Data Governance: AI applications in ensuring compliance and data quality standards

Abstract

Data governance is at the heart of modern organizations, ensuring compliance with regulations, safeguarding data quality, and fostering trust in enterprise data ecosystems. The emergence of artificial intelligence (AI) has revolutionized how businesses approach governance by introducing advanced tools and techniques that enhance efficiency, precision, and scalability. AI-powered solutions help automate compliance checks, identify anomalies in datasets, and enforce data quality standards across decentralized and complex environments. By leveraging machine learning algorithms, organizations can predict and prevent non-compliance risks, streamline audit trails, and ensure adherence to stringent regulatory frameworks like GDPR and HIPAA. AI also enables the continuous monitoring of data flows, detecting inconsistencies in real-time and flagging issues that could compromise data integrity. Natural language processing (NLP) also transforms how businesses interpret policy documents, map governance requirements, and ensure alignment across departments. As organizations increasingly adopt AI-driven governance tools, they minimize manual interventions and improve decision-making through enhanced data accuracy and transparency. Integrating AI into data governance frameworks creates a proactive culture of compliance and supports delivering trusted data to analytics and business intelligence teams. However, adopting AI comes with challenges, including addressing ethical concerns, managing AI biases, and establishing robust accountability measures. This paper explores the transformative role of AI in data governance, examining its potential to improve compliance and data quality while addressing critical considerations for successful implementation.