Organizations acknowledge the significance of a data governance program but encounter substantial challenges in implementing practical and effective solutions. Building a successful data governance foundation involves starting with the fundamentals—efficiently scanning, discovering, and classifying sensitive enterprise data.

Critical data often remains exposed in plain sight as businesses accumulate data at an astonishing pace, projected to reach approximately 175 Zettabytes globally by 2025, with the majority being unstructured, according to estimates from the International Data Corporation (IDC). The ongoing business challenge lies in efficiently scanning, discovering, and classifying diverse, fragmented enterprise data (structured, semi-structured, and unstructured) housed in various silos.

Manual and do-it-yourself (DIY) approaches prove inadequate in scaling to the extent required to manage the sheer volume, velocity, and veracity of entire data assets. This limitation renders data governance processes slow, cumbersome, expensive, and inefficient.

Adopting a ‘set it and forget it’ approach is ineffective in the dynamic data landscape, where the continuous identification, classification, and correction of weak security postures and inappropriate user access rights remain ongoing challenges for business data governance.

The task of ongoing privileged user data access reporting, essential for revealing who has specific access to sensitive data and their actions, poses a significant burden for many security teams within the realm of data governance.