What's included?
Last updated
Last updated
The diagnostic report captures the Validity, Structure and Relationship Validity. This guide contains some technical details about each property.
Does each column in the data contain valid data?
This property applies metrics based on the column types.
primary keys
Primary keys must always be unique and non-null
numerical, datetime
Continuous values in the synthetic data must adhere to the min/max range in the real data
categorical, boolean
Discrete values in the synthetic data must adhere to the same categories as the real data.
This yields a separate score for every column. The final Data Validity score is the average of all columns.
Does each table have the same overall structure as the real data? The structure includes the column names.
Does the synthetic data contain valid relationships between different tables?
Every relationship in your dataset is determined by a primary/foreign key connection. This property applies two metrics to the relationship to determine the validity:
The final Relationship Validity score is the average of all the sub scores.
This property applies the metric to each table of the dataset. This checks to see that there are the same set of column names in the synthetic vs. the real data.
: Does each foreign key refer to an existing primary key? If a foreign key refers to a non-existent primary key, it is known as an orphaned child, which is invalid in most databases.
: Does each primary key have the correct number of children? The correct number is based on the min/max bounds that are present in the real data.
