Utilizing Business Analytics, Big Data, and Visualization for Sales Performance Optimization




Abstract:
This research paper examines the role of business analytics and reporting in the sales sector, utilizing big data. The main goal is to analyse sales data, identify key insights, and present the results through visualization using Tableau. The introductory section discusses the research subject, objectives, and challenges that may arise when applying business analytics in sales. Types of content explores the technologies used for handling big data, which are crucial for the efficient storage, processing, and analysis of large datasets. These technologies include distributed systems like PIG, data warehouses such as Red Lake, and tools for data analysis and integration, including SQL Server, SSIS, and SSAS. The section also highlights the role of data visualization tools like Tableau and Power BI in presenting key insights and supporting business decision-making. The methodology describes the steps for collecting and analysing sales data, along with an overview of data visualization tools such as Tableau. A dedicated section provides a detailed description of the database, including its structure, and data types. The data analysis covers various types of sales performance, customer, and product analyses. Data visualization and interpretation are presented through Tableau, focusing on result interpretation and their application in business decision-making. The thesis concludes with a summary of key findings.

CITATION:

IEEE format

A. Radivojević, M. Mravik, M. Šarac, “Utilizing Business Analytics, Big Data, and Visualization for Sales Performance Optimization,” in Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2025, pp. 503-510. doi:10.15308/Sinteza-2025-503-510

APA format

Radivojević, A., Mravik, M., Šarac, M. (2025). Utilizing Business Analytics, Big Data, and Visualization for Sales Performance Optimization. Paper presented at Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2025-503-510

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