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Monthly Archives: February 2015

Consider this: Big Data and the Pot of Tea

17 Tuesday Feb 2015

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

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Analytics, Big Data, data management, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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 →

The amazing world of Fred’s Big Data

15 Sunday Feb 2015

Posted by Martyn Jones in Big Data, Consider this

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Big Data, data management, Good Strat, Good Strategy, information management, knowledge management, Martyn Jones, Martyn Richard Jones

Hold this thought: There are real golden nuggets of data that many organisations are oblivious to. But first let’s look at business process management. Continue reading →

A brief introduction to Knowledge Management

14 Saturday Feb 2015

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

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Big Data, Consider this, data management, information manageemnt, knowledge management

A helpful slideset that is used to explain the purposes, positions and roles of Knowledge Management.

A brief introduction to Knowledge Management from Martyn Jones

Enjoy! Please tell me what you think about this slide deck. Many thanks for viewing. 

Big Data, a promised land where the Big Bucks grow

12 Thursday Feb 2015

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

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Analytics, Big Data, Good Strat, Martyn Jones, statistics

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

Normally I would send such people to see a specialist – no, not a guru, but a sort of health specialist, but because this has happened to me so many times now, I eventually decided to put pen to paper, push the envelope, open up the kimono, and to record my advice for posterity and the great grandchildren.

So, here are my top seven tips for cashing in quick on the new big thing on the block.

1 – A business opportunity for faith

Like every new religion, trend or fad, Big Data has its own founding myths, theology and liturgy, and there is money to be made in it; loadsa lovely jubbly money. By predicating and evangelising Big Data you will be welcomed with open arms into the Big Data faith, and will receive all the attendant benefits that will miraculously and mysteriously fall upon you and your devout friends. Go on, I dare you. Be a Big Data guru, a shepherd to a flock of sheep, and enjoy the wealth, health and happiness that most surely will come your way. You too can look cool in red Prada slippers, a flattering and flowing gown and matching accessories.

2 – Acquire it, multiply it, weigh it, mark it up and sell it on

Simply stated, this is about acquiring other people’s data, by sacred means or profane, marking it up and then selling it on. The value you add is that you act as a trusted conduit, a conduit for good. You may care to enrich the data, swop the order of data, replicate and embellish data, make stuff up, etc. which all serves to ‘add value’ to the data. You may even consider adding nuggets of value to the data, just for kicks and giggles. My best friend’s favourite is injecting the good old ‘diaper and beer’ and ‘friends and family’ clichés into every Big Data collection, as it never fails to thrill, please and delight.

3 – Anything can be anything

The good thing about making money from Big Data is that it doesn’t need to be anything to do with Big Data. Make a 20GB Enterprise Data Warehouse? Call it a Big Data success. Sell 20 boxes of dodgy doughnuts down the alternative market? Proclaim a Big Data triumph. Sell your digital porn stash to your best mate? Point to the incredible invisible hand of the Big Data market at work. See what I’m doing there. Anything can be anything, and you too can cash in on that opportunity, big time.

4 – Big Data Patronage

Tense, nervous headaches? Do you like making up stories about Big Data, or for that matter anything else? Are you a natural born fibber but are strapped for cash? Then worry no longer. If you get a Big Data patron you will be sorted for ‘life’; get two and you’ll be sorted for the afterlife as well. With a Big Data patron you can get the most tenuous, crappiest and superficial of pieces published, promoted and vaunted – globally. Can’t make it up yourself, then outsource and offshore it, after all, just get the keywords right for SEO ranking and the gullible will flock to you in droves. The down side of this profession is that you will be targeted for writing half-truths, quarter-truths and downright lies, and you will be pilloried as a purveyor of rank hyperbole. But don’t worry, take heart and never lose the faith, you will be in good company. As one Big Data guru was want to say ” If you repeat a lie often enough, people will believe it, and you will even come to believe it yourself.” Amen! brother.

5 – Big Data Certification

By 2016 there will be global demand for 30 billion Big Data professionals. Are you prepared to cash in on that inevitability? No? Then consider this.

One of my best friends makes his living as a completely phony Big Data Scientist. For two hundred bucks he can make you a Data Scientist or a Big Data guru. Some guys give you an education but this guy gives you immediate access to high paying jobs, sex and a life in the city. Moreover, for an extra 250 bucks you can also become a certified Big Data Trainer, which will allow you to do unto others what has been done unto you.

6 – Creative Technology Reuse

Big Data has heralded in the biggest innovations known in the history of computing, and arguably in the entire history of humankind. One of those new inventions has been the now widely acclaimed and revolutionary ‘flat file data base’ (FFDB), and this has been accompanied with developments in low level operating system primitives that allow for the processing of these collections and hierarchies of FFDBs. So, if one has a mind to do so, one can get some real business leverage off of these new tendencies by borrowing 21st century technology found in old operating system hacks from the sixties and seventies and eighties and nineties and… Well, the point is that in order to get serious funding it is no longer good enough to have a half page business plan, it is also necessary to eke out ‘stuff’ that works within the new paradigms of Big Data and Big Data Analytics. For my next venture I will be looking for serious funding for my ‘Arbitrary Dawdle Down Data Street’ (AD3S) Big Data Analytics platform, a platform designed to support virtual 1k bit processing and the massively parallel provision of global regular expression search and match (S&M), concatenation and listing, and cooperative data-driven and streamed data extraction and reporting. I’m hoping to attract the attention of governments, the EU, the Manic Street Preachers, the UN, China, Vladimir Putin, the DOD, HP, Oracle, Gartner, Lana Del Rey, Deloitte and IBM. So, this is going to be absolutely massive. Word!

7 – Big Data Brokerage

According to leading management consultants and industry watchers Gartner, McKinsey and Deloitte, data needs to be managed and accounted like any other asset, such as money. To get into a similar view-point requires a massive leap of faith, but it is a conversion that might drive dividends. One avenue to be explored in eking out value from the apparently massively valuable Big Data lakes, silos and pools is through the operation of a Big Data Brokerage. A Big Data Brokerage is a business whose main responsibility is to be an intermediary that puts Big Data buyers and Big Data sellers together in order to facilitate a transaction. Big Data Brokerage companies are compensated via commission after the Big Data transaction has been successfully completed. They may also charge introductory fees. Just imagine the wealth of business opportunities in that. You could become the Goldman Sachs of data.

That’s it folks!

I hope you enjoyed this piece and would be pleased to hear your views on this and other subjects.

Whilst I understand the attraction and even the need of creating a new and significant growth industry, I would also advise a degree of restraint, and whilst I see that “Big Data” (the consideration of the potential value of All Data) has its allure, I also think that some good sense and informed caution should also prevail.

Thank you so much for reading.

Martyn Richard Jones

How to position Big Data

12 Thursday Feb 2015

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

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Big Data, Good Strat, Martyn Jones

To begin at the beginning

Fueled by the new fashions on the block, principally Big Data, the Internet of Things, and to a lesser extent Cloud computing, there’s a debate quietly taking please over what statistics is and is not, and where it fits in the whole new brave world of data architecture and management. For this piece I would like to put aspects of this discussion into context, by asking what ‘Core Statistics’ means in the context of the DW 3.0 Information Supply Framework.

Core Statistics on the DW 3.0 Landscape

The following diagram illustrates the overall DW 3.0 framework:

There are three main concepts in this diagram: Data Sources; Core Data Warehousing; and, Core Statistics.

Data Sources: All current sources, varieties, velocities and volumes of data available.

Core Data Warehousing: All required content, including data, information and outcomes derived from statistical analysis.

Core Statistics: This is the body of statistical competence, and the data used by that competence. A key data component of Core Statistics is the Analytics Data Store, which is designed to support the requirements of statisticians.

The focus of this piece is on Core Statistics. It briefly looks at the aspect of demand driven data provisioning for statistical analysis and what ‘statistics’ means in the context of the DW 3.0 framework.

Demand Driven Data Provisioning

The DW 3.0 Information Supply Framework isn’t primarily about statistics it’s about data supply. However, the provision of adequate, appropriate and timely demand-driven data to statisticians for statistical analysis is very much an integral part of the DW 3.0 philosophy, framework and architecture.

Within DW 3.0 there are a number of key activities and artifacts that support the effective functioning of all associated processes. Here are some examples:

All Data Investigation: An activity centre that carries out research into potential new sources of data and analyses the effectiveness of existing sources of data and its usage. It is also responsible for identifying markets for data owned by the organization.

All Data Brokerage: An activity that focuses on all aspects of matching data demand to data supply, including negotiating supply, service levels and quality agreements with data suppliers and data users. It also deals with contractual and technical arrangements to supply data to corporate subsidiaries and external data customers.

All Data Quality: Much of the requirements for clean and useable data, regardless of data volumes, variety and velocity, have been addressed by methods, tools and techniques developed over the last four decades. Data migration, data conversion, data integration, and data warehousing have all brought about advances in the field of data quality. The All Data Quality function focuses on providing quality in all aspects of information supply, including data quality, data suitability, quality and appropriateness of data structures, and data use.

All Data Catalogue: The creation and maintenance of a catalogue of internal and external sources of data, its provenance, quality, format, etc. It is compiled based on explicit demand and implicit anticipation of demand, and is the result of an active scanning of the ‘data markets’, ‘potential new sources’ of data and existing and emerging data suppliers.

All Data Inventory: This is a subset of the All Data Catalogue. It identifies, describes and quantifies the data in terms of a full range of metadata elements, including provenance, quality, and transformation rules. It encompasses business, management and technical metadata; usage data; and, qualitative and quantitative contribution data.

Of course there are many more activities and artifacts involved in the overall DW 3.0 framework.

Yes, but is it all statistics?

Statistics, it is said, is the study of the collection, organization, analysis, interpretation and presentation of data. It deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments; learning from data, and of measuring, controlling, and communicating uncertainty; and it provides the navigation essential for controlling the course of scientific and societal advances[i]. It is also about applying statistical thinking and methods to a wide variety of scientific, social, and business endeavors in such areas as astronomy, biology, education, economics, engineering, genetics, marketing, medicine, psychology, public health, sports, among many.

Core Statistics supports micro and macro oriented statistical data, and metadata for syntactical projection (representation-orientation); semantic projection (content-orientation); and, pragmatic projection (purpose-orientation).

The Core Statistics approach provides a full range of data artifacts, logistics and controls to meet an ever growing and varied demand for data to support the statistician, including the areas of data mining and predictive analytics. Moreover, and this is going to be tough for some people to accept, the focus of Core Statistics is on professional statistical analysis of all relevant data of all varieties, volumes and velocities, and not, for example, on the fanciful and unsubstantiated data requirements of amateur ‘analysts’ and ‘scientists’ dedicated to finding causation free correlations and interesting shapes in clouds.

That’s all folks

This has been a brief look at the role of DW 3.0 in supplying data to statisticians.

One key aspect of the Core Statistics element of the DW 3.0 framework is that it renders irrelevant the hyperbolic claims that statisticians are not equipped to deal with data variety, volumes and velocity.

Even with the advent of Big Data alchemy is still alchemy, and data analysis is still about statistics.

If you have any questions about this aspect of the framework then please feel free to contact me, or to leave a comment below.

Many thanks for reading.

Catalogue under: #bigdata #technology big data, predictive analytics

[i] Davidian, M. and Louis, T. A., 10.1126/science.1218685

Big Data Will Save the World

12 Thursday Feb 2015

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

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Big Data, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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 Martyn Jones in Good Strat, goodstartegy, Martyn Jones

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Big Data, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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: #IT #BigData

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

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Analytics, Big Data, data lake, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Joens, trading

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

OLYMPUS DIGITAL CAMERA

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

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Consider this: 7 Handy Phrases To Unhinge Your Boss

06 Friday Feb 2015

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

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Entertainment, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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.


Channels: #Careers: Getting Started #leadership

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

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Big Data, BS, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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 →

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  • You don’t need a data warehouse to do data warehousing March 22, 2023
  • Data Warehousing means having thousands of ETL jobs March 21, 2023
  • The data warehouse is the repository for the post-transactional data March 20, 2023
  • Does your way of providing data have business value? March 19, 2023
  • Data warehousing stands in the way of progress March 18, 2023
  • Data Trailblazers: 2022 Vision January 2, 2022
  • Tea with The Data Contrarian: Afilonius Rex December 10, 2021
  • Reality Check: Data Mesh and Data Warehousing   December 5, 2021
  • Myth-busting: Data Mesh and Data Warehousing – Revisited November 25, 2021
  • Heaven help us! Have you seen the latest Virtual Data Warehouse bullshit? June 26, 2020

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