Understanding data governance for AI

AI runs on data. Good AI runs on good data. Artificial intelligence works though ingesting massive amounts of training data and analyzing it to uncover patterns and correlations. An AI model then uses these correlations and patterns to generate predictions about future states.

An AI system’s ultimate performance and behavior lies in the data itself. If a model is trained on data that’s inaccurate, incomplete, biased, or inconsistent, the predictions or recommendations that this AI system generates can be unreliable or even harmful.

The accuracy and usefulness of AI models, then, depends on the availability of high-quality training data. Having high-quality data to feed the model depends on data governance. Good data governance is important for any business focused on making data-driven decisions, but it’s even more critical for AI.

This guide will break down the critical role data governance plays in AI applications, as well as how AI can enhance data governance in an organization — and the competitive advantages of mastering both.

What is data governance?

Data governance is a framework that manages the availability, integrity, security, and usability of data across their entire organization. It defines (and enforces) the policies, standards, roles, and responsibilities that control how your company’s data is used, shared, and protected. In AI — where the quality of performance and behavior of large language models are intimately connected to the quality of the data they get “fed” to train on and process — sound data governance is particularly critical.

Data governance in AI is focused on managing the data used by AI systems: the quality, integrity, security, and usability of an organization’s data throughout its lifecycle. AI data governance concentrates on the operational and technical aspects of data handling to produce clean and reliable data by creating standards and practices for data collection, storage, and use that prevent data mishandling and misuse.

Why data governance for AI is important

These AI governance standards are crucial because they directly influence the training, operation, and outcomes of AI models, shaping the algorithms' fairness, accuracy, and security. Solid data governance policies secure the data lifecycle processes that underpin the AI models themselves by ensuring that any data inputs do not compromise the integrity of AI outputs.

Some of the most significant responsibilities of AI data governance include:

Bias mitigation: AI models are only as good as the data they’re trained on. If your training data contains biases, the AI model will produce biased outputs. Governance ensures that data used for AI training is diverse, representative, and monitored for biases.

Model transparency and accountability: Clear data governance policies enhance transparency in AI systems, allowing users to understand how decisions are made and who is responsible for the outcomes. AI governance policies deliver transparency and explainability through ensuring that data lineage and metadata are meticulously tracked and accessible. This is especially important in sectors like finance, healthcare, and legal, where explainability is a regulatory requirement.

Accuracy and reliability: High-quality data is essential for AI to function effectively, and data governance ensures that the data used is accurate, complete, and consistent, preventing biased or misleading results.

Ethical considerations: Data governance helps mitigate ethical concerns by identifying and addressing potential biases in data. This promotes fairness in AI decision-making, especially in sensitive areas like recruitment or lending.

Compliance with regulations: Noncompliance with applicable regulations can cost a company up to USD $15M a year. Data governance frameworks can help organizations comply with data privacy laws and regulations when developing and deploying AI systems.

Data security: Data governance practices include measures to protect sensitive data used in AI systems, preventing unauthorized access and data breaches.

Ethical AI: Ensuring that AI applications are developed and used ethically is a top priority for many organizations. Data governance frameworks help establish rules to prevent the misuse of AI and ensure that AI decisions are fair and just.

How AI contributes to good data governance

Machines have real advantages over humans when it comes to working continuously, tirelessly, and accurately — three important factors for practicing good data governance. Here are some ways AI technologies such as machine learning and natural language processing can help automate and improve data governance processes to enhance your data platform’s performance.

Improving data quality: Managing data at scale is full of pitfalls, including incorrect, out-of-date, or hard-to-find data. AI can detect anomalies, cleanse data, and resolve inconsistencies across datasets.
Automating data quality control: AI can leverage metadata to continuously monitor data for quality issues, spotting inconsistencies, errors, or duplications at scale. Machine learning algorithms can be trained to detect anomalies and automatically flag or correct them, ensuring that data remains clean and reliable.
Enhanced data discovery and cataloging: AI-powered tools can automatically identify, tag, and classify massive volumes of data for fast and efficient data cataloging. By making data easier to find and understand, AI can grant access to valuable insights for any user anywhere in your organization.
Data security and risk management: AI can help identify potential security risks by detecting unusual access patterns or data breaches within your data governance platform so you can preemptively address risks and protect sensitive data.
Smart data discovery: AI-powered tools can help uncover hidden insights in large data sets while ensuring that access and usage are governed by strict policies. Besides being a major boost to data-driven decision-making, this can also ensure that data usage remains compliant.
Regulatory compliance: Applying AI within your company’s data governance program can help ensure that your company remains compliant by tracking and controlling how data is handled.

Competitive advantage through AI data governance

The operational, ethical, and competitive advantages of instituting and following robust data governance for AI are potentially powerful.

Trust and reputation: Following AI governance practices can significantly enhance customer trust. Transparent, explainable, and ethical AI systems build credibility — and can help differentiate your company from competitors who may not prioritize governance.

Regulatory compliance: Proper AI governance builds in adherence to evolving AI-specific regulations like the European Union AI Act — which protects your organization from the fines, legal repercussions, and reputational damage that can penalize the companies that find themselves out of compliance.
Optimized AI performance: Data governance policies that ensure AI models are trained on high-quality and diverse dataset result in an AI system that delivers more accurate and reliable outputs. These fast, efficient, and valuable outputs can then enhance the performance of AI-driven business initiatives and boost overall business success.
Faster innovation with safety nets: Having a solid governance framework allows for innovation within AI while maintaining control over potential risks — so your company can scale AI initiatives quickly and safely.
Speed up development cycles: Your AI initiatives only move as fast as the development of the data that underlies them. Data governance speeds up development by streamlining your data transformation pipelines and processes.

Conclusion

Data governance may not be the most exciting topic. However, it’s the bedrock of sustainable, reliable AI applications. Ensuring that AI systems are fed high-quality, well-governed data is not just a matter of improved performance. It’s also a matter of trust, compliance, and competitive survival.

Without strong data governance, AI can generate flawed insights, introduce bias, and compromise security. And that creates significant reputational and financial risks.

By prioritizing data governance in your AI operations, your organization can drive competitive advantages through enhanced decision-making, regulatory compliance, and operational efficiency. AI's role in automating and improving governance practices allows organizations to harness the full power of their data in a controlled, ethical manner.

Mastering data governance for AI is no longer optional. It’s essential for building trust, staying ahead of regulations, and accelerating innovation. In a world where data is a key differentiator, good AI is built on great data governance.

Great data governance also requires great data governance tools. dbt Cloud provides a suite of tools to manage data governance for AI and other data-driven projects. Rather than approaching data governance piecemeal, you can govern all of your data products in a single, unified data control plane.

Learn more about how to use dbt Cloud to elevate your data governance game.