Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions

The study of big data analytics (BDA) methods for the data-driven industries is gaining research attention and implementation in today's industrial activities, business intelligence, and rapidly changing the perception of industrial revolutions. The uniqueness of big data and BDA has created unprecedented new research calls to solve data generation, storage, visualization, and processing challenges. There are significant gaps in knowledge for researchers and practitioners on the right information and BDA tools to extract knowledge in large significant industrial data that could help to handle big data formats. Notwithstanding various research efforts and scholarly studies that have been proposed recently on big data analytic processes for industrial performance improvements. Comprehensive review and systematic data-driven analysis, comparison, and rigorous evaluation of methods, data sources, applications, major challenges, and appropriate solutions are still lacking. To fill this gap, this paper makes the following contributions: presents an all-inclusive survey of current trends of BDA tools, methods, their strengths, and weaknesses. Identify and discuss data sources and real-life applications where BDA have potential impacts. Other main contributions of this paper include the identification of BDA challenges and solutions, and future research prospects that require further attention by researchers. This study provides an insightful recommendation that could assist researchers, industrial practitioners, big data providers, and governments in the area of BDA on the challenges of the current BDA methods, and solutions that would alleviate these challenges.

Automated summary

This paper surveys the trends of BDA tools and methods, discusses potential applications and challenges, and provides insightful recommendations.