Many-objective optimization problems (MaOPs) are problems that feature four or more objectives, criteria or attributes that must be considered simultaneously. MaOPs often arise in real-world situations and the development of algorithms for solving MaOPs has become one of the hot topics in the field of evolutionary multi-criteria optimization (EMO). However, much of this energy devoted to MaOP research is arguably detached from the challenges of, and decision analysis requirements for, MaOPs. Motivated by this gap, the authors of this chapter organized a Lorentz Center workshop in 2019 entitled Many-Criteria Optimization and Decision Analysis (MACODA) bringing researchers and practitioners together to reflect on the challenges in many-objective optimization and analysis, and to develop a vision for the next decade of MACODA research. From the workshop arose the MACODA book, for which this chapter forms the introduction. The chapter describes the organizers’ perspectives on the challenges of MaOP. It introduces the history of MaOP principally from the perspective of EMO, from where the terminology originated, but drawing important connections to pre-existing work in the field of multi-criteria decision-making (MCDM) which was the source or inspiration for many EMO ideas. The chapter then offers a brief review of the present state of MACODA research, covering major algorithms, scalarization approaches, objective-space reduction, order extensions to Pareto dominance, preference elicitation, wider decision-maker interaction methods and visualization. In drawing together the vision for MACODA in 2030, the chapter provides synopses of the unique and varied contributions that comprise the MACODA book and identifies further under-explored topics worthy of consideration by researchers over the next decade and beyond.