Examination of Export, Import and DnD Datasets Through Advanced Data Visualization The Case of Maersk Ethiopia Plc
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Date
2021-06
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A.A.U
Abstract
In this era of Big Data, companies arrive at a decision based on objective and unbiased information- Data. Companies need to find the means and capabilities to analyze datasets that have been acquired for years. Leveraging their datasets at their fingerprints, leaders can make decisions backed by verifiable data. The traditional Business Intelligence capabilities lack the sophistication and flexibility to analyze larger datasets to discover meaning and insights that have otherwise remained hidden interactively. Big data analytics techniques such as Advanced Data Visualization, are efficient and easy to examine large and complex datasets to discover actionable insights. The Transportation and logistics sectors are among the most advantageous sectors to take advantage of the advanced analytical tools and Big data technologies available to examine their datasets. This study, Big Data Analytics: Examination of Export, Import and DnD Datasets Through Advanced Data Visualization: The Case of Maersk Ethiopia Plc, examined the Export and Import datasets from 2010 to 2020 and DnD Datasets for the year 2016-2020. The goal of the study was to extract meaning and insights and describe the results using data visualization and analytics technique. The selected case company, Maersk Ethiopia Plc, is a fully owned consultancy entity of Maersk A/S, a Shipping and Logistics company, in the Ethiopia Business area. A quantitative method approach to analyzing the secondary data obtained from the case company via a business intelligence tool WEBI and CXED tool was chosen in line with the research objective. Based on the research purpose, a descriptive case study design was chosen to describe the study's results. The data analysis was performed utilizing an advanced data visualization tool, Tableau. Several visualization charts of the tool were utilized in describing the results. Analyzing a larger dataset using data visualization was helpful to see trends in the various Trades, the commodities with higher contribution, the customers’ performance, the demand for Reefer containers, the DnD revenue performance, the Turn time and Free time along with the detention bucket of the returned containers.
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Big Data & Analytics, Advanced Data Visualization, Tableau, Export