Expert-based science is widely on the decline when it comes to computing- and data-based inference worldwide for land- and seascapes as well as the atmosphere. The status of associated biodiversity and its conservation is equally in a crisis-state. In this presentation I will show how open access to data -including citizen science – has provided a fundamentally different playing field for institutions, research, agencies and (global) governance. Secondly, I will show how ‘Big Data’ mining using machine learning algorithms, e.g. based on open source as well as high-performance commercial tools, provide a superior analytical platform for statistical analysis and real-world progress. I will present in-depth examples from the polar regions (Arctic, Antarctic and Hindu-Kush Himalaya) as well as from the tropics (Central America and Papua New Guinea), the oceans and the atmosphere for conservation applications. All of these examples experienced a profile of hindrance, lack of recognition, and constraints found in the century-old science model that is currently applied but still awaiting new ethics and concepts for unleashing the full power of computers in a good way for the wider public good.