The Importance of Data Governance When Implementing AI/ML

An article in the Data Administration Newsletter asks how can you maximize the benefits of AI/ML while minimizing the pitfalls and risks? It suggests that a big chunk of the answer is out there already — found in the deployment of “good old-fashioned” data management.

Why? Well… because it is! Too often, organizations assume that the solution to managing their AI/ML programs is through data governance. The truth is, the article, says, “they’re not wrong, but they’re only partially right. Data governance is actually just a component of data management. To fully embrace and take advantage of your AI/ML program, to leverage its capabilities while mitigating risk, you need a well-constructed, well-designed and mature data management program.”

The article goes on to say that data Management is about the supply chain of the data asset. “It’s about getting the right data to the right people at the right time. It’s about understanding and having full transparency of the data’s source, understanding how it is processed and curated, where it is persisted and how it is maintained. Data that feeds our analytics, AI/ML models, must be accurate, timely and trusted.

So, data governance alone is not enough. According to the article, “to successfully manage your AI/ML agenda, you need the full complement of your data management capabilities, successfully implemented, to have a successful AI/ML program. The EDM Council’s DCAM (Data Management Capability Assessment Model) aptly describes the key elements of your data management program. It begins with a fully defined and endorsed data management strategy. How will your program be managed and sustained and what data do you need to support your business objectives? The next consideration is the program itself. Do you have the right organizational structure, with the right levels of executive backing to support your program? And do you have the right skill sets in your organization to accomplish your data management objectives?”

Unfortunately, the article notes, many organizations are anxious to jump into AI/ML without having an established data management program. “You can lay the foundation of a building with inferior concrete, but it won’t stand the test of time. Data is the foundation of your AI/ML program.”

Next comes model management. “The same principles that apply to data management carry over into model management. Model development must adhere to a set of well-defined principles and standards and must be carefully developed and tested to ensure they are performing as intended. While developing these models to achieve a particular business objective, the modelers must constantly be aware of detecting bias in the algorithms and ensuring fairness in their design.”

The final leg of this journey is outcome management. For many firms, developing AI/ML solutions ended with the model going into production. And, in the past, as with traditional technology applications, after unit testing, that was sufficient. But in today’s AI/ML world, what comes out of these models must be constantly reviewed and evaluated.

The difference is the ML — “machine learning.” As the article points out, “even the best management programs overseeing large data sets can miss underlying bias in the data. This does not minimize the role of data management, but instead recognizes that the combination of unforeseen bias in the data, combined with the power of AI/ML, can bring these unintended outcomes to the forefront.”

As the article concludes, “bring together data, model and outcome and you will be on the path to leveraging the power and potential of this new AI/ML technology.”

For more, go to Eyes on Data: Importance of Data Governance When Implementing AI/ML –