The explosion of different “omic” technologies and methods has led to many advances in ecology, evolution, and developmental biology, identifying countless novel phenotypes and molecular variants of interest as well as establishing new biological principals. The logical next step is to integrate these methods to deepen our understanding of these phenotypes and uncover biologically meaningful relationships, yet the pace of this advancement has been slow. Omic data is inherently high dimensional and identifying structure through the vast amounts of background noise remains a challenge across multiple connected fields and methodologies. Various sources of omic data and their associated methodologies can be integrated to understand the genomic underpinnings of phenotypic variation. The utilisation of high dimensional morphological and molecular phenotypes can be used to uncover the complex molecular and developmental bases of these traits. We present current, and potential future, methods for integrating multiple omic data types to identify biologically relevant phenotypic patterns. We also highlight that the integration of developmental theories into multi-omic analyses will help better understand the evolution of complex phenotypes. Here we provide a roadmap for navigating the integration of complex and high dimensional datasets using a wholistic and integrative approach between ecology, evolution, and development.