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First things first. The Big Data Contrarians (“a hype free Agora for Big Data dialogue”) is now a community of over one thousand professionals. Continue reading
07 Mon Dec 2015
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First things first. The Big Data Contrarians (“a hype free Agora for Big Data dialogue”) is now a community of over one thousand professionals. Continue reading
07 Mon Dec 2015
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Towards the end of 2014 I gazed into the amazingly incredible crystal ball called Good Strategy, well known and admired by the readers of the Good Strat Blog, and made some predictions about Big Data for the year to come. The year of the goat. As I write now we are reaching the end of the wonderful year of 2015 – an anno quite-allrightus. So, equipped with good cheer, emboldened by the thoughts of passing the vacations with loved ones, friends and family, and heartened by my impending (albeit temporary) demobilization, I have decided to look back at my predictions of a year ago, to see how accurate or mistaken they ‘have become’, and to share those reflections with you. Continue reading
05 Sat Dec 2015
Nauseated by the non-stop crap, railroading and bullying tactics from a reduced group of snotty little techno bastards? Disgusted by the crass propaganda, crude instrumentalisation and fetid boloney from the likes of Bernie, Vinnie, Spats and an attendant entourage of snake-oil merchants and brain-dead sycophants? Sick and tired of the amazing, incredible and fabulous velocities, varieties and volumes of Big Data bullshit washing the decks of the SS LinkedIn? Well, be sick and tired no longer. Here is the antidote!
Some interesting Big Data facts to think about this weekend.
I. More Big Data bullshit has been created in the last couple of years, than in the entire history of humankind.
II. Big Data bullshit will grow faster than ever before, in spite of what Gartner say to the contrary.
III. By 2021, if the mega-trending nonsense does not go unabated, there will be 40 megabytes of Big Data bullshit created for every living woman, man and child, every sixty seconds.
IV. Also, in 2021 the accumulated digital universe of Big Data bullshit will grow from 8 spartabytes to 22 marrsabytes.
V. Every second people are thinking about creating new Big Data bullshit. For example, 20 million search queries alone (per minute) are generated with the sole intent of creating even more Big Data bullshit. This is set to grow to over 100 thousand brazilian bulslhit queries per year by 2020.
VI. Every minute an estimated 280 hours of Big Data oriented porn is uploaded to the ‘next greatest thing since sliced bread and butter pudding‘ network.
VII. By 2017 over 1 trillion Big Data bullshitters will be connected via Facebook.
VIII. Facebook usage by Big Data bullshitters will make the current social media scene look like a walk in the bullring.
IX. In 2015, an astounding 1 million trolleyloads of photos were uploaded to the web every single hour of the day. By 2017, nearly 80% of photos taken will include a cameo by one or more smartass Big Data bullshit artist.
X. This year, over 4 billion smartass Big Data bullshitters will be shipped – all packed with communication devices capable of collecting and communicating all kinds of Big Data bullshit, not to mention the Big Data bullshit the amazing Big Data babblers create themselves.
XI. By 2020, we will have over 8 billion Big Data idiot savants (overtaking sentient and rational human beings).
XII. Within five years there will be over 5 billion Big Data smartasses connected in the world, all developed to collect, analyze and share Big Data bullshit.
XIII. By 2020, at least a third of all Big Data bullshit will pass through the bullshit cloud (a network of Big Data bullshit servers connected over the Big Data bullshit Internet).
XIV. Distributed Big Data bullshitting (performing Big Data bullshitting tasks using a network of computers in the cloud) is very real. Google uses it every day to involve about 10 Big Data bullshitters in answering a single search query, which takes no more that 0.2 weeks to complete.
XV. The Hadoop Bullshit Ecosystem (open bullshit software for distributed bullshitting) market is forecast to grow at a compound annual growth rate 299,258% surpassing $111 billion by 2021.
XVI. Estimates suggest that by better integrating Big Data bullshit, we could save as much as $300Bn a year on smoking, drinking and having a wild time. That’s equal to reducing costs by $1000000 a year for every person on earth.
XVII. The White House, who first recognized Big Data as the bullshit it is, has already invested more than $200 in big data bullshit projects.
XVIII. For an archetypal Fortune 1000 company, just a 10% increase in data accessibility will result in more than $650 billion additional net income.
XIX. Retailers who leverage the full power of big data could increase their operating margins by as much as 36,660%
XX. 173% of organizations have already invested or plan to invest in big data bullshit by 2099.
Many thanks for reading. Think about it. I hope you get the message.
23 Mon Nov 2015
Having got your attention I would like to introduce you to a pragmatic, real-world and business centric approach to Big Data and Big Data Analytics. When I say that this is the best approach to Big Data you are ever likely to find in the whole universe and in your entire life, I am still significantly understating the magnificent utility, timeliness and the here-and-now facets of the approach. Continue reading
17 Tue Nov 2015
Many people come up to me in the street and beg me to write about the truths, myths and unwise things said about Big Data. I am offered gifts of goats, partners and riches beyond the dreams of avarice just to pronounce on such things. I am not in the habit of bowing to such street-pressure, but I have finally come round to doing something, if only to placate the river of rose-petal bearing infants’ tears flowing past my abode.
13 Fri Nov 2015
11 Wed Nov 2015
Posted in Big Data, Data Lake, data science, Martyn Jones, Martyn Richard Jones
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Normally I would send such people to see a specialist – no, not a guru, but a sort of health specialist, but because this has happened to me so many times now, I eventually decided to put pen to paper, push the envelope, open up the kimono, and to record my advice for posterity and the great grandchildren.
So, here are my top seven tips for cashing in quick on the new big thing on the block.

1 – A business opportunity for faith
Like every new religion, trend or fad, Big Data has its own founding myths, theology and liturgy, and there is money to be made in it; loadsa lovely jubbly money. By predicating and evangelising Big Data you will be welcomed with open arms into the Big Data faith, and will receive all the attendant benefits that will miraculously and mysteriously fall upon you and your devout friends. Go on, I dare you. Be a Big Data guru, a shepherd to a flock of sheep, and enjoy the wealth, health and happiness that most surely will come your way. You too can look cool in red Prada slippers, a flattering and flowing gown and matching accessories.

2 – Acquire it, multiply it, weigh it, mark it up and sell it on
Simply stated, this is about acquiring other people’s data, by sacred means or profane, marking it up and then selling it on. The value you add is that you act as a trusted conduit, a conduit for good. You may care to enrich the data, swop the order of data, replicate and embellish data, make stuff up, etc. which all serves to ‘add value’ to the data. You may even consider adding nuggets of value to the data, just for kicks and giggles. My best friend’s favourite is injecting the good old ‘diaper and beer’ and ‘friends and family’ clichés into every Big Data collection, as it never fails to thrill, please and delight.

3 – Anything can be anything
The good thing about making money from Big Data is that it doesn’t need to be anything to do with Big Data. Make a 20GB Enterprise Data Warehouse? Call it a Big Data success. Sell 20 boxes of dodgy doughnuts down the alternative market? Proclaim a Big Data triumph. Sell your digital porn stash to your best mate? Point to the incredible invisible hand of the Big Data market at work. See what I’m doing there. Anything can be anything, and you too can cash in on that opportunity, big time.

4 – Big Data Patronage
Tense, nervous headaches? Do you like making up stories about Big Data, or for that matter anything else? Are you a natural born fibber but are strapped for cash? Then worry no longer. If you get a Big Data patron you will be sorted for ‘life’; get two and you’ll be sorted for the afterlife as well. With a Big Data patron you can get the most tenuous, crappiest and superficial of pieces published, promoted and vaunted – globally. Can’t make it up yourself, then outsource and offshore it, after all, just get the keywords right for SEO ranking and the gullible will flock to you in droves. The down side of this profession is that you will be targeted for writing half-truths, quarter-truths and downright lies, and you will be pilloried as a purveyor of rank hyperbole. But don’t worry, take heart and never lose the faith, you will be in good company. As one Big Data guru was want to say ” If you repeat a lie often enough, people will believe it, and you will even come to believe it yourself.” Amen! brother.

5 – Big Data Certification
By 2016 there will be global demand for 30 billion Big Data professionals. Are you prepared to cash in on that inevitability? No? Then consider this.
One of my best friends makes his living as a completely phony Big Data Scientist. For two hundred bucks he can make you a Data Scientist or a Big Data guru. Some guys give you an education but this guy gives you immediate access to high paying jobs, sex and a life in the city. Moreover, for an extra 250 bucks you can also become a certified Big Data Trainer, which will allow you to do unto others what has been done unto you.

6 – Creative Technology Reuse
Big Data has heralded in the biggest innovations known in the history of computing, and arguably in the entire history of humankind. One of those new inventions has been the now widely acclaimed and revolutionary ‘flat file data base’ (FFDB), and this has been accompanied with developments in low level operating system primitives that allow for the processing of these collections and hierarchies of FFDBs. So, if one has a mind to do so, one can get some real business leverage off of these new tendencies by borrowing 21st century technology found in old operating system hacks from the sixties and seventies and eighties and nineties and… Well, the point is that in order to get serious funding it is no longer good enough to have a half page business plan, it is also necessary to eke out ‘stuff’ that works within the new paradigms of Big Data and Big Data Analytics. For my next venture I will be looking for serious funding for my ‘Arbitrary Dawdle Down Data Street’ (AD3S) Big Data Analytics platform, a platform designed to support virtual 1k bit processing and the massively parallel provision of global regular expression search and match (S&M), concatenation and listing, and cooperative data-driven and streamed data extraction and reporting. I’m hoping to attract the attention of governments, the EU, the Manic Street Preachers, the UN, China, Vladimir Putin, the DOD, HP, Oracle, Gartner, Lana Del Rey, Deloitte and IBM. So, this is going to be absolutely massive. Word!

7 – Big Data Brokerage
According to leading management consultants and industry watchers Gartner, McKinsey and Deloitte, data needs to be managed and accounted like any other asset, such as money. To get into a similar view-point requires a massive leap of faith, but it is a conversion that might drive dividends. One avenue to be explored in eking out value from the apparently massively valuable Big Data lakes, silos and pools is through the operation of a Big Data Brokerage. A Big Data Brokerage is a business whose main responsibility is to be an intermediary that puts Big Data buyers and Big Data sellers together in order to facilitate a transaction. Big Data Brokerage companies are compensated via commission after the Big Data transaction has been successfully completed. They may also charge introductory fees. Just imagine the wealth of business opportunities in that. You could become the Goldman Sachs of data.
I hope you enjoyed this piece and would be pleased to hear your views on this and other subjects.
Whilst I understand the attraction and even the need of creating a new and significant growth industry, I would also advise a degree of restraint, and whilst I see that “Big Data” (the consideration of the potential value of All Data) has its allure, I also think that some good sense and informed caution should also prevail.
Thank you so much for reading.
Martyn Richard Jones
Spain, 2015
11 Wed Nov 2015
Posted in All Data, Big Data, Cloud, Data Lake, data science, Inform, educate and entertain., IoT, Martyn Jones
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To the experienced observer, Big Data propaganda may well appear to be a disorganised surfeit of half-truths, sleights of hand and boloney. Indeed, the once famously alliterative characterisation of Big Data as defined by volumes, variety and velocity, seems now more appropriately applied to the quantity, invariability and quality of the incessant self-aggrandising hype, hokum and Hadoop being astro-turfed by every dog and his guru. Indeed, the very fact that such an inevitable mega-trend needs so much hype, disingenuousness and spin to support its passage to universal applicability, is a massive contradiction, a disservice to professionals, and an artless deception worthy of our criticism and condemnation. Continue reading
11 Wed Nov 2015
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In a city centre office block, somewhere in Scotland, the conversation between the IT Business Manager (Bill) and the Information Management Manager (Richie) is in full swing,, Bob is irate because his successfully delivered data mart has been derided as unusable rubbish by the business people it was meant to serve.
Let’s join the conversation:
Bill: I hate this job. Every time we try and help the business all we get back are complaints. Complaints because it’s not what they want, complaints because it’s in the wrong format, complaints because of the cost, or the performance, or the availability. All we get are complaints, complaints and complaints.
Richie: Well, to be fair, Little Bill, this was one clearly avoidable situation. We didn’t have to build the data mart.
Bill: I know what you’re thinking, but you are wrong. We had to do something. Anything.
Richie: I don’t agree, Little Bill, we always had the option of doing nothing.
Bill: And why would we do nothing?
Richie: Because. as I said at the time, Little Bill, without demand you don’t create supply, and at this level and on this scale, if you want to create supply, you first encourage demand. But it’s still fundamentally about meeting demand.
Bill: But, things don’t work like that in this organisation.
Richie: I think you will find that in fact that approach works remarkably well, Little Bill, and in almost any type of organisation. The problem is one of perception, if it has never been tried before there is no internal reference to whether it works or not, and of course repeating the mistakes of the past with absolute security, if better than doing something correct, but unproven in this setting.
Bill: No, I still think you fail to understand the nuances of this business.
Richie: You may well be right, Little Bill, but clearly if we really understood the even the nuances of the business, then we wouldn’t have wasted time on this effort, an effort that one of the business executive described as the expensive manifestation of an abject failure to understand the fundamentals of the business.
Bill: They said that?
Richie: Yes, they certainly did, Little Bill.
Bill: Well, if that’s the case then they clearly don’t know what they are talking about.
Richie: As may be the case, but that doesn’t help us either.
Bill: So, you with all of your ‘knowledge and experience’, what do you suggest?
Richie: I suggest that we take a proactive approach to encouraging demand.
Bill: Such as?
Richie: Well, I would revisit the recommendations that I made when I first joined this department.
Bill: Okay, just remind me of the key points.
Richie: We need part of IT to understand business process, and our business processes; in effect we need people who know the business of the business. These should be people who talk to the business in language the business understands, has a good grasp of a vast array of issues, and who can be confident in their everyday dealings with business.
Bill: But, the business always thinks it knows best, how will these people succeed where we have almost always failed in the past? They think we overly complicate things; they virtually try and tell us how to do our jobs.
Richie: That’s why we need people who can communicate with authority, persuasively and with ease, not from a basis of mistrust, lack of empathy and even disdain. We need people who can sell ideas, can frame discussions and articulate coherent and realisable proposals for business IT solutions using language the business grasps the first time. We need people who understand what is said, can lead discussion and can capture requirements in a way that IT can also understand.
Bill: But the refuse to talk to us.
Richie: Well, that’s perhaps rather unsurprising from people who seem to think they have articulated the same requirements to us, and repeatedly, over an extended period of time. The problem is that we have very rarely documented those requirements, and when it has happened it has not been in a way that business can understand and verify, they can’t take any of our requirements and actually understand them without resorting to a translator, so they don’t do it.
Bill: Okay, so apart from blaming IT, what do you suggest?
Richie: The first hurdle seems to be simple. We need to convince the business that we actually have something worth listening to, that we aren’t going back to waste their time, yet again.
Bill: And?
Richie: So, what I suggest is this. Part of my team will spend time on investigating existing and new technologies, methods and approaches and how these are applied in similar industries or even dissimilar settings, but with certain synergies. They will have a good grasp of the business but their focus will be on understanding technology and relating it to project opportunities within our business. They will then work with our Business Consultants to actually articulate, explain and sell the benefits of these ideas to the business.
Bill: This, as I have repeatedly told you, is what we do now.
Richie: I don’t think so, Little Bill. There is a marked difference between what we do now, with the “look what a marvellous data mart we have made for you, it has data and lots of menu options, and graphics and stuff” versus the “we would like you to that allows you to be able to identify tangible cross-selling opportunities between various lines of business and with a high degree of certainty, this driving increased revenue, and increased customer intensity, and therefore loyalty… and repeat business”
Bill: So, we go begging the business for projects, with silver-tongued rhetoric.
Richie: No, Little Bill, we give the business what it wants. They are our customers, and as any business person should know, giving the customers what they want is a sure fire route to success.
Bill: Yes, but it would never work here. We are a very conservative company.
Richie: If the rest of the organisation was that conservative, we wouldn’t even be in business.
Bill: So what happens if the business says yes?
Richie: The business consultants and the research consultants work with the architecture consultants in socialising the business requirements and in developing solutions architecture (or a domain architecture), and as part of this they will also interact with the enterprise architecture consultant. So at some point, we will have a Business Requirements document, an IT requirements document, and an IT / Business Process Architecture document, and a Project Proposal document. Then the Business Requirements document – including detailed financials, together with the Business Project Proposal are socialised with the business, and submitted to them for review and approval. We then negotiate.
Bill: You make it sound easy.
Richie: You have to know what you’re doing, and there is logic to it all, but it’s far more rewarding than working on projects that invariably fail to satisfy.
Bill: So, when do we get started on all of this…?
Richie: As soon as you want, Little Bill.
So as we leave Bill and Richie to hammer out the details of the new approach, what can we take-away from this piece of business voyeurism?
I sincerely believe that the hardest job of any data warehousing professional, at least one worthy of the name, is in convincing sometimes even senior IT management of the need for doing the right thing right, of the possibility of doing the right thing right, and of the dangers of confusing ignorance and wishful-thinking with pragmatism.
So, make sure you get an expert, that your expert really is a professional, is absolutely ethical and that they really know their stuff, and when they don’t know, will not try and pretend that they do know, then trust in their professionalism, judgement and expertise, even if you then verify what you are told – and please, don’t verify this knowledge with a charlatan, spend more money and get the verification of a trusted and proven expert. Do this, and in this way and you won’t go far wrong.
Many thanks for reading.
Oh, and one last thng…
According to SAP “Big Data is the ocean of information we swim in every day”. I disagree; Big Data hype is the ocean of crap that we have to navigate through every second of every day.
Moreover, SAP contribute to that shite.
In a big way.
So, not only do we have Software aus Polen, we have Big Data aus Polen. With apologies to the great Polish IT professionals I know and respect.
Champion!
SAP! Mend your ways.
The choice is yours.
Many thanks for reading.
11 Wed Nov 2015
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“In fact men will fight for a superstition quite as quickly as for a living truth – often more so, since a superstition is so intangible you cannot get at it to refute it, but truth is a point of view, and so is changeable.”
Hypatia
On the 1st of July, I decided to set up a professional group on LinkedIn in order to create a hype free Agora for Big Data dialogue. I called the group The Big Data Contrarians and although it is a closed group, all those with interest in an open, informed and honest exchange of ideas on data, from whatever angle they are coming from, are very welcome to join in. (URL: http://www.linkedin.com/grp/home?gid=8338976)
So, why is the group called The Big Data Contrarians and not something more generic, such as The Data Contrarians?
Continue reading