AI Governance, Data Governance, and AI Data Governance: Pillars of AI Success

How are AI governance and data governance related? Better still, what’s more important for an organization to focus on, AI-powered data governance or AI data governance? These are important questions, but before we answer these, let’s understand how AI and data governance are related to each other.

How are AI and data governance related?

At a cursory level, it appears that data governance and AI rely on each other in that you need high-quality data to train your AI models and systems and that to be able to govern data efficiently, you need to integrate AI into your processes. But therein lies the catch: technically, data governance itself is not dependent on AI. In fact, the role of AI in governance is mainly supporting and enhancing the management and governance of data.

The same, however, does not hold true when we reverse the roles. An AI model trained on data with questionable integrity is as good as ChatGPT struggling to respond accurately to a prompt with a simple math problem.

These errors occur due to a variety of factors, including insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model. The performance of an AI system, therefore, is contingent on the data’s health and by extension, the effectiveness of the overall data governance framework. The image above is a good example that illustrates this problem.

To return to the question of the relationship between the two, we must also realize that, today, AI has an increasingly important role in almost every area, including data management. Keeping in mind the needs of modern businesses and the benefits offered by the modern data stack, both AI and data governance rely on each other and work synergistically—and that is exactly how businesses need them to function to derive real value from them.

Using AI for data governance

But what about AI algorithms or models used to enhance data governance itself? Organizations leverage AI to automate much of the redundant effort that goes into ensuring that they comply with relevant regulatory requirements.

Specifically, AI-powered data management platforms enable organizations to automate data quality management, classification, discovery, lineage, and impact assessment, all the while enhancing metadata management, data access control, and privacy management.

However, to make the best use of such AI models and tools, they require proper oversight, which is why we have AI governance.

AI governance requires AI-readiness

We’ll get to governing AI models and systems in a bit— let’s first talk about being AI-ready. As is evident, being AI-ready, or AI readiness, is when your organization has the required framework and policies in place to embrace AI and incorporate it into its processes, tools, and systems. According to Gartner, 90% of the organizations globally will be using generative AI alongside their workforce.

One of the most important prerequisites to being AI-ready is access to AI-ready data, i.e., data that is accurate, clean, and well-structured for feature engineering and machine learning (ML). And to get data of such refined quality, you need a data governance framework built for AI.

AI governance refers to all the processes, policies, and tools your organization uses to ensure that AI is used responsibly and ethically. In other words, AI systems should be designed in a way that the safety of people, data, as well as the AI technology itself is guaranteed throughout their use.

AI governance is sometimes used synonymously with AI data governance, which is the oversight of the management and use of AI data in an organization. However, they are not entirely the same.

Is data governance really the umbrella?

To differentiate between data governance, AI governance, and AI data governance, we need to consider the key word, i.e., data, which means that AI data governance is governance principles applied to data to be used by AI models. It suffices to recall that the effectiveness of such models still depends on how the data they’re trained on is governed—do data governance right, and you will get AI-ready data. In other words, AI data governance is just a component of data governance.

As far as data governance and AI governance are concerned, there are contrasting opinions. Some are of the view that they should be kept separate as there are different focus areas to oversee and risks associated with each. Primarily, the scope, objectives, and operational dynamics of governance differ significantly.

Wrap up

From the perspective of an enterprise operating in a world where AI has become the norm, what’s important to note is that AI governance and data governance together set the guidelines and policies for data utilization, even for AI and ML models.

Such an enterprise relies on data governance to ensure that the data used by these models is of high quality, secure, and compliant with regulatory standards. Likewise, it needs AI governance, whether as part of the overall data governance framework or as a separate initiative, to oversee the ethical and transparent use of AI technologies, ensuring fairness, accountability, and explainability in decision-making.