Big Data Analytics: Applications and Barriers in Supply Chain

1. Introduction

In the twenty-first century, data flowed at a breakneck speed. The rapid proliferation of data over the last two decades has led to the birth of Big Data (Addo-tenkorang and Helo, 2016). The flood of digital technology further substantiates the rise of big data. As technology becomes accessible, citizens worldwide become increasingly accustomed to the sensors, communication tools, actuators, and data processors that come with it (Raj et al., 2019; Tiwari et al., 2018). Furthermore, according to a 2011 study by the International Data Corporation, the globe has already created one zettabyte of data. Rising numbers have been accelerating; the total amount of data had increased to 7ZB in 2014 (Shayaa et al., 2018) and reached more than 50 ZB of data in 2020. Textual data accounts for at least half of that, as produced by social media platforms like Facebook, Twitter, and mobile instant messaging applications like WhatsApp and Telegram. Big data growth estimates as per the recent Statista platform project that in 2025, data will reach the colossal figure of 180 zettabytes, which is 1.3 trillion terabyte growth over 2020. This surge in data volume is attributable to the growing demand for remote learning, working, and entertainment due to the rise in popularity of telecommuting, working remotely and taking leisure time online during the COVID-19 lockdown times (Novikov, 2020). In addition to this, millions of network sensors are embedded in smartphones, smart energy meters, cars, and industrial equipment in the physical environment. Advancements in digital sensors and communication technologies have fuelled the Internet of Things (IoT) (Saleem et al., 2019). According to IDC, 127 new devices are constantly joining the internet every second throughout the world. Over the next fifteen years, the developed connected devices will have generated about five quintillion bytes of data per day, equal over 79.4 Zettabytes of data. The primary functions of IoT devices depend on the intended use of the devices and the kinds of data the devices are designed to gather. Companies use the Internet of Things (IoT) to regulate their operations better and increase their market reach by adding sensors and security systems.

As businesses increasingly demand better decision-making processes to succeed, data becomes essential to help drive such improvements. While all companies can take advantage of information system infrastructure expenditures, not all can transform those investments into better results (Chavez et al., 2017). The ability to leverage big data is regarded as one of its advantages since it may be unique and challenging to reproduce in the near to intermediate-term. Once industries have used big data to expand their market shares, some experts believe that exploiting and harnessing big data is the forthcoming “blue ocean” in terms of overall business performance (Ali et al., 2020). The literature has reported that digital transformation and technological breakthroughs continue to be the leading drivers of rising Big Data investment. Businesses must continuously innovate to be relevant in the marketplace, especially with so much competition in every field (Shafiq et al., 2019). Industry specialists require the proper amount of data to make informed judgments, and Big Data analytics give just that. These judgments can help a company advance by properly recognizing a market trend that could increase revenue. Global expenditure on Big Data was already over $180 billion at the end of 2019, and it is expected to rise at a growth rate of 13.2 percent between 2020 and 2022 (Business Wire). According to reports, IT expenditures, hardware purchases, and business services might be the areas where the most money is spent on Big Data analytics.