Friends, peers and colleagues, lend me your bandwidth and 10 minutes of your time. Gather around and let me tell you about the greatest, most interesting and fantastically diverse Big Data and Data community right here in our very midst on this amazing LinkedIn community.
We have a new Big Data/Data group, and the group is aptly named The Big Data Contrarians, and yet it is neither a ‘me too’ group, of which there are too many to mention, or a ‘belief circle’, of which the less said, the better. Not, The Big Data Contrarians group is a place for cool opinion pieces, creative abrasion, practical insight and (within the realms of the possible) BS free comment.
However, before going into more detail about the group, I would like to digress for a moment.
Like many people, I take a lot of inspiration from outside my own professional spheres of practice, principles and technologies, and this is no less true when it comes to advertising.
Two of my real influencers – the real kind not the LinkedIn kind – are advertising legends Dave Trott (also author of Predatory Thinking) and Bob Hoffman (the Ad Contrarian), who are exceptionally experienced, talented and creative people, of the NoBS (no flim-flam) kind. Indeed, it was after reading some of Bob’s and Dave’s recent articles that I decided to get this group registered on LinkedIn, which, love it or loath it, is where many of us connect.
So, I hear you ask “What’s The Big Data Contrarians, Mart?”
Okay, to be fair, The Big Data Contrarians group is about far more than just being contrarian and a legitimate means of inciting discussion, for as reasonable as that is. It’s also about arguing against or openly rejecting mistakenly cherished and contrived Big Data beliefs and ‘institutions’ and established Big Data hype, speculation and opinion. It’s about separating Big Data fads, fantasises and folk-tales from Big Data reality.
What we seek to understand and convey is where, when, how and for what ends data (including Big Data) can be used to derive legitimate benefits. Moreover, stated from a position of reason and facts, and not simply projected as an issue of Big Data faith, speculation and clairvoyance.
On the other side, we can call out the Big Data hype for what it is, and just as Bob Hoffman calls out the social media and advertising BS babblers in his trade, this too lends a platform for people to do the same with the disreputable and dubious practices of Data gurus, courtesans and ‘influencers’.
“So, Mart, is being a Big Data Contrarian a bit like being a Big Data Luddite?”
Well, not really, but the problem with having so many people who are new to IT is that the past is a mystery top them, so anything that is new to them is actually taken as new, whether it is new or not.
Those who know will know that technologies of distributed file stores and search over unstructured data has been around for quite some time, and some of the “new” technologies that we big-up today, are actually simple developments of data technologies that go back to the seventies and eighties, or maybe even before.
However, this is not essentially about being anti-technology or even in advances in the application of technology, but of understanding that it isn’t helpful for the media, the big industry players and their indentured acolytes, to railroad, cajole and bully businesses into buying Big Data technology they don’t need, to solve Big Data problems and opportunities they don’t have.
That said, it’s up to the members of The Big Data Contrarians to decide on what shape the community should take, and as it is an open forum in democratic terms, the members have equal rights in presenting their own opinions, lessons learned and other insights.
So, if you haven’t yet drunk the Big Data kool-aid, come on down to The Big Data Contrarians, the place for everyone interested in Big Data/Data and its many potential uses.
Many thanks for reading.
Of course, this piece will also not feature on LinkedIn’s Big Data channel, because apparently that channel editor (naming no names) doesn’t like anyone raining on their particular Big Data flim-flam parade.