GDPR Compliance Use Case: Understand Full Data Lineage in Seconds

Companies processing sensitive personal data of European residents are having to make a lot of changes to the way they govern their data in order to comply with GDPR. A large part of this pertains to access – giving people access to their own personal data, enabling portability of the data, changing or deleting the data.

Before any company can make a change to its data, it must first locate the data, and metadata is key in this step.

Annie, a business user in a financial services company noticed an age column in a specific report that
needed to be deleted from the tables that built this report for GDPR compliance purposes (age can often be considered sensitive data). Sarah, the business analyst in the group, then had to understand which processes were involved in creating this particular report and what kind of impact the deletion of this age column would have on other reports.

To begin, Sarah would need to understand in which table this age column resides, and only then would she be able to identify which processes are loading data into this table and understand the impact of changing this age column on all the other reports in the company. This, of course, entails a lot of manual work including tracing and mapping which all takes a very, very long time to do.

Octopai’s automated, centralized metadata search engine does all the work for Sarah by discovering for her in which table the age field resides, and then presenting the full lineage from the specific report through the tables and ETL processes. This enables Sarah to easily understand the full data lineage and impact of the deleted column on all the tables so that she can ensure the deletion is implemented safely and in line with GDPR.

As changes are made in one table, Octopai identifies instantly which reports are affected by the changes we made, across all systems, providing a clear view of the entire data journey.