Monthly Archives: February 2015

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.

I have it on good authority that by 2016, Big Data will be at the forefront of everything we do, think and wish for. In the era of unending benefits, options and possibilities, what more can one say other than ‘All hail Big Data! Our spiritual leader and mystical guiding light’.

Which brings me to the subject of Harry Potter.

Big Data and Harry Potter

There’s a series of Harry Potter books written for children, yes, children’s books written for children. Not written for adults, but for children. Anyway, there are a lot of Harry Potter books, with an amazing range of popular titles, such as Harry Potter and the Mitten of Mirth (apologies to Stewart Lee), Harry Potter and the Piece of Wood and Harry Potter and the Crock of Crap, and so on and so forth. All of which reminded me of the fantastic Big Data promotional material that can be found on scientific, technical and management blogs, and also on the social media sites, such as LinkedIn, Facebook and Twitter.

As a tribute to Harry Potter, Big Data and those who vaingloriously and thanklessly schlepp the hype, I have rewritten some of the amazing and almost unbelievable Big Data success stories in the style of postmodern children’s literature.

Enjoy.

Big Data and the Pot of Tea

Now who doesn’t love a lovely cuppa? From Indore to Idaho, Dubai to Dortmund and Beijing to Birmingham, it’s the breakfast drink of champions, the soother of pains and the bringer of temporal peace.

But did you know that if it weren’t for Big Data, we wouldn’t know how to brew a proper pot of tea. You see it’s all in the value chain, the data and the fresh boiling water, naturally.

If it weren’t for Big Data, then tea growers wouldn’t know what they were doing, they wouldn’t know when to plant, when to nurture and when to harvest, they wouldn’t even know what to plant, or even why – without Big Data they would be completely stupid. Likewise, the processing of tea without Big Data would lose all sense of being and purpose, which leads me to logistics. Without Big Data it would be impossible to move the exabytes of tea from one side of the world to the other. Fact!

But the most important role of Big Data in all of this is simple. If we hadn’t been able to use Big Data to analyse the results of how everyone makes a pot of tea, and the feelings of satisfaction or disappointment they gain from that process, then no one could even make a decent pot of tea, anywhere, and they wouldn’t even be motivated to engage with the process never mind the tea brands.

Now that really is food for thought.

Big Data and the Piece of Work

It is official, the fact that dared not speak its name has now been revealed: Big Data is helping to take the conjecture out of hiring and firing.

We can now leverage the amazing power of Big Data in the cradle-to-grave Human Resource process. This is the true democratisation of the world of work.

Because of the amazing correlations made by data scientists across massive HR datasets and golden nuggets from social media, we now know how to place people exactly in the organisation, in the approptiate roles and aligned to the appropriate responsibilities.

In the past we used to hire on the basis of personality, values, recommendations, knowledge and experience, certifications, qualifications, cost, and so on and so forth. All that has changed.

Big Data has determined that the most important asset that a job candidate can possess is aggregated mediocrity. No more heroes, no more geniuses, no more hard workers and thinkers, and certainly no innovators, because what most established organisations need, according to in-depth Big Data research, are workers who are dull, boring, servile and above all, mediocre.

How can we ensure that an employee sticks to the game plan? Now here’s a thought: just wire them up with wearable Internet of Things probes, and if they start to think too much or do too much or just exude an air of knowing too much, the system just automatically issues them with their dismissal notice, alternatively, the more humane organisations may consider the use of incentivised mild electric shocks administered via those very same IoT devices.

No longer will HR departments and line managers take the rap for hiring the wrong suits. Now we can blame it all on Big Data, or even better, the belligerent, alienated and disgruntled ex-employee.

Big Data and the Demographics of Destiny

Thanks to Big Data we now know that people (that’s human adults of a certain age or maturity) give birth approximately 9 months from the process point of conception, the people are invariably female, and the offspring are invariably of one sex or the other, in no special order of appearance.

Thanks to Big Data we now know that most people live from a ripe young age of approximately nothing (years of age) to a grand old age (of something), or not, and that this applies to various levels of demographic extrapolation, and is generally accompanied by an attendant aging process.

Big Data and the Universal Heater Malfunction

Whichever way you slice and dice it, the great Universal Heater in the sky is warming up the planet, and probably this is happening because of something we did way back when, or conversely, something that we persist in doing in the here and now, or maybe a bit of both.

Climate Change is reality. Heuristics together with dialectic and historical materialism showed Climate Change to be a fact, then science proved it to be true, now Big Data substantiates conclusively that true is frequently correct.

So, either you think that Big Data will save the world, as it most evidently will, or you might just dismiss it, along with Global Warming, as yet another scary Marxist-Leninist inspired technology plot and fad, which you most evidently can do.

Personally I think that Big Data is generating far more tonnes of equivalent Greenhouse Gas emissions than some people will openly admit to, and that the charges of Big Data collectivism are somewhat premature, foolish and misguided, even if they are suspiciously, precociously and predictably present, or hopelessly, recklessly and witheringly absent. Which either way, represents a dilemma within an enigma inside a hypothesis wrapped in swaddling clothes – to various degrees of certainty, of course.

Maybe we need to consider hedging our bets a little with the ‘angels of our better nature’. Either way, Big Data will be inextricably linked to Climate Change, Global Warming, Immigration, Emigration, Inhalation and Global Governance, which will keep a lot of curtain twitchers busy for decades to come.

Big Data and the Wrong Trousers

In the Oscar winning film The Wrong Trousers, the accidental hero, Wallace, thinking that it will make his life easier, becomes the innocent victim to a pair of automated ‘Techno Trousers’, that take control and carry him off against his will in directions and to places that he does not wish to go.

Wallace uses the ‘Techno Trousers’ thinking that it will increase his power and stride when he is out walking the dog – that’s Gromit, if you remember – but he is gravely mistaken.

Things get worse when the felonious penguin Feathers McGraw attempts to make Wallace, now locked into Techno Trouser mode, an unwilling accomplice to the theft of a diamond. Thanks largely to Gromit, the plan does not succeed, and Feathers ends up in jail. After his traumatic experiences, Wallace realises that the Trousers are not the valuable addition to his lifestyle that he once thought they were.

Which is pretty much sums up what can be said about some of the vast quantities and qualities of ‘valuable’ Big Data flowing through the data lakes and digital drains of the corporate underground. Some of it is as potentially useful and risk free as ‘Techno Trousers’, and may not be the valuable addition to your corporate lifestyle that you might have initially been told to expect.

But your idol may be Feathers McGraw, in which case the only sensible recommendation for you may be: do your own thing, Big Data baby! Word!

That’s all folks

So if you are a CIO, CTO or even CEO who feels an extremely strong affinity to Harry Potter, a series of children’s books, then you might also be fascinated by the whole Big Data hoopla as well.

But before I leave you, back to the thought… what about Big Data being King? What’s all that about then?

Well, every courtesan needs his monarch and court, and this is no different with the Emperor’s Big Data. All those pushing the Big Data showboat by dodgy means or foul, need a royal reference point, a shining brilliant light of data aristocracy and patronage in which they can bathe and frolic, oblivious to the trials and tribulations of the proletariat, and the organisations they seek to fleece. In the old days the monarchs would have names such as Friedrich II der Grosse, Aethelred II the Unready or Alfonso X el Sabio. Now they are named after fruits, brown sauces and Greek and geek myths, and others with even less imagination than sense, simply go for the old three letter acronym solutions, such as BOB, SUE or HAL.

Am I bothered by this? Should you or your organisation be bothered by this? Does this affect the price of bread or the quality of beer? Will this be the mommy and daddy of brazen hyperbole, manipulation and fraud to end all credibility, sanity and reason anyone has ever had, everywhere and anywhere, the whole world over?

No, not at all. This is a minor blip, blown up out of all proportions by the media foghorn of published (not public) opinion. So, down with the data Aristos. Vive la République! Vive la Résistance!

Many thanks for reading.

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The amazing world of Fred’s Big Data

15 Sunday Feb 2015

Posted by in Big Data, Consider this

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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.

Business process management, what is there not to like. It has revolutionised the way we organise and do business. Right?

Your business is applying, measuring and managing the process, right?

The process is king, it’s a great process, and your organisation loves it, right?

There is no process other than the known, approved, socialised and proven process, right?

Well, we’ll see about that.

You may think that your best data is in the enterprise data warehouse (Bill or Ralph). I will not argue with that.

You may consider that the gold rush starts and ends in the exploitation of Big Data (what I like to call All Data). You may have a point, or not.

You may speculate that your best data may come from external social media sources? Well, that’s a commonly held view, and who am I to challenge a commonly held view, for as erroneous and popularly acclaimed as it may be.

But, if you thought all this were true, would you be right?

Now, consider this…

In companies, good, mediocre and bad, people follow processes, they follow these processes as best as they can until they work out how to do something better, faster, cheaper and easier. They may even skip process points completely or simply rearrange them, and in ways that business analysts never thought of. You see, processes can be clever, but people are really smart. But we knew that already.

People can be astute, honestly and innovatively so, and businesses function as well as they do, not only because of the assets, systems or the processes, or even due to the capital of the business, but because of the people who work for them, and conspire together to make things run effectively.

This fact now stated opens up the whole world of hidden data and knowledge, the real data gems of the business world. I mean ‘business world’ in the widest sense of all the businesses all around the world, whether for profit, non-profit or public.

From the top to the bottom of the organisation, in the matrix, left to the right, there is a wealth of data that refuses to be digitised, evades formal capture and defies measurement.

In my precocious youth I used to refer to this data as the “data that falls between the cracks”, and would got weird looks from my management as a result.

My first encounter with “data that falls between the cracks” was with Fred’s Book.

Fred’s Book was the mother of parallel processes, and then some. In my youth I was asked to model an industrial process in a factory. My focus was on logistics, production and distribution. In the course of my investigations I discovered some glaring anomalies between the senior management views of the process and the actual interpretation of that process. I dug deeper. Formally, I was told nothing that really veered from the official business operating model. Of course, it was different story after a couple of beers on a Friday night.

It turned out there was a whole sub-process in play, and that it was all being coordinated through Fred’s Book, an almanac of all things related to the off-book business process, curated by Fred, a shop-steward and supervisor of one of the departments at the core of the process.

I interviewed Fred to find out more of the fascinating process that he was curating, but at the end of a long session he asked me not to reveal his ‘book’ to senior management.

Fred’s book was the glue that held part of the business process together, it was the data, information and knowledge that kept things working in spite of inadequacies and contradictions in the formal process. But later I found out there were more ‘books’. There was Tommy’s book in distribution, Lily’s book in production, Maria’s book in quality, and Harry’s factory floor management cookbook. The whole business was running on an efficient and effective parallel process, documented in living notebooks (the paper kind), notebooks that underpinned and provided a workable framework for CRM, MIS and ERP.

Over the years I have encountered various artefacts of the Fred’s Book kind in many organisations. Not only that, sometimes Fred’s Book is virtual and codified, making it even less tangible to the digital data twitchers and information botherers. But it still has unmeasured value. We know it has value, but we might not be sure as to how much value. Which in general is more than can be said for faddish Big Data. The personal knowledge, experience and wisdom that people bring to bear in their working and private lives is one of the most valuable assets that a business potentially has access to, even if they don’t know it or incapable of appreciating what they have and of then encouraging people to share and apply that knowledge and experience, wisely.

So, how do we ensure we don’t lose Fred’s knowledge and experience? How do we stop the good sense from leaking out of organisations? How do we ensure that we don’t throw out the knowledge baby and keep the Big Data bath water?

Driving business agility, competitiveness and strength requires far more than Data Warehousing, Big Data, Business Intelligence, Machine Learning, or Analytics and Data Science. It takes far more than supervision by numbers, shapes and colours; decisions based on sophisticated coin flipping; or, the voodoo analytics of the causation-free data bodgers. It needs the data, information and knowledge that resides in people’s heads and the wisdom needed to know when, where and how to apply that knowledge. That’s where the real golden nuggets are.

This is why I am so sceptical of the claims of Big Data “gurus” and other assorted charlatans, who seem to believe in their Homeric powers to move whole armies across the Alps with the click of a mouse, and just backed up with the invisible safety-net of the promised benefits accruable from exabytes of trashy social media feeds and bloated internet logs.

Historian Paul Kennedy, while acknowledging that no single variable can explain success, maintains the metanarrative that wars are ultimately won by a superior organization imbued with a culture of innovation that actively encourages inquiry, experimentation, and interdisciplinary problem solving.

This to me means that an organisation must focus on what is important, what it is good at, and what it has that is really important, and then apply it.

For many companies diverting the corporate attention from the important to the peripheral is a bad idea, that comes with many risks, and if that marginal and peripheral shiny-thing turns out to be large volumes, varieties and velocities of data with little or no value, then even more reason not to get caught up in the facile, superficial and peripheral issues of little or no value.

I like football, many people do. I support two teams, Real Madrid and Cordoba. When Real Madrid play FC Barcelona what preparations are made in advance? What is the focus of the preparation? Some people say that fan motivation helps the team to perform better, so maybe we should analyse the entire global fan base of Barcelona, Big Data style, in order to work out how to influence the influencers. But would I suggest such a tactic to any of the big clubs? Of course not, they would tell me to go away, and quite rightly so.

Adland legend Dave Trott tells a great story about former Leeds United football club manager Don Revie:

Don Revie had a dossier on all referees.
He used to make his teams study the referee for the upcoming game, so they could ask about his family, his hobbies, etc.
Most teams get 50% of decisions go their way.
Leeds used to get 80% of decisions go their way.

No Big Data Kool-Aid, no Data Science, no voodoo analytics, just good old fashioned knowledge, experience and street smarts.

Many thanks for reading.

A brief introduction to Knowledge Management

14 Saturday Feb 2015

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

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

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

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

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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 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.

Robitussin also features in comedian Chris Rock’s running gag on poverty, illness and misfortune, and in spite of all of that, how some people got by.

In Chris’s story the cure for every ailment suffered by anyone in his childhood neighbourhood, apart from imminent death or death itself, was Robitussin.

So it was used to fix every ailment; asthma, cancer, a broken leg, and so on and so forth.

Whatever ailed you, Robitussin was the necessary remedy.

But, this wasn’t about a company like Pfizer making claims for their products. It wasn’t Bayer selling flim-flam snake oil. It wasn’t about Glaxo flogging a universal panacea. I’m sure they would never dream of being anything other than totally ethical, decent and honest.

When Chris Rock talked of Robitussin he was telling a story, for comedic effect.

However, people who ‘fluff up’ Big Data in return for favours are not in the business of comedy, or for that matter, have any issues with the truth, or lack of it.They are in the business of hustling vapourware, half-truths and arrant nonsense; for a price. Now I assume they do it for a price, as putting one’s ‘hiney’ and one’s soul at the service of ‘business’, purely out of the goodness of one’s heart, would be seriously dopey.

Big Data populists claim it’s all new. But it isn’t. And they are not presenting the facts and there is nothing about Big Data that we haven’t seen before the ill-advised name of Big Data was ‘invented’.

Big Data populists also claim that methods and technologies used with Big Data are new; but they aren’t, it’s ignorance that’s says things like that, ignorance or drugs.

Are Big Data populists aware of the fact that they are frequently inaccurate in what they claim?

I was once told to never underestimate human kind’s ability to push the frontiers of stupidity. But surely that can’t be the case.

I have no problem with most of the technology and methods that have been repackaged under the Big Data umbrella. The thing is, none of them are new.

And a lot of the claims that are now made for Big Data have been roundly disproven over the last three decades and more. But still the assertions come in thick and strong. Like a never ending waft of acrid obnoxiousness.

Want to do your job better? Big Data!

Need insight into your organisation? Big Data!

Need to know your customer? Big Data!

Need to stick the beer next to the diapers? Big Data!

Need to be pragmatic? Big Data!

Need to analyse data in a parallel distributed processing environment? Big Data!

Need to do search, sort, do ‘data science’ and present? Big Data!

Need to do business computing with data? Big Data!

Need to fight terrorism, organised crime and unorganised stupidity? Big Data! Big Data can cure itself?

Need to cure AIDs? Big Data!

Need to control Ebola? Big Data!

There is an endless stream of Big Data baloney, and every day there is more of it.

I read a book on Big Data last year. I won’t state the exact name of the title, to protect the shameless and guilty who were responsible for its making. But, it was something along the lines of ‘Big Data for The Intellectually Challenged’. A complete and utter piece of perfidious, deceptive and artless nonsense. But you got to laugh, it’s tragic, but it’s also very funny.

Sure, that kind of thing drags the professions through the mud again. But this nonsense is also protected under freedom of expression legislation. So what can be done?

This blog piece was not meant to be a technical critique of the artless and fact-free claims that are made in the name of Big Data. Although there are many artless claims made to support the ‘new’ technology of Big Data.

For example, one could call people on a wide range of claims made to bolster the idea that Big Data is new.

Or even better, to correct blatant misrepresentation of Big Data, the history of IT and the evolution of database technologies.

Some Big Data populists claim that databases evolved from the simple use of flat files and went directly to relational technology with no intervening developments.

This is nonsense.

It wilfully ignores a whole swath of database technologies, some of which are still in use in major organisations to support their core business IT applications.

Some Big Data populists claim that Big Data processing was never done before.

This is also nonsense.

Data analytics has been carried out on Very Large Data Bases (VLDB) since the file size capacity of open operating systems grew exponentially and the price tag the hardware it ran on plummeted considerably.

Big Data populists talk about Real-time Data Streams and Complex Even Processing as if they were new, and could only be contemplated if Big Data is an integral part of the mix.

This is worse than nonsense. It’s a fib.

Before Big Data ‘arrived’ on the scene, we could already do these things, and more. The only thing that was stopping some organisations from doing so was cost.

I recently read that when Big Data technology evolves there will no longer be a need to have multiple copies of the same digital data files?

What this particular Big Data guru didn’t realise was that this ‘feature’ was a fundamental aspect of operating systems developed in the sixties and seventies. Yes, four decades ago.

Another thing that Big Data populists do is to revisit the ‘360 degree view of the business’ claim. We had this with MIS, then with Information Centres, Enterprise Data Warehousing and now Big Data.

This claim is so old, misleading and contemptible that it should really be simply laughed at.

Don’t get me wrong, Enterprise Data Warehouses (Inmon or Kimball, take your pick), done right, can deliver interesting benefits and valuable insight. But claiming that EDW would drive a 360 degree view of the organisation was no better than claiming that Big Data will deliver that same total view. Which is nonsense on steroids, running on stilts.

But, for me, perhaps the biggest piece of boloney discharged by Big Data populists, is this. “Big Data can spot hidden patterns in petabytes of information”.

This is AAA+ grade nonsense that you can take to the Big Data laughing academy.

As anyone who knows anything about trying to identify hidden patterns in data, will know, there comes a point at which any increase in data volumes used in data analytics of a certain nature will actually diminish the probability of identifying any hidden patterns in that data.

I could go on about the nonsense claims of the Big Data populists all day, yes, the variety, volumes and velocity of Big Data hype knows no bounds. But it should be blatantly obvious what is going on here.

How many more examples do people need for them to at least consider that they may be getting hustled by industry supported ‘independent’ populists?

Big Data does not mean more and better insight, and it may frequently mean only one thing. That you have more valueless data to store.

On another tack, what about the environment?

People worry about leaving their domestic appliances on in standby mode, because of that little bit of energy that the standby light uses. So just imagine what is happening with vast amounts of valueless “Big data” kept on disks and in storage, 24x7x52, for years on end. How many standby lights does that all represent?

So, the only thing that the accumulation of very large quantities of valueless data is doing is this: indirectly producing more greenhouse gases and warming the planet. That’s climate change for you and me.

The mindless application of Big Data dogma is not contributing to the Climate Change battle nor is it contributing insight or financial advantage, it is not fixing real world problems, and it is not universally applicable.

So, beware of the Big Data populists, hustlers and snake-oil merchants. Big Data is not a hi-tech turbo-charged Robitussin, and it’s certainly not for everyone. In fact, the business models that make sense are those that Warren Buffet and other smart investors take a shine to, and these businesses are generally not based on grandiose, ‘innovative’ and flaky models that grab the popular attention and published opinion, nor do they have ‘value’ that is based more on irrationality than on hard facts.

Many thanks for reading this piece.

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 Faustian side of IT – Part 1

12 Thursday Feb 2015

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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: #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

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The Wow Wow Factor! Trading, Big Data and 7 Habits The Big Data 'Wow Wow' Factor Hi, 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.  Coco Dabbling in his Big Data Lake

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Consider this: 7 Handy Phrases To Unhinge Your Boss

06 Friday Feb 2015

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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.

Channels: #Careers: Getting Started #leadership

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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?

Now repeat the exercise, only this time make a conscious effort to pick out couples of older people who may also be out taking a stroll, shooting the breeze or rocking the gange – okay, maybe not in London, but you get the picture.

Have you gotten what it is yet?

Right! Bang on the money, honey.

Just where are all the couples of tall old people? WHOOSH!

Good question, right question. So, let’s look at big details in the big analytics of the big data.

Hand on heart, I can honestly state that big scientific Big Data analysis has revealed that there are very few taller older women – little men and women form the overwhelming majority, to the tune of 95,9915645289% (Source: Diese Zahl ist nicht verifizierten).

Wow, that’s amazing, isn’t it. A very reliable quoted German source and all. I bet you are sitting there all gobsmacked. Like as if you just had a super bowl moving Big Data epiphany; metaphorically, that is.

So what is going on there?

Again, conspiracy theory, big data analysis and good ole cracker-barrel common-sense pragmatism indicates that the short people are knocking off the tall people, or summit, and at an outrageously alarming rate, all over the civilised world. So much so, that very few tall people now reach what we in the trade call ‘old age’. It’s alarming, but true.

I know, I know, I hear you, I hear you, you want me to tell you it ain’t so, but I can’t, I have ten partners and 250 kids to feed, and Big Data flimflam is my only source of visible income, so… suck it up…

Sometime after I was born I encountered good fortune when I least expected it. At age seven I could have played for the New York Knicks or the Philadelphia 76ers. However, I got wise to it, the whole bad mojo, the massive uncool vibes, and the “I shouldn’t even be here today” mentality, and all thanks to big Big Data.

I got wise to the “tall and old” existential issue, and through a combination of intelligent redesign, positive feedback thinking, Facebook likes and faith based build-up workouts, I was able to reduce my loftiness (my vertical carbon figure-print) and the vertically-challenging growth projections to a level that would ensure my longer term, forward-reaching and sustainable growth and survival.

Which is amazing, with a capital M. Yes, ladies and gentleman, BD’s on the block and the roofs on fire. So let’s party like it’s BD 99.

Which leads me to this question.

With all the available evidence of Big Data’s value at out disposal why do I still get harangued by party-poopers who have the bare-faced cheek to ask me “and, what has Big Data ever done for humanity?”

Well, bugger that… let ‘em eat cake.

By the way, did you know that Sigmund Freud was a great basketball player? It’s miraculous what one can do with Big Data.

Finally, a take away. If you are planning on laying a new lawn this next weekend remember the golden rule of that famous NYC entrepreneur and Big Data tycoon Vincent ‘Vinnie’ Antonelli: “Green side up, fellas! Green side up!”

Many thanks for reading.

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