Data Domain Discovery Concepts Overview
You need to identify and understand the meaning of critical source data so that you can take measures to work effectively on it. Data domain discovery is the process of discovering the functional meaning of data in the data sources based on the semantics of data.
Create a profile to perform data domain discovery and you can identify critical data characteristics within an enterprise. You can then apply further data management policies, such as data quality or data masking, to the data. For example, discover product codes or descriptions to analyze which data quality standardization or parsing rules you need to apply to make the data useful and trustworthy. Another example is to find sensitive customer data, such as credit card numbers, email IDs, and phone numbers. You may then want to mask this information to protect it.
You can create and run a profile to perform data domain discovery in both Analyst and Developer tools. You can define a profile to perform data domain discovery based on the following rules:
- •Data rule. Finds columns with data that matches specific logic defined in the rule.
- •Column name rule. Finds columns that match column name logic defined in the rule.
You can create data domains from the values and patterns in column profile results. You can then use these data domains to discover critical data across multiple data systems or enterprise.
You can create a profile with a sampling option and filters to perform data domain discovery. When you run the profile, you apply the sampling option and filters on the data source and generate a data set. The data domain discovery process uses the data set to discover data domains.