Cheryl Ann Alexander and Lidong Wang

In 2020, the world faced a new threat when SARS-CoV-2 was introduced in Wuhan, China. Immediately, mathematicians, computational scientists, and others began to work on supporting technology to provide medical professionals and others with the assistance and backbone necessary to fight the pandemic. Enterprises rely on business intelligence (BI), which is a combination of sequenced structured, unstructured, and semi-structured data that can be used in business intelligence (BI) from numerous sources that embody a complex and multi-stage process often managed by information technologies. Extensive effort in leadership, technology, and methodology must also be combined to complete these tasks as the massive amounts of data collected most often require the use of advanced data management tools such as big data, machine learning, or artificial intelligence (AI) with advanced neural networks and the Internet of Things (IoT) to formulate a sustainable decision support model for the enterprise. Current data analysis models are simply unable to handle the enormous amounts of data required to process day-to-day data analysis for any enterprise. Regard the data as the raw ingredients necessary to create a decision support system (DSS) to manipulate or create a sustainable DSS or DSS model; however, a reduction in the cost of labor and supplies, decreases the amounts of time involved in performing the decision-making cycle, and speedier responses are generated from the workers. In this paper, we look at all aspects of deep technologies associated with DSS during the Covid pandemic.

Keywords: deep technology, big data, COVID-19, decision support system, business intelligence, artificial intelligence, data warehouse

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