This paper discusses the approaches and environments for conducting analytics on Clouds for Big Data applications, focusing on four key areas: data management and supporting architectures, model development and scoring, visualization and user interaction, and business models. The authors identify gaps in technology and provide recommendations for future research directions. They highlight the challenges of managing and analyzing large volumes of structured and unstructured data, the importance of efficient data storage and retrieval, and the need for advanced visualization tools to support interactive and real-time analytics. The paper also explores the role of Cloud computing in reducing costs and improving flexibility, while addressing issues such as data variety, velocity, and volume. Additionally, it discusses the integration of different data sources, the use of MapReduce and other programming models for data processing, and the importance of standards and interfaces for model building and scoring. The authors emphasize the need for further research to address these challenges and to develop more effective solutions for Cloud-supported Big Data analytics.This paper discusses the approaches and environments for conducting analytics on Clouds for Big Data applications, focusing on four key areas: data management and supporting architectures, model development and scoring, visualization and user interaction, and business models. The authors identify gaps in technology and provide recommendations for future research directions. They highlight the challenges of managing and analyzing large volumes of structured and unstructured data, the importance of efficient data storage and retrieval, and the need for advanced visualization tools to support interactive and real-time analytics. The paper also explores the role of Cloud computing in reducing costs and improving flexibility, while addressing issues such as data variety, velocity, and volume. Additionally, it discusses the integration of different data sources, the use of MapReduce and other programming models for data processing, and the importance of standards and interfaces for model building and scoring. The authors emphasize the need for further research to address these challenges and to develop more effective solutions for Cloud-supported Big Data analytics.