Reducing data downtime with data observability

Data downtime, a term introduced by Monte Carlo to describe periods of inaccurate or incomplete data, poses significant challenges for companies reliant on data-driven decisions, as exemplified by a real incident at Acme where inaccurate data in a key report led to a loss of confidence in data among the leadership team. This issue highlights the importance of data observability, which offers transparency and control over data pipelines to quickly identify and resolve issues, thus minimizing data downtime. Unlike monitoring, which addresses known issues, data observability tackles unknown problems, providing comprehensive visibility to ensure data reliability. Snowplow's approach to data observability focuses on key metrics like throughput and latency to diagnose bottlenecks efficiently, aiming to create the most observable behavioral data pipeline for aligning technical data with business outcomes.