Data quality symbols provide information about the reliability and accuracy of data. They identify potential issues, errors, or inconsistencies in the data, allowing people to make informed decisions about how to interpret and analyze the data.
Without data quality indicators, it can be difficult to determine whether the data is trustworthy and suitable for use in a particular context.
Check out the infographic below to learn more!
Below is an example of a data table from Statistics Canada that has data quality symbols ranging from A to D (excellent to acceptable).
The data quality was assessed by Statistics Canada based on the following four factors: non-response error, imputation errors, coefficient of variation, and subject mater expertise.
To learn more about Statistics Canada's data quality symbols, click here.
Why are data quality symbols important?
Data quality symbols play a crucial role in identifying potential issues, errors, or inconsistencies in data. These indicators provide information about the reliability and accuracy of data, allowing users to make informed decisions when interpreting and analyzing it.
Without the presence of data quality symbols, it can be challenging to determine whether the data can be relied upon and used for a specific purpose.
Data quality symbols are important because they indicate how much faith you can put in the evidence (the data) that is available.
Paying attention to data quality and the corresponding data quality symbols is particularly relevant when working with data that involves marginalized groups and/or rural communities. Due to small sample sizes, it is more difficult to ensure good data quality.
Interested in learning more about data quality? Check out Data to Decisions: Data Quality.
