Data Governance Practice at Provectus

In an age when AI is reshaping every business, data is the currency that keeps operations running — but getting an organization’s data to actually power its decisions is rarely straightforward. Modern data estates span clouds, warehouses, lakes, streams, and dozens of SaaS systems, and the gap between “we have the data” and “we can trust the data” is where most digital transformation efforts stall.

This guide is a practitioner’s view of how Provectus approaches data governance for enterprise clients. It covers what governance actually means in the cloud era, the eight aspects that make or break a working program, and how to staff and tool it so that data discovery, quality, and security stop being one-off projects and become an operating capability.

What’s inside

• The eight key aspects of data governance — discovery, lineage, quality, glossary, security, modeling, cost, and master data management
• How cloud, lake-and-warehouse architectures, and AI workloads change the governance problem
• A pragmatic framework for assessing the current state of governance in your organization
• The processes, roles, and tools required to run governance as an ongoing capability rather than a project
• Examples of data governance initiatives across security, quality, and discovery use cases

For data, analytics, and engineering leaders responsible for making enterprise data trustworthy, discoverable, and compliant — across modernization, transformation, and AI initiatives.