Assessing Data Quality

This process assesses the Data Quality of a dataset so that it can be compared to the requirements for reuse..

It is easy to assume that data that is received from another source, is of high quality, for example, that it is accurate, and up to date, and so on. However, the source organisation may know that the data has some issues, for example, it may only be updated once a year, or perhaps it does not contain a full set of records.

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The Government Data Quality Framework⁠

explains that Data Quality can be assessed as

Completeness - describes the degree to which records are present.

Uniqueness - describes the degree to which there is no duplication in records.

Consistency - describes the degree to which values in a data set do not contradict other values

Timeliness - describes the degree to which the data is an accurate reflection of the period that they represent, and that the data and its values are up to date.

Validity - describes the degree to which the data is in the range and format expected.

Accuracy - describes the degree to which data matches reality.

The provenance of data can be traced from its originating source, through to its current stage. This could include understanding

how the data was collected

transformations that have been applied

data cleansing processes to ensure that data continues to meet its intended quality

Steps

Resources

⁠

The Government Data Quality Framework: guidance - GOV.UK (www.gov.uk)

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Data Quality Assessment

Read the Government Data Quality Framework Guidance and conduct a data quality assessment.

Using the SAVVI Data Quality Assessment Template. See

Templates.

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SAVVI Data Quality Assessment.odt

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