Big Data: The Path to Maturity

Stephen H. Kaisler, William H. Money, Frank Armour and J. Alberto Espinosa
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Stephen H. Kaisler: SHK & Associates, Laurel, MD, USA
William H. Money: Baker School of Business, The Citadel, Charleston, SC, USA
Frank Armour: American University, Washington, DC, USA
J. Alberto Espinosa: American University, Washington, DC, USA

International Journal of Systems and Service-Oriented Engineering (IJSSOE), 2017, vol. 7, issue 2, 1-23

Abstract: Big Data refers to data volumes in the range of exabytes (1018th) requiring processing from distributed on-line storage systems with thousands of processors, mainframes or supercomputers where processing speed is measured in GFLOPS. The rate at which data are being collected are accelerating and will approach the zettabyte/year range. Other attributes of Bi Data are also concurrently expanding including variety/variability, velocity, value, and vital concerns for veracity. Storage and data transport technology issues may be solvable in the near-term. However, these communication, quantity management, and processing technologies also represent long-term challenges that require research, paradigms and analytical practices. This paper extends the authors' previous analysis of the issues and challenges with Big Data. It presents a table that contrasts their previous research finding and projects with the state of Big Data today, and their projections of what managers and decision makers will (or should) seek to accomplish as the Big Data universe continues to expand and evolve.

Date: 2017
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