Tag Archives: Big Data

Big Data, a promised land where the Big Bucks grow

11 Wednesday Nov 2015

Posted by in Big Data, Data Lake, data science, Martyn Jones, Martyn Richard Jones

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Consider this: Many people come up to me in the street, and, apropos of nothing, they ask me how they can make money from Big Data.

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.

That’s it folks!

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

The Big Data Shell Game

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Big Data, Cloud, Data Lake, data science, 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 →

I lied about Big Data! Have an issue?

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, Big Data, data science, Data Supply Framework, Information Supply Framework, Martyn Jones, statistics

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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. 

The Big Data Contrarians: The Agora for Big Data dialogue

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Analytics, Big Data, statistics, The Big Data Contrarians

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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 →

10 amazing reasons to join The Big Data Contrarians

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Big Data, Consider this, Martyn Jones, Martyn Richard Jones

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You love data. You eat, breathe and sleep data! You source it, clean it, integrate and then analyse it until it confesses. You represent, invent and present results. Data is your life and Big Data is your prophet. The Big Data Big Top is the place to be, and (passively) that is where you are headed. For you, Big Data drives everything we do! Is that the case?

Yes?

No worries, in spite of all of that, you too can also be a useful member of The Big Data Contrarians.

Continue reading →

Not banking on Big Data?

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Big Data, data science, Martyn Jones

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First, a request, please consider joining The Big Data Contrarians.

I have worked with clients across the entirety of financial industry for most of my career, and although this may surprise some people, I believe that I fully understand why they are being conservative about Big Data in general and Hadoop in particular. I can also understand why some people want to keep up or even ramp-up even more the Big Data market buzz, but with such a dearth of meaningful, well described and verifiable Big Data ‘success stories’, neither the banks nor I are going to be speculating in any big way on Big Data or Hadoop, anytime soon.

Based in Spain for almost three decades, I have been up close and intimate with a few of the biggest players in the Spanish financial industry. Indeed, Spanish banks have not only lead the way in the effective, innovative and business driven use of technologies in the Spanish market, but have applied that financial industry nous around the world.

In recent times, the big financial players in Spain have entered into the Big Data fields and stratospheres. From what I know, which may not be all, or so much, they are still watching and investigating rather than putting tangible things in production. Nevertheless, there are some interesting Big Data application ideas floating around the financial world. These are still relatively early days for Big Data in finance, and it will take some time for the hype to fade away and the cream of financial Big Data to rise to the top.

However, it has happened before.

If there was ever a country that quietly, diligently and consciously implemented Data Warehousing and Business Intelligence, then it has been Spain. Spanish companies were not only early adopters but also early beneficiaries of implementing Data Warehousing. Not for nothing did Bill Inmon’s company Prism Solutions chose Madrid as a major hub for its European Data Warehousing consulting, sales and support activities. Bill being the father of Data Warehousing and Prism being one of his commercial babies.

As an aside, at Prism I had the opportunity of working alongside fantastic professionals and great people with knowledge, values and experience, such as Don and Katherine. That great gig, I will never forget.

Which brings me to this.

I knew what Data Warehousing would be good for, and amplified this knowledge through reasonable, rational and coherent ways of addressing a wide range of requirements. My aim was to support my claims with coherent, simple and verifiable examples of Data Warehousing success stories.

I knew how to explain Inmon’s Data Warehousing, in business, management and technical terms. I saw when a company could benefit from DW and also when a company was not ready for DW. However, try as I might, I cannot achieve the same intensity of understanding with Big Data. Believe me I have tried.

I’m not a contrarian just because, but isn’t it about time the Big Data BS babblers put up or shut up?

So, if you are like me, then join The Big Data Contrarians.

Many thanks for reading.

Who’s afraid of the Big Data Contrarians? Here’s 500 reasons not to be

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Big Data, Business Intelligence, Cambriano, Consider this, Good Strategy, Strategy

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If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks.

When I first started The Big Data Contrarians group on LinkedIn I was thinking that maybe we would get 100 members within three or four months. Well, I was mistaken. Since the 1st of July, the membership ranks of The Big Data Contrarians has risen to over 500 members. However, it’s not about the quantity it’s about the quality, and The Big Data Contrarians is ‘the nicest Big Data community that you are ever likely to encoun Continue reading →

Big Data is Bullshit

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Analytics, Big Data, Data Lake, Martyn Jones

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“I’ve been accused of vulgarity. I say that’s bullshit.” – Mel Brooks

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Being brutally honest, Big Data is bullshit. Not only is Big Data bullshit but it comes with a surfeit of pranksters, pundits and prissy big data bullshit-babblers, all willing (cue narration by Morgan Freeman) to “big-up Big Data in a vulgar, ill-mannered and predictably nauseating dance of professional-hustling… as old as time.”

However, is all Big Data bullshit? Is it all a fad, a load of old tripe and a confusion of weasels together with their surfeit of weasel words? Or, is there something of value, substance and tangibility to be found amongst the volumes, velocities and varieties of brazen and opportunistic self-aggrandizement, toxic speculation and opinions and unverifiable miracles?

For Google, Facebook and Twitter, Big Data certainly isn’t BS. For example, Google rely on Big Data as the biggest irreplaceable element in their colossal advertising business – so I am lead to believe. A business that accounts for more than 90% of Google’s revenue. So clearly, for the masters of web-based unstructured and complex search, Big Data is an essential element in their business model. The biggest essential element by far.

However, let´s be honest, we should consider the obvious. How many of us are really going to do business like Google?

Big Data technology and service vendors benefit tangibly from the Big Data movement, at least this is the impression that I get. Indeed, there is much talk about the relationship between Big Data, the Hadoop ecosphere and the big wild world of open source, but what is more interesting is that companies are bringing in revenue on the back of Big Data by offering battle-hardened business and enterprise versions of open source software. Then there is the business in consulting, with such a demand for Big Data gurus, Master Data Scientists and Number Conjurors, there must presumably be real people working in these roles, and paid handsomely for doing so.

But apart from the ‘success’ associated with the foundation of so many Big Data start-up businesses and the market-based commitment of some of ITs’ ‘great and good’ to the new digital zeitgeist of data volumes, velocities and varieties, just where are the other success stories of Big Data coming from?

To help us in our quest, I earlier compiled a not-so-exhaustive list of Big Data success stories, celebrity-chef like, to help us out. Here are some of the Big Data gems that I managed to track down:

  • Thanks to Big Data, the taxi service alternative channel Uber is making massive waves and shaking things up in the transport sector.
  • By leveraging Big Data AirBnB is turning the hospitality business on its head, and what´s more, making friends, and influencing people as they are doing so.
  • Amazon would not be what they are today if it were not for Big Data, in fact, without Big Data, they would be nothing.
  • One of the industries that will suffer revolutionary transformation because of Big Data will be the banking industry.
  • Big Data will increase the GDP of the USA by at least 1% or more, and the Spanish GDP could likewise add an additional 1%, for similar reasons.

These would be all great headlines for Big Data success stories, apart from one small flaw. None of them is exactly a Big Data success story in the Big Data defining characteristics of volumes, varieties and velocities of mainly unstructured data or in terms of the Hadoop technological kitchen-drawer ecosphere.

Something is happening, and it is not exactly legitimate. Can you guess what it is yet?

When it rains it pours, and when it rains Big Data hype it quickly turn into a monsoon of cloying hysteria. Spotting and pointing at Big Data bullshit babblers on forums like LinkedIn Pulse, Forbes and Information Management is no fun, unless your fun is nuking a school of intellectually challenged fish floundering in a barrel of vintage Malmsey.

However, it not only is no fun, but also more times than not it is a complete and utter waste of time trying to get people to adopt a more critical approach to thinking. Because for every Big Data bullshit babbler, there is a battalion of intransigent Big Data believers stuck in untenable and absurd positions, marooned from reason and ways back to rationality. You can’t use logic against belief, and you can´t turn back a rising tide of IT refugees who are desperately seeking succour in the apparently safer-havens of Big Data, Data Science and Data-driven voodoo.

Only the other day I read that “The emergence of Big Data is now allowing CEOs to increasingly base decisions on current “reality” rather than past experience, but the risks in the integrity and fullness of the data that they are “seeing” and “hearing” is often a barrier to getting a clear picture of what is actually going on.” This is really taking shameless baloney and wilful ignorance to all new heights, but it doesn’t stop there.

Elsewhere another eminent Big Data bullshit babbler wrote, “Clearly big data and AI will change almost every industry this decade… but none more than these”, referring vaguely and vacuously to “Healthcare, Finance and Insurance”.  What species of shameless and fatuous willy-waving goes so far out on a limb that it becomes massively removed from even being a grandiose and beguiling ‘bigging-up’ of a fad?

Finally yet importantly, I almost choked on my supersized Big Data popcorn the other day when I read, “Today, with the rise of the Internet, we capture “data” on everything.  Therefore, the new term “Big Data” is honestly like 1985 again.  But this time, Big Data will actually be really big and by some forecasts, be a $40 billion industry by 2018.”

This is not hype, it is not even simple deceit, it is astroturfing of 22 carat bullshit, and in most cases it’s clearly deliberate, it´s intentional and it´s grossly misleading. So why do people do it?

Given that Big Data is very much a niche technology, with very much a niche appeal, why do so many buffoons go around pretending that Big Data is for all of us? Like as if it was some sort of digital universal-panacea, when at the moment, and at best, it is a walk on bit-player with just a couple of lies who aspires to B actor status. In this sense, at present Big Data isn´t even the hero´s best friend.

Before I close the piece, I will leave you with the thoughts of Dan Ariely. Why? Because it just irritates the hell out of a section of the community of Big Data bullshit babblers, and it´s actually very accurate. Here it is:

“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…”

Many thanks for reading.

In subsequent blog pieces I will be sharing my views on the evolution of information management in general, and the incorporation novel and innovative techniques, technologies and methods into well architected mainstream information supply frameworks, for primarily strategic and tactical objectives.

As always, please reach out and share your questions, views and criticisms on this piece using the comment box below. I frequently write about strategy, organisational, leadership and information technology topics, trends and tendencies. You are more than welcome to keep up with my posts by clicking the ‘Follow’ link and perhaps you will even consider sending me a LinkedIn invite if you feel our data interests coincide. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.

For more on this and other topics, check out some of my other posts:

Looking for your most valuable data? Follow the money – http://www.itworld.com/article/2982352/big-data/looking-for-your-most-valuable-data-follow-the-money.html

Stuff a great data architect should know –https://goodstrat.com/2015/08/16/stuff-a-great-data-architect-should-know-how-to-be-a-professional-expert

Big Data, the promised land where ‘smart’ is the new doh!–https://www.linkedin.com/pulse/big-data-promised-land-where-smart-new-doh-martyn-jones?trk=prof-post

Absolutely Fabulous Big Data Roles –https://www.linkedin.com/pulse/absolutely-fabulous-big-data-roles-martyn-jones?trk=prof-post

Not banking on Big Data? – https://www.linkedin.com/pulse/banking-big-data-martyn-jones?trk=prof-post

10 amazing reasons to join The Big Data Contrarians –https://www.linkedin.com/pulse/10-amazing-reasons-join-big-data-contrarians-martyn-jones?trk=prof-post

Amazing Data Warehousing with Hadoop and Big Data–https://www.linkedin.com/pulse/cloudera-kimball-dw-building-disinformation-factory-martyn-jones?trk=prof-post

The Big Data Contrarians: The Agora for Big Data dialogue–https://www.linkedin.com/pulse/big-data-contrarians-agora-dialogue-martyn-jones?trk=mp-reader-card

The Big Data Shell Game – https://www.linkedin.com/pulse/big-data-shell-game-martyn-jones?trk=mp-reader-card

Aligning Data Warehousing and Big Data –https://www.linkedin.com/pulse/aligning-data-warehousing-big-martyn-jones?trk=mp-reader-card

Big Data Luddites – https://www.linkedin.com/pulse/big-data-luddites-martyn-jones?trk=mp-reader-card

Data Warehousing Explained to Big Data Friends –https://www.linkedin.com/pulse/data-warehousing-explained-big-friends-martyn-jones?trk=mp-reader-card

Big Data, a promised land where the Big Bucks grow –https://www.linkedin.com/pulse/big-data-promised-land-where-bucks-grow-martyn-jones-6023459994031177728?trk=mp-reader-card

The Big Data Contrarians – https://www.linkedin.com/pulse/big-data-contrarians-martyn-jones?trk=mp-reader-card

Is big data really for you? Things to consider before diving in–https://www.linkedin.com/pulse/big-data-really-you-things-consider-before-diving-martyn-jones?trk=mp-reader-card

Big Data Explained to My Grandchildren –https://www.linkedin.com/pulse/big-data-explained-my-grandchildren-martyn-jones?trk=mp-reader-card

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

The Million-Dollar Big Data Briefing

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, agile, Big Data, Consider this, Data Lake, Martyn Jones, Martyn Richard Jones

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If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

Big Data, together with Cloud computing, the Internet of Things and Machine Learning, are topics that are very much to the fore in contemporary trends in Information Management. But is Big Data really the revolution that people have been waiting for or is it simply about the next steps in the evolution of business data architecture and management?  Continue reading →

Big Data and Catfish

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, Big Data, Business Intelligence, goodstrat, Martyn Jones, Martyn Richard Jones

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Many thanks, Martyn.

“Contrary to slanderous Eastern opinion, much of Iowa is not flat, but rolling hills country with a lot of timber, a handsome and imaginative landscape, crowded with constant small changes of scene and full of little creeks winding with pools where shiners, crappies and catfish hover.”

Paul Engle

Catfish are said to be named because of their passing resemblance to land-roving felines. Admittedly, it’s not like any cat I’ve seen around the house, but if you simultaneously squint your eyes – impressionist style, guzzle a quart of bourbon and smoke a stash of ganja then maybe the resemblance becomes more obvious.

Catfish come in all sizes and varieties, at times they are native and other times they are classed as an alien species, rather like this Welshman who finds himself living in the Spain of Evo Morales, Kirchner and King Mohammed. Nonetheless, you won’t find many thrilling and delightful catfish videos on YouTube nor will you see many entered for the best of breed category at the International Cat Show.

So, what have catfish got to do with Big Data?

Well, there’s loads of them, they come in many varieties, and when they aren’t eating, they can be quite swift. But that’s not what I really wanted to discuss.

Now imagine this. Given the immense geographic dispersion, varieties and volumes of catfish around the world, wouldn’t it be interesting to carry out the Ma and Pa of all Big Data experiments?

We capture – over time of course, this is not the work of one day – all the catfish in the world, and we not only electronically tag them but we also fit them with IoT (Internet of Things) devices that will tell us:

  • Where the catfish is
  • Who the catfish is with
  • What are they doing
  • What are they eating
  • How do they feel in general
  • How do they feel about certain things, like the food they just ate, the company they keep, and what they do for entertainment and distraction, etc.

We could then collect this data, in centers all around the world, and then bring it all together in a massive Catfish Big Data Processing Centre in, for example, Coney Island.

Then the data we have so carefully collected, multiplied twice, and then searched and word-counted, in parallel, can be put to revolutionary, evolutionary and amazing uses such as:

  • Analysing and forecasting the Amazon buying trends of the lost Fukawi tribe – yes, the very same tribe who used to wander around boasting about their culture and presence usually accompanied with cries such as “We’re the Fukawi” or “Where the Fukawi?”
  • Creating appealing, compelling and revenue-busting online interactive ads for Bob Hoffman
  • Predicting the outcome of the US Presidential election, the regional elections in Catalonia and the vote for Chairperson at the Hello Working Person’s Club, Hello Village, in Jolly Olde England.
  • Preventing the outbreak of a world-wide pandemic of universal proportions thanks to Big Data being used to intervene virus-bearing inter-terrestrial vehicles sent by radical-fundamentalist-Martians inhabiting the once munificent planet of Zog.
  • Providing a wealth of material success stories that can be liberally sprinkled like fairy-dust on amazing Big Data stories from the keyboards of some of the finest Big Data bullshit babbling princesses on the entire world wide webs.

Over time, the competence, repertoire and agility of Catfish of all varieties, species, volumes and velocities (did anyone mention Catfish voracity and veracity?) could be augmented, potentiated and expanded by invasive, elliptical and sublime manipulation and neuro-retraining. We could then start with in-aqua interactive stimulus, menu variation and programming and extra-sensory passivation. Later the experiments could be more complex and more all-inclusive, reaching greater and greater degrees of perfection and inclusivity and exclusivity as the Catfish Big Data bandwagon rolled on… Waterlogged, waylaid and none the wiser. Indeed, in the future, all individual decisions will also rely on Catfish input, insight and turbo-charged predictive analytics of great and lasting charm.

Diet manipulation, an habituation test, and chemical analysis of urinary free amino acids were used to demonstrate that bullhead catfish (Ictalurus nebulosus) naturally detect the body odors of conspecifics and respond to them in a predictable fashion. These signals are used in dominance and territorial relationships and lead to increased aggression toward chemical “strangers.” The results support the general notion that nonspecific metabolites, as well as specific pheromones, are important in chemical mediation of social behavior.

There is also one very important thing about catfish that not many people know – apart from Michael Caine, who of course is a leading authority on catfish – and not many people know that either. But, anyway… Catfish are also bottom feeders, this is because of some complex physiological configuration that I won’t go into here – for fear of hurting the sensibilities of the puerilely prudish and wasting valuable drinking time – so in terms of data, the Catfish are able to plumb the depths of the most obtuse, dark and murky data, gobble it up, transform it and… err… load it into Hadoop, to be analyzed with Spark and presented in Excel… or something like that.

So, you’re not convinced by this story? Okay, I didn’t want to tell you this, but here it goes…

Many of us worry about leveraging all data, and mainly we worry because we don’t really have a clue about what we are bullshitting about. We see Big Data, and we believe that is good, whether we know this to be true or not. We are grasping at straws like so many bottom feeders, so many feces-eating walking-catfish, motivated by ideas of maximizing the sale of useless and outdated crap to ignorant people who don’t need it and can’t derive any tangible benefit from it in the first place. This is the biggest takeaway from this current schizophrenic Big Data BS Kulturkampf. Beyond a limited set of interest stories and an even more limited set of peripheral benefits accruable in very specific circumstances, there is nothing tangible that really grabs the attention, apart from the razzle-dazzle, smoke and mirrors of vacuous cant dressed up as showmanship.

The biggest problem with Big Data isn’t so much the plethora of technology (which is more and more reminding me of box of half-eaten chocolates,) nor even the niche applications – for as miraculous and mysterious as most of them are. It’s more about Big Data being turned into a seriously creepy religion, where belief is paramount, and where there is little or no questioning of the tenets, the fables, the dogma and the liturgy, and where one person’s willful ignorance is just as valid as another person’s aspiration to gain knowledge and experience.

Make no mistake, Big Data can be useful for certain businesses and for certain situations. But for many of us in practice it’s either a peripheral player or doesn’t even make it to the bench.

A final thought. Treating Big Data as a religion is foolish, unhelpful and ultimately doomed to failure and ignominy. You have been warned!

For what it’s worth, I am currently writing the Ma and Pa of all Big Data parallel-analytics languages (details to follow), and I might even call it catfish (it’s sorta catchy) and I will have it represented by a muddy-looking open-source cartoon catfish, one worthy of a spot on YouTube.

Many thanks for reading.

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.