Driving data governance with discovery in depth
Data is the key to new business opportunities and new experiences for users. However, using data without in-depth knowledge can lead to poor decisions, missed opportunities, and negative outcomes.
But there is a better way: Machine learning techniques combined with cataloging, classification, and entity correlation create an automated data discovery model. This “discovery in depth” can run unsupervised processes that will create a complete catalog of all data, and tell you whose data it is, what it is, and what it is related to. This frees data professionals from tedious manual tagging and labeling, and allows them to focus on using the data to add value to the organization.
Join Dan Sholler from BigID and learn:
1.Why most catalogs do not include all data
2. Steps to unsupervised discovery: Catalog, classify and correlate your data
3. How to use discovery to drive governance and policy
4. How to build an automated and repeatable data catalog