Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complimentary and sometimes conflicting initiatives rather than a focused, integrated approach. When we think of a discipline like civil engineering, it's fairly obvious that a skyscraper requires deep and robust footings to support the load of each and every floor built on top of it. The same is true when considering data governance. We require a solid data architecture foundation in order to support the pillars of enterprise architecture. In turn, the entire structure is required to enable data governance. In this session, we will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline. It will highlight the importance of integrated modeling and metadata management to implement proactive data governance rather than reactive solutions to specific regulatory requirements.