Expectations toward business intelligence and data-driven decision-making are continuously growing. At the same time new practices and technologies emerge to address these needs—hence a key obvious question arises: How can we most effectively setup our organization around data, content and architecture and which approaches are most effective to use for which purpose? This is where BI governance plays a key role. In the past, we could observe two prominent patterns of BI governance. Either the governance of business intelligence artifacts was supported only tactically based on ad hoc requests and issue management, or it was centrally enforced to a degree that made an agile approach to BI almost impossible. Both approaches are not viable any longer. In addition, BI governance needs to increasingly address ethical and data protection concerns resulting, for example, from AI and big data usage. It needs to establish guidelines and principles for responsible use of data, including addressing issues like bias, fairness, and transparency. Hence in a nutshell, to be effective BI governance needs to organize collaboration among different stakeholders involved in data, AI, and big data initiatives. It should define and establish governance bodies and processes that bring together business leaders, data scientists, IT professionals, legal experts, and other relevant parties to shape an overall approach to corporate BI governance. This collaboration ensures alignment between strategic objectives, data management practices, and technological capabilities. This chapter will explore the general principles and dimensions of BI governance and discuss alternative ways of setting up BI governance in organizations. A BI strategy can be an effective enabler for promoting the importance of, and making a case for, an enterprise-wide BI governance setup with appropriate funding. It should also frame the boundaries of BI and corresponding governance layers to ensure the right balance between control and noncontrol.