Paige Broderick

In Part 1, we explored the foundational strategy, including data classification frameworks and tagging approaches. In this post, we examine the technical implementation approach and key architectural patterns for building a governance framework. We explore governance controls across four implementation areas, building from foundational monitoring to advanced automation.

Generative AI and machine learning workloads create massive amounts of data. Organizations need data governance to manage this growth and stay compliant. While data governance isn’t a new concept, recent studies highlight a concerning gap: a Gartner study of 300 IT executives revealed that only 60% of organizations have implemented a data governance strategy, with 40% still in planning stages or uncertain where to begin.

Infrastructure alerts pose a challenge for DevOps teams, particularly when they occur outside of regular business hours. The complexity isn’t merely in receiving notifications, it lies in rapidly assessing their severity and determining the root cause. This challenge is compounded when upstream service disruptions cascade into multiple downstream alerts, creating a confusion of notifications that mask the true source of the problem.