The phrase "big data" became popular in 2013 and was identified as a group of data sets that

were too huge and complicated for traditional database management systems. The 3 Vs (volume, velocity, and variety) or 4 Vs (adding veracity and Complexity) define its challenges, surpassing organizations' data, storage, and computing capacities. In essence, big data encompasses the digitalization of various aspects of life, generating dynamic data on the Internet and the Web. This work  explores the problems , techniques, and applications of big data, emphasizing future business intelligence (BI) architectures. Recognizing data as an "ocean of Universal Facts," The research explores the potential of big data for future technological advancements, serving as a crucial resource for students and professionals entering the field. Focusing on the 5 Vs attributes of data and domain-wise comparisons, it addresses applications in Wholesale Trade, Retail, Utilities, Education, Transportation, Banking, Communication, Manufacturing, Government, Healthcare, and more. Big data, with its challenges in storage, analysis, and visualization, is used with analytics to obtain knowledge and a competitive edge. The article gives a general overview of the types, content, architecture, technology, and attributes (Volume, Velocity, Variety, Value, and Veracity) of big data. The 7 Vs Ten Vs (Volume, Variety, Velocity, Veracity, Validity, Value, Variability, Venue, Vocabulary, and Vagueness) and (Volume, Velocity, Variety, Variability, Veracity, Value, and Visualization) of big data are also covered in this work. Big data is transforming sectors such as manufacturing, e-commerce, banking, and insurance, this paper identifies and defines fourteen characteristics, addressing the growing challenges and introducing three new characteristics for efficient handling of big data. along with exploring three new features , to enhance the efficiency of handling big data. Additionally, it presents an updated list of 42 Vs to provide a comprehensive framework for understanding big data and data science in the modern era.