EVALUATING DATA VISUALIZATION TOOLS BASED ON IMPORTING AND PROCESSING TIMES

Abstract

Following the rapid development of information and communication technologies, we witness the inevitable production of vast amounts of data, often referred to as Big Data. These large datasets are not just a result of technological advancements and smart devices; they are also closely tied to human activity. Data, as a concept, holds little meaning unless processed and presented in an understandable format with the power to convey information effectively. Alongside the development of software for processing Big Data, there is a parallel evolution of software for data visualization. Data visualization involves translating information into meaningful visual formats such as charts, maps, or diagrams to simplify data analysis and facilitate informed decision-making. Due to the complexity of Big Data, visualization is crucial and serves as a gateway through which the brilliance of information reaches humans. This study encompasses Big Data, recent data visualization tools based on Gartner's rankings, criteria checklists commonly used for tool comparisons, and the interpretation of results obtained from various measurements of data import and visualization times. The research employs scientific methods such as literature review, experimental methodology, and comparative analysis. This study evaluates popular data visualization tools, including Power BI, Tableau, Zoho Analytics, Qlik Sense, Domo, and TIBCO Spotfire, based on Gartner's rankings. Testing these tools across various data types and sizes, we found that they perform similarly for large datasets. The statistical analysis strongly corroborated the hypothesis that there exists a substantial disparity in data download and processing times among these tools, particularly for datasets exceeding 1 gigabyte in size. Data visualization remains crucial for effective analysis, especially with vast datasets. Optimizing visualization tools for performance and human-centric design is essential in a continuously evolving technological landscape. This study highlights the significance of understanding and fine-tuning data visualization tools to enhance data analysis quality and efficiency.