In the paper, some approaches of computer science are used for the digital transformation of the spiral model. We have added to the spiral model the processes of computer coding and accumulation of the generated explicit knowledge coded in the digital medium of computers. The addition of computer coding is the basis for creating digital spiral models as the theoretical framework for designing information technology of knowledge generation. Two types of digital spiral models are considered: abstract and specific ones. Building digital spiral models opens up new possibilities. First, coding the generated knowledge allows experts to regularly populate two computer knowledge bases which they created: one for their individual knowledge and the other for group knowledge, indicating those experts who have already coordinated their individual knowledge among themselves. Digital spiral models capture the authorship of new individual and group parts of knowledge generated by experts at spiral turns. Specifying the authorship of knowledge parts opens up new managerial opportunities in decision-making compared to the spiral model. For example, it will be possible to retrieve and compare knowledge generated by different teams of experts. Second, it becomes possible to use big data that can serve as potential sources of new knowledge. It is important to note that a part of knowledge is considered new if it is not represented in the conventional knowledge base of a decision-making system. And third, the group knowledge base created by experts can be used to train an artificial neural network and create an artificial intelligence database, which help experts analyse big data and generate new knowledge. The main aim of the paper is to develop digital spiral models of knowledge generation with knowledge bases and big data of potential sources of new knowledge by transforming the spiral model. The huge volume of digital data, which are positioned as potential sources of new knowledge, increases the significance of definitional clarity of the relationships between data, information and knowledge in Ackoff’s hierarchy. On the one hand, these relationships have helped us detail transformation processes of digital spiral models and described the states of their processes. On the other hand, these transformation processes have helped us clarify the relationships between data, information and knowledge in Ackoff’s hierarchy.