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Category Archives: Inform, educate and entertain.

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Big Data: And the Hype Played On

10 Thu Dec 2015

Posted by Martyn Jones in Ask Martyn, Big Data, Big Data Analytics, business strategy, Good Strat, Good Strategy, goodstrat, Inform, educate and entertain., IT strategy, Marty does, Martyn does, Martyn Jones, Martyn Richard Jones, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians, Uncategorized

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MARTYN RICHARD JONES

Despite the best efforts of Hadoop evangelists, consulting houses, and IT infrastructure and service vendors, Big Data – hailed as the greatest thing since the dawn of greatest things – is failing, and dramatically so, to produce the necessarily corresponding quantity and quality of tangible, detailed and verifiable success stories.
Continue reading →

The banality of Big Data hype

05 Sat Dec 2015

Posted by Martyn Jones in Big Data, Good Strat, Good Strategy, Inform, educate and entertain., Martyn Jones, Martyn Richard Jones, Rant, Uncategorized

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Good Strategy, Martyn Jones, Martyn Jones Data, Rant


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.

Big Data: 10 truths, 10 myths and 10 unwise things

17 Tue Nov 2015

Posted by Martyn Jones in Ask Martyn, Big Data, Data Warehousing, Inform, educate and entertain., Information Management, Uncategorized

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Big Data, enterprise data warehousing, Martyn Jones, Strategy


StartSMILESMany 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.

Big Data is for everyone

Continue reading →

Big Data, analytics and 4th generation data warehousing by Martyn Jones at Big Data Spain 2015

13 Fri Nov 2015

Posted by Martyn Jones in All Data, Analytics, Big Data, Data Supply Framework, Good Strat, Inform, educate and entertain., informatio Supply Framework, Martyn Jones

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4th generation data warehousing, Analytics, Big Data


STRAIGHT TALK: Leadership 7s

11 Wed Nov 2015

Posted by Martyn Jones in Good Strategy, goodstrat, Inform, educate and entertain., leadership, Martyn Jones

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goodstrat, leadership, Martyn Jones


AD: — JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976

To begin at the beginning

Here are the second seven talking points in this series that deal with aspects of leadership, coaching and management. Enjoy! Continue reading →

The Big Data Shell Game

11 Wed Nov 2015

Posted by Martyn Jones in All Data, Big Data, Cloud, Data Lake, data science, Inform, educate and entertain., IoT, Martyn Jones

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Big Data, data science, Martyn Jones


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 Wed Nov 2015

Posted by Martyn Jones in Big Data, data science, Data Supply Framework, Inform, educate and entertain., Information Supply Framework, Martyn Jones, statistics

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Big Data, Martyn Jones


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 Wed Nov 2015

Posted by Martyn Jones in All Data, Analytics, Big Data, Inform, educate and entertain., statistics, The Big Data Contrarians

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All Data, Analytics, Big Data, Martyn Jones


“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 Wed Nov 2015

Posted by Martyn Jones in All Data, Big Data, Consider this, Inform, educate and entertain., Martyn Jones, Martyn Richard Jones

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Big Data, goodstrat, Martyn Jones


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 Wed Nov 2015

Posted by Martyn Jones in All Data, Big Data, data science, Inform, educate and entertain., Martyn Jones

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Big Data, goodstrat, Martyn Jones


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.

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