I have looked into the immediate twelve-month future of Big Data and the immediate future looked back and said “Be more goat”.
Big Data Predictions for 2015 羊
30 Tuesday Dec 2014
Posted Big Data
in30 Tuesday Dec 2014
Posted Big Data
inI have looked into the immediate twelve-month future of Big Data and the immediate future looked back and said “Be more goat”.
27 Saturday Dec 2014
Posted Consider this
inTags
In the modern world if you question the lack of ethics, absence of morality or the scarcity of good sense of something, some claim, some recommendation or some piece of advice, especially if confronts or compromises the status quo, the wilfully ignorant and content and the obtuse conformists, then more often than not you will be labelled as a negative person, a mischief maker and a member of the awkward squad. Continue reading
27 Saturday Dec 2014
Posted Big Data, Consider this
inTags
Plus ça change, plus c’est la même chose.
Jean-Baptiste Alphonse Karr
I have been involved (all afternoon as a matter of fact) in an in-depth study of the changing face of IT, data architecture and data management, and the challenges that the profession faces.
In particular I have tried to focus on emerging and evolving roles and responsibilities, and in their significance, synergies and collaborative potential in a predictably high-speed, volatile and exotic future.
I know that many people will question the need to create new roles in statistical analysis, qualitative analysis, and data architecture and management, and I must admit that I also shy away from the invention of new terms, especially when they may seem to be superfluous and misleading, but I feel that the spirit of the times is calling out for a revolution in how we view and appreciate the world of data professionals.
Some of the new roles detailed here may not be immediately familiar or intuitive, and some of the responsibilities may seem to be somewhat onerous or even trivial. But this is not accidental. As what has lead me here is the desire to formulate a coherent and cohesive response to the IT industries sea-change with respect to disruptive and game-changing innovations such as Cloud data centres, the Internet of Things and Big Data.
So here is my take on what I see as being the new roles – 7+3 in all – and responsibilities within many if not all of the Next Generation Mega-Mega Data projects coming our way. The roles that will be discussed here, are as follows:
Data Trader – The Data Trader is the high flying market maker of the alternative data universe. They are essentially the gears and oil of the data market, introducing market providers of data to market consumers of data. The Data Trader identifies potentially undervalued data, and price and quality discrepancies in alternative data sources, and then seeks to leverage these discrepancies in order to ‘monetise’ their valuable role in keeping the data market healthy. Data Traders also seek out data instruments on the instructions of a client. They may also issue and buy options and futures contracts on commoditised data, to be optionally executed and delivered at a later date. Although it is technically feasible, Data Traders will rarely trade on their own account.
Data Hound –Although the Data Hound is a special pedigree breed of data management role, the job of the Data Hound is essential to the work of the Data Trader.
When the Data Trader gets a new requirement for novel, fresh or new data, the Data Hound is charged with searching out the best, cheapest and most reliable sources for that data, and to identify the owners and vendors of that data. Essentially, they assist in the data market-making responsibilities of the Data Trader. But there is more to the role than that. Only a Data Hound can bring infectious enthusiasm to a long walk on the data landscape. Only a Data Hound can be such a perfect, patient distraction for the knowledge workers. And only a Data Hound can dispel all gloom, tension and work-stress with a single explosion of excitement every time you walk through the data portal.
Not for nothing shall the motto of the Data Hounds be Ad grandior data, Winalot!
Data Plumber – The Data Plumber designs, builds and maintains the infrastructure to ensure that any validly supplied data reaches the data preparation stage prior to its selection, analysis and consumption. The Data Plumber is charged with ensuring that the required data correctly gets from the data provider to the data consumer, first time, every time.
Typical responsibilities of the Data Plumber may include:
Data Butcher – The Data Butcher works in conjunction with the Data Chef. The Data Butcher selects and prepares the desired parts of the supplied data which they then pass on to the Data Chef for data mining, ad-hoc predictive analysis and visualisation. The Data Butcher removes the fat data from the lean data, and provides quality data that can then be subsequently ‘sliced, diced and spiced’ in downstream analytics applications.
In years from now, IT archaeologists will marvel at the Tau influences inherent in the role of the Data Butcher in particular, and data architecture and management, in general. By way of evidence, the following is a philosophical anecdote from the future:
There was once a Data Butcher who was preparing a piece of Big Data for a customer who had been coming for many years.
“Pardon me, Sir” the customer asked, “But isn’t that the same ETL you used last year? Don’t you ever need to upgrade it or maybe go for a more sophisticated and sharper solution?”
“No…” replied the kindly Data Butcher “It’s the same ETL I’ve been using for the last 17 years”. He stared wistfully into the distance for a few moments, looking for inspiration, and then continued. “And I haven’t had to upgrade it, sunset it or change it even once. For, when I select, transform and integrate raw data, I allow the trusty ETL to find its own way through it without effort or stress. Just like Bill told me. And when I come to a tricky bit with lots of disconnected, superfluous and erroneous data, I just slow down and allow the mystery to solve itself and in no time the good data comes right through the process.”
Adapted from Chuang Tzu: The Basic Writings, 1964
Data Chef – If you’ve ever seen a great Chef working, up close and intimate (to use a west coast expression), then you will appreciate the need for the role of Data Chef.
First and foremost, the Data Chef is curator of all the organisations data analytics ‘recipes’. They have the data analytics ‘knowledge’. Ideally, the Data Chef has a solid grounding in formal statistical methods and a solid appreciation of data architecture. This may also be augmented by a wide range of other skills, such as in Nouvelle analyse des données. The Data Chef also works in conjunction with the Data Trader and the Data Butcher to determine and identify prime data material in the data markets. Based on the available of prime data the Data Chef is able to determine a menu of data analysis approaches that will dynamically change depending on what data is in season and available on the data market.
Data Taster – A Data Taster is a person that takes data (or information) to be provided to a person or entity to confirm that it is safe to issue. This is perhaps one of the oldest professions in data, coming, as it does, from the ancient Roman role of praegustator or data unini. The person or entity to whom the data is going to be issued is usually an important person or body (for example, a regulatory reporting body or an organisational strategy group) or any person or body that could possibly be placed at risk if the data (or information) is erroneous, misleading or compromised. For example, the Data Taster verifies the outcomes of Big Data Analytics and confirms that the data is plausible and that the models used are valid so that they do not permit either the accidental or intentional introduction of data contamination. The Data Taster may also be accountable for the preparation and provision of data. The hope is that the Data Taster will be conscientious and meticulous in preventing contamination from being introduced into data, in order to safeguard their own reputation and that of their organisation.
Data Server – The Data Server is a role that is closely tied to the roles of Data Whisperer and Data Czar.
At a superficial level, the Data Server presents the data menu and takes the data orders, then serves what has been ordered. The Data Server may also advise data clients on the optimal choices of data, based on the data that is available and the data preferences of other clients.
Because the role of the Data Server requires that they know a little about everything and a lot about something, the two most popular career progression paths for Data Servers are moves into the role of Data Whisperer or Data Shouterer.
Data Whisperer – Is integrally associated with the roles of Data Server and Data Czar. This role is an extremely important key-stone position within an organisation.
The Data Whisperer is an explainer, a storyteller and a stand-up philosopher. The primary responsibility of the Data Whisperer is to avoid that any senior executive or regulatory body throws a ‘wobbly’ when they fail to correctly interpret the data that they are provided. Therefore, the responsibility of the Data Whisperer is to correctly socialise data analysis outcomes with the intended audiences for those outcomes, and to jointly present and explain those outcomes in plain and simple language. Thea are required to have courage, strength and a high degree of empathy both with the data and also with the consumers of that data.
Data Czar – Typically this is a senior Board role, comparable to that of CFO. Indeed, the role of Data Czar (or Data Tsar, for the British) may also be held by the CFO. The role itself is that of visible figurehead of all data architecture and management activities within an organisation. Although it bears some striking resemblance with the now widely discredited role of bygone days, which I will delve into in subsequent articles, its remit goes much further. The Data Czar has the ability and the empowerment to break down barriers, cut through red tape and knock down walls that create organisational silos. They can free and easily engage with and involve senior organisation players in their data campaigns and battles, gaining commitment, trust and willing complicity along the way. Naturally, the Data Czar can also call on the skills, talents, knowledge and experience of the other 9 roles identified here.
Data Shouterer (aka Data Shouter) – Finally, we come to the last of the roles. The Data Shouterer is primarily the role of the data evangelist, the extoller of grand data ‘truths’, the purveyor of serendipitous comfort, and the herald of a brave new data world.
When things go well the Data Shouterer is called upon to holler out the successes of data analysis from the rooftops.
When success is reluctant to come forth, they must be there to ‘big up’ the inherent potential for success, with brave tales of data buccaneering, ace information pilots and glorious exponents of the Art of Data.
So there you have a brief explanation of the 7(+3) new big data roles for 2015. So, to conclude…
Necessity is the mother of invention and mother is the invention of necessity, and just as the judicious use of parallel grep, awk and bash could have been reinvented, rebadged and released as perhaps justifiably the next best thing in data, so too must there be concomitant and relevant roles to suit the revolutionary data spirit of the times. No?
But, to paraphrase John McEnroe, “surely this piece cannot be serious?” To which I might reply, maybe yes, or maybe no, it simply ‘depends’. But depend on what?
The English writer George Orwell once mused that “The most effective way to destroy people is to deny and obliterate their own understanding of their history”, to which I could add, “This may occur whether the act is intentional, accidental or systemic”. I think it is important that when we look at any new IT industry trend or fad, that we do so with a reasonable knowledge of IT history and the evolution of IT technology, and with a good understanding of contemporary and legacy technologies and architectures. This, to my mind, is how we respect both the IT/Information Architecture and Management profession and those whom we seek to help.
Finally, dear reader, although as I state my intention is quite serious, please do take this piece with a modicum of sodium chloride.
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.
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
25 Thursday Dec 2014
Posted Big Data, Business Intelligence, Consider this, Data Warehousing
inTags
I have worked in data architecture and management for three decades, I have become a recognised expert in my field, and as a result I have become almost oblivious to the fads, fancies and fashions that pass through IT. However, being an expert in a field also means that from time to time we are oblivious to the difficulties that some people may have when trying to understand issues and concepts that we simply take for granted – because, one simply knows. This is the case with data.
With the polemic and resourcefulness surrounding buzz-words such as Big Data, Cloud and the Internet of Things, one may be forgiven for assuming that there has been a massive inflection point in the generation, variety, understanding and use of data. Now, this isn’t strictly accurate, as, outside of the handful of speculative and high-visibility projects of social media and networking, data collators and indexers, search engines and online volume ad-sellers, there has been scant publicised evidence of significant ‘data revolutions’ elsewhere.
Things haven’t changed significantly simply because a handful of companies are making money with other people’s data, rather than in the more traditional organisation, where their own data is one of the most important assets. So what’s really out there in the world of data? First, let’s look at some broad-brush classes of data, namely:
Enterprise Operational Data – This is data that is used in applications that support the day to day running of an organisation’s operations. Typical data items in this space are sales transactions, purchase transactions, product information, client and contact information. Enterprise Operational Data may also include complexly structured data, such as contracts and other business documents. Applications in this space may include production control, logistics and stock control, as well as purchase order, supply chain management, management accounting and human resource modules.
Enterprise Process Data – This is measurement and management data collected to show how the operational systems are performing. In the past the recording of events went down to the level of a completed transaction – with a start and an end and nothing in between, and as transactions were kept as simple as possible, to maximize performance and throughput and minimise the risk of failure, very little process data was captured. Now, especially with the advent of Business Process Management and Web Logs, we collect a whole array of transaction and process performance data that was never previously captured.
Enterprise Information Data – This is primarily data which is collected from internal and external data sources, the most significant source being typically Enterprise Operational Data. Other sources for this aspect of data include Enterprise Process Data and data provided by 3rd party data providers. In this data space we find Enterprise Data Warehousing, Operational Data Stores, Data Marts and Special Project Data Stores. Applications in this space include support for strategic and tactical decision making, formal statistical analysis, speculative ad-hoc analysis, data mining, business intelligence and reporting (also called Management Information reporting), and qualitative as well as quantitative analysis.
As we can see, there are interdependencies, synergies between each of these broad areas of data generation and use. Of course, data in each of these areas can be augmented and enriched by new sources of data, whether that data is richer market data, competitive data or weather data. Now, this is a very simplistic view of data, but for the purposes here it is both coherent and cohesive.
Fig. 1 – Data Made SImple
In the above diagram I have identified an area labelled as ‘Data Transitioning’. This is usually referred to as ETL (Extract, Transform and Load), although in more and more cases, instead of extracting data directly from source systems (Enterprise Operational Data Management) we are capturing data sent via enterprise message queueing, this drip feed approach in most cases allows us to maximise the time available for data loading and analysis.
Another important point to note is that although Enterprise Information Data Management has been associated with Relational Database platforms, such as Oracle, Teradata and DB/2, in this IT domain we are also using a wide range of ‘databases’, from the humble ‘flat file’ to powerful column oriented database engines, such as EXASOL, Teradata and Vertica to provide information and analysis to business stakeholders. As more and more ‘exotic’ data formats and sources are incorporated into our Enterprise Information Data platforms, we will also witness the evolution of tools, technologies and techniques to meet those new requirements.
Last of all, there will be no revolution in data management, because in most cases data architectures are built around sound data engineering principles, constrained and governed by the limitations of mainstream hardware, the operating systems that bring them to life and the competencies of those charged with designing, building or using them.
In subsequent blog pieces I will be sharing my views on the evolution of information management in general, and the incorporation of ‘Ad hoc Speculative-Predictive Analytics’ into well architected mainstream information supply frameworks for primarily strategic and tactical objectives.
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
25 Thursday Dec 2014
Posted Books with influence, Creativity
inDialectic of Enlightenment is undoubtedly the most influential publication of the Frankfurt School of Critical Theory. Written during the Second World War and circulated privately, it appeared in a printed edition in Amsterdam in 1947. “What we had set out to do,” the authors write in the Preface, “was nothing less than to explain why humanity, instead of entering a truly human state, is sinking into a new kind of barbarism.”
From the 2007 Edition.
As a collection of philosophical and social musings, reflections and ideas planted firmly in the spirit and events and flux of the 1930s and 40s, Dialectic of Enlightenment, with its broad range of sketches, provides an accessible route to some of the thought of the times, and in a form that is easy to comprehend, think about and delve deeper into. I think that the problems associated with the reading of Dialectic of Enlightenment tend to be related to the fact that it is either treated as a coherent and cohesive master work, which it is not, that it is treated as highly accessible and therefore highly trivial, which it is not, and that frequently the subtle nuances of the works of thoughts and fragmentary ideas of the continental authors, especially when not originally written in English, tend to get lost in the translation.
What I also like about Dialectic of Enlightenment is that it is a subtle guide for the baffled by the perplexed, a handbook of philosophical survival, of political, social and mental health, a compendium of reassurance, and a book of ‘yes, we also think that our present paths will lead us to nowhere good’.
But the book is not without its detractors. There are some who have trouble with Dialectic of Enlightenment, for what it is, for what they want it to represent – but doesn’t, for what it tells us, for what it leaves for us to contemplate. That is, what it represents, what it represented and the uncomfortable messages it still sends no longer pleases so many.
The comfortably content have no such problems; they can just hate it, despise it and rubbish it, without actually understanding it or reading it. Even shallow thinkers are subject to dilemmas, but the ‘no thought zone’ of the ‘wilfully ignorant’ apart from perhaps being the most comfortable place on earth, is also one of its more sinister aspects.
Today, everyone’s opinion is treated equally, with the same validity, with the same value, with the same level of indifference or respectful disrespect, but at the end of the day, it is not the powerful, reasonable, logical and humane argument that wins, but the arguments that most closely flow with the system, for the benefit of the system and without upsetting the system to any large degree.
In this way we are suckered into accepting the status quo.
Just look at how we cannot think ourselves out of a paper bag. This is problem brought about in part by having no alternative to the current parties and their policies and leaders and interests, and that the only alternative would be for violent revolt, which most of us are strongly against. So we just accept what there is, without seeing the elephant in the room is saying “get off your arse and use the system to deinstall itself, and to install a more humane and reasonable system in its place”… too much like hard thinking and hard work I suppose, and no one is going to ‘provide’ that service over the internet or at the hyper-hyper store or mall.
The POMO nonsense of ‘everything goes’, ‘do your own thing, baby’ and ‘everything can mean anything’ is just that, self-entrapping crap that will ensure that quite a few people will rage against the effects of the central problems and causes of those problems by first exaggerating peripheral, marginal and frequently superfluous issues.
As Isaac Asimov so succinctly put it “Anti-intellectualism has been a constant thread winding its way through our political and cultural life, nurtured by the false notion that democracy means that ‘my ignorance is just as good as your knowledge.’”
In summary, these are some of the things I get from reading Horkheimer and Adorno’s sketches, a sense of ‘we are not alone’ when we think this way, but nonetheless, aspirations aside, this is what we have.
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
20 Saturday Dec 2014
Posted Big Data, Data governance, Data Warehousing
inTags
Alas, poor Yorick! I knew him, Horatio; a fellow of infinite jest, of most excellent fancy; he hath borne me on his back a thousand times; and now, how abhorred in my imagination it is!
From the play Hamlet by William Shakespeare
Big Data is dead! Long live Information Management. Continue reading
19 Friday Dec 2014
Posted Data Warehouse, Data Warehousing, EDW
in“A thing is not necessarily true because a man dies for it.”
Oscar Wilde
For well over two decades one of the most talked about benefits of Enterprise Data Warehousing (EDW) has been that it gives us a single source of business truth. Continue reading
16 Tuesday Dec 2014
Posted Consider this
inTags
Even before the Duke of Gloucester had berated Edward Gibbon for his “thick, square book”, the encouragement of ignorance was already a powerful force in the English speaking world.
In a young USA, both Thomas Jefferson and John Quincy Adams, were accused of being unsuitable for political office due to their intellectual pursuits.
Much later, Richard Hofstadter, in a classic study of the plight of intellectualism in the USA, noted that “It seemed to be the goal of the common man in America to build a society that would show how much could be done without literature and learning–or rather, a society whose literature and learning would be largely limited to such elementary things as the common man could grasp and use”.
Indeed, the widely popularised and populist Andrew Jackson, seventh President of the United States, delighted his followers with witty observations of the type “It’s a damn poor mind that can only think of one way to spell a word”.
In France, Sarkozy’s “Cultivated Anti-Intellectualism” used to be something of an embarrassment, given that the Gaullist right were never “quintessentially vulgar” or anti-intellectual. That was in sharp contrast to the situation in other G8 countries, where “bread and circuses” are served up as a daily substitute for political engagement.
Indeed, if people consider that a bit of rad chit-chat on the old iDog ‘n’ Bone is a form of political engagement, then where are we?
Which leads me to Isaac Asimov, as dead as he is, who thought that there was a ” false notion that democracy meant that ‘my ignorance is just as good as your knowledge'”.
Meanwhile, in the UK, a former Education Secretary told an audience, including “gifted and talented” children, that academic success is to be celebrated as much as sporting achievement, adding “being clever is sometimes seen as a term of abuse, for example: ‘Too clever for your own good’.”
Unfortunately this was the very same person who was also the cabinet colleague of a man who blamed the French for the Iraq War, because, according to this particular ‘brain box’ they had caused the war, because, they voted against it.
It didn’t stop there, because the justification for using the “blame the French” ruse was so mangled and indecipherable, that comedian Mark Steel was lead to remark that “the Government would have done better to have their policy explained by Po of Teletubbies”.
Anti-intellectualism extends to the political blogosphere, where the established principle seems to be that the way to attack the opposition is not to address it – because that would involve reading and comprehension and joined-up-writing. But, conversely, to mock, ridicule, trivialise, deny, invent, deflect, and misrepresent, is an acceptable opening gambit, which when all else fails, can be replaced by attack using blunt adjectives and deceitful indignation.
Of course, there is nothing new in that. Stewart Lee is on record as saying that “you can prove anything with facts, can’t you”, since, when an argument cannot be sustained by reason or evidence, then gut instinct and prejudice become your friends.
It seems that giving a reasoned and well placed opinion is actually a transgression. Something that decent chaps just don’t do. As if political life was a football match, between right and wrong.
The keeper in the red, effortlessly blocks a weak shot, boots the ball right up the field to his mate cruising in the opposition’s penalty area, who Gareth Bale like, sends the ball crashing into the back of the opponents net.
Of course, when things don’t go to plan, Team Right, in the blue strip for those watching in black and white, surround the man in black with appeals to reason.
“Eh, Referee! The other lot are cheating again. That argument was well offside”.
This cultivated anti-intellectualism is not confined to politics.
Just look at the manufactured hype and faddishness surrounding Big Data, and the wilful ignorance of those who truly want to believe.
Anti-Intellectualism is not clever or funny, and ultimately it is divisive and destructive.
Thanks for reading.
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
16 Tuesday Dec 2014
Tags
Apologies to the late Benny Hill
You could hear the dollar fall, then it crashed upon the ground
And the chatter from the White House as they spun, around and ’round
And he glided into Wall Street, his scam beneath his vest
His name was Bernie, and he sold the slickest secrets in the west
Now Bernie loved his Lipstick, a place where mom’s the word
He worked alone on Third Avenue, at 53rd at Third
They said that he was just da’bom[1]; they were greedy, vain and chic
But Bernie got his kickbacks[2] there, five days in every week
They called him Bernie, Bernieeeeeeeeeee!
And he finessed the slickest Hedge Fund in the west
She said she’d like to work in funds, he said, “All right, braveheart”
And when he’d finished fiddling, he loaded up his chart
He said, “D’you want to leverage? ‘Cause leverage is best”
She says, “Bernie, I’ll be happy if there’s money left to invest”
That tickled old Bernie, Bernieeeeeeeeeee!
As he pimped the slickest Hedge Fund in the west.
Now Bernie had a rival, a governmental man
Called quickstep Chris from Harvard Yard[3], and he drove S.E.C.’s[4] van.
He tempted her with his oversight, regulator’s feet of lead
And when she seen the size of his compliant eyes, Lipstick trader placed a spread[5]
She almost sold on his insider tips and he said, “If you put me right,
You’ll have issues every morning, dividends every night.”
He knew once she sampled his laissez faire, he’d have his hedging way,
And all Bernie had to offer was a NASDAQ loss a day.
Poor Bernie, Bernieeeeeeeeeee…
And he rode the slickest Hedge Fund in the west.
One bell time Chris copped Bernie, doing deals outside her floor,
It drove him mad to find them trading even after half past four.
And as he jumped up from his chair, hot issues through his veins did course,
And he went across to Bernie’s trades and didn’t half kick his Bourse[6].
[Of course, it was his horse]
Whose name was Ponzi, Ponziiii…
And he schlepped the slickest Hedge Fund in the west.
Now Bernie rushed out onto Wall Street, prospectus in his hand,
He said, “you wanna subscribe to Lipstick you’ll pay for her like a man.”
“Oh why don’t we place bets for her?” he derisively replied,
“And just to make it interesting we’ll do some shorting on the side.”
Now Bernie dragged him up from F Street and beneath the Times Square clock,
They stood there face to face, and Chris went for his stock.
But Bernie was too quick, things didn’t go the way Chris planned,
An unexpected corporate-action sent it spinning from his hand.
Then Lipstick rigged a Chinese Wall to keep them both onside
But Bernie, made a haircut deal and subprime caught him underneath his pride.
And as he looked up at BASEL II, an OFAC hardened trust
Of a next day trade, caught him in the PEP, and Bernie got ‘da bust’[7].
Poor Bernie, Bernieeeeeeeeeee…
And he blagged the slickest Hedge Fund in the west.
Bernie was only just 60, he didn’t wanna lie
But now he’s stopped making deliveries to that Hedge Fund in the sky.
Where the investors are quite stupid, and regulation banned,
And the trader’s life is full of dosh, in that alternative land.
Yet Lipstock’s needs are manifold, and soon she collared Chris
Strange things happened on their redemption day, as they talked a ‘loada pish’[8]
A KYC oversight? Or old secrets out the gate?
Ole Bernie’s market making? A rattled exchange rate?
They won’t forget Bernie, Bernieeeeeeeeee…
As he ‘vanished’ the slickest Hedge Fund in the west.
[1] Quite jolly good.
[2] A return of a part of a sum received often because of confidential agreement or coercion.
[3] Harvard Yard, in Cambridge, Massachusetts, is a grassy area of 22.4 acres enclosed by fences with twenty-seven gates. It is the oldest part of the Harvard University campus, its historic centre, and its modern crossroads.
[4] Securities And Exchange Commission
[5] An options position established by purchasing one option and selling another option of the same class but of a different series.
[6] A market organized for the purpose of buying and selling securities, commodities, options and other investments.
[7] Revealed
[8] Not of a superior quality
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
15 Monday Dec 2014
Posted Consider this
inTags
For their feet run to evil, and make haste to shed blood.
Proverbs 1:16
Fools rush in where angels fear to tread.
Alexander Pope, An Essay on Criticism
I’m not a psychologist, and people who read my blog will know this for sure. But I would like to examine the notion of wanting something so much that at best we don’t enjoy it when we obtain it, or worst still, that our eagerness means that we will never obtain that which we truly want. Continue reading