Tag Archives: Good Strategy

Contradictions of Big Data

01 Sunday Mar 2015

Posted by in Ask Martyn, Big Data, Consider this

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What we’ve been told

We’ve been told that Big Data is the greatest thing since sliced bread, and that its major characteristics are massive volumes (so great are they that mainstream relational products and technologies such as Oracle, DB2 and Teradata just can’t hack it), high variety (not only structured data, but also the whole range of digital data), and high velocity (the speed at which data is generated and transmitted). Also, from time to time, much to the chagrin of some Big Data disciples, a whole slew of new identifying Vs are produced, touted and then dismissed (check out my LinkedIn Pulse article on Big Data and the Vs).

So, beware. Things in Big Data may not be as they may seem.

It’s not about big

I have been waging an uphill battle against the nonsensical and unsubstantiated idea that more data is better data, but now this view is getting some additional support, and from some surprising corners.

In a recent blog piece on IBM’s Big Data and Analytics Hub (Big data: Think Smarter, not bigger), Bernard Marr wrote that “the truth is, it isn’t how big your data is, it’s what you do with it that matters!”

Elsewhere, SAS echoed similar sentiments on their web site: “The real issue is not that you are acquiring large amounts of data. It’s what you do with the data that counts.”

Can we call that ‘strike one’ for Big Data Vs?

It’s not about variety

It is claimed that 20% of digital data is structured, it is based on the problematic suggestion that structured data is uniquely relational. It is also claimed that unstructured data includes CSV files and XML data, and this makes up far more than the 20% of the data generated. But this definition is simply wrong.

If anything, CSV data is structured, and XML data is highly structured, and it’s typically regular ASCII data. So it does not add variety, even though it is not structured in the ways that some people might expect, especially if that someone lacks the required knowledge and experience. Simply stated, CSV data is structured, it’s just that it lacks rich metadata, but that doesn’t make it unstructured.

“But”, I hear you say “what about all the non-textual data such as multi-media, and what about the masses of unstructured textual data?”

Take it from me, most businesses will not be basing their business strategies on the analysis of a glut of selfies, home videos of cute kittens, or the complete works of William Shakespeare or Dan Brown. Almost all business analysis will continue to be carried out on structured data obtained primarily from internal operational systems and external structured data providers.

Strike two! Third time lucky?

It’s not even about velocity

So, if we accept that Big Data isn’t really about the data volumes or data variety that leaves us with velocity, right? Well no, because if it isn’t about record breaking VLDBor significant data variety, then for most commercial businesses the management of data velocity becomes either less of an issue or just is no issue. The fact that some software vendors and IT service suppliers set up this ‘straw man’ argument and then knock it down with the ‘amazing powers’ of their products and services, is quite another matter.

Strike three, and counting.

It’s not about the manageability of Big Data

We have been told and time again that the major difference between a data scientist and professional statistician is that the ‘scientists’ know how to cope very well with massive volumes, varieties and velocities of data. Now it turns out that this is also questionable.

According to Bob Violino writing in Information Management (Messy Big Data Overwhelms Data Scientists – 20 February 2015) “Data scientists see messy, disorganized data as a major hurdle preventing them from doing what they find most interesting in their jobs”. So, when it comes to data quality and structure the ‘scientists’ don’t really have an advantage over professional statisticians.

Last year Thomas C. Redman writing in the Harvard Business Review (Data’s Credibility Problem) noted that when Big Data is unreliable “managers quickly lose faith” and “and fall back on their intuition to make decisions, steer their companies, and implement strategy” and when this happens there is a propensity to reject potentially “important, counterintuitive implications that emerge from big data analyses.”

Strike four?

The new analytics aren’t new

Data science and Big Data analytics are the new kids on the block, aren’t they?

Well, here are some real life scenarios.

A major banking equipment supplier: A lot of banking equipment is hybrid analogic-digital, a simple example of this would be a photo copier or a physical document processing device. One major supplier decided to incorporate the capture of sensor data produced by their devices to predict failure and problems. Predictive preventive maintenance rules are created and corroborated using the data generated by sensors on each customer device, and these rules then get incorporated into the devices logic.

A major IT vendor: What happens when you create an intersection and convergence between technologies, techniques and method from areas of mainstream IT, data architecture and management, statistics (quantitative and qualitative analytics) and data visualisation, artificial intelligence/machine learning and knowledge management? This is precisely what one of the main European IT vendors did, and the idea proved to be quite attractive to customers, prospects and investors.

A major integrated circuit supplier: The testing of ICs at the ‘fabs’ (manufacturing plants) generates serious amount of data. This data is used to detect errors in the IC manufacturing process, it is captured and analysed in as near real-time as possible, which is necessary due to the costly nature of over-running the production of faulty ICs. To get around this problem the company uses a combination of fast data capture, transformation and loading of data into a data analytics area to ensure early and precise problem detection.

All Big Data Analytics success stories?

The first happened in 1989, the second in 1993 and the third in 2001. Yes, Big Data and Big Data analytics are sort of newish.

Strike five.

The science is frequently not very scientific

What is science?

According to Vasant Dhar of the Stern School of Business (Data Science and Prediction), Jeff Leek (The key word in “Data Science” is not Data, it is Science), and repeated on Wikipedia, “In general terms, data science is the extraction of knowledge from data”. Well, excuse me if I beg to differ. I have seen data scientists at work, and the word science doesn’t actually jump out and grab you. It’s difficult to make the connection, just as it is to accurately connect some popular science magazines with fundamental scientific research.

If a professional and qualified statistician wants to label themselves a data scientist then I have no issue with that, it’s their problem, but I am not willing to lend credibility to the term ‘data scientist’ when it is merely an interesting job title, with at most a tenuous connection to the actual role, and one that is liberally applied, with the almost customary largesse of IT, to creative code hackers and business-averse dabblers in data.

As Hazelcast VP Miko Matsumura suggested in Data Science is Dead “… put “Data Scientist” on your resume. It may get you additional calls from recruiters, and maybe even a spiffy new job, where you’ll be the King or Queen of a rotting whale-carcass of data” and ” Don’t be the data scientist tasked with the crime-scene cleanup of most companies’ “Big Data”—be the developer, programmer, or entrepreneur who can think, code, and create the future.”

Strike six.

And the value is questionable

DATA: “Data is a super-class of a modern representation of an arcane symbology.” – Anon

If I had a dollar for every time I heard someone claim that data has intrinsic positive value then I would be as wealthy as Warren Buffet.

If I have said it once, I have said it a hundred time. In order for data to be more than an operational necessity it requires context.

Providing valid data with valid context turns that data into information.

Data can be relevant and data can be irrelevant. That relevance or irrelevance of data may be permanent or temporary, continuous or episodic, qualitative or quantitative.

Some data is meaningless, and there are cases whereby nobody can remember why it was collected or what purpose it serves.

Taking all this into account we can ask the deadly pragmatic question: what value does this data have? Which is sometimes answered with a pertinent ‘no value whatsoever’.

Strike seven.

So what is it really about?

It is said that Big Data is changing the world, but for all intents and purposes, and shamed by previous Big Data excesses, some people are rapidly changing the definitions and parameters of Big Data, and to position it as being more tangible and down-to-earth, whilst moving it away from its position as an overhyped and dead-ended liability.

Big Data is a dopey term, applied necessarily ambiguously to a surfeit of tenuously connected vagaries, and its time has come and gone. So, let’s drop the Big Data moniker, and embrace the fact that data is data, and long live ‘All Data’, yes, all digital data. Let’s consider all data and for what it’s worth to the business, and not for what some chatterers reckon its value is – having as they do, little or no insight into the businesses to which they refer, or of the data in that these businesses possess.

So, when push comes to shove, is Big Data really about high volumes, high velocity and high variety, or is it in fact about much noise, too much pomposity and abundant similarity leading to unnecessary high anxiety?

Thanks very much for reading.

Big Data in Question – Again

01 Sunday Mar 2015

Posted by in All Data, Big Data, Consider this

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Big Data is now an inhospitable and unhealthy land inhabited by those who, through accident or design, deceive naïve and sentimental bystanders and those who are willingly mislead.

When all of this Big Data malarkey started it was sort of funny, humorous and occasional witty, especially in the affected, bizarre and the frequently uninhibited ways that freshly-minted self-appointed gurus and experts would “big it up”

Doctor Freud would have had a field day with all of that, being as it was, and for that matter still is, a postmodern mishmash of Riefenstahl, Freddy Mercury and Monty Python on steroids. However, after that extended, operatic and high-camp hiatus it all went downhill.

The Big Data scene is fast becoming an outrageous and brash festival of deception, disinformation and obliviousness. Which is a pity, because it does the industry no good whatsoever.

It is telling that Big Data evangelists, gurus and assorted sycophants cannot even define Big Data adequately, never mind discuss (or for that matter, point at) tangible success stories, without falling into contradictions on all of the key defining characteristics of volume, variety and velocity, and resorting to crude debating devices to avoid or finesse the concerns and the questions.

Almost every morning I check out the industry news, and almost invariably, it comes with new mind-boggling examples of Big Data nonsense.

However, it isn’t always nonsense for nonsense’s sake, there are agendas, there are rational explanations why Big Data has become at the same time, one of the most hyped up fads in the history of IT, and one that its supporters find so difficult to actually explain and justify, in any reasonable sort of way.

Therefore, when it comes to Big Data, beyond the surfeit of platitudes, clichés, bluff and bluster, the only thing in play are the interests of industry, the patrons, the courtesans and their entourage of the innocent and the beguiled.

One of the biggest deceptions in Big Data is in the misleadingly named ‘success stories’. The thing is that most of these success stories that I have ever read have been:

  • So vague that it’s difficult to know how success is being defined never mind reached.
  • So secretive and obtuse is the avoidance of naming names, locations and other relevant Big Data references that it’s impossible to corroborate if these claims are actually true or not. Disclaimer: I have worked for some of the biggest IT vendors, and in senior roles, and I know what is behind comments such as “the Big Data project is a success, although the client name and project are confidential” and “it’s delivering such major competitive advantages that we are obliged to keep it under wraps”.
  • Stories stolen from elsewhere, such as from Data Warehousing, Business Intelligence, VLDB or Business Application projects.
  • Borderline fantasies and badly contrived technology fan fiction.

However, it doesn’t stop there.

One of the clearest examples of the questionable nature of Big Data evangelism is when it is used to piggyback Big Data hype on simple, tangible and immediately recognisable artefacts or applications that have little in common with Big Data.

This is an extreme illustration, but it works like this: “iPhones are commercially successful, iPhones are part of Big Data, and therefore Big Data is commercially successful.”

As if the mere conjuring up of association, affinity and proximity will convince people of the great and growing value of Big Data.

What I am also referring to are publicity pieces that may as well have been titled:

  • Smith, Galbraith, Mies, Keynes, Homer SImpson and the economic justification of Big Data
  • Lovelace, Babbage, von Neumann, Eckert, Davies, Codd, Knuth, Naur and the technological underpinnings of Big Data
  • Einstein, Freud, Edison, Faraday, Recorde and the intellectual structure of Big Data
  • Socrates, Kant, Hegel, Marx , Adorno and the philosophical correctness of Big Data
  • Great quotes about Big Data, from the Cambrian era to the postmodern époque
  • Great jokes about Big Data, from Mel Brooks to Steve Martin
  • Sportspeople and Big Data, from Lottie Dodd and Babe Ruth to Rafa Nadal and CR7
  • Industry support of Big Data, from Henry Ford to Neutron Jack

Do you recognise similarities?

It’s no big deal, just the use of unreliable, misleading and inappropriate fallacies, dressed up as cute, plausible and accessible collateral. People may think that such things are clever and witty, but they aren’t, it’s just misleading.

Let’s continue with something simple.

Evasion is, in ethics, an act that deceives by stating a true statement that is immaterial or leads to a false deduction. For example, citing events, persons or anecdotes from the history of IT to justify the supposed or imaginary value of Big Data. This is close to the notion of a non sequitur, which of course is an argument, the conclusions from which do not follow from its premise. It falls short of being full-on sophistry, purely because the simplistic, puerile and superficial arguments put forward in favour of Big Data do not match those of the true sophist who seeks to reason with clever but fallacious and deceptive arguments. Too many of the Big Data arguments are fallacious and deceptive, but no one, equipped with a reasonable capacity for critical thinking, should take such ‘arguments’ as valid.

Hold this thought: Big Data hype is a viper’s nest of logical fallacies, white lies and disinformation.

Just when I think things could not get any weirder, they do, and Big Data ceiling of hyperbole rises even higher, up to the rarer atmosphere of extreme tendentiousness.

There is a growing mass of Big Data hoop-la, hyperbole and flim flam that exceeds all previously bounds of overstatement, solecism and confabulation. This is where the real volumes, varieties and velocities are in Big Data; in hokie.

We live, as Oscar Wilde said in his day, in and age of surfaces. Yes, superficiality, puerility and short-termism are the competing orders of the day. However, I am still amazed – and maybe wrongly so – by what ostensibly professional, experienced and knowledgeable people are willing, able and prepared to accept, especially when it comes to Big Data flim flam sauce.

Here are some examples of the nonsense about Big Data that is taken as gospel by ‘adults’:

Data Warehousing is part of Big Data: No comment.

Big Data will replace Enterprise Data Warehousing: People can’t even explain the features and benefits of Big Data. I try it make it as easy as possible, ‘if you can’t say it, point to it’. But, seriously, people can’t even relate tangible and credible Big Data success stories, never mind show how it will replace Enterprise Data Warehousing, whether that’s the Inmon or Kimball flavour, take your pick.

Everyone and every organisation can benefit from Big Data: If people can’t explain this, and they don’t in terms of tangible benefits, then the claim should remain questionable.

Data Scientists will replace Statisticians: Why is that so? It is claimed that Data Scientists are uniquely equipped to handle massive volumes, varieties and velocities of data – well, as it turns out, this isn’t certain either.

Big Data is in its infancy: I think we may be confusing infancy with lack of real traction, and of time and place utility.

You cannot be serious: Just what are people talking about here? I have read vague, naïve and ill-informed pieces about data management, data architecture, data warehousing, reporting, business intelligence and a plethora of etcetera that have been passed off as observations and commentary on Big Data. So, what makes people recycle hackneyed, misleading and badly conceptualised ‘content’?

In the commentary on one of Bernard Marr’s pieces on LinkedIn (a professional networking site) I observed that no one can adequately explain what Big Data is without falling into contradictions and fancies, and no one seems to be capable or willing to provide tangible success stories.

Bernard responded to this comment by pointing out “the reason for that is that Big Data means different things to different people.”

Fair enough. It’s an explanation.

That said, I have always had more than a tenuous dislike of postmodern thinking, in fact most things ‘postmodern’. Call me old fashioned, jaded or cynical, but to me, the idea that everything can mean anything is an aberration that I prefer to leave to others.

I am at a loss to explain why so many reasonable people are willing to embrace the hype surrounding Big Data and Big Data Analytics, including the attendant surfeit of nonsense, incongruences and contradictions, and from my perspective, it defies reason and good sense.

Therefore, I will just end again with a fabulous quote from Ben Goldacre:

“You cannot reason people out of a position that they did not reason themselves into”.

Many thanks for reading.

Contradictions of Big Data – Short

01 Sunday Mar 2015

Posted by in Big Data, Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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Please note: This is an edited version of a previous piece with a similar name, but focusing solely on the three main Vs of Big Data.

What we’ve been told

We’ve been told that business Big Data is the greatest thing since sliced bread, and that its major characteristics are:

  • massive volumes – so great are they that mainstream relational products and technologies such as Oracle, DB2 and Teradata just can’t hack it, and
  • high variety – not only structured data, but also the whole range of digital data, and
  • high velocity – the speed at which data is generated, transmitted and received

Which is a simple and straightforward means of classification. Big Data is about massive volumes, high variety and high velocity. Right?

It’s not about big

I have never bought into the idea that more data is necessarily better data, or that it provides better focus or leads to increased insight, in fact I have been quite vocal with my contrarian opinion, but now this view is getting some additional support, and from some surprising corners.

In a recent blog piece on IBM’s Big Data and Analytics Hub (Big data: Think Smarter, not bigger), Bernard Marr wrote that “the truth is, it isn’t how big your data is, it’s what you do with it that matters!”

Over at Fierce Big Data it was Pam Baker who stated that “the term big data is unfortunate because it’s really not about the size of the data”. (Big data is not about petabytes, but complex computing).

Elsewhere, SAS echoed similar sentiments on their web site: “The real issue is not that you are acquiring large amounts of data. It’s what you do with the data that counts.”

Well, apparently Big Data isn’t about “massive volumes” of data.

Strike 1!

It’s not about variety

It is claimed that 20% of digital data is structured, it is based on the problematic suggestion that structured data is uniquely relational.

It is also said that unstructured data includes CSV files and XML data, and this makes up far more than the 20% of the data generated. But this definition is wrong.

If anything, CSV data is structured, and XML data is highly structured, and it’s typically regular ASCII data. So there it does not add variety, even though it is not structured in the ways that some someone might expect, especially if that someone lacks the required knowledge and experience. Simply stated, CSV data is structured, it’s just that it lacks rich metadata, but that doesn’t make it unstructured.

“But”, I hear you say “what about all the non-textual data such as multi-media, and what about the masses of unstructured textual data?”

Take it from me, most businesses will not be basing their business strategies on the analysis of a glut of selfies, juvenile twittering, home videos of cute kittens, or the complete works of William Shakespeare. Almost all business analysis (whether done by a professional statistician or a data scientist) will continue to be carried out using structured data obtained primarily from internal operational systems and external structured data providers.

Variety, Sir? No problem.

Strike two!

It’s not even about velocity

So, if we accept that Big Data isn’t really about the massive data volumes or high data variety then that leaves us with velocity. Because if it isn’t about record breaking VLDB or significant data variety, then for most commercial businesses the management of data velocity becomes either less of an issue or just is no issue.

Even in some extreme circumstances, one can explore the suggestion that data sampling can remove issues with data volume as well as velocity.

However, the fact that some software vendors and IT service suppliers set up this‘straw man’ velocity argument and then knock it down with the ‘amazing powers’ of their products and services, is quite another matter.

So, is it really about velocity?

Strike three!

So what is it really about?

Big Data is a dopey term, applied necessarily ambiguously to a surfeit of tenuously connected vagaries, and its time has come and gone. Let’s dump the Big Data moniker, and the 3 Vs along with it, and embrace the fact that data is data, there will always be more of it.

So, let’s consider ‘all data’ and principally for its time and place utility.

If there is something that you are not sure about or have questions with then please leave a comment below or email me.

Thanks very much for reading.

Consider this: Big Data and the Pot of Tea

17 Tuesday Feb 2015

Posted by in Big Data, Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones, Strategy

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To begin at the beginning

Hold this thought: Big Data is King.

Is there just nothing that Big Data isn’t capable of fixing? From terrorism, world hunger, Ebola, HIV, fraud, money laundering and hiring the ‘right’ people through to winning the lottery, curing hangovers, arranging entrapment and finding the love of your life. Big Data is King. Continue reading →

Big Data Will Save the World

12 Thursday Feb 2015

Posted by in Big Data, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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Good morning fellow consumers; here’s a pop quiz question: What does Big Data have in common with Robitussin? Think about, take your time.

Okay, times up!

Robitussin is a legal pharmaceutical product commonly associated with coughs, colds and flu combinations. Continue reading →

The Faustian side of IT – Part 1

12 Thursday Feb 2015

Posted by in Good Strat, goodstartegy, Martyn Jones

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It’s no wonder that truth is stranger than fiction. Fiction has to make sense.
Mark Twain

It’s Friday morning in London’s trendy Canary Wharf, and I have been asked to facilitate a local meeting of the Digital Violence and Dogma Victims Group, the self-help recovery chain for those who have fallen foul of the pernicious and debilitating effects of IT dogma, organisational autism and insider thuggery and blackmail.

There are twelve of us in the old church hall. We sit in a circle, to facilitate communication. After a more formal welcome and brief introduction the floor is opened up for people to talk about whatever they want to talk about. There is silence. This is normal. There are a few new faces.

“Pantxo!” I look across at Pantxo; he is staring out the window at the falling rain. He can usually talk the legs off a giraffe, but today he is having none of it. Sensing that things are not going too well, I enter into my routine of floridly and inanely relating well-worn anecdotes from the distant annals of IT history.

As I am entering my tenth lap of the track of tedium, one of the new members picks up enough courage to chime in, first nervously and then with the increasing confidence of someone who knows exactly what they are talking about and precisely what they are going to say.

“Hello. My name is Crème”. A woman in a blue adidas tracksuit looks around the room.

“Yes. My name is Crème Brûlée; you may well have heard of it from twitter, the tabloids and the TV… oh, and the novel Absolute Beginners… I used to be the CIO of a major household name.”

She pauses and looks into the middle distance, searching for the truth, tip toeing around the pain.

“This is a bit embarrassing – awkward maybe would be a better word – but what I want to unburden upon you all today is the story of how I outsourced my Data Warehouse, my Business Intelligence, my Big Data, my MDM, my CRM, my family and my life”.

She takes a deep breath and continues; making a point of looking at each of her fellow members in turn as she does so.

“About five summers ago, I feeling a bit lost, which was unusual for me, a strange and novel experience, so I decided that I really needed to do something to turbo-charge my career prospects and to get things moving faster in my part of the organisation. I wanted to excel, and I wanted to be seen doing so, by the right people, and recognised as such.”

“In the spring of that year I had been to a management conference with some of our senior IT management team, some of whom are also here today. Okay, I won’t single out any one of you, because you know who you are.”

“As part of the week-long conference we were wined and dined, stroked and cajoled, flattered and sweet talked by a whole entourage of sales execs from the technology and service providers. They were telling us that the future was in outsourcing and offshoring as much as we could, yes even Big Data and Data Warehousing and Analytics, and they were bewitching us with stories of future successes, of IT paradise and professional nirvana. We in turn wanted to believe, needed to believe, desired to believe. All of this was reinforced by the so called independent industry analysts who insisted, in their agnostic way, that we should seize the moment, with courage, determination and illusion.”

“When I got back to the ranch my mind became occupied with other things, but I didn’t entirely forget the compelling messages that I had brought away with me from the conference.”

“Nothing happened for a couple of months until, one day and out of the blue, things came to a head.”

“We had recently acquired a media news and entertainments business – Media Macaroni International, and we were planning on integrating their general ledger into the corporate IT landscape. One morning I received a call from the CIO of the newly acquired company, inviting me to their site for a meet and greet event.”

“So I moseyed on down to Tinsel Town and got a briefing from not only the CIO, but the full board of directors of Media Macaroni, the ‘hasta la pasta’ of Big Data Analytics ad-hoc performance alignment.”

“To cut a long story short, they basically put me on the spot. Either I integrate the entire Data Warehousing, MIS, Big Data, Analytics and MDM across the expanded corporate body in 9 to 15 months, or we would have serious problems of convergence and market credibility. The message couldn’t have been clearer. Either I got our act together and made this acquisition work, or what looked like a humongous hot potato could land in my lap anytime soon. It was a career changing risk that I needed to address.”

“I told the directors there and then that the mission was going to be incredibly difficult to fulfil. However, the mood quickly changed.”

“Their CIO looks across the table and tells me that he can help me out of my hole. My hole? What the freak! You see, we have employed a service company that does most of our IT work for us, and according to us at Media Macaroni they are simply the bee’s knees, the best thing since chopped liver on rye, the biz.”

“So ‘who are these guys’, I ask. And after a brief hiatus that seemed to last forever, back came the ominous response: The Taffia Connection.”

To be continued…

Many thanks for reading.

Channel:

As always, please 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 even send me aLinkedIn invite. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.

File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

The Big Data ‘Wow Wow’ Factor

08 Sunday Feb 2015

Posted by in Big Data, Consider this, Good Strat, Good Strategy, Martyn Jones

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wFactor

The Wow Wow Factor! Trading, Big Data and 7 HabitsHi, I’m Ricky Jones, boss and co-founder of Becci Boo International Investments. Last week we said goodbye to our best ever Big Data energy commodity trader. He’d been with us for years.

Sadly, Coco Jones was determined to retire to the countryside, to his birthplace, to his real home, a snug little village in the hills of Montseny, and there was absolutely nothing we could do to convince him to stay. The thing about Coco is that he is not like you or me, he’s a highly intelligent Catalan sheepdog.

So, you might ask, how did Coco get to be a star trader at the Becci Boo Hedge Fund? Was it the tools he used? Was it the techniques he adopted? Was it the food he ate? What was so special about him and his amazing abilities? It’s a long story that I will relate as briefly as I can.

Back in time, there was one particularly disastrous week of trading at Becci Boo. Something had gone really wrong with our once reliable Big Data Trade Analytics platform, and wrong bets were being placed right, left and centre – and against trader’s better judgement. The CFO was livid. Out he comes onto the trading floor, swearing and blinding. “God! You guys are the damn pits! What the hell do you think you are doing? Can’t you get anything right? A Catalan sheepdog could trade more effectively than you feckless lot of feckless things.”

I try and diffuse the situation. “Come on, Jordi, don’t be like that, we’re only human and this is a tough business.” “You don’t believe me” he replies. “Sure, but you’re not going to convince me that a Catalan sheepdog could be a substitute for a highly experienced human trader are you?” “How much do you want to bet? These superior four legged beings would never place any faith in things that they don’t understand or can’t control, and they certainly wouldn’t need your Big Data gizmo to make a success of things.”

So, in the following weeks we arrange to run an experiment. We ring around all the owners of registered Catalan sheepdogs and ask them if they would like their dog to take part in our simple trading experiment. Food, lodging and generous expenses all included. We easily manage to get together 64 Catalan sheepdogs and their owners from all over Europe. The experiment we design is quite simple.

We give each dog a gadget (and actually for the more sophisticated dogs it was a smart phone and specially designed app) with two big buttons on it, a red button and a yellow button.

Every Monday morning we ask the assembled dogs to press one of two buttons depending on whether they think that the chosen energy commodities market will close higher or lower at the end of the week.

Red button for a higher closing price, yellow button for a lower closing price. As you might imagine dear reader it’s a walk in the park for these cunning canines. To make sure that all of the participating dogs have equal and fair chances in the experiment, we provide guardians with access to all our big data analytics, and we have multiple screens set up in each studio apartment so that each dog can follow all of the financial news that they need to take in and then view the results of machine learning, data mining and predictive analytics.

At the end of week one, 32 dogs remain in the experiment, and the other 32 are sadly sent home. Every week the exercise is repeated, and every week less dogs move on to the next round. By the end of week 5 only two dogs remain. By the end of week 6 we have a sole winner, Afi Bastò, who has accurately predicted the market movement of our energy commodities market for a record six weeks in succession. It’s an amazing success that proves Jordi right. Unfortunately, this is where things start to go wrong. Our marketing department tweets the news of the amazing Afi and it immediately goes viral.

Articles appear in the Wall Street Journal, Financial Times, Economist, Cinco Dias and Les Échos, hailing the amazing brilliance of the newly discovered Catalan commodity trader.

Hundreds of interviews are held and millions of photos make the rounds of the social and professional networks. Over the following year Afi is interviewed, researched, studied and investigated. Academic papers are written about him.

Amazing claims are made about his canine knowledge, wisdom and experience. This is shortly followed by the publication of a plethora of bestselling business books, with titles such as: The 7 Habits of the Highly Effective Canine – Personal Lessons in Trading; Be More Afi and Grow Rich; The Gos d’Atura That Conquered Chicago; Learn to Trade like Afi; Good Sheepdog, Great Afi; Who Moved My Dog Food; How Afi outperformed Big Data Analytics; etc.

Which unfortunately all goes to Afi’s head, and he begins to seriously lose his commodity trading mojo. So much so, that by the end of the year, we let Afi go, and we bring back “the incredible” Coco Jones, the dog that managed to get the market movements right for five weeks in a row, and who was only pipped at the post by “the amazing” Afi.

Since then, Coco has helped us to win far more than we lose, he has had an innate knack of being able to combine market knowledge with statistical analysis and a certain indescribable ‘insight’ that no one quite manages to understand never mind emulate, and all combined with an amazing caring character.

So, as you might imagine, we were really sad to see him go. After his epic leaving party, where he is acclaimed by all and sundry, but especially by Jordi, all the staff assembled outside the office entrance to see him off, and as he was slowly chauffeured away down the driveway he turned and looked back, and his face said it all. “I was lucky — I found what I loved to do early in life”.

Many thanks for reading.  

Here’s Coco paddling in his Big Data Lake

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File under: Good Strat, Good Strategy, Martyn Richard Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Consider this: 7 Handy Phrases To Unhinge Your Boss

06 Friday Feb 2015

Posted by in Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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See no evil, speak no evil, and hear no evil. Bad managers love to hear good news, leaders thrive on adversity, contradiction and criticism, but some bosses don’t know their right foot from their left ear.

Sure, there are things a lot of us would prefer not to hear. But sometimes things are just unavoidable. We are told that honesty is the best policy, but what happens when honesty goes wrong?

Here are some examples of comments that might piss your boss off, together with some suggestions on how to finesse your way out of such situations and crawl back into favour.

One: “Honey, I shrunk the Big Data…!”

This is a really difficult one. On the one hand your boss might take the news badly and run around like Chicken Licken for days on end lamenting the ‘fact’ that the sky has just fallen in. On the other hand, you might have a sensible, intelligent and sane boss, in which case you might like to follow it up with a “shall I stick it back in the spin dryer and give it another whirl?”

Two: “Isn’t it your bedtime already?”

No, no and no! This is so wrong, and on so many levels. First, avoid a question that ends with an ‘already’, this is far too formal for office banter. Next, consider the time. If it’s before 21:00 it is really not the moment to start asking these sorts of questions. Save this type of question for the regular night out with the project team or for anonymous SMSs.

Three: “Yes, your bum does look big in that 1k USD suit…”

Nobody likes being told that they have spent ‘loadsa’ money on sharp ‘schmutter’ that doesn’t exactly flatter, especially when it comes to naturally portly or big boned types. One way out of this difficult situation, if you really want a way out of this difficult situation, is to add a quick “sorry, only joking, you don’t look even half as bad as me dear old granddad”.

Four: “What did your last slave die of?”

Say you were busily serving tea in the Oval Office and President Obama asked you to pour some more milk into his cup, this would not be the phrase to use under any circumstances if this happened. In other circumstances it might be perfectly acceptable. Just imagine that instead of Obama it was Uncle Joe Biden who was asking. Then you would possibly be right to use that phrase, and would be free to follow it up with a “and who gave you permission to use the office of the POTUS to entertain your mates, huh?”

Five: “My Mum won’t be happy with this… and you know what that means”

Yes, he gets it, its blackmail, and he won’t like it. He won’t like the thought of not getting any… Well, you know what I mean. No need to spell out these sorts of things, is there, especially before the nine o’clock watershed. But, if this just slipped out accidentally then the best way of retracting it is to deny that you ever said it in the first place. Yes, I know this is not very ethical, but it’s the height of modern day ‘professionalism’.

Six: “You run like a girl…”

Your boss may behave like a highly socialised two year old, but the use of gender specific insults is a definite ‘no, no’. Of course there are exceptions. Your boss may be from a tribal ethnic minority and may have been named Runs Like Girl by his parents, so in that case it might be totally acceptable to use the name. But, it’s really best to ask first, just to be on the safe side.

Seven: “Okay, keep your hair on Mussolini, you’ll never sell any ice creams with an attitude like that!”

Dodgy one, under almost any conditions. There are however times when this might just work positively in your favour. For example, if the bald headed and rotund boss is a keen and nostalgic fascist sympathiser, one with a penchant for the old gelati celesti. In which case, you might want to follow it up with a rousingly jolly accusation such as “Fascist!” If that’s not the case or you are unsure, then it’s really best to avoid such language.

None of these expressions particularly bother me, but there are horses for courses, braces for races and boors for moors. If you are one of those charming ‘holier than thou’ thin-skinned puritans then you probably have a plethora of pet peeves of your own, in which case please join in the fun, and contribute your own ‘phrases I like to hate’, below.

Many thanks for reading.

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File under: Good Strat, Good Strategy, Martyn Richard Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Big Data in Social Studies

03 Tuesday Feb 2015

Posted by in Big Data, Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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You walk down the street, maybe in London, Paris and Rome – well, of course, not all at the same time.

You look out of your spacious and luxurious apartment in Manhattan, Unter den Linden or Mahim.

You are cycling slowly along the streets of Amsterdam, Bonn or Zurich.

Got the picture? Continue reading →