The article discusses the importance of big data analytics in decision-making processes across various industries. It highlights the challenges posed by the vast, diverse, and rapidly evolving nature of big data, which traditional management tools and methods are not equipped to handle. The paper explores different analytics approaches and tools suitable for big data analysis, as well as the potential benefits of leveraging big data analytics in decision-making domains. Big data is characterized by volume, variety, and velocity, with additional aspects such as veracity. The paper discusses the need for new technologies and tools to handle big data, including distributed computing, machine learning, and advanced analytics. It also covers big data storage, processing, and analytics, emphasizing the importance of scalable infrastructure and efficient data processing techniques. The paper reviews existing literature on big data analytics, discussing various tools, methods, and technologies applicable to big data analysis. It explores their potential applications and opportunities across different decision-making domains. The literature reviewed spans from 2008 to 2013, with a focus on big data discussions from 2011 to 2013. The paper also discusses the role of big data analytics in customer intelligence, supply chain and performance management, and risk management and fraud detection. It highlights the potential of big data analytics to provide valuable insights and support decision-making processes in various industries. The paper concludes by emphasizing the importance of big data analytics in decision-making, highlighting the need for frameworks like the B-DAD framework to integrate big data tools and techniques into the decision-making process. The paper also underscores the growing importance of big data analytics in unlocking economic value and making better, faster decisions by analyzing large datasets to uncover patterns, sentiments, and customer insights.The article discusses the importance of big data analytics in decision-making processes across various industries. It highlights the challenges posed by the vast, diverse, and rapidly evolving nature of big data, which traditional management tools and methods are not equipped to handle. The paper explores different analytics approaches and tools suitable for big data analysis, as well as the potential benefits of leveraging big data analytics in decision-making domains. Big data is characterized by volume, variety, and velocity, with additional aspects such as veracity. The paper discusses the need for new technologies and tools to handle big data, including distributed computing, machine learning, and advanced analytics. It also covers big data storage, processing, and analytics, emphasizing the importance of scalable infrastructure and efficient data processing techniques. The paper reviews existing literature on big data analytics, discussing various tools, methods, and technologies applicable to big data analysis. It explores their potential applications and opportunities across different decision-making domains. The literature reviewed spans from 2008 to 2013, with a focus on big data discussions from 2011 to 2013. The paper also discusses the role of big data analytics in customer intelligence, supply chain and performance management, and risk management and fraud detection. It highlights the potential of big data analytics to provide valuable insights and support decision-making processes in various industries. The paper concludes by emphasizing the importance of big data analytics in decision-making, highlighting the need for frameworks like the B-DAD framework to integrate big data tools and techniques into the decision-making process. The paper also underscores the growing importance of big data analytics in unlocking economic value and making better, faster decisions by analyzing large datasets to uncover patterns, sentiments, and customer insights.