Glossary
What is data model?
A data model describes how data is organised: which tables there are, which fields they contain and how they relate to each other. A good data model makes reporting faster and answers more reliable.
Data model explained
You can look at a data model on three levels. The conceptual model describes in plain language which concepts exist, such as customer, order and product. The logical model defines the fields and relationships, and the physical model is the actual implementation in a database or semantic model.
For reporting and analysis, a dimensional model such as a star schema is usually used. It is easy to understand, quick to query and helps make calculations work the same way everywhere.
A good data model also matters for AI. Clear table names, column names and relationships make it much easier for a language model to write a correct query than a jumble of export tables.
Example
In Acme Groothandel's data model, every order line belongs to exactly one order, every order to one customer and every order line to one product. Because those relationships are fixed, a report never double-counts revenue per customer.
Data model in ENABLE
ENABLE uses the data model of the customer's PostgreSQL data warehouse. The AI in the SQL Explorer receives the tables, columns, data types and relationships as metadata, so a clear model directly leads to better queries.
FAQ
Frequently asked questions
What is the difference between a data model and a database?
A data model describes how data is organised, while a database is the system in which it is stored. The model defines the tables and relationships; the database implements them.
What is a dimensional data model?
A dimensional model splits data into facts, such as orders and amounts, and dimensions, such as customer, product and date. The star schema is its best-known form.
Why does a data model matter for AI?
An AI model that writes SQL relies on the names and relationships in the data model. The clearer the model, the more likely the query is correct.
Further reading
- SolutionBuild a data platform: layers, choices and approachFrom sources to dashboards and AI: how a data platform is structured.Read more
- ResourcesWhat is a data warehouse? Explained and comparedA data warehouse is a central database in which you bring together data from different source systems, such as your ERP, CRM and webshop, clean it up and organise it so that you can report on it and analyse it effectively. A source system is built to process transactions; a data warehouse is built for reading, comparing and keeping history. A data lake stores raw data in any format, and a data lakehouse combines the storage of a lake with the structure of a warehouse.Read more
- SolutionSingle source of truth: one version of the truthOne central source for your business data, so every department sees the same figures.Read more
- ResourcesGlossary of data, AI and Power BI termsWhat is a service principal, row-level security, a semantic model, text-to-SQL or RAG? Short, clear definitions of terms around data, Power BI and AI.Read more
Use AI on your own data. Securely, and without the headache.
In an online demo we show how ENABLE makes your data securely available and how you build on it: describing an app in the AI Builder, plain-language questions, dashboards and APIs. And how we set it up and manage it for you.