Sarcouncil Journal of Multidisciplinary

Abstract: The Explainable Data Pipelines (EDP) framework tackles issues concerning transparency and debugging in data engineering processes enhanced by AI. By integrating explainability features throughout the entire pipeline lifecycle—from data ingestion to transformation, modeling, and deployment—EDP ensures ongoing visibility that greatly boosts error traceability, minimizes debugging duration, and elevates stakeholder trust. The structure comprises four vital elements: Data Lineage Tracker, Explainable Model Interface, Anomaly Detector & Root Cause Engine, and Pipeline Debug Console, which together provide comprehensive observability while maintaining performance. Applications in finance, healthcare, and retail demonstrate notable progress in compliance with regulations, stakeholder satisfaction, and efficiency in operations. Though results are encouraging, obstacles persist, such as computational burden, integration difficulties, and skill deficits that necessitate coordinated technical and organizational responses. Future directions point toward explanation personalization, causal reasoning approaches, and interactive interfaces that will further enhance the utility of explainable data pipelines. The EDP architecture stands apart from traditional monitoring approaches by acknowledging the heterogeneous nature of explanation needs across different pipeline stages and stakeholder groups, enabling contextualized insights that bridge the gap between technical implementations and business understanding. This multi-faceted strategy for pipeline transparency aids not just in reactive debugging but also in proactive governance, promoting a culture of responsibility and ongoing enhancement in AI-enhanced data environments.