Rule Category | Description |
|---|---|
Profiling | Profiling rules are of the following types: • Some rules can be used to generate aggregate statistics, which help identify inaccuracies by examining aggregated values over large datasets. • Other rules are test rules that generate pass and fail statistics. Some of these rules also provide a method to invert the results into pass and fail files. For a complete list of Profiling rules, see Profiling Rules. |
Remediation | Remediation rules can help you to identify and correct errors, inconsistencies, and inaccuracies in source data. This can include tasks such as removing duplicate records, standardizing format and data types, and filling in missing values. For a complete list of Remediation rules, see Remediation Rules. |
Data Prep | Data Preparation rules are a set of Function rules and Conversion rules that help transform raw data into a clean, consistent, and structured format that is suitable for analysis. They generate pass and fail statistics based on success or failure, and they create derived fields (see Derived Fields) of the converted type. These derived or converted fields can then be used to build additional rules. For a complete list of Data Prep rules, see Data Prep Rules. |