What is data quality?

Data quality refers to the quality of data. There are six dimensions of data quality. Considering and addressing these dimensions when creating and saving data will help improve data quality: 

  1. Accuracy. How well does a piece of information reflect reality? 

  1. Completeness. Is it comprehensive / are all required fields filled?  

  1. Consistency. Does information stored in one place match relevant data stored elsewhere? 

  1. Timeliness. Is it available when you need it? 

  1. Validity. Is it in the right format / follow business rules? 

  1. Uniqueness. Is there a single ‘source of truth’, or have you accidentally recorded the same data multiple times? 

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Why data quality is important

Data is valuable, however its value is heavily determined by its quality. Good quality data provides confidence in inferences, while poor quality data hampers opportunities to utilise it. 

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Your responsibilities in relation to data quality

All staff have responsibilities around data quality. It is important that data is created and maintained considering the six dimensions of data quality listed above. In addition, the different types of data roles defined at UQ all have specific data quality responsibilities. For example: 

  • Information Creators are responsible for accurately capturing information and data (e.g. is it in the right format? Is the required metadata entered correctly?).  

  • Information Consumers are responsible for checking the quality of the data they are using is appropriate for the purpose (e.g. is it complete or accurate?). 

  • Information Stewards are responsible for implementing strategies for quality improvement and resolving quality issues. 

  • Information Domain Custodians are responsible for defining the domain specific procedures and rules to ensure proper quality. 

Find out more about the different responsibilities in the Information Governance and Management Framework

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