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Data Quality
Data quality measures value provided by data. High quality data provides value when it meets the business needs and expectations of its consumers.
Why do Data Quality?
Data quality refers to the usability and applicability of data used for an organization’s priority use cases — including AI and machine learning initiatives. Data quality is usually one of the goals of effective data management and data governance.
Benefits
- Enhancing trust and informed accurate decision-making
- Operations Efficiency
- Critical to university functions and business processes
- Improves the accuracy and efficiency of the processes that rely on the data
- Reducing risks and losses associated with incorrect or unavailable data
Have you encountered an issue with the quality of GW data?
Data quality measures value provided by data. High quality data provides value when it meets the business needs and expectations of its consumers.
Data quality dimensions and business rules are two concepts that help determine data quality. A data quality dimension is “a measurable feature or characteristics of data” and provide a vocabulary to measurably quantify data quality. Business rules “describe expectations about the quality characteristics of data” and therefore form the foundation of defining data quality. There are many different sets of data quality dimensions and even significant overlap between dimensions, but some of the more common dimensions are:
- Data Quality Dimensions: A measurable feature or characteristics of data
Common Data Quality Dimensions
- Accuracy – degree to which data correctly represents real world values or entities
- Completeness – presence of required data
- Consistency / Integrity – consistency of records and their attributes across systems and time
- Reasonability – data meets the assumptions and expectations of its domain
- Timeliness – data is up-to-date and/or available when it is needed
- Uniqueness – degree to which data is allowed to have duplicate values
- Validity / Conformity – data conforms to the defined domain of values in type, format, and precision
- Business Rules: Expectations about the quality characteristics of data
An example business rule is: a state code is required and must be in the ISO 2-character format.
- This rule involves the completeness and validity dimensions.
- "Required" indicates the state code have a certain degree of completeness.
- To meet the validity dimension, the state code also needs to conform to a specific format.
- This rule involves the completeness and validity dimensions.
Rules and dimensions are used as the basis for defining data quality metrics, and metrics are used to estimate the cost of poor quality data.
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