From Nigerian banks to the UK’s NHS, the way we design and evolve data systems is about to change. Data models are the blueprints of enterprise systems. They define what counts as a “customer,” a “transaction,” or an “admission date.”
But anyone who has worked in data engineering knows: building and maintaining these models is painfully slow.
In Nigeria, banks often struggle to merge fintech acquisitions because their data definitions don’t align. In the UK, the NHS faces constant battles over inconsistent fields like admission dates, leading to reporting delays and errors.
Traditional modelling is manual, knowledge-intensive, and brittle often taking weeks or months before systems can “talk” to each other.
Enter Generative AI
Instead of long workshops and endless iterations, AI can now generate draft data models in hours by analysing business glossaries, existing schemas, and even raw datasets. It can suggest alignments, flag duplicate definitions, and automatically produce documentation for compliance.
Imagine a Nigerian insurer unifying its legacy systems with new digital products far more quickly. Or a UK hospital network automatically generating a shared patient data model that works across multiple trusts. Suddenly, integration that once felt impossible becomes achievable.
Why Now?
This idea is fresh. Large language models like GPT-4, capable of this kind of semantic work, have only become available in the last couple of years.
Early adopters are discovering that AI-assisted modelling doesn’t just save time, it preserves institutional knowledge, reduces human error, and produces clearer documentation for auditors and regulators.
The Caveats
Of course, AI isn’t magic. It’s only as good as the data it learns from, and biased or incomplete sources will lead to flawed models. Explainability also matters: decision-makers need to understand why the AI generated a certain schema. That means human oversight is still essential. The role of the architect doesn’t vanish, it evolves. Instead of manual builders, they become reviewers, validators, and guardians of quality.
Why It Matters
If widely adopted, generative AI could allow enterprise data models to evolve as fast as the businesses they support. For Nigeria, that means leapfrogging bottlenecks that have held back digital transformation.
For the UK, it means taming the complexity of massive, fragmented systems in healthcare and finance.
It’s not flashy like self-driving cars or chatbots. But make no mistake: generative AI in data modelling may be one of the quiet revolutions shaping the future of enterprise data.
About Soji Olaleru
Soji Olaleru is a data engineering and enterprise architecture professional with a focus on how emerging practices can transform the way organisations manage information. With experience at the crossroads of innovation. My work often draws on lessons from both Africa and the UK, where the challenges are different but the need for reliable, transparent data is universal
