> For the complete documentation index, see [llms.txt](https://iea-task-43.gitbook.io/iea-task-43-glossary/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://iea-task-43.gitbook.io/iea-task-43-glossary/terms/data-model.md).

# Data Model

A data model is an abstract model that organises elements of data and standardises how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the colour and size of the car and define its owner.

{% hint style="danger" %}
**Disambiguation**

The term "data model" has a variety of meanings depending on context.\
\
In the context of ML/AI or data fitting (a common practice within the area of digitalisation) a data model is an entirely different concept: it's a representation of a dataset itself (like a fitted hypersurface) which can be used as a rapid lookup.\
\
Even within the context of knowledge engineering, the interpretation of "data model" isn't clear - for example, [this discussion](https://medium.com/p/3bab439132cd) suggests that a "data model" is more conceptual than the more explicit "[schema](/iea-task-43-glossary/terms/schema.md)" or "[ontology](/iea-task-43-glossary/terms/ontology.md)".<br>

Within Task 43, there are a number of references to "data model" such as the WRA data model within Work Package 4. We see no need to retroactively change these, however.\
\
Going forward, use of the more explicit "schema" or "ontology" is preferred where appropriate.
{% endhint %}

{% embed url="<https://en.wikipedia.org/wiki/Data_model>" %}


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