Before AI can reason with your data,
your business needs to agree on what that data means and
what you want to achieve.

The data isn't the hard part but understanding each other is.

I've been in data for over 15 years, and sometimes even I feel like I'm decoding a foreign language.
We've turned simple ideas into jargon that makes non-data people tune out.

Here's what these terms actually mean and why they matter for AI:

▶️ Ontology
A shared definition of your core business concepts and how they relate.
It gives AI clear concepts to reason about instead of guessing.

▶️ Entity
A real world thing like a customer, product or event.
It helps AI tell the difference between people, products and moments in time.

▶️ Metadata
Data that explains other data.
It tells AI what something means, how fresh it is and whether it can be trusted.

▶️ Physical layer
Where data is stored and processed.
It shapes how fast, scalable and reliable AI workloads can be.

▶️ Logical layer
How data is organised conceptually, not physically.
It shields AI from raw technical mess.

▶️ Semantic layer
A business friendly layer with agreed definitions and metrics.
It stops humans and AI arguing over what a number actually means.

▶️ Schema
The formal structure of what data exists and what type it is.
It gives consistency so AI knows what to expect.

▶️ Data modelling
How entities and their relationships are designed.
It reduces confusion in how AI interprets data.

▶️ Data virtualisation
Accessing data from many sources without copying it all.
It lets AI work across systems seamlessly.

▶️ Vector database
A database that searches by similarity, not exact matches.
It enables richer retrieval and context for AI.

▶️ Data pipeline
How data flows from creation to consumption.
It keeps AI fed with timely and relevant inputs.

▶️ Orchestration
Coordinating when and how pipelines run.
It keeps jobs reliable and in the right order.

▶️ Data quality
How accurate, complete and consistent the data is.
It directly affects confidence in AI outputs.

▶️ Observability
Seeing what data systems are doing and spotting issues early.
It helps catch drift and weird behaviour before damage is done.

▶️ Data lineage
Where data comes from, how it changes and where it’s used.
It adds transparency and explainability to AI decisions.

None of this is magic.
But together, it’s the foundation AI stands on.

What other terms would you add as essential?

♻️ Repost to help someone decode their data landscape.
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