Data quality is fundamental to reliable decision-making and effective services. Accurate, complete, consistent, and timely data provides greater value and enables the University to use its information assets with confidence.
The Data Office works with data-owning units to enhance data quality by defining quality requirements, measuring performance, identifying root causes of issues, and improving the processes through which data is created, maintained, and used.
Data Quality Dimensions
Data quality may be assessed using dimensions appropriate to the nature and use of the data, including:
- Accuracy: The extent to which data correctly represents reality.
- Completeness: Availability of all required data.
- Consistency: Absence of conflicting information across related data.
- Timeliness: Availability and updating of data when needed.
- Validity: Compliance with defined rules and formats.
- Uniqueness: Prevention of unnecessary duplicate records.
Improving Data Quality
The data quality improvement cycle includes:
- Identifying priority data.
- Defining quality rules and requirements.
- Measuring data quality.
- Identifying root causes of issues.
- Implementing improvement actions.
- Monitoring results periodically.
Data Quality Is a Shared Responsibility
Data quality begins at the source. Everyone who creates, updates, manages, or uses data contributes to maintaining its quality.