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.