Efforts toward Research and Development on Inconsistencies and Analytical Tools of Big Data

Big data denotes to data volumes in the range of zettabytes (1021) and beyond. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s, as of 2012, every day 2.5 exabytes (2.5×1018) of data were created, as of 2014, every day 2.3 zettabytes (2.3×1021) of data were created [4, 5]. Every year, NASA and the National Science Foundation host a contest across the scientific communities, the results often resonating in both the academic and business worlds. The latest challenge: How can organizations pull together all the right data from a variety of sources before performing analysis, drawing conclusions and making decisions [1, 7]? Sounds like big data, right? We are encountered with many limitations due to advancement and the large set of data generated and captured in sciences, engineering and technologies, and various social, economical and human activities [8]. The limitations also affect Internet search, finance and business informatics. With big data, the world has gotten far more complex for IT managers and those in charge of keeping a business moving forward. So how do you simplify your architecture and operations while raising the value of the innovative tools you've crafted to meet your business goals? How do we make dissimilar data sets uniformly accessible? And how do we extract the most relevant information in a fast, scalable and consistent way? In this paper, we first focus on various dimensions in big data and big data analysis, and highlight some major issues in those dimensions. We then took a close look at the inconsistencies in big data and their effect on the outcome of big data analysis. Then we discussed comparison between enterprise big data analytical tools and open source big data analytical tools, selecting right kind of tools for big data analytics. Finally we conclude the paper with remarks on future work.