Why AI Can’t Thrive Without Governance: A Strategic 4-Step Framework

Published on March 20, 2025

The rapid rise of AI in the workplace is undeniable. In a recent McKinsey survey, 78% of respondents say their organizations are regularly using generative AI in at least one business function, up from 72% last year. AI offers immense value across a wide range of use cases, from automating repetitive tasks to generating creative content and powering data-driven decision-making.

However, as AI becomes increasingly integrated into business operations, the need for robust AI-focused data governance cannot be overstated. Ethical oversight is crucial to mitigate the risks associated with AI systems and ensure their responsible development and deployment. A lack of ethical oversight can have severe consequences, as exemplified by the case of Zillow's failed iBuying algorithms.

Zillow, an online real estate marketplace, was forced to shut down its Zillow Offers business in 2021 because of failed iBuying algorithms. A derailed algorithm on property valuations led the company to reduce the estimated value of the houses it purchased in Q3 and Q4 by more than $500 million. At the time, Zillow announced $304 million in Q3 losses. An analyst estimated that possibly two-thirds of the homes that Zillow purchased in 2021 were valued below what Zillow paid for them.

This cautionary tale highlights the importance of implementing a comprehensive AI-focused data governance strategy. A data catalog plays a pivotal role in this endeavor, enabling organizations to manage and govern their data effectively.

So how do you get started? This blog provides a step-by-step guide for data management professionals to build an AI-focused data governance framework, leveraging the power of a data catalog and addressing critical aspects such as data quality, traceability, and ethical oversight.

Understanding AI governance

AI governance refers to the processes, policies, and frameworks that organizations implement to ensure the responsible and ethical development, deployment, and monitoring of artificial intelligence (AI) systems. It is a critical component of an organization's overall data governance strategy, as AI systems are heavily reliant on data and can significantly impact decision-making, operations, and customer experiences.

AI governance differs from traditional data governance in its focus on the outputs and decision-making processes of AI systems. While data governance primarily deals with the management, quality, and security of data assets, AI governance is concerned with the ethical and responsible use of AI technologies, including the decisions, predictions, and autonomous content generated by these systems.

One of the key distinctions between AI governance and data governance is the emphasis on the potential risks and unintended consequences associated with AI systems. As AI algorithms become more sophisticated and are deployed in high-stakes scenarios, there is a growing need to ensure that these systems are transparent, accountable, and free from biases or discriminatory outcomes.

AI governance also addresses the unique challenges posed by the complexity and opacity of AI models. Unlike traditional software systems, AI models are often "black boxes," making it difficult to understand how they arrive at their decisions or predictions. This lack of transparency can raise concerns about fairness, privacy, and accountability, particularly in sensitive domains such as healthcare, finance, and criminal justice.

Implementing effective AI governance requires a multifaceted approach that involves various stakeholders, including data scientists, domain experts, risk managers, and ethical review boards.

Some of the key pillars of AI governance include:

Ethical considerations: Ensuring that AI systems are developed and deployed in an ethical manner, respecting principles such as fairness, privacy, and transparency.
Bias mitigation: Identifying and mitigating potential biases in AI models, which can arise from biased training data or algorithmic flaws.
Explainability and interpretability: Developing techniques and tools to make AI models more interpretable and explainable, allowing for better understanding and accountability.
Regulatory compliance: Ensuring that AI systems comply with relevant laws, regulations, and industry standards, particularly in highly regulated sectors.
Continuous monitoring and auditing: Implementing processes for ongoing monitoring and auditing of AI systems to detect and address any issues or unintended consequences.
Stakeholder engagement: Involving diverse stakeholders, including domain experts, end-users, and affected communities, in the development and deployment of AI systems.

By addressing these challenges and opportunities, organizations can foster trust, mitigate risks, and unlock the full potential of AI technologies while ensuring responsible and ethical practices.

Step 1: Establish data quality standards

Data quality is the foundation of successful AI models. Inaccurate, incomplete, or inconsistent data can lead to flawed predictions, biased decisions, and unreliable outputs from AI systems. Ensuring high data quality is crucial for AI models to produce valid and reliable results that organizations can trust.

Data quality encompasses several key dimensions:

Accuracy: Data should correctly represent the real-world entities or phenomena it describes. Data accuracy should be tailored to the AI use case. Inaccurate data can introduce errors and distortions in AI model predictions.

Completeness: AI models require comprehensive data to capture all relevant features and patterns. Missing or incomplete data can result in biased or skewed outputs.

Consistency: Data must be consistent across different sources, formats, and systems. Inconsistencies can lead to conflicting information and unreliable AI model performance.

Timeliness: AI models, especially those used for real-time decision-making, require up-to-date and timely data to ensure their outputs are relevant and actionable.

Maintaining high data quality for AI initiatives can be facilitated by leveraging a data catalog. A data catalog acts as a centralized repository for metadata, providing a comprehensive view of an organization's data assets. By using a data catalog, organizations can:

Discover and understand data: Data catalogs enable users to search, browse, and comprehend available data assets, including their quality, lineage, and usage.
Enforce data quality rules: Data catalogs support the definition and enforcement of data quality rules, enabling organizations to identify and address data quality issues proactively.
Collaborate on data curation: Data catalogs foster collaboration among data stewards, subject matter experts, and data consumers, facilitating the curation and improvement of data quality.
Track data lineage: Data catalogs provide visibility into the end-to-end data lineage, helping organizations understand how data flows through various systems and transformations, enabling them to identify and mitigate potential quality issues.
Monitor data quality metrics: Data catalogs can integrate with data quality monitoring tools, allowing organizations to track and report on key data quality metrics over time.

By establishing robust data quality standards and leveraging the capabilities of a data catalog, organizations can lay a solid foundation for their AI initiatives, ensuring that their AI models operate on high-quality, trustworthy data.

Step 2: Implement data traceability and lineage

Data traceability and lineage are critical components of an effective AI governance strategy. Data lineage in the context of AI refers to the process of tracking the journey of data from its origin through various transformations and processing steps, all the way to its final use in an AI model. It essentially provides a detailed audit trail to understand how data influences the AI's outputs and identify potential issues with data quality or bias.

Maintaining data traceability and lineage is crucial for several reasons:

Transparency: By tracking the data's journey, organizations can ensure transparency in their AI systems, which is essential for building trust and accountability.

Debugging and troubleshooting: If an AI model produces unexpected or incorrect results, data lineage allows teams to trace back and identify the root cause, whether it's an issue with the data itself or the way it was processed.

Regulatory compliance: Many industries, such as healthcare and finance, have strict regulations around data usage and privacy. Data lineage helps organizations demonstrate compliance by providing a clear audit trail of how data is handled and processed.
Bias mitigation: Data lineage can help identify potential sources of bias in the data or the algorithms used, enabling organizations to take corrective action and ensure their AI systems are fair and ethical.

To enhance transparency and maintain data lineage, organizations can leverage data lineage tools, such as a comprehensive data catalog. By leveraging data lineage capabilities from the data catalog, teams can easily track the flow of data from source to consumption, understand data transformations, and identify potential issues or dependencies.

Some key features of data lineage tools within a data catalog include:

Visual lineage diagrams: Graphical representations of the data's journey, making it easier to understand and analyze complex data flows.

Impact analysis: The ability to analyze the impact of changes to data sources or transformations on downstream systems and processes.

Collaboration and governance: Shared visibility into data lineage across teams, enabling better collaboration and governance around data usage and AI model development.

Automated lineage capture: Automated tracking and recording of data transformations, reducing the manual effort required to maintain accurate lineage information.

By implementing data traceability and lineage as part of an AI governance strategy, organizations can enhance transparency, facilitate debugging and troubleshooting, ensure regulatory compliance, and mitigate the risk of bias in their AI systems.

Step 3: Leverage AI-assisted metadata

AI-assisted metadata plays a crucial role in improving the performance and accuracy of AI models. By automatically generating rich metadata descriptions, organizations can enhance the discoverability, understanding, and governance of their data assets, surfacing the context AI creators need to build impactful models, and ultimately leading to better outcomes.

One Alation feature, "Suggested Descriptions", leverages large language models (LLMs) to assist data stewards in creating comprehensive metadata descriptions. Here's how it works:

When a data steward needs to describe a data asset, such as a table or dataset, they can simply click a button, and the LLM will generate a detailed description. This description includes a summary of the table's contents, an explanation of its purpose, and suggestions for how it can be used. The LLM analyzes the metadata securely and crafts a response based on a carefully designed prompt.

This AI-assisted metadata generation not only saves time for data stewards but also ensures that metadata descriptions are consistent, accurate, and informative. By leveraging the power of LLMs, organizations can overcome the challenges of "data steward's writer's block" and maintain high-quality metadata at scale.

To effectively integrate metadata management into your AI governance strategy, consider the following practical tips.

How to integrate metadata into your AI governance strategy

Establish metadata standards: Define clear guidelines and standards for metadata creation, including required fields, naming conventions, and formatting rules. Consistent metadata standards will ensure that your AI-assisted metadata generation aligns with your organization's best practices.
Train and involve data stewards: While AI-assisted metadata generation can significantly streamline the process, it's essential to involve data stewards in the review and validation of the generated descriptions. Provide training to data stewards on how to effectively use the AI-assisted metadata tools and how to refine the generated descriptions when necessary.
Integrate with data catalog: Seamlessly integrate your AI-assisted metadata generation with your data intelligence platform. This will ensure that the generated metadata is easily accessible, searchable, and can be leveraged by various stakeholders, including data analysts, data scientists, and AI model developers.
Continuously improve: Regularly review and analyze the performance of your AI-assisted metadata generation. Gather feedback from users and stakeholders, and use this information to refine the prompts and fine-tune the LLM models for improved accuracy and relevance.

By leveraging AI-assisted metadata generation and following best practices for metadata management, organizations can unlock the full potential of their data assets, enabling more accurate and trustworthy AI models while ensuring compliance with data governance and ethical standards.

Step 4: Ensure ethical oversight and compliance

Importance of ethical oversight in AI development

Ethical oversight in AI development is crucial to mitigate the risk of perpetuating biases and stereotypes, especially against historically marginalized groups. AI systems heavily rely on data, and in the case of large language models (LLMs), the training data often reflects societal biases, leading to decisions and outputs that mirror and potentially amplify these biases.

Strategies for maintaining compliance in regulated industries

In regulated industries such as healthcare and financial services, maintaining compliance with relevant regulations is paramount when implementing AI systems. Failure to comply can result in hefty fines, legal consequences, and reputational damage.

To maintain compliance, organizations must establish robust governance frameworks that address data privacy, security, and ethical considerations specific to their industry. This includes implementing measures such as:

Data protection and privacy: Ensuring that sensitive data used in AI models is handled in compliance with regulations like HIPAA (Health Insurance Portability and Accountability Act) for healthcare or GDPR (General Data Protection Regulation) for consumer data.
Auditing and monitoring: Implementing processes to continuously monitor AI systems for compliance violations, bias, and other ethical concerns, and maintaining detailed audit trails for regulatory audits.
Model risk management: Assessing and mitigating the risks associated with AI models, including model drift, data quality issues, and potential biases, to ensure compliance with regulatory requirements.
Explainable AI: Developing AI systems that are transparent and explainable, enabling stakeholders to understand how decisions are made and ensure compliance with regulations around fair lending practices, equal opportunity employment, and other areas.
Responsible AI practices: Adopting industry best practices and frameworks for responsible AI development, such as the AI Principles proposed by the Organisation for Economic Co-operation and Development (OECD) or the Ethics Guidelines for Trustworthy AI by the European Commission.

By implementing these strategies, organizations can foster trust in their AI systems, mitigate compliance risks, and ensure that their AI initiatives align with ethical principles and regulatory requirements.

Conclusion

In today's AI-driven landscape, implementing a robust AI-focused data governance strategy is paramount for organizations seeking to harness the power of AI while mitigating risks and ensuring ethical, compliant operations. By following the steps outlined in this guide, you can establish a solid foundation for effective AI data governance.

Take the next step towards building a future-proof AI data governance strategy by downloading our free whitepaper, the "Data & AI Readiness Strategy Guide."