This discussion converges focus on a case study that speaks about a company named Anyi-VirUs AVU that collects, analyzes and visualizes data gathered to provide with decision making strategy that helps big organizations and states that follow AVU to build precautionary measures against the pandemic encountered and help in fighting the second wave of the Covid more effectively defeating the infection, since many states and governments are approaching their ways towards AVU it needs to build a strategic decision making power to answer their questions, thus AVU is operating a data warehouse scheme to initiate this process. The discussion further highlights some questions asked and what more could AVU incorporate to provide the organizations with efficient decisions and strategies to tackle this pandemic. Furthermore, the discussion concludes with that is deduced through the case study in my research and analysis.
According to the case study given, I came to know that Anti VirUs provides with what decisions should be made in what sort of situations being faced. Thus, to make global decisions, Anti- VirUs needs to operate a data warehouse, as it fundamentally collects, concocts, investigates and visualize the acquired data and information processing and researching on it to build the power to make a decision. In the current pandemic met, Anti VirUs is being looked up to in order to deduce a decision to handle this situation, I state that it needs to operate a data warehouse since data warehouse provides with a system that extracts and brings forth data from numerous diverse resources for reporting and analyzing the data found. To come up with accurate decisions to handle the situation, I think that Anti VirUs needs to analyze all historic data since the pandemic started and pre pandemic stages so that the company can deduce the results in a better way and come up with better decisions to handle this situation (Mohanty & Jagadeesh & Srivatsa, 2013). This repository system will help the company analyze more amount of data from vast diverse sources and provide with better results. I would personally recommend that AVU should use enterprise Data Warehouse since it is strategic and provides with efficient ways to do analytical analysis of the data so it is necessary for AVU to operate and maintain a data warehouse for efficient manipulation of data and fetching better results transforming them to helpful decisions to save the future.
Since governments and health organizations who only have access to their own domestic and in-house data and information only while have knowledge only bout their selves and what is going on in their country and about the rising danger from the outside world, for example this pandemic named Covid. Since AVU will have accesses to multiple resources from all around the globe and that will include what is going on around the globe, governments and those health organizations that stay intact will get to acquire new knowledge about the uprising conditions and what troubles and problems the world is facing today and if it reaches their states what precautionary measures are to be taken and can be implemented to avoid and reduce the effects of the encountered pandemic in their region (Padmanabhan & Patki, 2016). Also, if these governments and organizations shake hands with AVU it will helps them in making enterprise level decision making by proving support by using the data collected. As, data warehouse provides with a better way of analyzing and accessing critical information stored in large databases and AVU uses data warehousing scheme it will provide with more efficient ways for organizations and governments to support decision making strategies and come up with better solutions to tackle the situation at hand (Mascarenhas & Pan & Santhanam & Venkatasubramanian, 2015).
AVU in addition to data warehouses can use data marts too that provides with data related to client facing. It is like a subset of data warehouse that is oriented to a specific business field, data marts can help to focus on the businesses and organizations that are mainly accused of contributing a rising situation. I would personally recommend usage of data marts in AVU to provide with better focus over the analysis and visualization of data to deduce results that can be further processed to support decision making power of the organization in later terms helping to control and fight against the pandemic (Scriney & McCarthy & McCarren & Cappellari & Roantree, 2019). All that sort of data and information AVU must gather that can link to the causes and reasons of the rising pandemic at hand, so that when the second wave strikes, we are more prepared to deal with it and in a more efficient way breaking down the pillars of this infection and finally restoring peaceful and healthy environment. This pandemic at hand requires data related to medical organizations, hospital records, disease rates and all related sectors in this respective analysis. Data marts can really help focusing and gathering data from respective required field for analysis (Arfaoui & Akaichi, 2015). I would also recommend that AVU uses ODS that stands for operational data stores that provides with operational reporting and integrates with enterprise data warehouse helping in building strategies that help in better decision making regarding the pandemic at hand.
I conclude this discussion with the results that AVU can prove to be a well and efficient working company if it operates a data warehouse efficiently and follow the other schemes discussed above for better results. The collection, analysis, concatenation and visualizing of data and information gathered will become easier, more effectual and competent to provide with better results to deduce better decision making strategies. AVU will be able to support the relying governments and organizations helping them build cautious and strong defense against the rising pandemic since the situation is likely to occur coming with a second wave, governments need to come up with better ways to handle it and lessen the losses thus AVU provides with active support for this matter.
Mohanty, S., Jagadeesh, M., & Srivatsa, H. (2013). Big data imperatives: Enterprise ‘Big Data’warehouse,‘BI’implementations and analytics. Apress.
Padmanabhan, R., & Patki, A. U. (2016). U.S. Patent No. 9,430,505. Washington, DC: U.S. Patent and Trademark Office.
Mascarenhas, A., Pan, H., Santhanam, A., & Venkatasubramanian, R. (2015). U.S. Patent No. 9,218,408. Washington, DC: U.S. Patent and Trademark Office.
Scriney, M., McCarthy, S., McCarren, A., Cappellari, P., & Roantree, M. (2019). Automating data mart construction from semi-structured data sources. The Computer Journal, 62(3), 394-413.
Arfaoui, N., & Akaichi, J. (2015, May). Automating schema integration technique case study: generating data warehouse schema from data mart schemas. In International Conference: Beyond Databases, Architectures and Structures (pp. 200-209). Springer, Cham.
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