Optimizing Business Intelligence Using Machine Learning And Deep Learning In Cloud-Based Big Data Platforms

Abstract

Today, companies are more interested in optimizing data analytics solutions to gain in-depth insights and generate value from them. It is not a trivial task to extract the benefits and insights from operational data. Analyzing and using the data to generate insights helps make informed decisions and predict the future based on historical data, which is essential for everyone. Especially, making smart decisions instead of relying solely on expertise is especially important for individuals, organizations, research institutions, and governments. By utilizing advanced computing technologies, these decisions can be made, and the results are more accurate and reliable in every market. In this ambitious world, all kinds of technologies are available in the market and business to reach the global market in our strategy. Hence, it is utmost necessary for each enterprise, from small to large businesses, to integrate the latest technology to improve decision-making according to the market’s strategy. If this is not addressed, organizations lose time, money, and customer satisfaction with existing services. Thus, information has become a major factor in recent years. They have transformed data into useful information.
We investigate cloud-based business intelligence and the advanced and promising technologies like machine learning and deep learning techniques, along with BI tools in big data platforms. Cloud-based big data platforms usually focus on the distributed processing of large datasets using distributed computational platforms, working with various business strategies and opinions. The information interrelated with the reflections observed from the cloud-based environment is then technically converted into useful reports. Using this approach, the development process will guide the organization in making recommendations. It aims to enhance the development of existing objects in the BI system data report using cloud and Hadoop. As a significant part of the analysis, we specifically observed an important outcome in the development task of the proposed design for data quality and decision-making reporting. As a continuation of our study, there are many challenges that need to be addressed; we require collaborative BI-digitalization data for Industry 4.0 and to integrate a recommendation system with the existing literature of our study. Finally, we implemented our use case for forecasting to test the above methods in our research.