Original Article ISSN (Online): 2582-7472 ShodhKosh: Journal of Visual and Performing Arts June 2024 5(6), 1117–1123 How to cite this article (APA): Bhandare, P., and K.R., J. (2024). Big Data and AI in Marketing: Unleashing the Power of Data-Driven Decision Making. ShodhKosh: Journal of Visual and Performing Arts, 5(6), 1117–1123. doi: 10.29121/shodhkosh.v5.i6.2024.2109 1117 BIG DATA AND AI IN MARKETING: UNLEASHING THE POWER OF DATA-DRIVEN DECISION MAKING Dr. Pradnya Bhandare 1 , Dr. Jayalekshmi K.R. 2 1 Controller of examinations and Associate Professor, Indus Business School, IIEBM, Pune 2 Associate Professor, NCRD'S Sterling Institute of Management Studies, Nerul, Navi Mumbai ABSTRACT This study investigates the use of neural networks with respect to big data analytics, emphasizing the ways in which these potent tools may be used to mine massive data sets for insightful information. Using data-driven techniques, researchers explore the methods that allow the efficient using neural networks to improve big data processing and understanding. They go over how neural networks' innate ability to manage intricate relationships and trends in huge datasets makes it easier to find useful insights. We also emphasize how crucial it is to combine various data sources and use strong approaches to preprocessing in order to maximize neural network performance in big data analytics. Researchers illustrate the prospective effect of using neural networks in a variety of sectors, including finances, marketing, and healthcare, using research results and actual- life scenarios. This paper's principal objective is to provide a thorough analysis of the methods and approaches for using neural networks to their fullest capacity in analytics of large amounts of data, highlighting the significance of making decisions based on data for fostering invention and commercial success. Corresponding Author Dr. Pradnya Bhandare, pradnyab.26@gmail.com DOI 10.29121/shodhkosh.v5.i6.2024.210 9 Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Copyright: © 2024 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. With the license CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author. Keywords: Artificial Neural Networks, Big Data Analytics, Data-Driven Techniques, Pattern Recognition, Predictive Modelling, Data Preprocessing, Multi-Source Data Integration, Scalability, Explainable AI, Industry Applications, Healthcare, Finance, Marketing, Data-Driven Decision Making, Innovation, Commercial Success. 1. INTRODUCTION Within the ever-changing field of Big Data Analytics, the increasing amount, speed, and diversity of data provide both possibilities and difficulties (Yang and Ge, 2022). Organisations are facing a growing demand for sophisticated statistical instruments as they struggle with huge databases. A subset of machine learning and the foundation of deep learning, neural networks have become a transformative tool for identifying mysterious trends in enormous datasets. The purpose of this study is to examine and clarify the function of artificial intelligence in the Big Data Analytics age, highlighting the opportunity for these technologies to provide insights grounded in data. During the discussions experts will delve into the design and capabilities of networks highlighting their adaptability in recognizing relationships and patterns. Moreover, the conversation will delve into applications as artificial neural networks have proven adept, at uncovering connections and patterns that traditional analysis methods may overlook. By delving into real life scenarios, the aim is to provide readers with an understanding of how AI powered neural networks facilitate data driven decision making