Why Data Observability Is the Trust Layer Your AI Strategy Depends On
Author: Ole Olesen-Bagneux, Chief Evangelist at Actian | O’Reilly Author of ‘Fundamentals of Metadata Management’ (2025) and ‘The Enterprise Data Catalog’ (2023)
AI adoption is no longer experimental. It’s strategic. As organizations roll out copilots and deploy machine learning models into production, one foundational issue keeps surfacing: can we trust the data steering those decisions?
Modern platforms have made it easier than ever to find data through catalogs, marketplaces, and semantic search. But discoverability only gets you so far. The more important question is whether the data that’s been found is reliable enough to use.
That’s where data observability comes in. Not as a buzzword, but as a necessary capability in any data-driven organization.
From Discovering Data to Trusting It
Observability is about confidence. It gives teams the ability to monitor how data behaves, how it moves across infrastructure, how it transforms, and whether it meets the quality expectations defined by the business.
When data goes wrong—and it often does—observability makes it visible. It helps you detect issues before they become business problems. It helps you fix them quickly when they do.
In the context of AI, this becomes even more important. A model trained on poor-quality data can reinforce bias, produce unreliable predictions, or trigger automated decisions based on flawed assumptions. Without visibility into the health of your data, it’s impossible to know whether you’re building innovation on solid ground—or sand.
Why Lineage Matters
Observability isn’t just about catching errors. It’s about understanding how data gets from point A to point B, and everything that happens in between.
That’s why data lineage plays such a critical role. Without it, you can see that something is broken—but you can’t see where or why. With it, you gain the ability to trace data through every transformation, system, and interaction, which makes root cause analysis and quality assurance faster and far more effective.
Lineage gives you the context you need to make observability meaningful.
The Limits of Traditional Approaches
Many legacy data quality tools rely on sampling, batch validation, or rigid rule sets. They weren’t built for today’s hybrid architectures or the speed of real-time analytics. They often add complexity, cost, or latency, and they rarely offer the kind of contextual insights data teams actually need.
Even newer tools that promise observability often require invasive integrations or fall short when it comes to scale or business alignment.
What’s needed is something simpler, faster, and smarter—observability that’s deeply connected to your metadata, policies, and business terms. Not something you bolt on, but something that comes built in.
A New Layer in the Modern Data Stack
At Actian, we’ve made observability a core capability of our Data Intelligence Platform. It’s powered by graph-based lineage and designed to work across hybrid, multi-cloud environments without putting stress on source systems.
But this isn’t just about one platform. It’s about a mindset shift—recognizing that trust in data isn’t something you assume. It’s something you build, by design.
Watch this 3-minute explainer to see how it works.
Come learn from Ole in person at Big Data Paris during his talk about ‘Metadata Management in the era of Artificial Intelligence’ on 02 October at 16h30 in Salle de conférence 2.