# Data model fundamentals

The data model is how Nominal relates your time-series data to the [units](https://dev.nominal.io/platform/reference/glossary.md#unit-under-test-uut "The device or system being tested, also called a device under test (DUT).") and tests it came from.

## Understanding the data model

[Data model core concepts](https://dev.nominal.io/platform/data-model.md) defines each part of the data model and shows how time-series data relates to metadata about units, tests, and the issues flagged for review.

## Applying the data model

The stories in [Organize and view your data](https://dev.nominal.io/platform/data-model/paradigms.md) use one or more of these approaches:

- [The default approach](https://dev.nominal.io/platform/data-model/organization/per-asset.md) uses a time-series dataset for each of your long-lived, high-value units tested over many campaigns.
- [Tagging data in a single dataset](https://dev.nominal.io/platform/data-model/organization/tagged-data.md) tags the data itself with metadata. This works well for production/end-of-line test because the tester only needs to know an identifier like a serial number.
- [Simulations](https://dev.nominal.io/platform/data-model/organization/simulation.md) use a variant of the tagged approach which prioritizes making comparisons across executions.

[Combining the default and tagged approaches](https://dev.nominal.io/platform/data-model/organization/combined.md) covers units with datasets of their own alongside a shared source, such as a ground station, that tags its data by unit.

[Complex and nested assets](https://dev.nominal.io/platform/data-model/organization/assemblies.md) is an extension of the tagged approach for units built from components that are units under test themselves and can move from one unit to another.