Big Data Analysis and Analytics to Support the Acceleration of Innovative Business Projects in the Modern Dynamic Conditions of Uncertainty, Turbulence and Systemic Risks of the Global Environment

Purpose of the work. The study is devoted to the theoretical and methodological substantiation and development of a conceptual model for the application of intelligent Big Data analysis & analytics – as a strategic tool for accelerating innovative business projects. The work is aimed at resolving the fundamental scientific contradiction between the need for forced commercialization of innovations and the avalanche-like growth of entropy, uncertainty and systemic environmental risks in the conditions of permanent macrosystemic transit of the global economy. Methodology. The methodological basis of the work was the systemic, comparative and evolutionary analysis of macroecological paradigms, as well as the conceptual modeling of complex socio-economic systems. The instrumental and analytical basis is formed by Data Mining methods, machine learning technologies, artificial intelligence, neural network architectures of natural language processing and graph-based modeling. The approaches of predictive, management and cognitive analytics, principles of digital twins, knowledge graphs and intelligent decision support systems are used. Scientific novelty. The work conceptualizes for the first time the cumulative nature of the genesis of global macroenvironments, consistently traces the transit from the VUCA and TUNA models to the fragile nonlinearity of BANI, the chaotic fragmentation of SHIVA markets and the geoeconomic hostility of the WARMI environment. The complete incapacity of classical management based on retrospective diagnostics in the conditions of SHIVA and WARMI environments is proven. The conceptual five-level model of analytical maturity of innovative business projects in the conditions of macrodynamics VUCA → WARMI has been developed, which is distinguished by a synergistic combination of technologies of digital twins of projects and ecosystem of knowledge graphs. The author’s model of the four-level architecture of the intelligent acceleration platform (including the levels of Data Ingestion/Collection, Data Ingestion, Data Processing & Cleansing, Data Analysis & Analytics, intelligent DSS in real-time mode) has been developed, which transforms uncertainty and chaos in the external environment into a formalized matrix of uncertainties (probabilities for mass events and fuzzyness for individual projects/cases). Conclusions. It is established that Big Data analytics provides a transition from reactive management to proactive business resilience. The essence of innovation acceleration is redefined as the system’s ability to high-speed pivoting of business models based on the detection of weak market signals. It is proven that the implementation of the proposed platform minimizes the risks of startups passing through the “valley of death”, optimizes resource allocation and reduces decision-making time from weeks to minutes. The results obtained are applicable in the design of digital innovation management platforms, corporate analytical systems, accelerators and venture ecosystems. The practical significance of the work lies in the possibility of applying its provisions for the design of startup accelerators, digital innovation management platforms, corporate analytical systems and venture funds. Promising areas of future research related to the use of multi-agent systems, large language models, generative AI and cognitive modeling of complex systems are highlighted.