Data and AI Governance: Evolving Traditional Data Governance in the Age of Artificial Intelligence

Artificial intelligence (AI) is transforming how organizations manage and govern data. As AI permeates across industries, a profound shift is underway from traditional data governance to a more evolved framework that unifies data and AI governance. This evolution is critical to harness AI’s potential while governing its risks and complexities.

The Pillars of Traditional Data Governance

Data governance provides the overarching management framework to ensure data availability, usability, consistency, security and alignment with policies across the enterprise data ecosystem.

It revolves around six key pillars:

• Data Quality: Ensuring accuracy, completeness, reliability and validity of data for business usage through validation, monitoring and remediation processes.
• Data Security: Safeguarding data from unauthorized access and breaches through controls like encryption and cybersecurity measures.
• Metadata Management: Cataloging and documenting data context, meaning, structure, interrelationships and lineage across systems.
• Data Policies: Establishing policies and standards aligned to regulations for data acquisition, storage, retention, usage, privacy and lifecycle management.
• Data Access: Provisioning authenticated, policy-based access to enterprise data by various personas across lines of business.
• Data Lifecycle Management: Managing data from inception through usage till retirement across the data supply chain.

While this traditional governance model serves structured data scenarios reasonably well, AI introduces new challenges.

The AI-Induced Disruptions

AI systems are only as effective as the data they are trained on. But governing AI data poses several complexities:

• Data Volume: AI training data can comprise billions of points from diverse sources, challenging traditional management methods.
• Poor Data Quality: Low-quality, biased, or inconsistent training data significantly impacts the performance and fairness of AI models. Ensuring consistently high data quality is exponentially harder at AI data volumes.
• Model Opaqueness: The inner workings of complex AI models are often black boxes, making specific decisions opaque and posing Governance Challenges in AI.
• Algorithmic Bias: Training data containing human biases can lead AI models to make prejudiced and unethical decisions. Continuously detecting and managing bias is crucial.
• Increased Privacy Risks: The depth of insights uncovered by AI from data patterns heightens privacy concerns. Data anonymization also provides limited protection against re-identification.
• Regulatory Compliance: Increased use of consumer data by AI applications raises compliance requirements like GDPR, CCPA etc.
• Shortage of Skills: Governing AI requires a blend of data governance and data science skills which are scarce, hampering oversight of AI systems.

The Evolution to Unified Data and AI Governance

Governing AI necessitates extending traditional data governance to encompass AI’s unique risks and requirements.

Key aspects of this Unified Data and AI Governance model include:

• Holistic Data and Model Catalog: A comprehensive catalog of all data and AI model metadata providing visibility into relationships, lineage and meaning to enhance traceability.
• Continuous Data Quality Validation: Multilayered data quality checks using statistical analysis, rules-based profiling etc., to ensure training data and model input consistency.
• Algorithmic Audits: Proactive bias assessment by testing model outcomes across diverse datasets and user groups.
• Privacy Protection: Deploying data minimization, anonymization, federated learning and encryption to mitigate privacy risks.
• Model Risk Management: Formal evaluation of risks across the AI model lifecycle pre-deployment to ensure controls adherence.
• Human Oversight: Maintaining meaningful human oversight of data and models across the lifecycle.
• Actionable AI Insights: Providing visibility into key metrics on model accuracy, data quality, bias rate and AI vs. human decision ratios.
• Regulatory Compliance: Embedding compliance to data protection and AI regulations within data sourcing, model development and operations.
• Cross-functional Teams: Developing blended teams encompassing data engineers, scientists, and governance experts.
• Enabling Tools: Deploying integrated tools spanning metadata, data quality, bias detection and model risk management.

Implementing a unified approach enables continuous assessment and improvement across the AI data and model lifecycle while delivering the transparency, explainability and risk mitigation imperative to scale AI responsibly.

{Continue reading...}