Big Data: The Path to Maturity

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.

[1]  Richard F. Deckro,et al.  A Flow Model Social Network Analysis of the Iranian Government , 2003 .

[2]  Michael Stonebraker,et al.  Researchers' big data crisis; understanding design and functionality , 2012, Commun. ACM.

[3]  H. Rittel,et al.  Dilemmas in a general theory of planning , 1973 .

[4]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[5]  D. Boyd,et al.  Six Provocations for Big Data , 2011 .

[6]  Claudio Cioffi-Revilla,et al.  Politics and Uncertainty: Theory, Models and Applications , 1998 .

[7]  Nassim Nicholas Taleb,et al.  The Black Swan: The Impact of the Highly Improbable , 2007 .

[8]  Yangyong Zhu,et al.  The Challenges of Data Quality and Data Quality Assessment in the Big Data Era , 2015, Data Sci. J..

[9]  Flemming Nielson,et al.  Constraint Solver Techniques for Implementing Precise and Scalable Static Program Analysis , 2009 .

[10]  Anwar M. Ghuloum,et al.  ViewpointFace the inevitable, embrace parallelism , 2009, CACM.

[11]  Milos Jenicek,et al.  List of Cognitive Biases , 2010 .

[12]  J. Alberto Espinosa,et al.  Big Data: Issues and Challenges Moving Forward , 2013, 2013 46th Hawaii International Conference on System Sciences.

[13]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[14]  Stephen J. Cohen,et al.  A Decision Framework for Cloud Computing , 2012, 2012 45th Hawaii International Conference on System Sciences.

[15]  Michael Stonebraker,et al.  New opportunities for New SQL , 2012, CACM.

[16]  R. Nisbett The geography of thought : how Asians and Westerners think differently--and why , 2003 .

[17]  Adam Jacobs,et al.  The pathologies of big data , 2009, Commun. ACM.

[18]  D Meiron,et al.  Data Analysis Challenges , 2008 .

[19]  G. Hardin,et al.  The Tragedy of the Commons , 1968, Green Planet Blues.