Data Observability and Data Quality Automation: Building Self-Healing Data Pipelines

Modern data architectures have become increasingly complex, creating new challenges in ensuring data quality and reliability. This paper explores the emerging field of data observability and quality automation frameworks that enable organizations to build self-healing data pipelines. We present a comprehensive analysis of current challenges in data quality management, examine the evolution of observability practices from DevOps to DataOps, and propose a reference architecture for implementing intelligent data quality systems. Through case studies and empirical evidence, we demonstrate how organizations can significantly reduce data downtime, accelerate issue resolution, and build greater trust in their data assets through automated detection, diagnosis, and remediation capabilities. The paper concludes with a roadmap for future developments in self-healing data systems and guidelines for implementation across various organizational contexts.