Data Minimization: How to Use ML to Reduce Risk on Duplicate Data

With data growing exponentially, data sources spread across disparate data sources, centers, and clouds, it’s more difficult than ever to proactively reduce risk, classify, and protect critical and sensitive data.

One of the largest sources of risk comes from duplicate and redundant sensitive data migrating across multiple data sources and stores. Blind spots into your derivative data can create unnecessary data exposure risks, stall cloud migration initiatives, data minimization initiatives, and M&A processes, and present an additional layer of compliance challenges across the board.

Join Michael Long from BigID to explore these risks – and how to apply ML and NLP to discover, identify, and minimize duplicate and similar data.