Consequences in Acceptance and Application of Big Data Analytics in Micro, Small, and Medium Enterprises (MSMEs) in India

Introduction

Data is a very privileged resource in the highly competitive business world, which is essentially required to resolve the number of challenges everyday businesses face. Based on the current imperative situation of the vast data related to finance, marketing, operations, and human resources associated with specific target markets, particularly in the developing world, businesspeople are eying big data and its analytics these days. Irrespective of the magnitude, sector, and range, multinationals to MSMEs investigate avenues to burden and exploit the data. The application of big data analytics and its technologies is adjusting how businesses crosswise over enterprises work. Therefore, there is a desperate requirement for MSMEs to genuinely consider significant data adoption to address their voluminous data challenges. Big data includes several data types, including traditional enterprise data, machine-generated data, and social data (Opresnik & Taisch, 2015). Machine-generated data could consist of several formats, including weblogs, smart meters, and data originating from multiple sources. Big data contains data in both structured and unstructured data having five dimensions: namely volume, variety, velocity, integrity, and value. Capacity refers to the terabytes and Exabytes of data generated every day, while speed refers to creating big data quickly in real-time (Coleman et al., 2016). Variety refers to the numerous data sources, including textual data, image data, and much more. In contrast, veracity refers to detecting and correcting noisy and unreliable information (Coleman et al., 2016; Zheng et al., 2013).

Big data analytics analyzes big data to uncover hidden patterns, unknown correlations, and other information using sophisticated algorithms (Zheng et al., 2013). SMEs can benefit substantially from big data analytics, but the challenges associated with significant investment in technology and the workforce hinder these firms from benefiting from big data analytics (Zulkernine, Bauer & Aboulnaga, 2013). Agreeing to (Assuncao et al., 2015), more than 70% of large enterprises and 56% of SMEs in advanced countries have either already deployed or are intending to implement big data projects. According to Dobre and Xhafa (2014), 2.5 exabytes of data are produced every day, with more than 90% of the data generated in the last few years. However, Coleman et al. (2016) indicated that SMEs in developing and emerging economies are slow in embracing big data analytics because of several challenges. Unless these challenges are addressed, there is a risk that MSMEs will be left behind in benefitting from this new technology. Since MSMEs are the backbone of developing countries' economies, such a lapse can prove detrimental to developing countries' growth.

According to Zheng et al. (2013), data volume doubles every two years, and with several setups forming the core of SMEs in the last two years, more than 90% of their data was generated in the previous two years. Big data analytics involve considerable investment in hardware and software resources. A potential explanation for these challenges is in the form of cloud computing technology, which enables omnipresent and accessible provisioning of computing resources (Zulkernine et al., 2013). Cloud computing could offer these challenges through computing resources' pervasive and scalable provisioning (Zulkernine et al., 2013). The researchers have initiated this book chapter to know the Indian Micro, Small, and Medium Enterprises (MSMEs) challenges based on the Big-Data's significance. A similar kind of study was also conducted in Iran, and the results offer evidence of a big data analytics mediation effect in the relationship between technological, organizational, and environmental contexts and SMEs performance (Maroufkhani et al., 2020).