Quality
The Quality tab provides a detailed view of data quality across various dimensions, supported by different test types. Hereās what you can expect:
Quality Dimensions and Test Types
Our platform currently supports four quality dimensions, each associated with specific test types:
Accuracy: Measures how close the data values are to the true values. Tests include āRegexā and āValue in.ā
Completeness: Measures the extent to which all required data elements are present. Tests include āNot Null.ā
Uniqueness: Checks each data record to ensure it is unique within the dataset. Tests include āIs Unique.ā
Validity: Ensures data conforms to acceptable standards, such as ranges and formats. Tests include āIs Emailā and āIs UUID.ā
The Data Quality (DQ) score is calculated daily using the formula:
The health score for each dimension is the average of all monitors over the selected time period.
Data Health Score
The Data Health Score represents the average score for all dimensions over the selected time period. The scores are color-coded for easy interpretation:
ā¢ Green (> 98%): Excellent health
ā¢ Yellow (95% - 98%): At risk
ā¢ Red (< 95%): Poor health
Custom Date Range and Filters
The custom date range supports up to six months, allowing for in-depth analysis over a quarter. The Quality dashboard also includes various filters to help you narrow down your data view, such as:
ā¢ Domains
ā¢ Data sources
ā¢ Data Owners
ā¢ Monitor mode (Scheduled, On-demand)
ā¢ Row creation preferences (filter for 'All Records' scan only)
ā¢ Tags
ā¢ Classifications
Source/Domain Summary
The Source/Domain Summary in the Quality tab provides results based on selected domains and shows scores for key quality metrics. This helps you gain a deeper understanding of your dataās health across different data sources and domains, making it easier to pinpoint areas for improvement.
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