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Data Quality Dimensions Cheat Sheet

In this cheat sheet, you'll learn about data quality dimensions, allowing you to ensure that your data is fit for purpose.
Mar 2023  · 3 min read

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What are Data Quality Dimensions?

Data Quality is a measurement of the degree to which data is fit for purpose. Good data quality generates trust in data. Data Quality Dimensions are a measurement of a specific attribute of a data's quality.

Completeness

Completeness measures the degree to which all expected records in a dataset are present. At a data element level, completeness is the degree to which all records have data populated when expected.

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Completeness Example

All records must have a value populated in the CustomerName field.

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Validity

Validity measures the degree to which the values in a data element are valid.

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Validity Example

  • CustomerBirthDate value must be a date in the past.
  • CustomerAccountType value must be either Loan or Deposit.
  • LatestAccountOpenDate value must be a date in the past.

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Uniqueness

Uniqueness measures the degree to which the records in a dataset are not duplicated.

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Uniqueness Example

All records must have a unique CustomerID and CustomerName.

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Timeliness

Timeliness is the degree to which a dataset is available when expected and depends on service level agreements being set up between technical and business resources.

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Timeliness Example

All records in the customer dataset must be loaded by the 9:00 am.

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Consistency

Consistency is a data quality dimension that measures the degree to which data is the same across all instances of the data. Consistency can be measured by setting a threshold for how much difference there can be between two datasets.

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Consistency Example

The count of records loaded today must be within +/- 5% of the count of records loaded yesterday.

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The count of records loaded today must be within +/- 5% of the count of records loaded yesterday.

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Accuracy

All records in the Customer Table must have accurate Customer Name, Customer Birthdate, and Customer Address fields when compared to the Tax Form.

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Accuracy Example

All records in the Customer Table must have accurate Customer Name, Customer Birthdate, and Customer Address fields when compared to the Tax Form.

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