Truth as the Center of Order: From Big Data to Small Data and the Engineering Interpretation of Reality (CTMinfo)

This work introduces a deterministic approach to system design based on the concept of truth as exact correspondence to reality. Modern technological systems are predominantly built on probabilistic models. Machine learning, statistical inference, and Big Data assume that increasing the volume of data leads to better approximations. However, approximation does not guarantee correctness. This paper proposes an alternative paradigm: → from probabilistic approximation (Big Data) → to deterministic correctness (Small Data) Truth is defined in engineering terms as: information reflecting reality with 100% accuracy, free from ambiguity and probabilistic assumptions. The study demonstrates that truth functions as a structural stabilizing factor, analogous to gravity in physical systems. While gravity holds physical systems together, truth holds cognitive and informational systems together. The concept is formalized through the CTMinfo framework, which enables: unambiguous description of objects deterministic verification elimination of semantic distortion The approach is currently applicable to non-organic domains, where exact correspondence between description and reality can be achieved. Limitations at the quantum level and within biological systems are acknowledged as areas for future development. Key implications: elimination of ambiguity in engineering and classification systems reduction of computational and energy overhead acceleration of industrial and supply chain processes reduction of systemic errors, bias, and corruption This work positions truth not as a philosophical abstraction, but as a necessary structural condition for stable systems. Author’s publications are also available via Google Scholar: https://scholar.google.com/citations?user=9Fj9_X8AAAAJ&hl=ru