Copyright@ Cheryl Ann Alexander | Biomed J Sci & Tech Res | BJSTR. MS.ID.006726. 33458 Short Communication ISSN: 2574 -1241 DOI: 10.26717/BJSTR.2022.42.006726 Data Warehouses, Decision Support Systems, and Deep Technologies During the Global COVID-19 Pandemic Cheryl Ann Alexander 1 * and Lidong Wang 2 1 Institute for IT innovation and Smart Health, USA 2 Institute for Systems Engineering Research, Mississippi State University, USA *Corresponding author: Cheryl Ann Alexander, Institute for IT Innovation and Smart Health, Mississippi, USA Introduction Data storage for businesses involves the storage of such information as stocks, raw materials, deposits, and other such information related to the daily operations of the business. The architecture of such a system needs to be aimed at data management. Data warehousing uses technologies that allow data from multiple sources to be compared and analyzed so that businesses can the consolidated data to make decisions (Simion, et al. [1]). The database is built to implement the volume and the requirements of the system and help project managers and organizational managers make decisions related to the development of the business structure or further daily operations (Simion, et al. [1]). Furthermore, database applications improve the reliability and efficiency of the user and the ability to make decisions, store, update, and get answers through reports (Simion, et al. [1]). Communication with the essential departments of the organization is facilitated by the efforts of the component dialog. The analysis highlights the structure between the data analysis and the simplest ARTICLE INFO ABSTRACT Received: February 22, 2022 Published: March 01, 2022 Citation: Cheryl Ann Alexander, Lidong Wang. Data Warehouses, Decision Sup- port Systems, and Deep Technologies During the Global COVID-19 Pandem- ic. Biomed J Sci & Tech Res 42(2)-2022. BJSTR. MS.ID.006726. Abbreviations: IoT: Internet of Things; DW: Data Warehouse; DSS: Decision Sup- port System; JHU CCSE: Johns Hopkins University Center for Systems Science and Engineering Society produces a massive amount of data. Engineers, scientists, physicians, financial officers, legal personnel, etc., continue to look for methods to organize, analyze, and categorize the data, making society data driven. The basic method used to organize and categorize the historical data necessary for the building of business intelligence is a data warehouse. While the Data Warehouse (DW) organizes and categorizes the business data, it is not, however, a Decision Support System (DSS). But a DW is used as the first building block of a sustainable DSS, containing historical data about the company, its assets, goals, mission, vision, competition, etc. With the continued growth of data driving these systems, traditional methods of DW and DSS outgrew their viability once deep technologies and Big Data became widely available and more popular as a tool for manipulation of massive data circuits. Once the world found itself facing the most important data crisis in the last two centuries, deep technologies based on Big Data really began to emerge as the backbone of the COVID-19 pandemic models, statistics, and data reserves for the historical data. In this paper, we examine the use of deep technologies in DWs and DSSs. The use of models and factors that challenge the data or may interfere with successful algorithms are also discussed. Three examples of successful DW examples are looked at and critiqued. Finally, future research into what could be the future of deep technology and DWs is examined. Keywords: Data Warehouse; Decision Support System; Deep Technology; Machine Learning; Apache Hadoop; Big Data