BI & Analytics teams spend a lot of time on data mapping in order to locate their data that is scattered throughout their multi-system environments. Data mapping is also necessary to understand how data flows between systems, which is important when making changes to any processes or reports or for locating the source of an error in a report. Data mapping is the process of relating fields in one data object (database, flat file, spreadsheet file, or other object containing data) to those in another data object.
Data privacy regulations, such as the European Union’s GDPR requires businesses to be able to show how they protect personal data, or erase it if requested to do so. Auditors may require the business to show where the data is stored and how it is processed through the organization’s various business systems. With so much data scattered throughout so many different systems and different data descriptions used throughout the different systems, it is no simple task to demonstrate compliance with these regulations. For example, personal identifiable information (PII) like credit card information must be masked or erased under GDPR, but with so many different possible data descriptions for credit card information, it is extremely difficult to locate every single place throughout an entire data landscape where this data lies. Simply searching “credit card number” won’t cut it, as there might very well be some columns in some reports where this information is described differently – perhaps as cc# or cc_no. You have no way of knowing this without automated metadata management tools for data mapping. GDPR data mapping can simplify this compliance task by providing an up-to-date graphical representation—that is, a map—of the data landscape.
