USE OF MODERN ANALYTICAL TOOLS AND BIG DATA IN BUSINESS VALUATION

Abstract

The article explores the application of modern analytical tools and Big Data technologies in business valuation, emphasizing the growing importance of data-driven approaches in the context of digital transformation. The aim of the research is to identify effective directions for using artificial intelligence, machine learning algorithms, and large-scale data processing to increase the accuracy, objectivity, and adaptability of valuation procedures. The relevance of the topic stems from the rapid development of digital platforms, the diversification of data sources, and the rising significance of non-financial indicators and intangible assets in business performance assessments. The study employs analytical methods and systems modeling, focusing on the integration of traditional valuation approaches (income, cost, and market methods) with AI-driven technologies. The methodology includes the classification of data sources, the application of predictive algorithms (such as regression models, decision trees, neural networks), and the use of sentiment analysis and natural language processing for the interpretation of unstructured information from media, social networks, and customer feedback. The results demonstrate that the implementation of Big Data and machine learning tools enhances the transparency and adaptability of valuation models. These technologies allow analysts to capture complex dependencies, quickly adjust to market changes, and include dynamic behavioral and reputational factors that were previously difficult to quantify. Practical examples from technology startups, manufacturing firms, and service companies confirm the cross-sectoral relevance and effectiveness of the proposed analytical approaches. The practical value of the article lies in its contribution to the modernization of valuation practices in rapidly changing economic conditions. By leveraging Big Data and AI, companies can improve the quality of strategic decision-making, gain competitive advantages, and increase investment attractiveness. The findings encourage broader implementation of innovative analytics in corporate finance, supporting the transition from static assessment methods to dynamic, data-informed valuation strategies.