Using Data Analytics to Derive Business Intelligence: A Case Study

• URL: http://arxiv.org/abs/2305.19021v1
• Date: Tue, 30 May 2023 13:21:12 GMT
• Title: Using Data Analytics to Derive Business Intelligence: A Case Study
• Authors: Ugochukwu Orji, Ezugwu Obianuju, Modesta Ezema, Chikodili Ugwuishiwu, Elochukwu Ukwandu, Uchechukwu Agomuo
• Abstract summary: Big data analytics is already at the forefront of innovations to help make meaningful business decisions. Business intelligence and analytics has become a huge trend in todays IT world. This paper aims to demonstrate the data analytical process of deriving business intelligence via the historical data of a fictional bike share company.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The data revolution experienced in recent times has thrown up new challenges and opportunities for businesses of all sizes in diverse industries. Big data analytics is already at the forefront of innovations to help make meaningful business decisions from the abundance of raw data available today. Business intelligence and analytics has become a huge trend in todays IT world as companies of all sizes are looking to improve their business processes and scale up using data driven solutions. This paper aims to demonstrate the data analytical process of deriving business intelligence via the historical data of a fictional bike share company seeking to find innovative ways to convert their casual riders to annual paying registered members. The dataset used is freely available as Chicago Divvy Bicycle Sharing Data on Kaggle. The authors used the RTidyverse library in RStudio to analyse the data and followed the six data analysis steps of ask, prepare, process, analyse, share, and act to recommend some actionable approaches the company could adopt to convert casual riders to paying annual members. The findings from this research serve as a valuable case example, of a real world deployment of BIA technologies in the industry, and a demonstration of the data analysis cycle for data practitioners, researchers, and other potential users.

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