How to structure reliable, traceable, and compliant data for successful AI projects

Artificial intelligence is now at the heart of corporate transformation strategies. From automation and decision support to improved customer experience, use cases are multiplying and the potential is vast.

However, a reality quickly emerges in the field: an AI project is only as good as the data that powers it.

Many organizations invest in artificial intelligence tools without always questioning the quality, availability, or understanding of their data. The result: projects that struggle to deliver value, results that are difficult to interpret, and limited user trust.

Even before talking about algorithms or models, it is essential to ensure that data is reliable, well-organized, and understood by all teams. This is precisely the role of data governance.

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Data governance: a much simpler subject than it seems

Data governance involves establishing a clear framework to define which data is used, who is responsible for it, how it is updated, and under what conditions it can be shared.

The goal is not to add constraints, but to make data more reliable, understandable, and useful on a daily basis.

When an organization knows where its data comes from, who manages it, and how it is used, it gains efficiency, confidence, and the ability to deploy AI projects at scale.

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Why has it become essential for AI?

Artificial intelligence learns and produces results based on the data provided to it. If that data is incomplete, inconsistent, or outdated, the results are likely to be biased or unusable.

Conversely, reliable and well-structured data leads to more relevant analyses, more precise recommendations, and higher-performing AI applications.

As noted by Xavier Trigano, VP Product at Craft AI:

"Trustworthy AI relies less on the quantity of data than on its quality and the mastery of its usage."

This reality explains why many companies today begin their AI initiatives by focusing on structuring their data.

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The main challenges faced by companies

In practice, the obstacles are often the same:

• Data scattered across multiple tools;
• Duplicated or conflicting information;
• Poorly defined responsibilities;
• Difficulties with access or sharing;
• Increasingly significant compliance and security challenges.

For Hatem Mamlouk, Data Architect at ADEO Services:

"The real hurdle when you want to better manage data for AI use cases is rarely the AI itself: it’s primarily data quality, accessibility, and how it’s organized."

Without a clear framework, teams waste time searching for the right information, verifying its reliability, or fixing errors that could have been avoided.

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How do you get started with a data governance initiative?

The good news is that you don't need to transform everything overnight.

A phased approach is often the most effective:

• Identify the most important data for the business;
• Designate owners for key data;
• Define a few simple rules for management and sharing;
• Implement regular quality monitoring;
• Gradually extend best practices to the entire organization.

As highlighted by Nicolas Coton, Data Business Operations Manager at LexisNexis:

"Data quality must be a company-wide project championed by the CEO."

The key is to move forward in stages to achieve quick, tangible results while fostering team buy-in.

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A collective responsibility

Contrary to popular belief, data governance is not just a technical issue.

Business teams, operational managers, leadership, and IT teams all have a role to play. Business units understand how data is actually used, while technical teams ensure its availability and security.

This collaboration is essential for building governance that is truly useful and tailored to the company's needs.

As noted by Matthieu Boussard, Head of R&D at Craft AI:

"It is fundamental to have a designated data officer. It is important to be able to identify the person who can provide a comprehensive view of the data, its quality, its evolution, and its accessibility."

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Building the foundations for the AI of tomorrow

AI is more than just a technological choice. Its success relies primarily on the quality of the data that powers it.

Reliable, traceable, understandable, and compliant data not only reduces risk but also accelerates projects and increases confidence in the results produced.

Data governance is therefore not an added burden. It is the essential foundation for turning AI ambitions into concrete, sustainable, and controlled results.

Would you like to go further and discover best practices, expert feedback, and the key steps to effectively structure your data?

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Download our free white paper "Data Governance at the Heart of AI Deployment" and discover how to implement reliable, traceable, and compliant data to ensure the success of your artificial intelligence projects.

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