Data is the lifeblood of AI. However, many organizations struggle with fragmented systems, inconsistent data quality, poor data accessibility, and governance issues. These challenges can not only hinder AI adoption but also cause serious setbacks, from biased models to security breaches.

So, how can businesses overcome these challenges and set the stage for successful AI deployment?

Data governance: an old concept with a new role

Data governance is not a new idea—it’s been a cornerstone of data management for decades. Historically, it focused on ensuring data was secure, compliant, and well-organized. But with AI on the rise, the role of data governance has evolved.

In the age of AI, data governance isn’t just about securing data and meeting compliance standards. It’s about ensuring that the right data is available at the right time and in the right format for AI models. With AI’s growing complexity, it’s become more important than ever to have strong data governance in place to ensure that data is high-quality, ethically sourced, transparent, and accessible.

The growing concerns in the age of Generative AI

While data governance traditionally focused on data quality and compliance, the adoption of Generative AI has brought new concerns to the forefront. The adoption of more autonomous agentic system makes it crucial to manage the data and the governance around it. These include:

• Data security: With AI systems processing vast amounts of sensitive information, the risk of data breaches increases. Ensuring that sensitive data is protected from cyber threats is critical.
• Data privacy: AI models often rely on personal and sensitive data, which raises concerns about privacy. Governance frameworks need to ensure that data is collected and used responsibly, in compliance with privacy laws like GDPR or CCPA.
• Access control: The increasing demand for AI data requires strict management of who can access and modify data. Ensuring that only the right people can view or use certain data is crucial to prevent unauthorized access and maintain data integrity.

Steps to implement a strong data governance framework for AI

For organizations to successfully implement data governance, they need to follow a structured approach. Here are the key steps to get started:

1. Define a data governance strategy
   • The first step is to clearly define what data governance will look like for your organization. This includes setting objectives, aligning the strategy with business goals, and ensuring it supports AI initiatives. Define the scope of data governance and identify key stakeholders.
2. Assign data stewardship roles
   • Designate data stewards responsible for data quality, access, and security across various departments. These stewards will ensure that data is used correctly and consistently across the organization.
3. Develop data classification and metadata management systems
   • Use data catalogs to categorize data according to its business relevance and sensitivity. Implement metadata management systems to help users understand the data’s lineage, origin, and transformation. This is critical for ensuring transparency and ethical AI practices.
4. Establish data quality standards
   • Create data quality rules that ensure data is accurate, complete, and up-to-date. Introduce automated tools to flag any discrepancies and ensure data consistency, which is crucial for AI models that depend on high-quality input.
5. Implement data access controls
   • Establish strict access control policies to ensure that only authorized personnel can access sensitive data. This is particularly important when working with Generative AI, where large datasets may include personal or confidential information.
6. Monitor compliance with regulations
   • Ensure compliance with data privacy and security regulations such as GDPR, CCPA, HIPAA, or industry-specific standards. Implement automated checks to ensure that data collection, storage, and processing practices align with legal and ethical guidelines.
7. Promote data transparency and lineage
   • Establish systems to track data lineage—the journey of data from its origin to its final use. This helps not only with auditing and compliance but also with improving transparency in AI decision-making, which can mitigate biases and enhance trust in AI systems.
8. Foster a data-driven culture
   • Encourage a data-driven culture across the organization. This includes providing training on data governance best practices, promoting collaboration between IT and business teams, and empowering employees to use data responsibly.
9. Invest in AI specific governance tools
   • Leverage advanced data governance tools that are AI-friendly. Tools with capabilities like automated quality checks, AI-specific compliance frameworks, and integrated AI model monitoring are crucial in this age of generative AI.

Sogeti’s thoughts on this journey

At Sogeti, we understand that implementing a comprehensive data governance framework can seem overwhelming and resource-intensive. The steps mentioned above can be time-consuming and expensive, which is why we don’t believe in embarking on an all-encompassing, multi-year data governance journey.

Instead, we emphasize a shift-left approach—starting with data governance at the source level or as close to the business function as possible. This approach ensures that data governance is manageable, practical, and tailored to specific business needs, rather than trying to govern all organizational data at once.

We believe that the true value lies in implementing AI that delivers measurable business value, and data is the fuel that powers this engine. By focusing on governance at the functional level, we can quickly enable AI-driven solutions that create impact, while ensuring the underlying data remains secure, accurate, and usable. This incremental and agile approach allows businesses to start reaping the benefits of AI without waiting years for a full-scale governance overhaul.

Benefits of implementing data governance for AI

When organizations implement data governance effectively, the benefits are multifaceted:

• Enhanced AI outcomes: AI systems based on governed data produce better, more accurate insights, leading to informed decision-making and business success.
• Reduced risk: Data governance minimizes the risk of security breaches, privacy violations, and non-compliance with regulations, thus protecting the organization from legal and reputational damage.
• Faster AI adoption: With clear rules around data management and availability, AI initiatives can move faster, enabling organizations to capitalize on opportunities quicker.
• Increased trust: Transparent data usage and ethical AI practices foster greater trust among customers, stakeholders, and regulatory bodies.
• Scalability: A solid governance framework ensures that AI systems can scale across departments or geographies without compromising data quality or security.

As part of our ongoing commitment to help clients innovate with confidence, Sogeti has launched a new campaign: Make Way for Innovation. One of the key pillars of this campaign is Make Way for Modern Data, where we emphasize the power of modern data platforms like Microsoft Fabric in reshaping how organizations manage, govern, and use their data. Microsoft Fabric offers an integrated and intelligent data foundation that unifies data engineering, data science, real-time analytics, and business intelligence—all underpinned by strong, scalable data governance. Through this campaign, we are helping organizations rethink data governance not as a constraint, but as an enabler of innovation and trust in AI, allowing businesses to accelerate their data-to-insight journey while maintaining control, compliance, and confidence.

In the rapidly evolving world of AI, data governance is not just a regulatory or IT function—it’s a strategic enabler. With the rise of Generative AI, organizations need to prioritize robust data governance to handle new challenges around security, privacy, and ethical use.

By following the right steps to implement a comprehensive data governance strategy, organizations can ensure that their AI systems are built on a foundation of trustworthy, accessible, and ethical data. After all, AI is only as good as the data it learns from, and good data governance is what makes that possible.