AI-Driven Data Governance Frameworks

AI-driven data governance frameworks enhance traditional governance by automating key tasks such as data classification, quality monitoring, and compliance checks. AI also allows organizations to implement dynamic data policies that evolve with shifting business models and regulatory changes. This adaptive, real-time governance ensures that organizations can respond to changes quickly while maintaining data integrity, security, and compliance across the enterprise.

Key Takeaways

• AI-Enhanced Data Classification: AI automates data classification by analyzing patterns, content, and context, continuously updating categories as new data is ingested, reducing manual effort.
• Improved Data Quality Monitoring: AI detects data quality issues like anomalies and inconsistencies in real-time, ensuring higher accuracy and proactively recommending fixes before problems escalate.
• Automated Compliance Checks: AI streamlines compliance by monitoring data for regulatory requirements (e.g., GDPR) and automatically adjusting policies, reducing the need for manual audits.Ability to read Excel datasets URL from sharepoint.
• Adaptive Data Policies: AI-driven policies automatically evolve with business changes and new regulations, ensuring real-time updates for data governance rules.
• Policy Evolution for Business and Regulatory Shifts: AI anticipates shifts in business models and regulations, dynamically adjusting governance policies to ensure continued compliance and security.

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Bio

Dr. Usha Balasundaram- Head of Data Governance | S&P Global| Malaysia

Dr. Usha Balasundaram is an accomplished leader in the field of data management and governance with over 20 years of experience. She excels in driving enterprise data strategies, enhancing data quality, analytics, and digital transformation initiatives. Usha has led cross-functional teams across various industries, enabling organizations to maximize the value of their data assets. She is also deeply committed to promoting diversity and inclusion, mentoring women in data professions, and advocating for flexible learning and career development.