It is important to note that e-commerce dominates the current global market, which gave rise to large corporate entities, such as Amazon. In order to properly understand this massive boom in the given area, one should note that such growth is primarily due to big data analytics utilized in these companies. The most comprehensive approach used in the assessment combines both qualitative and quantitative methodological frameworks in order to gain critical insight into big data analysis measures deployed by the largest e-commerce organization, Amazon. Therefore, big data analytics is responsible for bringing substantial cost advantages, monitoring constantly changing customer needs, and significantly improving the overall performance in regard to decision-making processes.
Literature Review
One needs to be aware of the fact that big data analytics is becoming a major influencing force in the operations of modern enterprises. It is stated that “analytics applications that can deliver a competitive advantage appear all along the supply chain decision spectrum—from targeted location-based marketing to optimizing supply chain inventories to enabling supplier risk assessment” (Sanders, 2016, p. 26). In other words, such an advantage is delivered in a multitude of ways, which include, but are not limited to supply chain optimization, enhanced marketing efficiency, and risk analysis for potential threats, such as losses. In addition, it is important to point out that there are five fundamental characteristics of the given form of analytics, which are value, variability, velocity, variety, and volume (Mishra et al., 2016). The latter element manifests itself in a data magnitude, whereas value is reflective of the potential economic benefits. Variability is connected to the complexity of operations, such as supply chains, whereas variety is attributed to structural heterogeneity. Lastly, velocity is critical for understanding the overall rate of data generation.
References
Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2016). Big data and supply chain management: A review and bibliometric analysis. Annals of Operations Research, 270, 313–336.
Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26–48.