Discover the comprehensive guide to all nine data quality (DQ) dimensions in one place. This essential resource covers completeness, accuracy, validity, consistency, timeliness, uniqueness, adaptability, traceability and accessibility (availability). Understanding and mastering these dimensions ensures your data remains robust, trustworthy, and valuable for informed decision-making and effective operations in any organization.
DQ Dimension 1 : Completeness
Completeness, as a data quality dimension, refers to the extent to which all required data is present. It ensures that datasets contain all necessary values and records, with no missing information. High completeness means that the data fully represents the required fields, enabling accurate analysis and reliable decision-making.
In essence, all required data from all required sources are included to ensure the complete portfolio.
For Example: Null Check
DQ Dimension 2 : Uniqueness
Uniqueness, as a data quality dimension, refers to the degree to which each record in a dataset is distinct and free from duplicates. It ensures that no two records are identical, preventing redundancy and confusion. High uniqueness is crucial for maintaining data integrity and enabling precise data analysis and decision-making.
In essence, data values reflect the single source of truth and are not recorded more than once.
For Example: Duplicate Check
DQ Dimension 3 : Timeliness
Timeliness, as a data quality dimension, refers to the degree to which data is up-to-date and available when needed. It ensures that data is collected, processed, and accessible within an appropriate timeframe to meet business requirements. High timeliness means data is current and ready for use, supporting accurate and timely decision-making.
In essence, data represent reality from the required point in time and is delivered according to the timeliness demands.
For Example: Frequency check, Tracking changes in data as per business need
DQ Dimension 4 : Validity
Validity, as a data quality dimension, refers to the extent to which data conforms to the defined rules, formats, and standards set by the organization. It ensures that the data values are within acceptable ranges and comply with business rules. High validity means that data is accurate, reliable, and suitable for its intended purpose.
In essence, data is valid if it confirms to the syntax (format, type, range) of its definition.
For Example: Format check, data type check, range check etc.
DQ Dimension 5 : Accuracy
Accuracy, as a data quality dimension, refers to the extent to which data correctly represents the real-world objects or events it is intended to describe. It ensures that data values are precise and free from errors, providing a true and exact reflection of the original source. High accuracy is essential for reliable analysis, decision-making, and operational efficiency.
In essence, the degree to which data correctly describes the “real world” object or event being described.
For Example: Cross verification, Validation against known values,
DQ Dimension 6 : Consistency
Consistency, as data quality dimension, refers to the extent to which data is uniform and standardized across different systems and datasets. It ensures that data values are the same when they should be, and that they are represented in a common format and structure. This dimension is crucial for data integration, comparison, and analysis, as it allows for coherent and meaningful insights.
In essence, values of related data attributes are consistent with each other.
For Example: Reference data match check, List of values check, Cross system consistency
DQ Dimension 7 : Traceability
Traceability, data quality dimension, refers to the ability to track and verify the origins, history, and movement of data throughout its lifecycle. It involves documenting the data’s source, how it has been transformed, and where it has been stored or used. Traceability ensures transparency and accountability, allowing organizations to understand the data’s lineage, validate its accuracy, and identify any changes or errors that may have occurred. This dimension is critical for compliance, auditing, and ensuring the integrity of data across various processes and systems.
In essence, the history, processing and location of the data under consideration can be easily traced.
For Example: Data lineage tracking, Version control, Audit trails, Transformation logs
DQ Dimension 8 : Adaptability
Adaptability, data quality dimension, refers to the ability of data to remain relevant, useful, and applicable in different contexts, environments, and for various purposes over time. It involves ensuring that data can be easily adjusted, extended, or repurposed to meet changing business needs, technological advancements, and evolving regulatory requirements. Adaptability emphasizes the flexibility of data structures, formats, and processes, enabling seamless integration with new systems and the efficient support of diverse analytical and operational tasks. This dimension is essential for maintaining the long-term value and usability of data within an organization.
In essence, the organization should be able to generate data to meet a broad range of on-demand, ad-hoc reporting requests.
For Example: Change execution process check
DQ Dimension 9 : Availability/Accessibility
Availability or Accessibility, data quality dimension, refers to the degree to which data is present and accessible when needed, pertains to the ease with which users can retrieve and use data.
In essence, data is made available to the relevant parties and parties must be able to access at ease following access controls.
For Example: Uptime, Latency, Permissions, Usability/Usages