Синергия между статистическата наука и бизнес анализа: адаптиране към ерата на големите данни и изкуствения интелект
Synergy between Statistical Science and Business Analytics: Adapting to the Era of Big Data and Artificial Intelligence

Author(s): Stefan Fenerski
Subject(s): Social Sciences, Economy, Business Economy / Management, Sociology, Methodology and research technology, Social Informatics, ICT Information and Communications Technologies
Published by: Университет за национално и световно стопанство (УНСС)
Keywords: synergy; business analytics; big data; artificial intelligence (AI); linear regression
Summary/Abstract: This report explores the synergy between statistical science and business analysis, focusing on their adaptation to the era of Big Data and Artificial Intelligence (AI). As businesses face an increasing volume and complexity of data, statistical methods provide the essential tools for interpreting and analyzing data effectively. The report highlights the importance of techniques like linear regression, which is widely used for forecasting and analyzing trends in large datasets. It also examines how AI technologies, such as machine learning and deep learning, complement statistical methods by enabling automated analysis and more accurate predictions. Through practical examples, such as predicting sales based on advertising expenses, the report demonstrates the application of statistical methods in real-world business scenarios. Furthermore, the integration of AI enhances the value extracted from data, optimizing decision-making and improving operational efficiency. Despite the challenges of handling vast amounts of data, the report emphasizes the importance of combining statistical science with modern AI tools to achieve competitive advantages in today’s fast-paced business environment. The report concludes with recommendations for businesses to invest in training, improve data quality, and integrate AI with traditional statistical methods for better decision-making and long-term success.

• Details
• Contents