> For the complete documentation index, see [llms.txt](https://docs.telm.ai/academy/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.telm.ai/academy/data-quality-indicators/uniqueness.md).

# Uniqueness

**The number of records that can be identified uniquely based on a predefined key.** 

{% hint style="info" %}
**Good to know:** Uniqueness is the inverse of duplicates.
{% endhint %}

![](/files/wcMy9pG2FATcZcsjSbZg)

## **Measuring Uniqueness**

**Prerequisite for this is that primary key defined**

Formula : 1 – primary\_key\_count / total\_row\_count

**Common unit of Measure:** Value count or percentage

## **Examples** 

Three different problems uniqueness could have occurred:

One record with one key value occurs more than once in a dataset (duplicate with identical key values). The two records are not unique.

Key  |    Student Name 

22     |    John snow

22     |     John snow

Often times Datastore constraints can easily help avoid this issue.

Multiple records with same values occur more than once in a dataset (duplicate with different key values). Object John is not unique in the dataset.

Key  |    Student Name 

22     |    John snow

37     |     John snow

A record has the same key as another record, and both occur in a dataset (false duplicate). Key 22 is not unique.

Key  |    Student Name 

22     |    John snow

37     |     John snow

Most often users will need use sophisticated **master data management and Identify resolution systems** for resolving duplicates like these.

**Related dimension:** Consistency