Effective data governance ensures your data is trustworthy, usable, and compliant—ready to support real business decisions. The challenge? Figuring out how to actually make that happen. Todd Slind, VP of Technology at TRCA, shares an article on Fast Company that makes a powerful case: to succeed with AI, businesses must radically rethink how they approach data governance. Traditional, top-down governance models—designed to control how data is collected, stored, and accessed—are no longer effective in today’s fast-paced, data-rich environments. The problem? These rigid frameworks don’t reflect how data is actually created and used. Frontline employees, field teams, IoT systems, and AI tools like ChatGPT are now generating massive volumes of valuable data—often more than what’s centrally managed. These users aren’t just data consumers; they’re creators and curators.

To adapt, organizations need to shift to a user-empowered governance model. Instead of dictating how data should be handled, leaders should ask:

• What data do users find most valuable?

• Where are the quality issues?

• How can teams be supported in organizing and improving the data they generate?

This bottom-up approach means involving employees across all levels—not just those at desks—in shaping governance policies. Gathering feedback on how data is used helps identify what’s truly valuable and where governance guardrails are needed. Slind concludes that by democratizing data governance, organizations can improve data quality, increase its business impact, and build a more agile, AI-ready foundation.

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Data and artificial intelligence (AI)—when used together—are key drivers of business success, but unlocking their full value requires integrated governance to ensure responsible and effective use. A recent article from IBM by Ray Beharry, Senior Product Marketing Manager for Data Intelligence, and Sahiba Pahwa, Product Marketing for watsonx.governance, makes a compelling case: data and AI governance aren’t just parallel efforts—they’re deeply interconnected pillars of modern enterprise strategy.

Data governance ensures data is accurate, consistent, secure, and managed responsibly. It sets the standards and controls that make data usable and trustworthy—critical when feeding AI systems.

AI governance, meanwhile, focuses on the behavior of AI models. It ensures systems are ethical, explainable, and compliant with evolving regulations like GDPR and the EU AI Act. As AI grows more complex, governance becomes essential to manage risks like bias, hallucinations, and lack of transparency.

The article emphasizes that without strong data governance, AI governance can’t succeed. High-quality, traceable data is what makes AI accountable and explainable. Likewise, AI governance pushes data governance to meet new standards of compliance, fairness, and trust.

Organizations that integrate both frameworks benefit from:

• Smarter, more reliable decision-making
• Greater stakeholder and regulatory trust
• Simplified compliance
• Reduced risk
• Enhanced efficiency and innovation

In today’s digital landscape, managing data and AI separately is no longer enough. As the authors put it, these governance practices are “complementary elements” that, together, help organizations unlock the full value of their data and AI investments—securely and responsibly.

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Enterprise resource planning (ERP) systems are often pitched as game-changers for finance transformation. But as Miles Ewing, principal, Finance & Performance at Deloitte Consulting, reports in The Australian, around 70% of finance leaders say their ERP initiatives have been slower or less impactful than expected. Why the gap? Ewing’s interviews with 26 CFOs suggest a key reason: unclear or unmeasured finance goals. Projects that focused on specific, measurable outcomes—like cost reduction or enabling M&A—delivered real value. But vague goals such as “reducing manual effort” or “improving analytics” were often deprioritized in favor of hitting go-live deadlines. In many cases, the urgency to launch on time meant finance priorities were sidelined. Some CFOs admitted they had no way to quantify whether finance actually improved post-implementation. Other recurring issues included poor-quality data from upstream functions, limited system scope, and lack of change management. Even with modern ERP systems, teams often reverted to manual processes due to these gaps.

Ewing identifies three persistent challenges:

1. Finance is broad, but ERP systems often cover only slices like accounting.
2. Finance depends on external data, which tech alone can’t fix.
3. Transformation needs policy/process change, not just new systems.

The lesson here is without clear goals, cross-functional coordination, and strong change leadership, ERP investments may not deliver the finance transformation CFOs expect.

(This is part one of a two-part series. The next article explores how CFOs can lead more successful transformation efforts.)

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