Analysis of costing issues in big data

Abstract

In the present scenario, Big Data analytics is an important activity that is driven by the pervasive diffusion and adoption of, for example, RFID (radio-frequency identification), mobile devices, and social media tools. The Internet of things allows for the connection and interaction of smart devices as they move and exist within today's value chain. This allows for unprecedented process visibility that creates tremendous opportunities for operational and strategic benefits. However, the effective management of this visibility for improved decision making requires the combination and analysis of data from item-level identification using RFID, sensors, social media feeds, and cell phone GPS signals; in short, Big Data analytics. Millions of essential data are lost every day due to traditional storage technologies, and this doesn't allow one to adapt the health service to the needs of patients or diseases, since nowadays there are no tools able to store and manage so much information, although the technology needed exists. The current architectures and systems used in the sector do not allow for managing greater volumes of data and even less in reasonable time. To allow for mass data storage in real-time technologies, Big Data are needed. Although Big Data analytics have tremendous potential for transforming various industries, many scholars and practitioners are struggling to capture the business value. This chapter focuses on the functional and analysis issues of Big Data in industries. © 2017 by Taylor & Francis Group, LLC.