March 2020 Summaries

Snowplow's unique approach to structuring events and entities significantly enhances data richness, quality, and flexibility, distinguishing it from other vendors. By defining custom data structures for events, which represent user actions, and entities, which provide context to these actions, Snowplow enables businesses to collect granular and tailored data aligned with their specific needs. Each event is automatically collected with 130 properties, allowing for detailed insights into user behavior, while entities can be appended to events to offer additional context. This setup, governed by self-describing schemas, ensures data accuracy and governance, as each piece of data is validated against these predefined schemas before being used. Moreover, Snowplow's data structures are not bound to specific platforms, making it easier to unify data from diverse sources like mobile, web, and third-party platforms into a common format, thus facilitating comprehensive data analysis. This flexibility allows companies to adapt their data collection as business needs evolve, offering a robust framework for deriving meaningful insights and optimizing data-driven strategies.

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.