Optimize Retail System Performance by Analyzing Big Data and Visualizing with Power BI
Keywords:
Big data, Visualization reports, Power BI, Digital transformationAbstract
Digital transformation significantly improves business performance and customer experience and adapts to the pace of change in the business environment. Applying digital transformation technologies to business causes the retail industry to gain data very quickly, creating a big data system requiring the ability to perform extensive data analysis and management. Businesses can improve their operational capacity and competitiveness by discovering the data source better. The article presents the method of designing and organizing data and the relationship of data objects in a data warehouse system. Data in the warehouse will be synthesized using computational algorithms to extract useful information and then visualized using data analysis tools, representing data with the output in the reporting charts. They help turn digital data into easy-to-understand visuals, helping retailers see patterns, trends and essential information. Businesses can achieve many meaningful benefits, such as improving operational performance, increasing revenue, predicting trends, etc... by using many charts. Data representation into understandable information is accomplished with the intelligent analysis tool Microsoft PowerBI.
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Le Viet, H.; Dang Quoc, H., The Factors Affecting Digital Transformation in Vietnam Logistics Enterprises, Electronics 2023, 12, 1825. DOI: 10.3390/electronics 12081825.
Olszak, CM (2022). Business intelligence systems for innovative development of organizations. Procedia Computer Science, 207, 1754-1762. DOI: 10.1016/j.procs.2022.09.233.
Bharadiya, JP (2023). A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics. American Journal of Artificial Intelligence, 7 (1), 24. DOI: 10.11648/j.ajai.20230701.14.
Caserio, C., Trucco, S., Caserio, C., & Trucco, S. (2018). Business intelligence systems. Enterprise Resource Planning and Business Intelligence Systems for Information Quality: An Empirical Analysis in the Italian Setting, 43-73. DOI: 10.1007/978-3-319-77679-8_3.
Azma, F., & Mostafapour, MA (2012). Business intelligence as a key strategy for development organisations. Procedia Technology, 1, 102-106. DOI: 10.1016/j.protcy. 2012.02.2020.
Grabińska, A., & Ziora, L. (2019). The application of Business Intelligence systems in logistics. review of selected practical examples. System Safety: Human-Technical Facility-Environment, 1 (1). DOI: 10.2478/czoto-2019-0130.
Moyano, Diego Marcelo Bermeo, and Milton Alfredo Campoverde Molina, Implementación de Data Mart, en Power BI, para el análisis de ventas a clientes, en los Econegocios “Gransol”, Polo del Conocimiento: Revista científico-profesional 5.1 (2020): 647: -673.
Bermeo-Pérez, S. K., & Campoverde-Molina, M. A. (2020). Implementación de inteligencia de negocios, en el inventario de la Cooperativa GranSol, con la herramienta Power BI. Revista Científica FIPCAEC (Fomento de la investigación y publicación científico-técnica multidisciplinaria). ISSN: 2588-090X. Polo de Capacitación, Investigación y Publicación (POCAIP), 5(16), 240-266.
Edhya, Billah Faktha Putra, Business intelligencce data marketing mengunakan metodekimball dan ETL dengan Power B, Kurawal-Jurnal Teknologi, Informasi dan Industri 5.2 (2022): 87-97, DOI: 10.33479/kurawal.v5i2.642.
Gray, P. and H.J. Watson (1998) Decision Support in the Data Warehouse, Upper Saddle River, New Jersey, Prentice-Hall. DOI: 10.5555/572865.
Watson, H. (2001). Recent Developments in Data Warehousing. Association for Information Systems “AMCIS 2001 Proceedings”, 2289-2292. DOI: 10.17705/1CAIS.00801.
Nambiar, A., & Mundra, D. (2022). An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management. Big Data and Cognitive Computing, 6(4), 132. DOI: 10.3390/bdcc6040132.
Inmon, W. H. (1995). What is a data warehouse. Prism Tech Topic, 1(1), 1-5.
El-Sappagh, S. H. A., Hendawi, A. M. A., & El Bastawissy, A. H. (2011). A pro-posed model for data warehouse ETL processes. Journal of King Saud University-Computer and Information Sciences, 23(2), 91-104. DOI: 10.1016/j.jksuci.2011.05.005.
Kimball, R., & Ross, M. (2011). The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons.
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