The Ontology Opportunity: Why Building Your Data's Common Language is About to Pay Dividends

Ontologies and semantic knowledge graphs have lurked in the academic shadows for years. Now they’re finally having their moment, not because of some theoretical breakthrough, but because AI is hungry for context and most organisations are serving it data chaos.

Beyond the Buzzwords

An ontology is your organisation’s definitive dictionary. Not just what words mean, but how concepts relate to each other. The difference between knowing that “customer” and “client” both refer to people who give you money, and understanding that “high-value customer” triggers specific retention workflows whilst “churned client” activates win-back campaigns.

A semantic knowledge graph becomes the living implementation of this dictionary, connecting your actual data through defined relationships. It’s structured knowledge representation that machines can reason about.

The AI Context Problem

Large language models and AI systems are only as intelligent as the context you provide them. You can have sophisticated AI, but if it doesn’t understand that “Product A” in your inventory system is the same as “SKU-123” in your warehouse management system, you’re asking a multilingual genius to work with incoherent data.

Modern AI knowledge bases use algorithms that understand context and semantics, not just keyword matching. They can find relevant information even when the search doesn’t exactly match stored data. But they need semantic structure to work with.

Who’s Actually Making This Work

Netflix unveiled their Unified Data Architecture (UDA) in June 2025, a knowledge graph-based system that addresses a fundamental problem: as their offerings grew, core business concepts like “movie” and “actor” were being modelled differently across numerous isolated systems. UDA enables them to model concepts once at the conceptual level and reuse those definitions everywhere. Built on RDF, SHACL, and OWL standards, it unifies domain models, schemas, and mappings across their entire data ecosystem. Their system can automatically generate consistent schemas for GraphQL, Avro, and Iceberg whilst preserving semantic meaning.

In financial services, JPMorgan uses the Financial Industry Business Ontology (FIBO) to map relationships between entities like regulatory bodies and financial institutions. This enables sophisticated Know Your Customer (KYC) and sanctions compliance systems. When you’re moving billions and one wrong classification could trigger regulatory investigation, systems that truly understand entity relationships become essential.

The pharmaceutical industry shows perhaps the most compelling implementations. AbbVie’s internal knowledge graph platform, called ARCH (AbbVie R&D Convergence Hub), contains over 1.7 billion knowledge graph connections. It harmonizes data from more than 200 internal and external sources, connecting molecules, drugs, genes, and health conditions. In one case study, AbbVie scientists used ARCH’s embedded logic to discover that Ruxolitinib, a JAK1/2 inhibitor already approved for treating myelofibrosis, could potentially treat Carney Complex, a rare disease with no approved treatments. Companies like e-Therapeutics are using knowledge graphs to identify non-obvious relationships between diseases, genes, and processes. When bringing a new drug to market costs approximately £1 billion and takes 15 years, finding ways to repurpose existing compounds becomes rather compelling.

Implementation Realities

Building effective ontologies takes time. AbbVie started ARCH in 2020 and it took years of work to reach its current state. The challenge isn’t usually the technology. It’s getting different teams to agree on data definitions.

The tooling has improved. Platforms like Protégé, TopBraid, and newer cloud-based solutions can get you from zero to basic knowledge graph quite quickly with clean sample data. Real-world implementation with actual data complexity is different.

Most companies adopt a hybrid approach. You might start with an industry-standard ontology like FIBO for finance or SNOMED for healthcare, then adapt it to your actual business requirements. Because whilst businesses are different, they’re also often similar enough that standard frameworks provide solid foundations.

You’ll need someone to own long-term maintenance and evolution. Ontologies change as businesses change. Without planned change management, even well-designed ontologies can become less useful over time.

Making the Business Case

For convincing stakeholders, lead with the pain, not the technology. How much time do analysts spend finding the right data? How many decisions get delayed because nobody can agree on the numbers? How often do AI initiatives produce results that make everyone question the outcomes?

The competitive advantage isn’t about having better technology. It’s about having AI that actually understands your business context. Whilst competitors wrestle data from seventeen different systems into coherent views, you’ll have systems that can reason about relationships and make intelligent connections.

Getting Started

Start with something focused and painful. Pick one area where data confusion causes the most problems. Customer data works well, or product catalogues if you’re in retail. Don’t try to address everything at once.

Get everyone involved: business users, analysts, and the people who maintain systems afterward. The best ontologies emerge from conversations with people who know which classifications actually matter in practice.

Plan for evolution. Your first attempt will be wrong in interesting ways. That’s learning, not failure. No ontology survives first contact with reality unchanged.

Resist the urge for perfection. Teams can spend months debating whether “customer” should be a subclass of “person” or “organisation” when they could be solving actual business problems. Good enough to start beats perfect in theory.

The Reality

Ontologies aren’t magic. They’re structured ways of organizing knowledge that work rather well with modern AI systems. Organisations implementing them successfully treat them as long-term investments in data coherence, not quick fixes for immediate problems.

Will this completely sort out your data challenges? Probably not entirely. Will it make your AI initiatives significantly more likely to produce useful results? Quite possibly, if you approach it properly.

In the coming AI-driven world, the winners won’t necessarily be those with the most data. They’ll be those whose data actually makes sense to the systems trying to use it.