What is an ontology?
An ontology explained without jargon, a shared, written-down agreement about what things are and how they relate, and why it is the most valuable asset a data platform holds.
Taxonomy, ontology, knowledge graph
The three terms distinguished clearly, with examples, what each one adds, and which one you are building at any given moment.
Knowledge graphs
What a knowledge graph is, why following relationships answers questions that table joins cannot, and what it feels like to use one.
What is contextualization?
Attaching every piece of operational data to the real-world thing it describes, what that work involves, and what a business gets out of it.
What is a digital twin?
A digital twin explained honestly, a live, connected digital counterpart of a physical operation, why you grow one rather than build one, and how the knowledge graph, time series and events are its anatomy.
The three layers of a model
Assets, functions, and business and operational knowledge, the three kinds of thing a DataHub model describes, and why the top layer is the one that pays.
Naming and standards
How to name resources, labels, external ids and relationships, and the industrial tagging standards that already encode most of your model.
Lineage and data quality
The planned lineage capability, why every derived number will be traceable back to the raw measurements it came from, and what that changes about reporting.
What is data governance?
The rules and accountabilities for how an organisation's data is managed, and how data sets, policies, functions and the knowledge graph each carry a piece of it.
Building your model
A practical recipe for the modelling workshops that produce a DataHub ontology, who to invite, what to decide, and what to leave until later.