Define data quality dimensions metrics for:
Accuracy: Refers to the correctness and precision of the data. Data is considered accurate if it correctly represents the real-world values it is intended to model.
Completeness: Measures whether all the required data is present. Incomplete data can lead to gaps in analysis and decision-making.
Consistency: Ensures that the data does not contain conflicting or contradictory information across the dataset or between multiple data sources.
Timeliness: Pertains to the availability of data when it is needed. Timely data is crucial for decision-making processes that rely on up-to-date information.
Relevance: Assesses whether the data is applicable and helpful for the context in which it is used. Data should meet the needs of its intended purpose.
Reliability: Focuses on the trustworthiness of the data. Reliable data is sourced from credible sources and maintained through dependable processes.
Uniqueness: Ensures that entities within the data are represented only once. Duplicate records can skew analysis and lead to inaccurate conclusions.
Validity: Measures whether the data conforms to the specific syntax (format, type, range) defined by the data model and business rules.
Accessibility: Data should be easily retrievable and usable by authorized individuals, ensuring that data consumers can access the data when needed.
Integrity: Refers to the maintenance of data consistency and accuracy over its lifecycle, including relationships within the data that enforce logical rules and constraints.
Define data quality dimensions metrics for: Accuracy: Refers to the correctness and precision of the data. Data is considered accurate if it correctly represents the real-world values it is intended to model. Completeness: Measures whether all the required data is present. Incomplete data can lead to gaps in analysis and decision-making. Consistency: Ensures that the data does not contain conflicting or contradictory information across the dataset or between multiple data sources. Timeliness: Pertains to the availability of data when it is needed. Timely data is crucial for decision-making processes that rely on up-to-date information. Relevance: Assesses whether the data is applicable and helpful for the context in which it is used. Data should meet the needs of its intended purpose. Reliability: Focuses on the trustworthiness of the data. Reliable data is sourced from credible sources and maintained through dependable processes. Uniqueness: Ensures that entities within the data are represented only once. Duplicate records can skew analysis and lead to inaccurate conclusions. Validity: Measures whether the data conforms to the specific syntax (format, type, range) defined by the data model and business rules. Accessibility: Data should be easily retrievable and usable by authorized individuals, ensuring that data consumers can access the data when needed. Integrity: Refers to the maintenance of data consistency and accuracy over its lifecycle, including relationships within the data that enforce logical rules and constraints.