摘要：In 2008, Chris Anderson (2008), at that time the Editor–in–Chief of Wired , proposed that in the age of the petabyte, there was no longer any need for the scientific method, nor for models or theories. Although it might be contended that this was more provocation and journalistic hubris than formal or substantiated claim, the issue was taken up and has gathered momentum ever since. Indeed within a year or so of Anderson’s article, and a series of rejoinders published on the Edge Web site, ‘The Age of Big Data’ was being heralded, and the measure had increased from petabytes to exabytes, zettabytes, and yottabytes. Diebold (2012) usefully distinguishes between Big Data ‘the phenomenon’, ‘the term’, and ‘the discipline’; arguing that the phenomenon ‘continues unabated’, the term is ‘firmly entrenched’, and the discipline is ‘emerging’. In what follows we focus initially on the term and the phenomenon, but our main objective is to argue that it is critical that there is general understanding of the emerging discipline. In particular we aim to justify the assertion that in the age of Big Data the ability to be able to develop abstractions and concepts is at least as important as it was previously; perhaps even more so. Moreover that these skills and techniques need to be understood and available to all of us in an era where we are all analysts and researchers at least to the extent of our use of the internet and its potential for affording search and investigation of online resources. We seek to offer some critical insights into these activities — modeling, conceptualizing, and theorizing — by comparing and contrasting Knowledge Discovery from Data (KDD) with the Grounded Theory Method (GTM). The former a technical orientation, that although predating Big Data, lies at the heart of the emerging tools and techniques. The latter a widely used approach to qualitative research aimed at developing conceptual models ‘grounded in the data’.
