Big Data is now an inhospitable and unhealthy land inhabited by those who, through accident or design, deceive naïve and sentimental bystanders and those who are willingly mislead.
When all of this Big Data malarkey started it was sort of funny, humorous and occasional witty, especially in the affected, bizarre and the frequently uninhibited ways that freshly-minted self-appointed gurus and experts would “big it up”
Doctor Freud would have had a field day with all of that, being as it was, and for that matter still is, a postmodern mishmash of Riefenstahl, Freddy Mercury and Monty Python on steroids. However, after that extended, operatic and high-camp hiatus it all went downhill.
The Big Data scene is fast becoming an outrageous and brash festival of deception, disinformation and obliviousness. Which is a pity, because it does the industry no good whatsoever.
It is telling that Big Data evangelists, gurus and assorted sycophants cannot even define Big Data adequately, never mind discuss (or for that matter, point at) tangible success stories, without falling into contradictions on all of the key defining characteristics of volume, variety and velocity, and resorting to crude debating devices to avoid or finesse the concerns and the questions.
Almost every morning I check out the industry news, and almost invariably, it comes with new mind-boggling examples of Big Data nonsense.
However, it isn’t always nonsense for nonsense’s sake, there are agendas, there are rational explanations why Big Data has become at the same time, one of the most hyped up fads in the history of IT, and one that its supporters find so difficult to actually explain and justify, in any reasonable sort of way.
Therefore, when it comes to Big Data, beyond the surfeit of platitudes, clichés, bluff and bluster, the only thing in play are the interests of industry, the patrons, the courtesans and their entourage of the innocent and the beguiled.
One of the biggest deceptions in Big Data is in the misleadingly named ‘success stories’. The thing is that most of these success stories that I have ever read have been:
- So vague that it’s difficult to know how success is being defined never mind reached.
- So secretive and obtuse is the avoidance of naming names, locations and other relevant Big Data references that it’s impossible to corroborate if these claims are actually true or not. Disclaimer: I have worked for some of the biggest IT vendors, and in senior roles, and I know what is behind comments such as “the Big Data project is a success, although the client name and project are confidential” and “it’s delivering such major competitive advantages that we are obliged to keep it under wraps”.
- Stories stolen from elsewhere, such as from Data Warehousing, Business Intelligence, VLDB or Business Application projects.
- Borderline fantasies and badly contrived technology fan fiction.
However, it doesn’t stop there.
One of the clearest examples of the questionable nature of Big Data evangelism is when it is used to piggyback Big Data hype on simple, tangible and immediately recognisable artefacts or applications that have little in common with Big Data.
This is an extreme illustration, but it works like this: “iPhones are commercially successful, iPhones are part of Big Data, and therefore Big Data is commercially successful.”
As if the mere conjuring up of association, affinity and proximity will convince people of the great and growing value of Big Data.
What I am also referring to are publicity pieces that may as well have been titled:
- Smith, Galbraith, Mies, Keynes, Homer SImpson and the economic justification of Big Data
- Lovelace, Babbage, von Neumann, Eckert, Davies, Codd, Knuth, Naur and the technological underpinnings of Big Data
- Einstein, Freud, Edison, Faraday, Recorde and the intellectual structure of Big Data
- Socrates, Kant, Hegel, Marx , Adorno and the philosophical correctness of Big Data
- Great quotes about Big Data, from the Cambrian era to the postmodern époque
- Great jokes about Big Data, from Mel Brooks to Steve Martin
- Sportspeople and Big Data, from Lottie Dodd and Babe Ruth to Rafa Nadal and CR7
- Industry support of Big Data, from Henry Ford to Neutron Jack
Do you recognise similarities?
It’s no big deal, just the use of unreliable, misleading and inappropriate fallacies, dressed up as cute, plausible and accessible collateral. People may think that such things are clever and witty, but they aren’t, it’s just misleading.
Let’s continue with something simple.
Evasion is, in ethics, an act that deceives by stating a true statement that is immaterial or leads to a false deduction. For example, citing events, persons or anecdotes from the history of IT to justify the supposed or imaginary value of Big Data. This is close to the notion of a non sequitur, which of course is an argument, the conclusions from which do not follow from its premise. It falls short of being full-on sophistry, purely because the simplistic, puerile and superficial arguments put forward in favour of Big Data do not match those of the true sophist who seeks to reason with clever but fallacious and deceptive arguments. Too many of the Big Data arguments are fallacious and deceptive, but no one, equipped with a reasonable capacity for critical thinking, should take such ‘arguments’ as valid.
Hold this thought: Big Data hype is a viper’s nest of logical fallacies, white lies and disinformation.
Just when I think things could not get any weirder, they do, and Big Data ceiling of hyperbole rises even higher, up to the rarer atmosphere of extreme tendentiousness.
There is a growing mass of Big Data hoop-la, hyperbole and flim flam that exceeds all previously bounds of overstatement, solecism and confabulation. This is where the real volumes, varieties and velocities are in Big Data; in hokie.
We live, as Oscar Wilde said in his day, in and age of surfaces. Yes, superficiality, puerility and short-termism are the competing orders of the day. However, I am still amazed – and maybe wrongly so – by what ostensibly professional, experienced and knowledgeable people are willing, able and prepared to accept, especially when it comes to Big Data flim flam sauce.
Here are some examples of the nonsense about Big Data that is taken as gospel by ‘adults’:
Data Warehousing is part of Big Data: No comment.
Big Data will replace Enterprise Data Warehousing: People can’t even explain the features and benefits of Big Data. I try it make it as easy as possible, ‘if you can’t say it, point to it’. But, seriously, people can’t even relate tangible and credible Big Data success stories, never mind show how it will replace Enterprise Data Warehousing, whether that’s the Inmon or Kimball flavour, take your pick.
Everyone and every organisation can benefit from Big Data: If people can’t explain this, and they don’t in terms of tangible benefits, then the claim should remain questionable.
Data Scientists will replace Statisticians: Why is that so? It is claimed that Data Scientists are uniquely equipped to handle massive volumes, varieties and velocities of data – well, as it turns out, this isn’t certain either.
Big Data is in its infancy: I think we may be confusing infancy with lack of real traction, and of time and place utility.
You cannot be serious: Just what are people talking about here? I have read vague, naïve and ill-informed pieces about data management, data architecture, data warehousing, reporting, business intelligence and a plethora of etcetera that have been passed off as observations and commentary on Big Data. So, what makes people recycle hackneyed, misleading and badly conceptualised ‘content’?
In the commentary on one of Bernard Marr’s pieces on LinkedIn (a professional networking site) I observed that no one can adequately explain what Big Data is without falling into contradictions and fancies, and no one seems to be capable or willing to provide tangible success stories.
Bernard responded to this comment by pointing out “the reason for that is that Big Data means different things to different people.”
Fair enough. It’s an explanation.
That said, I have always had more than a tenuous dislike of postmodern thinking, in fact most things ‘postmodern’. Call me old fashioned, jaded or cynical, but to me, the idea that everything can mean anything is an aberration that I prefer to leave to others.
I am at a loss to explain why so many reasonable people are willing to embrace the hype surrounding Big Data and Big Data Analytics, including the attendant surfeit of nonsense, incongruences and contradictions, and from my perspective, it defies reason and good sense.
Therefore, I will just end again with a fabulous quote from Ben Goldacre:
“You cannot reason people out of a position that they did not reason themselves into”.
Many thanks for reading.
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There are three main concepts in this diagram: Data Sources; Core Data Warehousing; and, Core Statistics.
GOOD STRATEGY 