This article first appeared in Fortified Quarterly, Q1 2026. Download the full issue.
What Four Ondaro Experts Want Leaders to Understand About Data Foundations
Panelists: Christine Morris, Sr. Director of Technical Service Innovation · Angi Williams, Principal Business Consultant · Ian Cahall, Associate Director and Principal Architect · Eric Smith, VP of Global Solution Consulting
This team didn't come together to talk about AI features. They talked about the disciplines, decisions, and tradeoffs leaders must get right if they want AI to deliver value at scale.
Are Data Foundations a One-Time Cleanup Project?
Data foundations are not a cleanup project. One of the biggest myths the panel challenged was the idea that data work is a one-time effort. Strong data foundations behave more like an ongoing operating discipline. They require ownership, standards, and constant upkeep — especially as data volumes explode and platforms sprawl. "The platform can't solve every problem," Morris said. "It's something that you've got to get right and then maintain."
Does More Data Mean Better Data?
More data does not mean better data. Smith shared an early-career lesson from a client who loaded tens of millions of items into their CMDB. The result was technical debt — not insights. Good data isn't about having everything. It's about relevance, reliability, and usability. "Good data is data you can actually run the business on," Morris said. It doesn't have to be perfect. It just has to be consistent, clear, and understood across the org.
How Does AI Respond to Weak Data Foundations?
AI amplifies both strengths and weaknesses. Weak data foundations don't just limit AI — they actively undermine it. AI finds insights by going through your data. If data is bad, the insights won't be usable. Leadership won't trust them. But when you have a reliable data foundation, teams stop asking where to try AI and start asking which outcomes they're optimizing.
Is There a Universal Checklist for “AI-Ready” Data?
Data readiness is contextual, not universal. There's no single checklist for "AI-ready" data. "Asset Management data readiness looks very different from IRM or SPM," Cahall explained. Clarity of intent matters more than blanket maturity scores.
Is Governance a Blocker or an Accelerator?
Governance is an accelerator, not a blocker. Without clear ownership and decision rights, platforms sprawl and priorities skew toward the loudest voices. "What data do you allow AI models to see? What data should never be exposed? Those are governance questions," Smith said. Done well, governance doesn't slow things down. It makes progress last.
What Does the Next Three to Five Years Look Like?
Looking ahead, the panel agreed that data will shift from a compliance need to a true competitive asset. AI will force clarity where ambiguity once lived. And most importantly, AI will amplify what makes each org unique — if the data reflects that uniqueness.
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