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Why so many ‘fake’ Big Data Gurus?

Where do you all come from?

Where do you all come from?

All your integrity’s gone

Now tell me, where do you all come from?

From ‘Where Do You All Come From‘ by Mott the Hoople

A note from the editor:

Readers should be well aware that the comedian who wrote this piece is the self-styled founder of The Big Data Contrarians, which is quite possibly the most belligerently intelligent Big Data group you will ever come across in your entire life. You have been warned. If you need to verify these facts for yourself, then take a look here at your own risk: https://www.linkedin.com/grp/home?gid=8338976

And now for something completely different…

You may have noticed the massive relative-growth in the number of people who are describing themselves as Big Data gurus, data science Kaisers or analytics evangelists. Okay, I exaggerate to evidence the trend, but you’ll hopefully get the gist. Your fellow comrade on the picket line, that sweetie you met at the Pitt Club — even your darling masseuse has had their carte de visite transformed according to the prevailing désir de Jour.

Many people out there in the big data world suddenly call themselves ‘Big Data gurus’ simply because it is the latest vogue. The Caerfyrddin Good Pub Guide even went so far as to say that, “adding Big Data to your job title was the equivalent of sexing up a dodgy dossier”. They also later suggested that boosting your resume with the judicious incorporation of a titillating title, such as Big Data analytic pole-dancer, may get you a few chuckles, even if most people don’t understand and appreciate its broad multi-faceted and humoristic ramifications.

That stated, the cruel and harsh reality is that many who call themselves Big Data gurus appear to be lacking the full Big Data picnic by quite a few sandwiches when it comes down to looking at the nitty gritty of their visible resumes.  Moreover, and to be Frank and Earnest – Frank in Zurich and Earnest in Pontypridd – if I was hiring a top notch Big Data guru I wouldn’t even know where to start.

What I see are quite a number of courageous fellows who don’t really have a Scooby[1] (that’s a ‘clue’ for readers overseas) about Big Data, whose are enriched and swelled by others of their mind, who know just enough to be dangerous.

What I also see are Big Data hacks who cannot bring themselves to articulate one coherent, cohesive and verifiable Big Data success story. They are calling themselves Big Data gurus, presumably because of the incredible value accruable from their unrevealed bullion-class information.

The best offenders amongst the Big Data gurus being incredibly precocious with the hype and amazingly prudish with the facts.

Why, only the other day, one of these Big Data hype chappies – whose name escapes me for the moment – was lamenting the dearth of true data scientists whilst simultaneously lambasting and misrepresenting the profession of the contemporary statistician.

Now, I I am not fundamentally averse to a bit of hype, so long as it’s in moderation, and it doesn’t frighten the horses. Take my Dad, for example, he never curses, and he never has. Me, I use it for dramatic effect and emphasis – occasionally. However, a lot of the Big Data ‘hackery’ that we are entertained with is like having Big Data Derek and Clive playing in your ear, 24x7x52. It’s just too much, and most of the time it should be toned down or turned off. You know “So, this bloke comes up to me and says ‘Hello!’ ”

Then there are the people from the consultancies, the IT vendors and the service providers (alright, not all, just a few) who have a good grasp of the superficialities, the business, analytics and Big Data terminology, even if  they have at best a tenuous grasp of the underlying structures, concepts and relationships that these terms relate to. Of course, it’s all hooked on context.

It’s not important if it’s just about a bit of a chit-chat down ‘The Black’ or a bit of banter over Kaffee und Kuchen, or a passing comment from the umpire at the crease. As Afilonius Basto put it to me “in a seriously professional business setting, many of the predominant Big Data gurus who rock the Big Data bull**** babble, just lack a certain creativity, knowledge and experience in order to act as truly reliable informers and trusted Big Data advisers.” So I ask you, who am I to argue such matters with a highly intelligent Gos D’atura?

Part of the problem here is, to borrow the unwritten style guide of Big Data ‘hackdom’, simple supply and demand voodoo and superfluous use of important and amorphous terms tagged on to such examples. To wit, the incongruous use of terms such as ‘economics’, ‘performance’ and ‘science’. To say nothing of the irritatingly banal use of ‘amazing’, ‘amazing’ and ‘amazing’. In addition, one thing that has been puzzling me is this. Why do some Big Data pundits have to overegg their verbosely literal output with US slang that even professional bloggers in the USA would tend to use very sparingly? I know, I know what you’re thinking. “Gerroutta heres”, right? But for me it is like the IT equivalent of the mock cockney accents that are used by some celebrity chefs. I kid you not! It’s awful and it’s going viral, just like the bubonic plague and Greensleeves.

Now back to the main story thread. There simply are not enough true Big Data gurus out there to fill the demand, and so barely qualified (or ‘make it up as you go along’) aspirants make it into the higher stratified stratosphere of the Big Data saloon.

Second, just like many Big Data success stories themselves, the role of a Big Data guru is often poorly demarcated within the ambit of association and relation and even indeed within a solitary business.  People bandy around terms such as Big Data guru, Big Data whizz and Big Data who’s your daddy, willy-nilly postman style, These are terms that can mean everything and anything. From “he’s hot on Big Data that chap is” to “there goes thick Jack the Spratt the densest Big Data guru in Christendom”, and upwards and onwards to “did you see Marty, the Big Data party? Got Carmen Miranda from Data Analytics a leave of absence, although rumour has it that the she is down with predictive impregnation after reading a particularly sordid Big Data hype-piece on LinkedIn”.

A true Big Data guru/expert is so much more, so much more than what we have now. In my opinion, a Big Data guru/expert is about:


Facts. The Big Data guru should stick to the facts. For example, if a Big Data guru does not understand the roles and responsibilities of a statistician then they should keep mum, and be admired for their discretion, rather than opening up a floodgates of mental garbage and thereby inviting questions regarding credibility, and introducing the risk of being viewed as a buffoon. Exempli gratia, if you do not know what a statistician does, then ask, rather than simply making things up. Also ensure that you never get into a position of belief and thought that you did not rationalise yourself into. Because you will be stuck there and no one will be able to reason you out of that belief, because it’s a position that is not in itself based on reason, truth and logic.

As Malcolm X put it “Despite my firm convictions, I have always been a man who tries to face facts, and to accept the reality of life as new experience and new knowledge unfolds. I have always kept an open mind, a flexibility that must go hand in hand with every form of the intelligent search for truth.”

So, aspiring Big Data boys and girls? Please stick to the facts! Your Big Data god, friends or family will thank you for it.


Integrity. What Big Data definitely does not need is more hype-schlepping hypocrites, bamboozling babblers and conniving charlatans. What is needed are people who exude virtuous truthfulness, candour and pedagogical ethics.

According to Integrity Action, integrity is “the set of characteristics that justify trustworthiness and generate trust among stakeholders. Integrity creates the conditions for organisations to intelligently resist corruption and to be more trusted and efficient.” More broadly, Wikipedia puts it this way: ” Integrity is the quality of being honest and having strong moral principles; moral uprightness. It is generally a personal choice to uphold oneself to consistently moral and ethical standards.”


Trust. A Big Data guru/expert should be trustworthy, and seen to be trusty. Just like Caesar’s wife or the quality of brunch bar at Tiffany’s, the Big Data guru must be beyond reproach. Not that they aren’t allowed a little journalistic license, simply that the gaping abyss that separates barefaced porkies[2] from simple embellishments, is frankly enormous.


Knowledge. A Big Data guru/expert should be knowledgeable in all things data, and not just Big Data. Knowledge means you know what Data Warehousing is and don’t fib about it or grossly misrepresent it in order to score ill-gotten brownie points for Big Data babble. Knowledge means you know, not that you have a bit of an idea, know a friend of friend of a friend, or can ask the audience, in cases where one is caught-out, ill-advisedly pretending to know something one doesn’t know. My advice is this. Temper knowledge with humility, honesty and decency, and you won’t go far wrong.


Experience. A Big Data guru/expert must have walked the talk. Knowledge must go hand in hand with experience. Clearly a few self-labelled Big Data gurus doing the rounds these days do not fit the bill in this respect (or for that matter in many of the other ‘respects’). Alas, not all is lost. You too can acquire both the data knowledge and data experience to become a Big Data guru. How? Try working at it for a while.

There is another way at looking at experience, in a half-comic and half-serious way. To paraphrase a popular joke “Do not argue with an idiotic Big Data guru. He will drag you down to his level and beat you with experience.” And it happens…


Vocation. No, this is no time for a holiday. The Big Data guru must assume the mantle of Big Data stardom as a vocation and not just as an early-adopting fashion follower. As Voltaire put it, speaking of Newton but also commenting more broadly on education and the Enlightenment: “I have seen a professor of mathematics only because he was great in his vocation, buried like a king who had done well by his subjects.”


Simplicity. A Big Data guru/expert must be able to explain complex Big Data ideas in a simple ways, but without losing the essence or the credibility of what requires conveying. Tesco had a slogan, “you cannot bullshit simplicity”, which tried to convey this essence, and a German retailer took this even further with “Every Lidl helps”.  So remember, keep it simple. “Sophistication is the ultimate sophistication” – Leonardo da Vinci.


Journalism. Although not necessarily a master of joined up handwriting and maths 101, a Big Data guru must also write like a journalist. He or she does not have to be a great scribe, simply being competent with words, concepts and numbers is a high enough bar. Okay, there is a little more to it than that – or at least, there should be. So I will explain.

These are a few things (also mentioned elsewhere in this piece, and influenced by the World Journalism Institute), that a good Big-Data-guru journalist should possess or be aware of:

  • It’s mainly about people. What we write about will influence and affect people, so we should remember that, and act accordingly, with empathy and with compassion.
  • Don’t ever put-up and shut-up when presented with bullshit.
  • Be sceptical and be prepared to verify.
  • Have great and reliable sources.
  • Continually check your biases.
  • Be adaptable and welcome change.
  • Don’t be intimidated.
  • Be tenacious.
  • Be open minded.
  • Always maintain one’s own integrity.


A good story. According to the Writers Store there are seven elements that make a god story: ” the change of fortune, the problem of the story, the complications, crisis, climax and resolution of the classical structure, and the threat, which is by far the most important.” A truly great Big Data guru will be able to take this advice and apply it to the field of Big Data with little difficulty. It would make a great change from wading through Data Lakes of ‘bleh’ in search of the Holy Big Data grail. So, having set the scene, let’s take a closer look.

A real Big Data guru must be able to tell a compelling and credible story. However, they must also show as well as tell. Telling alone is fiction, which is fine, but a Big Data needs to back great fiction up with fact. A Big Data guru worth their salt must be able to tell and show. Nothing less than a verifiable story is acceptable. Don’t be promiscuous with the facts in a story, especially when one heralds a Big Data success, without providing any hard evidence to back it up. Remember this, just because some people are incredibly gullible when it comes to Big Data, it doesn’t mean you should lead them in ignorance down the garden path, like so many innocent lambs to the slaughter. That sort of thing is quite despicable even by our regular standards. So don’t go out of your way to prove Dan Ariely right yet again, especially with regards to his very accurate comment that “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…”


Technology. A Big Data guru should know the technology. They should also know the origins of the technology and its influences. For example, a Big Data guru should have a sound grasp of the principles of the following:

  • Distributed file stores and the many varieties that exist and have existed. Examples of these are Lustre, GPFS and HDFS.
  • Database models, architectures and technologies. From 1960 to today. Flat files and hierarchical, network, relational, object, and, dimensional models. There are more, but this is the place to start.
  • In-memory relational database technologies. E.g. EXASol and Vertica.
  • Distributed processing and orchestration.
  • Shared everything, shared nothing and conditional sharing.
  • Function shipping.
  • Parallel search, count and merge.
  • Extract, transport, transform and load technologies and techniques.
  • The history, architectures and technologies of Information Management, Information Centres, Executive Information Systems, Decision Support Systems, Management Information Systems, Report generators, 4GLs, End-user computing, Business Performance Management and even operational reporting.
  • Enterprise Data Warehousing architecture, process and technology. (See Bill Inmon for this aspect).
  • Statistics, data analytics, visualisation and presentation.

This is far from being an exhaustive list, but it should give you a flavour for what is required.

Also, take heed of the words of Pablo Picasso, who stated, “Computers are useless. They can only give you answers.”


Agnostic. An ethical and professional Big Data pundit that really rocks the Kasbah, must also be as agnostic as it is possible to be, yet without letting an insistence on ‘fair’ and ‘balance’ upset the civic order of things. This simply means that you do not have to give equal merit to all and sundry, especially when equal merit is palpably not inherently the case in the broad range of technical, project and business offerings that wash over the decks of the SS Big Data. At the end of the day, in all cases the postmodern interpretation of fair and balanced is also a massive contradiction in terms.

The Swiss philosopher, poet and critic Henri Frédéric Amiel once wrote that “A belief is not true because it is useful”, and this is we should take special care in taking an agnostic stance with regards to Big Data and its technologies. There is a tendency for Big Data gurus to big up Hadoop and ignore the rest of the field. Not only is this narrow-minded view an injustice, but it is ultimately detrimental to those who seek to understand and obtain benefit from deploying Big Data solutions.


Versatile. I have seen many people try to accommodate the meagre competence of some self-anointed Big Data gurus within a far too wide an area of acceptance. This is not an issue if people are aware of the rampant bias, babble and boloney factors, but nonetheless extreme caution needs to be exercised if awkward unintended consequences are to be avoided. Remember a good Big Data guru can come from a variety of backgrounds — and not all of them will necessarily require a degree from a prestigious centre of educational excellence such as Oxford, the LSE or Prifysgol Cymru, Y Drindod Dewi Sant. In fact, one could say that attendance at St Trinian’s School for Young Ladies, Hogwarts School of Witchcraft and Wizardry, or Cambridge Finishing School could quite possibly be a negative factor, although I would not associate a certainty factor with any of these bets. Although to be fair, I am reliably informed by the Headmistress that Saint Trinian has an excellent Data Science faculty.


Business understanding. The ideal Big Data guru must be business aware, savvy to the point of shrewdness, and cunning, preferably “as cunning as a fox who’s just been appointed Professor of Cunning at Oxford University”[3]. They must understand business process, the commonalities and differences of business sectors and players, and the motivations, competitive forces and key influencers in business. They must also understand the meaning of business-strategy, how it is developed, chosen and executed. Finally, the Big Data guru must have a good handle on irrationality and its degrees of predictability. So, know your business, and understand the business of others. So when someone gives you a bucket and tells you to go down to Tesco’s to buy a petabyte of data and a Euro Millions Lottery ticket, you’ll know what it’s all about.


Analytical. A good Big Data guru must be naturally analytical, but not to the point of being anally analytical, and should possess an ability to spot patterns in behaviour as well as in data. CIA veteran Dick Heuer put it this way: “Thinking analytically is a skill like carpentry or driving a car. It can be taught, it can be learned, and it can improve with practice. But like many other skills, such as riding a bike, it is not learned by sitting in a classroom and being told how to do it. Analysts learn by doing.”


That’s all folks

If you encounter a candidate Big Data guru with all of these traits — or have a candidate who ticks most of the boxes but is willing to acquire more ticks — then you’ve found someone who might deliver unbelievable value to the cause of Big Data, your struggle, your reason, your content, and your living-room. So, delight in your find.

However, be sparing with candidates on any of these inherent individualities, and you run the risk of acquiring a graded and coarse-grained pretender, someone just hoping to travel the Big Data bullshit babbling bubble until it bursts in their brazen time-pieces[4].

I know, I know, I hear you say “but does this all really matter?” Probably not. Probably in the grand scheme of things this is yet another boom and bust fad destined to become matter of fact in some reduced circles and a plain waste of time, money and patience, in others. No doubt we shall see, as the story unfolds and the ‘sublimely absurd’ metamorphoses into the ‘I don’t bloody believe it’, or not.

So, now over to you. What would you add to this compendium of convenient characteristics? I would really love to receive your views, opinions and perspectives in the comments section that follows on from this piece.

Many thanks for reading.

A note from the Prime Minister:

Data is only as good as its time and place utility. If it has none, it has no present value, unless of course, someone wants to pay something for nothing, but that is constructing a con not an economy, an aberration destined to be hated and then forgotten. Don’t only think about how to use the data you have, but also about what data should be captured and how it should be used. By the way, join the Big Data contrarians here on LinkedIn: https://www.linkedin.com/grp/home?gid=8338976

Vote for the Big Data Contrarians. Vote early! Vote often!

[1] Scooby, Scooby doo, rhyming slang for ‘clue’

[2] Porkies, pork pies, lies

[3] Blackadder Goes Forth, Richard Curtis and Ben Elton

[4] Clock, face.