DataOps-Driven Governance Frameworks for Enhancing Automated Decision-Making Systems in Big Data Environments

Abstract

The increasing reliance on automated decision-making systems in big data environments has created a critical need for efficient, reliable, and governance-driven data pipelines. Traditional data management approaches often suffer from inefficiencies, high error rates, and limited transparency, which can negatively impact decision accuracy and operational performance. This study proposes a DataOps-driven governance framework designed to enhance automation, improve data quality, and support scalable decision-making systems. The proposed framework integrates DataOps principles such as continuous integration, automated validation, real-time monitoring, and collaborative workflows into the data pipeline lifecycle. The analysis of pipeline efficiency demonstrates that DataOps significantly improves performance across key stages, including data ingestion, processing, governance enforcement, and deployment. By automating workflows and reducing manual intervention, the system achieves faster processing times and more consistent data quality. The study further evaluates the impact of DataOps on error reduction, showing that automated validation and real-time monitoring mechanisms significantly minimize system errors compared to traditional approaches. This improvement enhances the reliability of automated decision-making systems and reduces the propagation of errors across the pipeline. Additionally, the results indicate a strong correlation between increased automation and improved decision accuracy. Fully automated DataOps-driven systems outperform manual and semi-automated approaches by delivering more consistent and data-driven outcomes.