Effective data privacy governance practices require a deep and continuous inventory of your data sources, metadata, and classifications. Unfortunately, most data governance and privacy teams still use spreadsheets and manual surveys to inventory their data.  In fact, our 2019 Data Privacy Maturity Study found that 77% of organizations rely on these manual methods to track personal information.  

An effective data privacy governance structure needs to include the ability to quickly connect to any data type, at rest or in-motion and uses machine learning and natural language processing to create a live map of your data landscape that honors your data governance classifications. This framework is made even more valuable when working towards CCPA or GDPR compliance. 

A 4-Step Framework for Data Privacy Governance Success

Here are 4 steps to better data governance with data privacy automation:

1. Eliminate manual setup and updates to your data inventory

Utilize data privacy automation technology for automated discovery and classification of all your data, then push that work into your data governance platform.

2. Identify privacy issues

Data privacy automation can scan your data repositories and match them against rules and policies defined in your data governance platform.

 3. Fulfill subject access requests (e.g. right to access personal information, right to be forgotten)

Data privacy automation enables you to search for individuals and identify sources, attributes, purpose, and categories/classification of information, which can further enrich data governance workflows.

4. Enable end-to-end data privacy automation workflows

Real-time detection of new metadata, classification issues, or policy violations can trigger data governance workflows.

The advantages of these 4 best practices include:

  • Know exactly what data you have and how it maps back to data privacy governance policies and processes
  • Connect to any data source – file storage, big data systems, structured databases, data lakes, and streaming data sources all need to be connected to and scanned.
  • Flag data-handling issues related to data residency, retention, proliferation, misclassifications and mislabeling.
  • Create controls with automated data acquisition to ensure your data governance workflows are effective and defensible.
  • Quickly respond to breaches, audits, and lawsuits.
  • Give stakeholders a unified view of company data and risk.
  • Assess data risk prior to M&A transactions.

If you work with data governance tools like Collibra, Informatica, Alation or others then you might consider the benefits of integrating your data governance platform with a data privacy automation platform like Integris Software.  

To learn more about the benefits, download our data governance integration solution brief: https://integris.io/data-governance-solution-brief/