How to Use Data Governance for AI/ML Systems


Data governance plays a pivotal role in ensuring data is available, consistent, usable, trusted and secure. There are many challenges faced with maintaining data governance, and the ante is upped for systems such as artificial intelligence and machine learning.

AI/ML systems function differently from traditional, fixed record systems. The objective isn’t to return a value or a status for a single transaction. Rather, an AI/ML system sifts through petabytes of data seeking answers to queries that may be vast and multifaceted.

Furthermore, data can come from many different internal and external sources, each with its own way of collecting, curating and storing data, which may or may not conform to your organization’s governance standards. Then, there’s a matter of making sure AI/ML systems are trained on trustworthy data to ensure accuracy.

These are just some of the concerns companies and their auditors face as they focus on data governance for AI/ML and look for tools that can help them.

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Why is data governance necessary for AI/ML systems?

According to the IBM Global AI Adoption Index 2022, the global AI adoption rate is at 35% and ubiquitous in some industries and countries around the world. This rapid adoption of AI and ML systems to drive innovation and decision making makes the integrity and management of the underlying data paramount.

SEE: Learn more about data governance.

Compared to traditional computing systems, AI and ML systems are more nuanced, underscoring the importance of data governance. There are two main reasons why a robust data governance framework is necessary for AI/ML systems:

  • Dynamic structure: Compared to traditional data systems, AI/ML systems are dynamic — constantly evolving and learning from both structured and unstructured data.
  • Data volume and variety: The efficacy of an AI/ML system is directly proportional to the volume and variety of the datasets it trains on and learns from.

Because of these factors, without strict governance, AI/ML systems can produce inconsistent, inaccurate and even biased outputs.

How does data governance work with AI/ML systems?

AI/ML systems are designed to handle vast amounts of data simultaneously and asynchronously. This means multiple threads of data are fed into the processor at the same time, allowing for faster and more efficient data processing.

However, this also introduces complexities. The primary goal of an AI/ML system is to search through massive datasets to find answers, ranging from predicting future trends based on historical data to identifying patterns in e-commerce data. If the data from one source is corrupted or biased, it can influence the overall output, making the results unreliable.

Therefore, it’s critically important to incorporate rigorous data governance into the process to ensure each thread of data is accurate, relevant and free from biases.

The role of IT in speeding up data processing

IT departments play a pivotal role in the AI/ML data governance process. By preprocessing and weeding out irrelevant or redundant data, they can significantly speed up data processing times for AI/ML systems. This ensures the AI/ML models run efficiently and work with the most relevant and high-quality data.

SEE: Explore these top data preparation tools.

In addition, IT teams can implement tools and protocols to automate many governance tasks, such as data validation, ensuring consistency across data sources and monitoring for potential security breaches.

Challenges in implementing data governance for AI/ML systems

The integration and management of data for AI/ML systems pose several data governance challenges organizations need to navigate.

Integrating data from several sources

When organizations gather data from multiple sources, each with its own governance standards, ensuring consistency becomes a significant obstacle. This diversity can result in data mismatches, redundancies and inaccuracies.

Data must be harmonized to provide a comprehensive view that’s essential for efficacy. Integrating the data into a unified format is a complex process that involves cleaning, transformation and normalization.

To avoid flawed models, it’s critical to ensure the vast datasets used by AI/ML systems are accurate and relevant.

Trusting recommendations

The training data of some AI/ML models is secret, making it difficult for organizations to fully trust and comprehend the recommendations provided by these systems. Without insight into how decisions are made, there’s a risk of misinterpretation or misuse.

For example, AI/ML models sometimes reflect or amplify biases in the data. According to a study by Obermeyer et al, an algorithm that used health costs as a proxy for health needs, assigned Black patients, who were sicker than other White patients, the same level of health risk.

Knowing what training data is used for the model and that rigorous data governance is practiced can help in identifying and rectifying these biases, ensuring fairness in model outcomes.

Maintaining data quality

Since AI/ML systems heavily depend on high quality data, it’s crucial to ensure the data is clean, accurate and up to date. Poor data quality can lead to wrong model predictions and insights.

For example, poor data quality can lead to biases in predictions. A discontinued Amazon hiring model is another great example where a ML trained on a decade’s worth of résumés in 2014 developed a bias against female candidates.

Implementing a data governance for AI/ML systems ensures the data used is always of the highest quality, which can help to eliminate any biases or inaccuracies.

Data security and privacy

Handling high volumes of processed data requires constant vigilance in protecting sensitive information and complying with regulations. Greater volumes of data come with an increased security and compliance risk that demands adherence to many different data privacy and protection laws that cut across borders.

SEE: Explore these top data quality tools.

Lapses in data security can have dire consequences, such as unauthorized access, data tampering and breaches. It can also undermine trust in the AI system and lead to legal consequences that damage a company’s reputation and result in financial losses through declining sales or regulatory fines.

A data governance policy proactively ensures data security complies with data protection regulations, employs encryption methods and monitors data access regularly through audits

How to use data governance for AI/ML systems

The future of data governance in AI/ML isn’t only about managing data but also ensuring it’s leveraged responsibly and effectively. As the landscape of AI/ML evolves, so does the importance of robust data governance. Organizations must be proactive, adaptable and equipped with the right tools to navigate this terrain.

Ensure data is consistent and accurate

When integrating data from internal and external transactional systems, the data should be standardized, so it can communicate and blend with data from other sources. Application programming interfaces that are prebuilt in many systems facilitate this, so they can exchange data with other systems. If there aren’t available APIs, businesses can use ETL tools, which transfer data from one system into a format another system can read.

When adding unstructured data such as photographic, video and sound objects, there are object-linking tools that can link and relate these objects to each other. A good example of an object-linker is a geographic information system, which combines photographs, schematics and other types of data to deliver a full geographic context for a particular setting.

Confirm data is usable

We often think of usable data as data users can access, but it’s more than that. If data has lost its value because it’s obsolete, it should be purged. That said, IT and business users have to agree on when data should be purged. This will come in the form of data retention policies.

PREMIUM: Take advantage of this electronic data retention policy.

There are other occasions when AI/ML data should be purged. This happens when a data model for AI is changed, and the data no longer fits the model.

In an AI/ML governance audit, examiners will expect to see written policies and procedures for both types of data purges. They will also check to see that data purge practices are in compliance with industry standards. To keep up with these standards and practices, businesses should consider investing in data purge tools and utilities.

Make sure data is trusted

Circumstances change. An AI/ML system that once worked quite efficiently may begin to lose effectiveness. This is known as model drift. This can be confirmed by regularly checking AI/ML results against past performance and against what is happening in the world. If the accuracy of the AI/ML system is drifting away from current data, it’s essential to fix it.

PREMIUM: Make sure your business is outfitted with an AI ethics policy.

There are AI/ML tools that data scientists use to measure model drift, but the most direct way for business professionals to check for drift is to cross-compare AI/ML system performance with historical performance.

To address the challenges of implementing data governance in AI/ML systems, organizations can invest in data governance tools. Here are some of the top tools:

  • Collibra: A holistic data governance platform suitable for comprehensive data management and governance.
  • Informatica: Renowned for data integration, it’s ideal for integrating data from multiple sources.
  • Alation: Automates data discovery and cataloging using machine learning.
  • Erwin: Provides data modeling capabilities, helping businesses understand their data landscape.
  • OneTrust: Emphasizes data compliance, helping businesses adhere to regulations.
  • SAP Master Data Governance: Offers robust data processing and governance for enterprises.

For a more detailed analysis of data governance tools and how they can benefit your organization, read our review of the Top Data Governance Tools of 2023.


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