Create a column in a data coverage task to analyze combinations of data values in a
for density of data coverage. You can also create columns to use as filters in the data coverage analysis.
You can analyze and plot the data coverage in different ways based on the kind of data in the
. You can use individual column values in the analysis or assign the data to ranges that you create. You can create mapping values and map the data values to mapping values.
You cannot use source columns with binary data type in a data coverage task.
You can create columns to use the data in the following ways:
Use as is
Use individual data values in the analysis to plot the data coverage of distinct values. Individual values are plotted in the graph in the data coverage task. Use data as is when you have a small number of distinct values in the column and you want to view data coverage for individual values. For example, low cardinality columns.
Create ranges of values and analyze the data based on these ranges. For example, a table on employee information includes a Salary column. You want to analyze the data coverage for different salary values across different locations. You can create ranges for the salary values. The data coverage analysis indicates the data density for different salary ranges across locations.
You can use ranges for numeric and date data types
Create mapping values to analyze data in groups. Map each of the data values to a mapping value. You can then use the mapping value in the analysis to plot data density across groups of values. For example, a test case requires data in a few regions. You therefore want to analyze the distribution of data across regions. The data contains a States column. You can create mapping values such as East, West, North, South, and assign states to a mapping value. You can then analyze the data distribution across regions.
You can map data values to a single mapping value. You can map multiple data values to the same mapping value.