Description

About The Book

The Data-Driven Enterprise: From Infrastructure to Intelligence is an insightful exploration of how data is redefining the modern business landscape. Authored by Dhruvit Kumar Patel, Priyam Vaghasia, Dhruvil Saliya, and Rishil Vaghasia, this book provides a comprehensive journey from the foundations of data infrastructure to the cutting-edge world of artificial intelligence and decision intelligence. It explains how organizations can transform raw data into actionable insights through cloud computing, big data technologies, AI analytics, and ethical data governance.

Covering topics such as data architectures, machine learning, natural language processing, automation, and real-time intelligence, the book empowers readers to understand how enterprises evolve in a data-centric world. With real-world case studies from companies like Netflix, Uber, and Tesla, it bridges theory with practical applications, demonstrating how data has become the most valuable strategic asset of the digital age.

Ideal for business leaders, data professionals, and technology enthusiasts, The Data-Driven Enterprise offers a clear roadmap to harnessing the full potential of data — responsibly, intelligently, and effectively. It’s not just a technical guide, but a vision for the future of innovation, ethics, and intelligence in the age of data.

About The Author

Dhruvit Kumar Patel is a seasoned Data Analysis Researcher with over eight years of experience translating complex data into strategic insights across diverse industries, including finance, healthcare, and banking. He is known for his remarkable ability to convert raw data into actionable intelligence that fuels strategic decisions and organizational expansion. His hands-on success includes implementing efficient data pipelines and automating key business processes with Python scripts, all contributing to significant business optimization.

Priyam Vaghasia, a master’s graduate in Computer Science from Stevens Institute of Technology, is an innovator in Big Data Analytics, integrating AI, deep learning, and cloud computing to deliver real-time insights and predictive intelligence. With 6+ years of experience in healthcare, telecom, finance, and retail, his award-winning research—published in IEEE Xplore, WRFER, and other journals—has advanced secure, scalable machine learning in cloud environments. Holding patents in modular data storage and real-time AI systems, and serving as an IEEE paper reviewer, speaker, and hackathon judge, he drives the creation of intelligent, secure, and scalable data platforms for smarter decision-making.

Dhruvil Saliya is a data-driven analyst with over 4 years of experience in data analytics, trading strategy development, and portfolio optimization. Skilled in futures/options trading, statistical modeling, and financial risk management, he has delivered actionable insights, automated trading models, and improved ROI in high-pressure environments. A Master’s graduate in Data Science from Stevens Institute of Technology, he has published research papers in IEEE and other journals and holds a patent for his innovative work. Combining expertise in Python, Excel, Power BI, and Amibroker with strong strategic and communication skills, he excels at solving complex financial and business challenges.

Rishil Vaghasia is currently pursuing his Master’s in Computer Science, specializing in AI, Data Analytics, and Big Data. He has presented at an IEEE conference and published award-winning research, earning the Best Paper Award for his innovative contributions. Beyond academics, he has actively participated in hackathons and competed in events such as CREATO, showcasing his creativity and problem-solving skills. With expertise in analyzing complex datasets, developing intelligent models, and leveraging big data technologies, Rishil is passionate about transforming raw data into actionable insights. His academic journey, hands-on competition experience, and recognized research position him to create scalable, intelligent solutions that address real-world challenges.