# Data Quality

Data quality refers to the assessment of a dataset's overall state. It measures objective elements such as completeness, accuracy, and consistency. But it also measures more subjective factors, such as how well-suited a dataset is to a particular task.

Data Quality within VDA is addressed using the below features

### Profiling

Data profiling is the process of uncovering and investigating data quality issues, such as duplication, inconsistency, inaccuracies, and incompleteness. It involves analyzing one or multiple data sources and gathering metadata that reflects the condition of the data. This metadata allows data stewards to trace and investigate the origins of data errors effectively.

{% content-ref url="/pages/Q3g4WXEofv2VjNhXPKIy" %}
[Profiling](/how-to-guides/data-quality/profiling.md)
{% endcontent-ref %}

### Expectations

Expectations refer to predefined criteria or rules that data must meet to be considered accurate, complete, consistent, and reliable. These expectations help ensure data integrity and usability by setting standards for data quality.

{% content-ref url="/pages/wgGPdIP4Rp2iCaQSy7Eh" %}
[Expectations](/how-to-guides/data-quality/expectations.md)
{% endcontent-ref %}


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