Spot the Differences: Data Governance vs. AI Governance

Data Basics, Scaling AI, Featured David Talaga

AI Governance and data governance are both crucial for modern organizations, but they serve distinct purposes. Can you tell the difference between them? Understanding these differences is vital, especially with the introduction of the EU AI Act, which requires organizations to rethink their AI management strategies. In this blog post, we’ll explore these distinctions and explain why it's important to differentiate between the two. Keep reading to learn more.

AI Governance: The Next Layer

Back in 2018, at a prominent European Data & Analytics Summit, a leading analyst excitedly proclaimed, "Data Catalog is the New Black.” Since then, companies have centralized and controlled their data through various means, such as data catalogs, data inventories, and data collections. Meanwhile, AI, particularly Generative AI, has grown exponentially. The rise of machine learning (ML) models, analytics projects, code, Generative AI, and the use cases in which they are being deployed demands more specific and rigorous governance. Data governance was never designed to handle the democratized ML required in the age of AI, necessitating new governance frameworks. This is where AI Governance comes into play.

But how does AI Governance differ from data governance? Here are four key differences you need to understand:

Difference #1: Definitions

  • Data Governance focuses on managing an organization's data availability, usability, integrity, and security. Its goal is to ensure data is accurate, consistent, and used responsibly, adhering to regulations and internal policies. Key capabilities include data quality management, data security, metadata management, data stewardship, and data lifecycle management.
  • AI Governance oversees the processes, policies, and controls surrounding the development and deployment of AI projects. It orchestrates and enforces rules, processes, and requirements that align AI initiatives with organizational objectives. Key activities include model documentation, risk management, bias and fairness assessment, auditability, and accountability of AI systems.

Understanding and implementing both governance frameworks are essential for organizations to maintain trust and compliance as they scale their AI and data initiatives. AI Governance is inherently broader, encompassing objectives beyond data protection compliance, such as bias prevention and model explainability.

Difference #2: Regulatory Landscape

  • Data Governance: Driven by regulations like GDPR, DPA, CCPA, PIPEDA, and other regional or industry-specific data protection laws that focus on privacy and data security.
  • AI Governance: Governed by emerging regulations specifically targeting AI, such as the EU AI Act, which addresses ethical considerations and risk management. Industry-specific approaches complement AI-specific regulation.

Why it’s important: While companies are familiar with GDPR, few understand AI regulations. Governments globally are advancing regulatory and non-regulatory interventions to shape how organizations build, buy, and deploy AI. The U.S. has introduced the NIST AI Risk Management Framework, the AI Bill of Rights, an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence; Singapore’s IMDA released AI Verify; the U.K. published an AI regulation policy paper and launched the AI Safety Institute; and the EU AI Act has set a precedent by establishing requirements associated with risk-levels and complementary severe penalties for non-compliance. Understanding the EU AI Act and managing compliance with its requirements through governance practices is becoming a major criterion for any global company operating in Europe.

Difference #3: Operational Implementation

  • Data Governance: Implemented through data policies, centralized data catalogs, data stewardship, and data quality management processes.
  • AI Governance: Encompasses diverse objectives, extending from ethical guidelines, risk assessment frameworks, assessing operational efficiency, monitoring value through sign-off and approval workflows, and observability systems for AI applications.

Why it's important: An AI Governance framework should be distinct from cataloging datasets. It requires adherence to rules and operational implementation to prevent missteps in AI development and deployment. This is crucial for safely scaling AI and avoiding setbacks due to non-compliance with governance frameworks. Generative AI applications, like those involving large language models, also require governance measures to prevent downstream consequences related to risks such as data exposure, hallucination effects, and toxic outcomes.

Difference #4: Stakeholders

  • Data Governance: Involves data stewards, data teams, IT departments, compliance officers, and business users.
  • AI Governance: Requires a broader range of stakeholders, including data scientists, ML engineers, risk managers, ethicists and legal advisors, along with business domain experts.

Why it's important: Seamless collaboration among diverse stakeholders is paramount for successful AI Governance. Given the rapid proliferation of AI models, business teams need a unified platform with IT, data science teams, and managers and leaders to share visibility into ongoing projects, performance, and status, facilitating effective prioritization and decision-making.

How Dataiku Stands Out

In 2021, Dataiku launched its AI Governance offering to help organizations gain more control and visibility over AI projects. These capabilities complement the platform's native data control and data collaboration capabilities, facilitating data sharing and data governance.  

Since then, Dataiku has expanded its capabilities to adapt to emerging regulatory frameworks. Thanks to its customization capabilities and resources  to accelerate time to value, Advanced Govern is the perfect solution for accelerating preparation for current and future regulations, starting with the EU AI Act. Dataiku's advanced AI Governance offering connects with existing systems and seamlessly integrates into production workflows without sacrificing data science teams' autonomy. Our built-in LLM Mesh, a common backbone for enterprise Generative AI Applications, ensures proper GenAI governance with features like toxicity detection and PII protection. 

It’s the integration of end-to-end AI and MLlifecycle governance, collaborative risk management, and GenAI control within a single platform that has positioned Dataiku as a leader in the newly released IDC Marketscape for AI Governance Platforms 2023.

Conclusion

Both AI Governance and data governance are essential. Data governance ensures the accuracy, security, and trustworthiness of data feeding into AI models. AI Governance ensures these models are deployed, operated, and monitored ethically, transparently, and aligned to societal values and organizational priorities.

AI Governance is now becoming a must have for managing all AI projects, whether predictive or generative. Beyond technological innovation, the formalization of the EU AI Act requires companies to commit to AI Governance at all levels. This includes understanding regulations and adapting change management accordingly, improving AI literacy, and integrating these rules into AI project lifecycles. Only then will companies successfully transition from experimentation to sustainable AI industrialization.

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