The nowadays strategic management of an enterprise is determined by various criteria, one of which, increasingly, is the interaction with the environment. Advanced business analytics based on artificial intelligence allows organizations to achieve business goals by optimizing the use of resources while reducing the negative impact on the environment. This requires the improvement of existing and the invention of new tools for creating, sharing and consuming data, which can be compared with digital evolution. As a result of this evolution, new creators and consumers of data appear. Data, its creators and consumers, is a network of interconnected elements, part of a data ecosystem that is constantly evolving. Obviously, the ecosystem includes data that needs to be integrated from disparate sources; various types of analysis and skills to obtain information; actions in accordance with the acquired knowledge; as well as tools, applications and infrastructure for storing, processing and distributing data, depending on the purpose. Such multidimensionality makes the training of data-related specialists a rather complicated process. There is a need to constantly search for such key skills and knowledge that would make it easy to adapt to the new qualities and properties of the data ecosystem. The purpose of this work is to analyze the properties of the data ecosystem and highlight the key aspects of methodology for data science with respect to environmental sustainability of the data ecosystem. We illustrate the proposed methodology on the example of taxi demand prediction.