The document discusses considerations for building an enterprise data lake. It notes that traditional data warehousing approaches do not scale well for new data sources like sensors and streaming data. It advocates adopting a data lake approach with separate systems for data acquisition, management, and access instead of a monolithic architecture. A data lake requires a distributed architecture and platform services to support various data flows, formats, and processing needs. The data architecture should not enforce models or limitations upfront but rather allow for evolution and change over time.