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Inspect data
Missing values and outliers
Learn how to find missing values, detect outliers, and use filtering and sorting.
Missing values and outliers are primary signs of data quality issues. Identifying them before creating a cleaning plan helps you decide how to handle them.
IMD provides tools to highlight missing values and outliers, and supports filtering and sorting to examine data in detail.
Finding missing values
In the data table view, missing values appear as empty or marked specifically. Use the Diagnostics panel to quickly see which fields have high missing values.
If an important field has many missing values, decide in the cleaning plan whether to fill, remove, or keep them.
Finding outliers
Outliers are values that differ significantly from other data points. Checking the range, distribution, and extreme values of numeric fields reveals outliers.
Filtering extreme values in the data view quickly locates possible outliers. If a value seems unreasonable (such as an age of 999), it may need correction or flagging.
Filtering and sorting
Use filtering to filter rows by field value or condition. Use sorting to sort data by one or more fields.
Filtering and sorting help you focus on specific subsets, such as rows with many missing values or records with outliers.