Data observability in the age of AI

Nathi Dube, Director of PBT Innovation

As businesses evolve toward increasingly complex IT environments, data observability is emerging as a critical function—not just for IT teams, but for business success. At its core, data observability is about understanding the health and state of your data estate. It allows organisations to assess whether systems are behaving as expected based on the data those systems produce.

The problem? Observability often sits at the end of the data management value chain, only kicking in once a system is live. But by then, if the data isn’t structured for visibility, businesses are left reacting to problems they didn’t see coming.

Consider this common scenario: A bank executive reports an issue with interest accrual. But when analysts dive in, they realise the data required to confirm or diagnose the problem wasn’t captured in the first place. Why? Because no one planned for observability during the system build.

This is why data observability should no longer be an afterthought. It must become an active design principle – one that is embedded upfront in every system, Application Programming Interface (API), and feature. And in a world where data is increasingly event-driven, stored in complex formats, and generated at high velocity, this challenge is becoming harder to manage with human effort alone.

That’s where AI-powered tools come in.

AI is quietly transforming the way organisations approach observability. Today, AI is being built directly into modern data tools and platforms—often so seamlessly that users aren’t even aware it’s there.

These tools support observability in several ways:

• They improve data quality. AI systems can detect anomalies, highlight inconsistencies, and identify gaps before humans even know there’s a problem. When projections or insights seem off, it’s often AI that flags the need to revisit the source data.
• They can boost productivity. By automating repetitive tasks such as code generation, AI can help teams move faster. This can free up human data analysts to focus on more strategic thinking.
• They enable better service. With real-time analytics, organisations can respond faster to customer needs as well as uncover new revenue streams or optimise existing ones.
• They support regulatory compliance. AI can assist with data lineage, audit trails, and documentation—all of which are increasingly essential in highly regulated environments.

But to truly capitalise on these capabilities, organisations must bring data teams into the feature-build process from the start. Cross-functional teams embedded within business units—a model supported by data mesh architecture—are ideal for ensuring that data observability is prioritised early. This gives analysts and data engineers the opportunity to ask the right questions: What data do we want to collect? What insights do we expect to generate? What does success look like in the system’s data output?

The most effective organisations today are treating observability not as a technical afterthought, but as a business-critical function. Because in many cases, the data is the only indicator of whether systems are working at all. Business users don’t need to understand the technical workings of onboarding flows or API frameworks. They look at the data: Did we onboard 200 customers? Did the process work?

This shift towards embedded observability, supported by AI, is helping businesses respond more quickly, improve continuously, and stay competitive in a data-driven economy. The sooner data observability becomes a priority in your strategy, the sooner you’ll start reaping its full benefits.

And that’s not just good for IT. It’s good for business.