Application of Generative AI in Big Data Visualization for Enhancing International Business Decision-Making

Abstract

In this thesis, the author, a professional in the data visualization field at the commissioner's company, who has been engaged with data visualisation of customer-related datasets for their b2b clients across multiple industries, explores the challenges in big data visualization and examines the potential of generative AI as a transformative force in this domain. Starting with a comprehensive review of the challenges in big data visualization, the thesis delves into the potential of generative AI as a transformative force in this domain. The research explores the application of generative AI in big data visualization to enhance decision-making in the field of international business. The author of this thesis has leveraged the ability of generative AI in combination with tools; SQL (Structured Query Language), Google Sheets and WordPress to generate multiple charts related to products and customers of b2b clients. This thesis investigates the integration of generative AI and big data visualization specifically tailored for international business decision-making, addressing the existing research gap in this area. By leveraging generative AI, which offers innovative techniques for generating visual representations of large and complex data sets, The commissioner company seeks to extract insights and make informed decisions in the face of increasing data volume and complexity in the global business environment. With data's increasing volume and complexity in the global business environment, effective visualization plays a vital role in extracting insights and making informed decisions. Generative AI, an emerging technology, offers innovative techniques for generating visual representations of large and complex data sets. However, limited research exists on the integration of generative AI and big data visualization specifically tailored for international business decision-making. This study aims to address this gap by investigating the potential of generative AI in enhancing big data visualization, identifying challenges and limitations, and developing recommendations and best practices for its effective utilization. The research questions focus on the application, challenges, benefits, and best practices of incorporating generative AI in international business decision-making. The methodology includes a qualitative research approach which interprets and analyses past research papers and literature reviews, data collection from relevant sources, analysis and observation of generative AI techniques, and case studies with international businesses and other industries such as education, sports, medical fields and medicine. The expected outcomes include a better understanding of the application of generative AI in big data visualization and analytics for international business decision-making, identifying challenges and limitations, and developing recommendations for effective utilization. This research contributes to the field of international business by providing insights into leveraging generative AI to enhance decision-making processes and effectively utilize big data visualization in an increasingly data-driven global business landscape. By exploring the potential of generative AI in enhancing big data visualization, this research contributes to the field of international business, providing valuable insights to Commissioner Company and similar companies seeking to leverage AI-driven data visualization as a service to their b2b clients and to find out whether or not it will be more effective in decision-making processes for their b2b clients. In an increasingly data-driven global business landscape, this research sheds light on the transformative power of generative AI and its role in extracting actionable insights from complex data sets in the form of effective graphs and charts and perhaps the ability to be interactive to users.