Symbols-Meaning-Value (SMV) space as a basis for a conceptual model of data science - Yao, Yiyu (International journal of approximate reasoning, 2022)
By applying the principles of three-way decision as thinking in threes, in this paper I introduce a conceptual model of data science in three steps. First, I examine examples of triadic thinking in general and trilevel thinking in specific in data science. Then, based on Weaver's trilevel categorization of communications problems, I propose the concept of the symbols-meaning-value (SMV) space and discuss three perspectives on the SMV space from the viewpoints of information science and management science, cognitive science, and computer science. I label the operations on the SMV three levels metaphorically as seeing, knowing, and doing. Finally, I put forward a SMV-space-based conceptual model of data science, in which data are a resource, the power of data is the knowledge embedded in data, and the value of data is the wise decision and the best course of action supported by data. The goals and functions of data science at the SMV three levels are, respectively, making data available, making data meaningful, and making data valuable. To demonstrate the potential contributions of the conceptual model, I comment on some of its practical values and implications.