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19 Types of Data Quality

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Data quality is the degree to which information fits its purpose. It can be difficult for organizations to agree on data quality criteria because each team may use data towards different purposes. The following are commonly used criteria to define data quality.


Data that can be accessed as required.


The data accurately reflects reality.


An audit trail records the source of data and all access and changes.


The data includes the required elements and attributes.


The data includes context information such as source.

Factual accuracy

Data that is true.


Free of unauthorized alteration and data corruption.


There is sufficient data that describes the data.


Evidence that data such as transactions, documents and communications are authentic.


The data has a named data custodian and data steward.


Recorded at an appropriate level of detail and granularity.


The data is not meaningless or low value.


Data that can be trusted.

Single source of truth

Data can have copies but it needs to be clear which copy is the master.


The data complies to standards such as templates and standard formats.


The data is well architected and designed.


Up-to-date information that has arrived fast enough.


There is a record of the data source and changes to data.


The data has been validated against defined rules, controls and standards.
Critical to data quality:


Data that is useful to support processes, procedures and decision making.


How quickly data is created, updated and deleted.


The exactness of data. For example, a company that has annual revenue of $3,451,001,323 as opposed to a 3 billion dollar company.


Data that is free of errors, omissions and inaccuracies.


Data that is compete relative to your business purpose. For example, an order for an economy car may need configuration details such as color, wheel size and electronics package. An order for a luxury car may require additional details such as engine type, seat and interior package.


Data that stems from reputable sources such as verified company press releases as opposed to social media rumors.


Data that can be traced to its source. If someone changed your prices, you should be able to figure out who.
Overview: Data Quality
Information that fits its purpose.
Related Concepts
Next: Master Data
More about data quality:
Data Artifact
Data Cleansing
Data Corruption
Data Degradation
Data Integrity
Data Rot
IT Quality
Legacy Data
More ...
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