Using Data Analytics to Derive Business Intelligence: A Case Study

TL;DR

This paper demonstrates how data analytics can be applied to derive business intelligence, using a case study of a bike share company analyzing Chicago data to find strategies for converting casual riders into paying members.

Contribution

It provides a practical example of applying data analysis steps to derive actionable business insights from real-world bike sharing data.

Findings

Identified key factors influencing rider conversion.

Proposed data-driven strategies for increasing membership.

Showcased the data analysis process in a real-world context.

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.