Ensuring Data Quality and Consistency in AI Systems through Kafka-Based Data Governance

Abstract

In the ever-evolving landscape of Artificial Intelligence (AI), the accuracy and reliability of insights heavily rely on the quality and consistency of the underlying data. As AI systems increasingly become integral parts of various industries, ensuring robust data governance practices becomes paramount. This paper delves into the significance of leveraging Kafka-based data governance to maintain high standards of data quality and consistency within AI systems. Kafka, as a distributed event streaming platform, offers a versatile framework for managing data pipelines, facilitating real-time data processing, and ensuring the smooth flow of information across diverse components of AI infrastructure. By utilizing Kafka's capabilities in data governance, this study explores how organizations can establish comprehensive data quality standards, implement efficient data validation mechanisms, and enforce stringent consistency checks. It elucidates the role of Kafka in enforcing data governance policies, encompassing data lineage, metadata management, and access controls to guarantee the reliability and integrity of AI-driven insights. This paper highlights the challenges encountered in maintaining data quality and consistency in AI systems and proposes strategies utilizing Kafka's functionalities to address these issues. It discusses the significance of monitoring, alerting, and remediation strategies embedded within Kafka's ecosystem to proactively identify and rectify discrepancies, thereby upholding the reliability of AI-driven decisions.