Abstract
Big data analytics (BDA) are gaining importance in all aspects of business management. This is driven by both the presence of large-scale data and management's desire to root decisions in data. Extant research demonstrates that supply chain and operations management functions are among the biggest sources and users of data in the company. Therefore, their decision-making processes would benefit from increased use of BDA technologies. However, there is still a lack of understanding of what determines a company's ability to build BDA capability to gain a competitive advantage. In this study, we attempt to answer this fundamental question by identifying the factors that assist a company in or inhibit it from building its BDA capability and maximizing its gains through BDA technologies. We base our findings on a qualitative analysis of data collected from field visits, interviews with senior management, and secondary resources. We find that, in addition to technical capacity, competitive landscape and intra-firm power dynamics play an important role in building BDA capability and using BDA technologies.
Highlights
Big data (BD), which consists of high-volume, high-velocity, and high-variety data assets [1], has virtually become accessible to all businesses
Through our study context of France and India, we find that enhanced global interconnectedness has led to similar issues in supply chain departments' big data analytics (BDA) capability development ex ercise
Our model posits that the different constructs we identified through our analysis determine the capacity of the firm to build BDA capability and gain competitive advantage
Summary
Big data (BD), which consists of high-volume, high-velocity, and high-variety data assets [1], has virtually become accessible to all businesses. Tremendous amounts of real-time or almost real-time data are continuously produced and made available in multiple forms and from various sources, like social media applications, shopping portals, search engines, sensors, smart applications, and internet of things (IoT) Using such data by applying analytics offers promising opportunities for gaining insights in various domains and business sectors, as shown by Chen et al [2] in their seminal paper “Business Intelligence and Ana lytics: From Big Data to Big Impact.”. Literature reviews on this concept (see [12,13] for detailed reviews) point out that in addition to the high volume, BD definitions commonly refer to the high velocity at which data is created and transmitted and the high variety of data types and sources (Web sources, business processes, sensors, and tags) Drawing on these characteristics, McAfee et al [3] claimed that companies could radi cally improve their performance and gain competitive advantage through exploiting BD. The concept of BDA is pro posed as a means to analyze BD and capture its maximum business value
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