Data Reliability in a Self-Service World

The demand for more consumable datasets and analytics has led to increased pressure on data engineering teams. As a result, the role of "analytics engineer" has emerged, with individuals skilled in both data and SQL taking on the responsibility of creating their own datasets for new analytics. This collaborative process between data engineers and analysts allows for faster delivery of new analytics and enables data engineering teams to focus on critical data pipelines. However, one area that is often overlooked is data reliability and quality. Automation tools can help address this issue by providing data profiling, AI-driven recommendations, advanced data reliability policies, and no/low-code options for creating custom rules. Additionally, monitoring compute and spend helps maintain optimal performance and cost efficiency in self-service data environments. Embracing automation and operational intelligence features in data observability platforms can help scale data reliability efforts by empowering more virtual team members with self-service capabilities while providing the necessary guardrails and optimization facilities for smooth operations.