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Data observability refers to the comprehensive monitoring, management, and understanding of an organization's entire data ecosystem. These tools enable businesses to better manage their data by identifying and resolving real-time data issues, providing complete visibility into the health of their data systems. Data observability helps accelerate data adoption across various departments, empowering organizations to make data-driven decisions that enhance overall performance. The concept of data observability draws from best practices in DevOps, applied to the management of inaccurate, incomplete, or erroneous data. These practices, including optimizing logs and gaining real-time insights, help ensure the creation of reliable and trusted data across the entire data stack—covering data sources, data warehouses, ETL processes, machine learning (ML) tools, business intelligence (BI) platforms, and more. Data observability tools are integral to DataOps platforms, which combine various data management tools into a unified, integrated environment. These platforms streamline development and operations in data workflows, with data observability software specifically focusing on monitoring the health of data pipelines and systems. Unlike traditional monitoring software, which tracks pre-defined metrics to spot bugs, data observability emphasizes real-time detection and resolution of issues. It differs from data quality software in that its primary goal is to reduce the frequency of data incidents while speeding up resolution times. Ultimately, strong data observability leads to improved data quality across the modern data stack.