Data Engineering in Retail vs. Telecommunication: A Sectoral Analysis

This paper presents a comparative study of data engineering practices within the Retail and Telecommunication sectors, highlighting how each leverages data to drive business strategies, enhance operational efficiency, and foster innovation. Despite their shared reliance on data, these sectors face unique challenges and employ distinct approaches to managing and analyzing data to achieve their objectives. Through a detailed examination of sector-specific data engineering challenges, technological solutions, and implementations, this study uncovers the nuances of data management in these industries, including data volume, velocity, variety, and the application of advanced analytics and machine learning. Additionally, it explores the potential for cross-sectoral learning and innovations, particularly in data privacy, security, and real-time analytics. By analyzing the impact of data engineering on sectoral performance and strategic decision-making, this paper aims to provide insights into the adaptation of data engineering practices in response to industry-specific challenges and opportunities. Furthermore, it discusses future directions in data engineering, emphasizing the role of emerging technologies such as the Internet of Things (IoT), edge computing, and blockchain. This comparative analysis sheds light on data engineering in the Retail and Telecommunication sectors. It contributes to the broader academic discourse on leveraging data engineering to optimize data-driven strategies in different industry contexts.