Data Cleaning Terms Explained
Subscribe and stay up to date with the latest tips and news from Dromo.
Merging two or more fields in a dataset.
Dropping Invalid Rows
Removing rows from your dataset that do not meet certain validation rules or conditions.
Dynamic Data Validation
Validating data against a dynamic set of rules or conditions.
Field Name Matching
Match the fields in a dataset to a set of expected fields.
Finding Text Within a String
Identifying and possibly replacing specific sequences of characters (substrings) within a larger string.
Flagging Invalid Rows
Identifying and marking rows that do not meet certain validation criteria.
Modifying how a number is displayed without altering the underlying data.
Imputing Missing Values
Replacing missing or null values with substituted values.
Converting all text data to a uniform case, such as lower case or upper case.
Eliminating duplicate entries from your dataset.
Ensures specific fields in a dataset are not empty or undefined.
Separating the contents of a single field into multiple separate fields.
Standardizing Date Formats
Standardizing dates into a single, consistent format.
Removing unnecessary characters, typically whitespace, from the start and end of a string.
Checking if a field's value matches the structure of a valid URL.