Data Governance for Analytics Engineering

The text discusses the critical role of data governance and analytics engineering in transforming raw data into actionable insights, emphasizing the importance of quality data pipelines. Analytics engineering serves as a bridge between data consumers and data engineering teams, facilitating the organization of datasets into models for business intelligence. It highlights the necessity for teams to have skilled analytics engineers or individuals with a combination of technical and business understanding to ensure data context for decision-making. The text outlines three pipeline phases—source, transformation, and governance—that are essential for addressing data quality issues, with governance ensuring data integrity through documentation, ownership, and metadata management. It warns against excessive documentation and stresses the need for governance frameworks that fit specific business needs. Additionally, it introduces Select Star as a tool that aids analytics engineering teams by providing a modern data catalog, seamless data source connectivity, and insights into data utilization, thus enhancing data governance and enabling efficient data management and migration.