Clive: Yeah, well, you had to, didn’t you? You had to stand up for what you stood for, didn’t you? I mean, the only time I remember a similar occasion was, I was in, errm… I was at Spurs, Tottenham Hotspurs.
Clive: I was watching a game against Arsenal, and this bloke come up to me and said, “Hello”.
Derek: Oh no…
Derek and Clive – This Bloke Came Up to Me
Many people come up to me in the street and ask me what Big Data is. It has happened so many times in the past that I am convinced that it might just happen to you as well.
The first time a complete stranger came up to me in public and said “Hello, will you tell me what this Big Data lark is all about then?” I was lost for words. Later that day I read a book and adopted a strategy.
So, in the spirit of seasonal goodwill to all men and women, I have put together this blog piece that hopefully can be used in such situational encounters.
What is big data?
Big Data can be characterised by the 4+3+2+1 Vs. Which, in my book, is more than enough to bring up-to-speed an average John or Jane that one meets on the street, and who wish to be informed of such matters.
These 4+3+2+1 individualities herein described are designed to help one understand the harnessing of the synergies of Big Data awareness and to purposefully empower the breaking down of the entrance barriers to the understanding of cross-organisational silo-integration.
In layperson’s terms this a series of landmarks and pointers in the analytics space used to frame and guide the didactic aspects of Big Data.
It’s a Big Data cheat sheet. (Yes, I do know)
The fundamental Vs of the Big Data canon are these:
- Vendible (yes, I do know)
So, let me now explain what each of these characteristics mean to those who might know and for those who might want to know.
Vagueness – A very good place to start on our journey into the discovery of Big Data is with vagueness. Although, to be honest, if I was going on such a journey, and had a choice, I would start from somewhere else.
So, how does vagueness define Big Data? This is perhaps the trickiest of questions to address, given the vast panorama that is cast before this incredibly complex yet easily graspable concept. But let me state this, and let there be no mistake about it. At this point in time, what makes Big Data vague is also what makes Big Data specific, explicit and certain. That is to say, in order to ‘come to an understanding’ of Big Data, it is necessary to completely embrace the dialectic of knowing the unknowable. So belief is an absolute essential element – belief and data, that is.
I sincerely hope that I haven’t laboured the points too much, as that is very easy to do. But, in order to comprehend Big Data, in all its magnificent vastness, it is imperative that we understand, reconcile and internalise ambiguity, polysemy and especially vagueness.
Vagueness is a starting point, an end-point and a journey, and it will give us a basis from which we can push the envelope with respect to the other key characteristics of Big Data.
Volume – If there ever was a time to “pump up the volume”, we have it here with Big Data.
Big, voluminous, gorgeously rotund and infinite. Big Data is called Big Data because there is a lovely, roly-poly, likeable never-ending load of it. Its volumes can be measured in zeta-bytes, which you can be assured, is a helluva lot of data.
The name for a ginormous volume of ‘things’ was chosen to honour the massive talent of that great acting diva, Ms Catherine Zeta Jones, of the USA’s very own Spartacus Family, and to pay tribute to her magnificent efforts in leading the campaign to put Wales back on the map. So, you should know very well that there will always be a Big Data welcome for the Big Data ‘believers’ who venture down the valleys.
Big Data is proving Yoda wrong. Size does matter and Star Wars is for wimps
Variety – What constitutes variety in Big Data is a matter of intense debate, leading to some minor difficulties in defining what exactly the sense of the term is supposed to be. So, this is a curiously polemic aspect for sure.
But, as they might say down my way, “variety is the spice of life, innit”. This is what makes Big Data so special. So appealing.
Because before Big Data there was absolutely no variety in anything, at all. We lived in a bland world, bereft of detail, nuance and diversity. Nothing could be measured, analysed or explained, because we lacked Big Data. We were ignorant. So ignorant and stupid that we couldn’t see the sense of putting the diapers next to the beer, or of offering three for the price of two.
This now should be plainly obvious to anyone. But there are none so blind as those who will not see the Big Data.
Fortunately, today this is no longer the case if we don’t want it to be, and thanks to Big Data we have a veritable sensorial explosion. No longer is IT just a couple of symbols scribbled in crayon on someone’s school notebook. IT (and consequently humanity itself) has suddenly been expanded to include the perceptions of sight, hearing, taste, smell and touch, not to mention temperature, kinesthic sense, pain and balance.
Virility – Move over Smart Data, the new kid on the block is Big Data.
If Big Data were described in the manner of a religious text, it would be accompanied by a never ending narrative of begets.
So, what does that mean?
Simply stated, Big Data creates itself, in and of itself. The more Big Data you have, the more Big Data gets created. It’s like a self-fulfilling prophecy in 360 degree, high-definition, poly-faceted and all-encompassing knowing. The sort of thing that governments would pay an arm and a leg to get their mitts on.
But, we are getting a little ahead of ourselves here. So now I will backtrack.
We’ve all heard the expression ‘Big data, little feet’, or something along those lines. But what does it actually mean?
It’s understandably important when it comes to Big Data to speak in riddles, to be creative with euphemisms and to gild the lily.
Put it this way, if Big Data was a ‘ride’ that could be ‘pimped’, MTV style, then Big Data would be an all singing and dancing Nightrider, fully loaded, bells and whistles, with go fast stripes, flashing LED lights and ultra-shiny alloy ‘dubs’. Big Data has become the bling of IT.
As the ace yachtsman, MIG flying, master of relational data business might have put it (or not) “You’ve got 99 problem and the data ain’t one”. I happen to agree, even if the meaning is somewhat obscure.
So, just hold this thought for now: Big Data will expand to fill the whole of the known universe, so you’d better buy plenty of disk storage now, whilst you can afford them.
Velocity – Velocity is of the essence. Velocity kills the competition. More velocity, less haste.
We demand that service is ‘velocious’. ‘Everything’ must be ‘now’, or it’s too late.
This means we need to be able to handle Big Data at velocity – at the speed of need.
Big Data is so big, so squishy, so slippery and so fast that it can go from real-time input to real-time output without touching the sides. Which in and of itself is just absolutely fabulous. Moreover, the heat that this process generates could light up the whole of the Big Apple, and you would still have some left over to power a plethora of Ozzy Osbourne concerts. (And yes, Sir, I know my informal grammar sucks, and that… I’m using… incomplete sentences… but, this is a blog piece). But I digress,
Charles Babbage once stated (or maybe it was more than once) that “whenever the work is itself light, it becomes necessary, in order to economize time, to increase the velocity.”
But remember, we are dealing with mega-velocity here, so don’t drink and drive the Big Data Steamship, Star-ship or Mustang.
Hark! Did I hear you ask: “No drink, not even beer?” To which I might sensibly reply “Hell, no! Not even water”. So, be forewarned, forearmed and forward thinking.
Vendible – If you can sell it, and sell it as Big Data, then it ‘is’ Big Data. If you can’t, then it’s not. The saleability of Big Data proves its existence. The very existence of client’s for Big Data demonstrates conclusively that it is tangible – at least in market terms, and it’s the market that rules.
So, what are the vendible aspects of Big Data?
For some people, Big Data is like a crock of fertilizer. The ideal formula for nurturing and growing responses to significant challenges.
For other people, Big Data is the next big bandwagon of which to jump.
Then there are those who see the magic dollar signs in the glittering prize of Big Data success.
Big Data is both palpable and incorporeal, it cannot be touched, yet it can touch.
Big Data is both transient and enduring, it is like a moveable and yet unspecified feast.
It is a game-changing and strategy-energising shape-shifter.
It has the power to remould itself into a ‘potentiator’ of corporate riches, as a cure for all the important human ailments and afflictions, and as a solver of the most pressing issues facing humankind today.
More importantly it can drive whole new markets of supply and demand.
Demand for hardware, demand for software, demand for ‘appliances’, demand for implementation services and ‘instant experts’, and demand for litigation and legal services.
It can also be used to mobilise armies of commentators, industry analysts, publicists, punters, writers, bloggers, gurus, futurologists, conference organisers, conference speakers, educators, customer relationship managers, salespeople, marketers and admen.
Indeed. It can be confidently stated that never have the words, ‘mark it up, and sell it on’ been as apt as in this age of Big Data.
Vaticination – Edmund Burke is down on record as stating that “you can never plan the future by the past”. Now Burke may have been a clever person when it came to many things, but he wasn’t exactly a whiz when it came to Big Data.
There are people in the world who are in no doubt that Big Data provides the sort of visionary and predictive powers only previously obtainable through ritual sacrifice, magic potions and the casting of spells. Others are highly critical of the understatement implicit in this belief.
For many, Big Data will make the Oracle of Delphi look like a mere call centre.
This is why the power of vaticination plays a characteristically important role in the world of Big Data.
If it weren’t for Big Data’s unique set of prophetic value-propositions we may as well have gone back to being cave dwelling hunter-gatherers.
Voracity – This is based on the quasi-rationalist argument that Big Data is big and it has an omnipresent and insatiable self-fulfilling desire.
Big Data comes with an attendant requirement for hardware, even if it is a whole load of consumer hardware tacked together in a magnificent and miraculous mesh of magic.
Big Data can be characterised by voracity, but this comes hand in hand with the ‘ventripotent’ IT industry.
Unfairly in my view, some people claim that Big Data satisfies the fetishist appetites and whims of the rapacious, greedy and insatiable. I would disagree. I would argue that Big Data is for people who just ‘like a lot’.
Although, I do generally ascribe to the view of Ms Piggy that one should never eat more than one can lift.
But beware, treat the leviathan with a lot of caution. Big Data is potentially so voracious that it may attain the clout, control and the capability to eat itself, alive.
Veracity – The eminence of the data being captured for Big Data handling can vary significantly. The quality or lack of quality of the data naturally has the potential to impact the accuracy of analysis using that data.
Before Big Data arrived on the scene we knew nothing about Data Quality or data verification. This is why ETL and Data Cleansing tools lacked the power to effectively quality check and verify data, to ensure that any erroneous or anomalous data was rejected or flagged.
But now, with the sophistication of tools such as ‘grep’ and ‘awk’ at our disposal, we have the power in our hands to ensure nothing ‘dodgy’ gets into the analytical mix.
We are now able to sequentially clean, map and reduce datasets at will.
I can well imagine why a company like Oracle would be kicking themselves now for not designing and implementing a method of being able to distribute data across multiple channels and controllers, and of providing the capability of running queries “split and distributed across parallel nodes and processed in parallel”, and of then constructing a result set. Okay, they had these and other features in their products from Oracle 7.3 onwards, but it was not Big Data, was it? And anyway, this section is about veracity, it is not about MapReduce, Oracle RDBMS or of the history of advances in relational database technology.
Vanity – To paraphrase Max Beerbohm, ‘to say that data is vain means merely that it is pleased with the effect it produces on other people. Conceited data is satisfied with the effect it produces on itself’.
In my opinion, to fully grasp the underlying and profound meaning of Big Data, it is essential for us to understand the difference between vanity and conceit. Max Counsell claimed that “Vanity is the flatterer of the soul”. Goethe characterised vanity as being “a desire for personal glory”. After an incident with an Anarchist (presumable a Big Data Anarchist), Blackadder remarked to Baldrick that “The criminal’s vanity always makes them make one tiny but fatal mistake. Theirs was to have their entire conspiracy printed and published in plain manuscript”.
So that ends the brief rundown of the defining characteristics of Big Data.
So, to summarise. That, which has passed before, necessarily divulges both the upside and downside of Big Data. By reaching out, opening up the kimono and relating the 4+3Vs we are disclosing that which cannot be disclosed, exhibiting the absence of essential essence, and thereby opening up the entire field, discipline, profession, science and art to examination, questioning and ridicule.
Finally, I hope that as we move forward, in time and space, onwards and upwards to greater, bigger and better data, that we do not forget the fundamental lessons of life. Especially the “laugh at nonsense” bit.
Thanks for reading.
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