Data Quality
Data Quality refers to the quality of the data that is measured by several factors such as accuracy, completeness, and many other more. Data Quality can be used to detect any errors so that the problems could be resolved.
Data Quality is related to data consistency and data governance as both of its aim to make the data consistent through the system.
Data Quality Dimensions
There are several factors that determine the data quality, such as:
- Availability
- Accuracy
The Data must be valid and suitable for the actual real-world reality. In short, accuracy is the extent to which data is correct, reliable, and certified free of error.
- Reliability
Data can be relied on to convey the right information and can be seen as the truth of the data.
- Comparability
- Completeness
All required records and values should be available with no missing information. The completeness does not measure accuracy or validity; it measures missing information.
- Consistency
Data Consistency does not always mean complete or accurate. Data consistency represents the uniformity of data when it comes from various sources.
- Flexibility
- Plausibility
- Relevance
- Timeliness
Data should be available whenever needed. Also, ensure that data is always available and accessible. And the extent to which the age of data is appropriate for the task at hand.
- Uniqueness
- Validity
Data Validity describes how data accords to the rules and standards of business parameters that have been pre-defined by the organization.
Implementation
The demand for Data Quality in public health systems has increased in recent years. for example, applying data on HIV/AIDS, Tuberculosis, and Malaria requires a strong monitoring and evaluation system. However, the resulting data is often incomplete, inaccurate, and tardy. MEASURE Evaluation understood that the data must be of high quality so that the data can be relied on for decision making, therefore World Health Organization (WHO); The Global Fund; Gavi; The Vaccine Alliance; and the MEASURE Evaluation have collaborated on the Data Quality Review (DQR) toolkit to ensure the quality of data reported from public and national health facilities.
Importance of Data Quality
As more companies are becoming more data-driven, data quality is essential as low-quality data might resulting the company to have the wrong decision. Current technologies that are using artificial intelligence and automation also depends on good data quality to have more accurate data and better result.