
Why buy when you can get it for free?
Back at you! Here is the fourth fantastic delivery of an amazing, fabulous selection of free, widely available business analytics learning content, prepared… just for you. Continue reading
09 Wed Mar 2016
Posted in All Data, Analytics, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, business strategy, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Good Strat, Good Strategy, goodstrat, Inform, educate and entertain., IT strategy, Marty does, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, statistics, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians

Why buy when you can get it for free?
Back at you! Here is the fourth fantastic delivery of an amazing, fabulous selection of free, widely available business analytics learning content, prepared… just for you. Continue reading
08 Tue Mar 2016
Posted in All Data, Analytics, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, business strategy, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Good Strategy, Inform, educate and entertain., IT strategy, Marty does, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, Process, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians
Why buy when you can get it for free?
Back at you! Here is the third fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you. Continue reading
07 Mon Mar 2016
Posted in All Data, Analytics, Big Data, Big Data 7s, Big Data Analytics, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Inform, educate and entertain., pig data, The Amazing Big Data Challenge, The Big Data Contrarians
Why buy when you can get it for free?
Back at you! Here is the second fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you. Continue reading
05 Sat Mar 2016
Posted in All Data, Analytics, Big Data, Big Data 7s, Big Data Analytics, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Inform, educate and entertain., pig data, statistics, The Amazing Big Data Challenge, The Big Data Contrarians
Why buy when you can get it for free?
Here is the first fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you. Continue reading
05 Sat Mar 2016
Posted in All Data, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, business strategy, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Good Strat, Good Strategy, goodstrat, Inform, educate and entertain., IT strategy, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians
Martyn Richard Jones
Dusseldorf, August 2006
Data Warehousing provides possibly one of the best opportunities for IT organizations to deliver a valuable business solution in order to address a set of business needs; requirements that go well beyond the area of day to day operational support, and traditional applications (web enabled or not), and when Data Warehousing is done the right way, and for the right reasons, its payback to all of its stakeholders can be positively significant. Continue reading
05 Sat Mar 2016
Posted in All Data, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, hadoop, Inform, educate and entertain., Marty does, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, The Amazing Big Data Challenge, The Big Data Contrarians
This is the story of how the amazing Hadoop ecosphere revolutionised IT. If you enjoy it, then consider joining The Big Data Contrarians.
Before the advent of Hadoop and its ecosphere, IT was a desperate wasteland of failed opportunities, archaic technology and broken promises.
In the dark Cambrian days of bits, mercury delay lines and ferrite cores, we knew nothing about digital. The age of big iron did little to change matters, and vendors made enormous profits selling systems that nobody could use and even fewer people could understand. Continue reading
23 Tue Feb 2016

In the beginning was the Big Iron, the Big Data, and the Big Data Plan.
And then came the Big Data Assumptions.
And the Big Data Assumptions were without form.
And the Big Data Plan was without substance.
And the Big Iron was without movement.
And the Big Data was without velocity, variety and volume.
And darkness was upon the face of the data workers.
And they spoke amongst themselves, saying: “Big Data, is a crock of shit, and it stinketh mucho”.
And the data workers went unto their Data Supervisors and said: “This here Big Data is a pile of putrid crappy keech”, for they were from Govan, and continued, “and none may abide the odour thereof”.
And the Data Supervisors went unto their Information Managers, saying: “Big Data is a container of excrement, and it is very strong, such that none may abide by it.”
And the Information Managers went unto their Business Directors, saying: “This here Big Data doodoo is a vessel of fertilizer, and none may abide its strength.”
And the Business Directors spoke amongst themselves, saying to one another: “Big Data contains that which aids plant growth, and it is very powerful.”
And the Vice Presidents went unto the President, saying unto him: “This new Big Data will actively promote the growth and vigour of the company, with powerful effects.”
And the President looked upon the Big Iron, the Big Data and the Big Data Plan, and saw that they were good.
Many thanks for reading
Join The Big Data Contrarians
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
Tags
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.