Abstract
There exist large amounts of heterogeneous digital data. This phenomenon is called Big Data which will be examined. The examination of Big Data has been launched as Big Data analytics. In this paper, we present the literature review of definitions for Big Data analytics. The objective of this review is to describe current reported knowledge in terms of what kind of Big Data analytics is defined in the articles that can be found in ACM and IEEE Xplore databases in June 2013. We found 19 defining parts of the articles for Big Data analytics. Our review shows that Big Data analytics is verbosely explained, and the explanations have been meant for professionals. Furthermore, the findings show that the concept of Big Data analytics is unestablished. Big Data analytics is ambiguous to the professionals - how would we explain it to laypeople (e.g. leaders)? Therefore, we launch the term data-milling to illustrate an effort to uncover the information nuggets. Data-milling can be seen as an examination of heterogeneous data or as part of competitive advantage. Our example concerns investments of coal power plants in Europe.
Highlights
Big Data has as a term appeared literally first times towards the end of the 1990's [1]
When we searched “big data analytics” we found 29 articles (Table 3)
When we tried to find information nuggets for indicators for investments in the coal power plants in Europe until the year 2020, we realized that we need a lot of data, for example, from social media, TV, news, and politics
Summary
Big Data has as a term appeared literally first times towards the end of the 1990's [1]. “the important aspects of “big data” analytics: Big: the vast volumes and fast growth of datasets, requiring cost-effective storage (e.g., HDDs) and scalable solutions (e.g., scale-out architetures); Fast: the need for low-latency data analytics that can keep pace with business decisions; Total: the trend toward integration and correlation of multiple, potentially heterogeneous, data sources; Deep: the use of sophisticated analytics algorithms (e.g., machine learning and statistical analysis); Fresh: the need for near real-time integration as well as analytics on recently generated data.” [29]. The following statements can be taken to crystallize it: – “Big Data analytics involves analyzing large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information” [21] –“Big data analytics has become critical for industries and organizations to extract useful information from huge and chaotic data sets to support their core operations in many business and scientific applications” [24] –“big data analytics is a workflow that distills terabytes of low-value data . The following statements can be taken to crystallize it: – “Big Data analytics involves analyzing large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information” [21] –“Big data analytics has become critical for industries and organizations to extract useful information from huge and chaotic data sets to support their core operations in many business and scientific applications” [24] –“big data analytics is a workflow that distills terabytes of low-value data . . . down to, in some cases, a single bit of high-value data . . . The goal is to see the big picture from the minutia of our digital lives” [25] –“Big data analytics is the process of examining large amounts of data (big data) in an effort to uncover hidden patterns or unknown correlations” [30]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
