Metadata is essentially "data about data." It provides descriptive, structural, and contextual information, making other data easier to understand, locate, and use effectively. By capturing essential details—such as a dataset's origin, structure, purpose, relationships, and meaning—metadata enables data to be organised and contextualised.

Here is a key insight: Semantics gives data meaning. By adding semantics to the metadata, we make all metadata meaningful.

Many organisations are reorganising their data around data products, and the most advanced are connecting their data with knowledge graphs for use with Gen AI. These efforts should not be separate endeavours; the real value lies in bridging them.

This is where DPROD comes in. It is a freely available semantic ontology that defines data products, serving as both a specification and a first small step in creating a Distributed Knowledge Graph.

Here's the idea: the router initially maps the question to relevant classes within the organisation's upper ontology—a structured representation of key concepts that your business is focused on. Utilising these classes, the router then retrieves the corresponding 'Semantic Data Product' from the organisation's Semantic Layer

So let's get Data Mesh and Data Contracts right, by building them upon the solid foundations provided by Knowledge Graph technology. Let’s reinvent the wheel in the right way, by founding it upon a proven technology that honours interconnectivity.

Some of us are talking about Data Meshes, while others are talking about Semantic Layers and yet another group is talking about Enterprise Search etc. I can’t help wondering if we are all just talking about different aspects of the same thing. When each aspect is connected they combine to form one thing: a Knowledge Graph.