Data Quality

Last updated: January 24, 2017

What Does Data Quality Mean?

Data quality is an intricate way of measuring data properties from different perspectives. It is a comprehensive examination of the application efficiency, reliability and fitness of data, especially data residing in a data warehouse.

Inside an organization, adequate data quality is vital for transactional and operational processes, as well as the longevity of business intelligence (BI) and business analytics (BA) reporting. Data quality may be affected by the way in which data is entered, handled and maintained.

Data quality assurance (DQA) is a procedure intended to verify the efficiency and reliability of data.


Techopedia Explains Data Quality

Effective data quality maintenance requires periodic data monitoring and cleaning. In general, data quality maintenance involves updating/standardizing data and deduplicating records to create a single data view.

Key data quality components are as follows:

  • Completeness: Level at which desired data attributes are supplied. Data does not need to be 100 percent complete.
  • Accuracy: Represents data's real world status. May be calculated by using an automated method with the help of various lists and mapping.
  • Credibility: Extent to which data is considered credible and true. May differ from by source.
  • Timeliness (age of data): Extent to which data is adequately updated for a current venture.
  • Consistency: Assesses whether various dataset facts match.
  • Integrity: Assesses reference validity and accurate joining of various datasets.

Data quality is vital for the following reasons:

  • It provides precise and well timed information to handle accountability and services.
  • It offers prompt information to handle service effectiveness.
  • It helps prioritize and guarantee effective resource utilization.

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