"Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it..." - Dan Ariely, Professor at Duke University, 2013
Two years later and everyone is calling themselves a Data Scientist. But what is a Data Scientist anyway - and how useful is this description?
Put simply, a Data Scientist is merely a statistician with a different title - a buzzword for the second decade of the 21st Century. The term was originally coined back in the 1960s but only came into the mainstream consciousness after it was used by DJ Patil and Jeff Hammerbacher from social media giants LinkedIn and Facebook to describe their respective jobs. I often wonder whether 'data science' courses or graduate 'data science' jobs would be so over-subscribed if they were referred to simply as 'statistics' - a triumph for marketeers perhaps?
Well no, not in my view, at least. I think the metaphor of a technology enabled statistician being a 'data' version of his or her 'real-world' alter-ego is a helpful one. For in the same way as we expect our scientists to experiment, fail, iterate and discover - so do we hope to unlock value in our exploration of data.
Also too do I believe that there is a place for Data Artists - people who devote their professional lives to making data look beautiful. David McCandless, and others have done much to further the study of data visualisation and how great design can help stimulate better understanding of the underlying meaning presented in the data. This is surely the digital equivalent of the landscape artist conveying on canvas a sense of wonder and awe in the natural world.
The 'real-world' label can play a useful role in helping us think about our digital professions in a better way. In the same way that real world Architects collaborate with Project Managers, Structural Engineers, and Council Planning Departments to create their great wonders - so should we encourage our Data Architects and Data Engineers to think about their roles in an equivalent way and encompass similar approaches to their work.
So, what of the Data Philosopher? Well, "I know that I know nothing": it falls on Data Philosophy surely to provoke the debate, stimulate thinking about the issues, and perhaps once in a while to formulate an original idea or two...