Tag Archives: Martyn Jones

Who’s afraid of the Big Data Contrarians? Here’s 500 reasons not to be

11 Wed Nov 2015

Posted by in All Data, Big Data, Business Intelligence, Cambriano, Consider this, Good Strategy, Inform, educate and entertain., Strategy

≈ 1 Comment

Tags

, , , , , ,


If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks.

When I first started The Big Data Contrarians group on LinkedIn I was thinking that maybe we would get 100 members within three or four months. Well, I was mistaken. Since the 1st of July, the membership ranks of The Big Data Contrarians has risen to over 500 members. However, it’s not about the quantity it’s about the quality, and The Big Data Contrarians is ‘the nicest Big Data community that you are ever likely to encoun Continue reading →

Big Data is Bullshit

11 Wed Nov 2015

Posted by in All Data, Analytics, Big Data, Data Lake, Inform, educate and entertain., Martyn Jones

≈ 2 Comments

Tags

, ,


“I’ve been accused of vulgarity. I say that’s bullshit.” – Mel Brooks

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Being brutally honest, Big Data is bullshit. Not only is Big Data bullshit but it comes with a surfeit of pranksters, pundits and prissy big data bullshit-babblers, all willing (cue narration by Morgan Freeman) to “big-up Big Data in a vulgar, ill-mannered and predictably nauseating dance of professional-hustling… as old as time.”

However, is all Big Data bullshit? Is it all a fad, a load of old tripe and a confusion of weasels together with their surfeit of weasel words? Or, is there something of value, substance and tangibility to be found amongst the volumes, velocities and varieties of brazen and opportunistic self-aggrandizement, toxic speculation and opinions and unverifiable miracles?

For Google, Facebook and Twitter, Big Data certainly isn’t BS. For example, Google rely on Big Data as the biggest irreplaceable element in their colossal advertising business – so I am lead to believe. A business that accounts for more than 90% of Google’s revenue. So clearly, for the masters of web-based unstructured and complex search, Big Data is an essential element in their business model. The biggest essential element by far.

However, let´s be honest, we should consider the obvious. How many of us are really going to do business like Google?

Big Data technology and service vendors benefit tangibly from the Big Data movement, at least this is the impression that I get. Indeed, there is much talk about the relationship between Big Data, the Hadoop ecosphere and the big wild world of open source, but what is more interesting is that companies are bringing in revenue on the back of Big Data by offering battle-hardened business and enterprise versions of open source software. Then there is the business in consulting, with such a demand for Big Data gurus, Master Data Scientists and Number Conjurors, there must presumably be real people working in these roles, and paid handsomely for doing so.

But apart from the ‘success’ associated with the foundation of so many Big Data start-up businesses and the market-based commitment of some of ITs’ ‘great and good’ to the new digital zeitgeist of data volumes, velocities and varieties, just where are the other success stories of Big Data coming from?

To help us in our quest, I earlier compiled a not-so-exhaustive list of Big Data success stories, celebrity-chef like, to help us out. Here are some of the Big Data gems that I managed to track down:

  • Thanks to Big Data, the taxi service alternative channel Uber is making massive waves and shaking things up in the transport sector.
  • By leveraging Big Data AirBnB is turning the hospitality business on its head, and what´s more, making friends, and influencing people as they are doing so.
  • Amazon would not be what they are today if it were not for Big Data, in fact, without Big Data, they would be nothing.
  • One of the industries that will suffer revolutionary transformation because of Big Data will be the banking industry.
  • Big Data will increase the GDP of the USA by at least 1% or more, and the Spanish GDP could likewise add an additional 1%, for similar reasons.

These would be all great headlines for Big Data success stories, apart from one small flaw. None of them is exactly a Big Data success story in the Big Data defining characteristics of volumes, varieties and velocities of mainly unstructured data or in terms of the Hadoop technological kitchen-drawer ecosphere.

Something is happening, and it is not exactly legitimate. Can you guess what it is yet?

When it rains it pours, and when it rains Big Data hype it quickly turn into a monsoon of cloying hysteria. Spotting and pointing at Big Data bullshit babblers on forums like LinkedIn Pulse, Forbes and Information Management is no fun, unless your fun is nuking a school of intellectually challenged fish floundering in a barrel of vintage Malmsey.

However, it not only is no fun, but also more times than not it is a complete and utter waste of time trying to get people to adopt a more critical approach to thinking. Because for every Big Data bullshit babbler, there is a battalion of intransigent Big Data believers stuck in untenable and absurd positions, marooned from reason and ways back to rationality. You can’t use logic against belief, and you can´t turn back a rising tide of IT refugees who are desperately seeking succour in the apparently safer-havens of Big Data, Data Science and Data-driven voodoo.

Only the other day I read that “The emergence of Big Data is now allowing CEOs to increasingly base decisions on current “reality” rather than past experience, but the risks in the integrity and fullness of the data that they are “seeing” and “hearing” is often a barrier to getting a clear picture of what is actually going on.” This is really taking shameless baloney and wilful ignorance to all new heights, but it doesn’t stop there.

Elsewhere another eminent Big Data bullshit babbler wrote, “Clearly big data and AI will change almost every industry this decade… but none more than these”, referring vaguely and vacuously to “Healthcare, Finance and Insurance”.  What species of shameless and fatuous willy-waving goes so far out on a limb that it becomes massively removed from even being a grandiose and beguiling ‘bigging-up’ of a fad?

Finally yet importantly, I almost choked on my supersized Big Data popcorn the other day when I read, “Today, with the rise of the Internet, we capture “data” on everything.  Therefore, the new term “Big Data” is honestly like 1985 again.  But this time, Big Data will actually be really big and by some forecasts, be a $40 billion industry by 2018.”

This is not hype, it is not even simple deceit, it is astroturfing of 22 carat bullshit, and in most cases it’s clearly deliberate, it´s intentional and it´s grossly misleading. So why do people do it?

Given that Big Data is very much a niche technology, with very much a niche appeal, why do so many buffoons go around pretending that Big Data is for all of us? Like as if it was some sort of digital universal-panacea, when at the moment, and at best, it is a walk on bit-player with just a couple of lies who aspires to B actor status. In this sense, at present Big Data isn´t even the hero´s best friend.

Before I close the piece, I will leave you with the thoughts of Dan Ariely. Why? Because it just irritates the hell out of a section of the community of Big Data bullshit babblers, and it´s actually very accurate. Here it is:

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”

Many thanks for reading.

In subsequent blog pieces I will be sharing my views on the evolution of information management in general, and the incorporation novel and innovative techniques, technologies and methods into well architected mainstream information supply frameworks, for primarily strategic and tactical objectives.

As always, please reach out and 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 you will even consider sending me a LinkedIn invite if you feel our data interests coincide. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.

For more on this and other topics, check out some of my other posts:

Looking for your most valuable data? Follow the money – http://www.itworld.com/article/2982352/big-data/looking-for-your-most-valuable-data-follow-the-money.html

Stuff a great data architect should know –https://goodstrat.com/2015/08/16/stuff-a-great-data-architect-should-know-how-to-be-a-professional-expert

Big Data, the promised land where ‘smart’ is the new doh!–https://www.linkedin.com/pulse/big-data-promised-land-where-smart-new-doh-martyn-jones?trk=prof-post

Absolutely Fabulous Big Data Roles –https://www.linkedin.com/pulse/absolutely-fabulous-big-data-roles-martyn-jones?trk=prof-post

Not banking on Big Data? – https://www.linkedin.com/pulse/banking-big-data-martyn-jones?trk=prof-post

10 amazing reasons to join The Big Data Contrarians –https://www.linkedin.com/pulse/10-amazing-reasons-join-big-data-contrarians-martyn-jones?trk=prof-post

Amazing Data Warehousing with Hadoop and Big Data–https://www.linkedin.com/pulse/cloudera-kimball-dw-building-disinformation-factory-martyn-jones?trk=prof-post

The Big Data Contrarians: The Agora for Big Data dialogue–https://www.linkedin.com/pulse/big-data-contrarians-agora-dialogue-martyn-jones?trk=mp-reader-card

The Big Data Shell Game – https://www.linkedin.com/pulse/big-data-shell-game-martyn-jones?trk=mp-reader-card

Aligning Data Warehousing and Big Data –https://www.linkedin.com/pulse/aligning-data-warehousing-big-martyn-jones?trk=mp-reader-card

Big Data Luddites – https://www.linkedin.com/pulse/big-data-luddites-martyn-jones?trk=mp-reader-card

Data Warehousing Explained to Big Data Friends –https://www.linkedin.com/pulse/data-warehousing-explained-big-friends-martyn-jones?trk=mp-reader-card

Big Data, a promised land where the Big Bucks grow –https://www.linkedin.com/pulse/big-data-promised-land-where-bucks-grow-martyn-jones-6023459994031177728?trk=mp-reader-card

The Big Data Contrarians – https://www.linkedin.com/pulse/big-data-contrarians-martyn-jones?trk=mp-reader-card

Is big data really for you? Things to consider before diving in–https://www.linkedin.com/pulse/big-data-really-you-things-consider-before-diving-martyn-jones?trk=mp-reader-card

Big Data Explained to My Grandchildren –https://www.linkedin.com/pulse/big-data-explained-my-grandchildren-martyn-jones?trk=mp-reader-card

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

Big Data and Catfish

11 Wed Nov 2015

Posted by in Big Data, Business Intelligence, goodstrat, Inform, educate and entertain., Martyn Jones, Martyn Richard Jones

≈ Leave a comment

Tags

, , , ,


If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

“Contrary to slanderous Eastern opinion, much of Iowa is not flat, but rolling hills country with a lot of timber, a handsome and imaginative landscape, crowded with constant small changes of scene and full of little creeks winding with pools where shiners, crappies and catfish hover.”

Paul Engle

Catfish are said to be named because of their passing resemblance to land-roving felines. Admittedly, it’s not like any cat I’ve seen around the house, but if you simultaneously squint your eyes – impressionist style, guzzle a quart of bourbon and smoke a stash of ganja then maybe the resemblance becomes more obvious.

Catfish come in all sizes and varieties, at times they are native and other times they are classed as an alien species, rather like this Welshman who finds himself living in the Spain of Evo Morales, Kirchner and King Mohammed. Nonetheless, you won’t find many thrilling and delightful catfish videos on YouTube nor will you see many entered for the best of breed category at the International Cat Show.

So, what have catfish got to do with Big Data?

Well, there’s loads of them, they come in many varieties, and when they aren’t eating, they can be quite swift. But that’s not what I really wanted to discuss.

Now imagine this. Given the immense geographic dispersion, varieties and volumes of catfish around the world, wouldn’t it be interesting to carry out the Ma and Pa of all Big Data experiments?

We capture – over time of course, this is not the work of one day – all the catfish in the world, and we not only electronically tag them but we also fit them with IoT (Internet of Things) devices that will tell us:

  • Where the catfish is
  • Who the catfish is with
  • What are they doing
  • What are they eating
  • How do they feel in general
  • How do they feel about certain things, like the food they just ate, the company they keep, and what they do for entertainment and distraction, etc.

We could then collect this data, in centers all around the world, and then bring it all together in a massive Catfish Big Data Processing Centre in, for example, Coney Island.

Then the data we have so carefully collected, multiplied twice, and then searched and word-counted, in parallel, can be put to revolutionary, evolutionary and amazing uses such as:

  • Analysing and forecasting the Amazon buying trends of the lost Fukawi tribe – yes, the very same tribe who used to wander around boasting about their culture and presence usually accompanied with cries such as “We’re the Fukawi” or “Where the Fukawi?”
  • Creating appealing, compelling and revenue-busting online interactive ads for Bob Hoffman
  • Predicting the outcome of the US Presidential election, the regional elections in Catalonia and the vote for Chairperson at the Hello Working Person’s Club, Hello Village, in Jolly Olde England.
  • Preventing the outbreak of a world-wide pandemic of universal proportions thanks to Big Data being used to intervene virus-bearing inter-terrestrial vehicles sent by radical-fundamentalist-Martians inhabiting the once munificent planet of Zog.
  • Providing a wealth of material success stories that can be liberally sprinkled like fairy-dust on amazing Big Data stories from the keyboards of some of the finest Big Data bullshit babbling princesses on the entire world wide webs.

Over time, the competence, repertoire and agility of Catfish of all varieties, species, volumes and velocities (did anyone mention Catfish voracity and veracity?) could be augmented, potentiated and expanded by invasive, elliptical and sublime manipulation and neuro-retraining. We could then start with in-aqua interactive stimulus, menu variation and programming and extra-sensory passivation. Later the experiments could be more complex and more all-inclusive, reaching greater and greater degrees of perfection and inclusivity and exclusivity as the Catfish Big Data bandwagon rolled on… Waterlogged, waylaid and none the wiser. Indeed, in the future, all individual decisions will also rely on Catfish input, insight and turbo-charged predictive analytics of great and lasting charm.

Diet manipulation, an habituation test, and chemical analysis of urinary free amino acids were used to demonstrate that bullhead catfish (Ictalurus nebulosus) naturally detect the body odors of conspecifics and respond to them in a predictable fashion. These signals are used in dominance and territorial relationships and lead to increased aggression toward chemical “strangers.” The results support the general notion that nonspecific metabolites, as well as specific pheromones, are important in chemical mediation of social behavior.

There is also one very important thing about catfish that not many people know – apart from Michael Caine, who of course is a leading authority on catfish – and not many people know that either. But, anyway… Catfish are also bottom feeders, this is because of some complex physiological configuration that I won’t go into here – for fear of hurting the sensibilities of the puerilely prudish and wasting valuable drinking time – so in terms of data, the Catfish are able to plumb the depths of the most obtuse, dark and murky data, gobble it up, transform it and… err… load it into Hadoop, to be analyzed with Spark and presented in Excel… or something like that.

So, you’re not convinced by this story? Okay, I didn’t want to tell you this, but here it goes…

Many of us worry about leveraging all data, and mainly we worry because we don’t really have a clue about what we are bullshitting about. We see Big Data, and we believe that is good, whether we know this to be true or not. We are grasping at straws like so many bottom feeders, so many feces-eating walking-catfish, motivated by ideas of maximizing the sale of useless and outdated crap to ignorant people who don’t need it and can’t derive any tangible benefit from it in the first place. This is the biggest takeaway from this current schizophrenic Big Data BS Kulturkampf. Beyond a limited set of interest stories and an even more limited set of peripheral benefits accruable in very specific circumstances, there is nothing tangible that really grabs the attention, apart from the razzle-dazzle, smoke and mirrors of vacuous cant dressed up as showmanship.

The biggest problem with Big Data isn’t so much the plethora of technology (which is more and more reminding me of box of half-eaten chocolates,) nor even the niche applications – for as miraculous and mysterious as most of them are. It’s more about Big Data being turned into a seriously creepy religion, where belief is paramount, and where there is little or no questioning of the tenets, the fables, the dogma and the liturgy, and where one person’s willful ignorance is just as valid as another person’s aspiration to gain knowledge and experience.

Make no mistake, Big Data can be useful for certain businesses and for certain situations. But for many of us in practice it’s either a peripheral player or doesn’t even make it to the bench.

A final thought. Treating Big Data as a religion is foolish, unhelpful and ultimately doomed to failure and ignominy. You have been warned!

For what it’s worth, I am currently writing the Ma and Pa of all Big Data parallel-analytics languages (details to follow), and I might even call it catfish (it’s sorta catchy) and I will have it represented by a muddy-looking open-source cartoon catfish, one worthy of a spot on YouTube.

Many thanks for reading.

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

The Amazing Big Data Challenge – 2015

11 Wed Nov 2015

Posted by in Analytics, Big Data, goodstrat, Inform, educate and entertain., Martyn Jones, Martyn Richard Jones, Strategy, The Amazing Big Data Challenge

≈ Leave a comment

Tags

, , ,


For those of you who are familiar with the world of Big Data you will also be aware of the vanguard data community known as The Big Data Contrarians (the most fabulous Big Data community online).

Launched today (23 September 2015), the Big Data Contrarian’s Challenge is destined to fast become the most prestigious, enviable and prized challenge on the entire global world-wide-web. Continue reading →

The Princess Diana Memorial Data Lake

11 Wed Nov 2015

Posted by in Big Data, Data Lake, hadoop, Inform, educate and entertain., Martyn Jones

≈ Leave a comment

Tags

, ,


If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

If Princess Diana had been alive during the formative years of the Big Data revolution there would have been a plethora of influential Big Data bullshit babblers issuing their gushingly awful pieces in places like Forbes, the WSJ and professional blogging forums about the Big Data humanitarian causes closest to the heart of the peoples’ princess. And if tragedy had repeated itself, and had been reported not as paparazzi driven schmaltz or morbid vulgarity, but as something even more rancid and farcical, we would now have a Princess Diana Memorial Data Lake in some regal park in London or Milton Keynes –powered by Hadoop. Because, as the bullshit babblers would have it, “that is what she would have wanted”.

But, is this entirely fair? Should we view the outpourings of the biggest Big Data bullshit babblers on the entire internet as the inevitable result of free will, or is Big Data a message from God, in the same way that hard drugs are a signal to certain rock stars that they have too much available cash?

Which brings me to another issue. In a recent interview, I was given a list of data related terms, and was asked which one I preferred. Big Data, Smart Data, Small Data… you know what I mean. Anyway, I went off on a tangent about domestic pets and anthropomorphism. Okay, so it was logical entrapment, but I wanted to make a point. “Don´t you think that ascribing human behavior and thought to pets is a bit weird?” I asked. “No, came back the reply”. It wasn´t the answer I wanted, because the answer I wanted was “Yes, it certainly is” not a “No, that’s what my mum thinks as well”. I wanted to say see, people who ascribe human characteristics to dogs strike us as being a bit fanciful, but people who do the same for data? How can a bunch of recognizable symbols embody smartness? I mean, data by itself, of itself, is dumber than a rock.

So why do we pretend that the information, knowledge and the smarts are in the data and that data itself, without the need for any intervention (other than Hadoop, Sparke or Hive, etc.), is capable of revealing this smartness?

And the only thing I can think is that we are so desperate to sell useless crap that no one needs or wants, that we are even capable of saying the most dopiest of things in order to do so.

Anyway, I was at a Big Data conference recently, and every presenter selling a tool made exactly the same type of pitch. The amazing ways that their tools could establish correlations. Some of the examples of the correlations were so contrived, so obviously the creation of PR than the outcome of hands-off automated analysis, that it became seriously embarrassing, not as a professional, but as a human being. What´s more, no one mentioned the absent elephantine concept of causation, so everyone who went in clueless stayed happy in their ignorance throughout the whole wham-bam-tank-you-mam dog and pony session.

Now, I do think that the sort of data processing associated with Big Data does have a place in the old IT toolkit, but the levels of hype, misappropriation and downright lies is seriously queering the pitch. Just look at some of the Big Data articles in places like Forbes, Information Management and LinkedIn. If you haven’t yet noticed the tendency to use tremendous volumes, varieties and velocities of bullshit to push the Big Data envelope, then you really haven’t been paying enough attention.

Many thanks for reading.

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

Whither Big Data bullshit?

11 Wed Nov 2015

Posted by in All Data, Big Data, Good Strategy, goodstrat, Martyn Jones

≈ Leave a comment

Tags

, , ,


If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

Pundits far and wide are hailing the end of the period of big data babble, hyperbole and bullshit and are looking forward to an epoch of practical, tangible and verifiable Big Data success stories.

Gartner themselves came out some time ago and declared that Big Data was no longer in the hype cycle. Some took this as a sign that the Big Data bullshit bonanza was over, others were more cynical and suspected a highly orchestrated ruse, a move to the next level in the game plan.

But does this new attitude towards Big Data really ring true?

Accompanying this apparent bold openness, frankness and humility in the ranks of the rehabilitated Big Data bullshit babblers there is an awful lot of what appears to be ‘more of the same’. Or as the people of Thailand might say, “same, same, but different”.

As some of you might know, I am the administrative owner of The Big Data Contrarians community group on LinkedIn, and even I was somewhat taken aback by a recent piece by Bernard Marr entitled 20 Stupid Claims About Big Data. So much so that I wrote a fairly complimentary comment on LinkedIn about it. The thing is, even as a posted it I was thinking to myself “you’ll be sorry”.

Today I read yet another Big Data ‘reformation’ piece on LinkedIn Pulse, this time from Matthew Reaney and with the compelling title of The 5 Myths of Big Data.

Call me naïve, call me illusory, and a believer in humankinds need for basic decency, but I frequently have the idea that praising moderately acceptable behaviour leads to even more good behaviour. But it was not to be, and as fast as one could say ‘what the hell is going on here?’ back came a surfeit of astroturfed Big Data bananas – from all directions – bigger, brasher and more bogus than ever before.

Make no mistake, Big Data hype hasn’t gone away, it has become more subtle, more cunning and even more misleading.

Leading the charge is the initiative to discredit Data Warehousing by all means possible, and the amount of bullshit, disinformation and blatant lies doing the rounds is beginning to look like Big Data hype reflecting Big Data itself, if only in terms of the vast volumes, varieties and velocities that this Big Data babbling bullshit comes in.

But seriously, we are simply getting more of the same, as the end of the Big Data hype war is declared, we are subject to a bombardment of Big Data boloney via Cloud, IoT, the Hadoop ecosphere (as if using Hadoop was someone linked to ecology and saving the planet), and especially this incredibly obnoxious and dopey vehicle for Big Data tripe known widely as the Data Lake – more on that stupidity at some other time. But onwards and upwards…

This all reminds me of a joke from many decades ago, retold in part from memory.

A teacher was looking for a subject about which her class pupils could write, to set as a homework exercise.

After much deliberation she decided to as ask the children to write about what they thought of the police?

Sure, not a good question, I know, and as I stated, this was many decades ago, when even grown-ups could be innocent and naïve and hopeful.

Anyway, when the children had handed in all their essays, the teacher read the essays and was disappointed to find that most of them were very wishy-washy and that the children were almost all unanimously indifferent or grudgingly respectful of the police, except for one. One of the children, let’s call him Dave, was very critical and had written “I don’t think much of the police.” When the teacher asked Dave why he had written that, he replied “All police is bastards, Miss”. The teacher was vexed by the reply, but being a good and caring teacher she considered how she could change this obviously hostile view of the bobby on the beat and the police detective taking evil doers out of circulation, so she decided to do something about it.

She had a bright idea and took her problem to the police and discussed what could be done to give the children a much more positive view of the police and the work they did, so they would see the police as a necessary part of society, to be respected but not feared.

As a result, the teacher and the police organised a police day at the school. It was a big party, with lots of free goodies, badges and posters, rides in patrol cars, sirens, interesting stories and a movie, and a big discussion with the police dog handler and his faithful and brave police-dog, Ajax. The police took special interest in Dave, he was the one they wanted to convince the most, and he was the one they made the most fuss of.

At the end of the day, the teacher again asked the children to write about what they got from the school police day that she had organised.

The following Monday, after all the essays had been handed in by the children, she sought out and read Dave’s essay, eager with anticipation.

This time it contained the surprising phrase of “I really, really don’t think much of the police.”

Again, the teacher asked Dave why he had written what he had wrote, especially considering all the effort the police had gone to in order to leave a good and lasting impression with the children in general, and Dave in particular.

He simply replied “the Police is cunning bastards, Miss.”

Personally, I have respect for the professionalism, courage and hard work of many officers in our police forces, but when it comes to my view of certain Big Data pundits – and naming no names, just watch my eyes – the feeling is not the same.

Make of that what you will.

Many thanks for reading.

If you enjoyed this piece or found it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks,

Martyn.

Big Data, ESP and Transubstantiation

19 Wed Aug 2015

Posted by in Big Data, good start, goodstart, goodstrat

≈ Leave a comment

Tags

, , , , ,


vocationIf you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks.

To the layperson anxious for answers to complicated questions, the very idea of bringing together sets of disparate data and turning it into precious insights may seem like magic, a modern day alchemy, a goal placed well beyond the grasp of mere mortals. Fortunately, this is no longer the case, thanks in part to bagatelle-proportioned advances in Big Data and Big Data analytics and massive advances in imagination; we are able to look into the past, the present and the future, with absolute certainty. Continue reading →

Stuff a great data architect should know

16 Sun Aug 2015

Posted by in Consider this, data architecture, goodstart, goodstartegy

≈ 2 Comments

Tags

, , , , , , ,


Stuff a really great Data Architect should know

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks. Continue reading →

Why so many ‘fake’ Big Data Gurus?

16 Sun Aug 2015

Posted by in Big Data, Consider this, good start, goodstart, Strategy

≈ Leave a comment

Tags

, , , , , , , , ,


Why so many ‘fake’ Big Data Gurus?

Where do you all come from?

Where do you all come from?

All your integrity’s gone

Now tell me, where do you all come from?

From ‘Where Do You All Come From‘ by Mott the Hoople Continue reading →

Big Data, the promised land where ‘smart’ is the new doh!

03 Mon Aug 2015

Posted by in Big Data, Consider this, good start, goodstart, Martyn Jones, Strategy

≈ Leave a comment

Tags

, , , ,


If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks.

So you want to ‘do’ Big Data

Now everyone is doing Big Data you don’t want to be the odd one out, right? Of course not.

Now, if you are serious about looking at Big Data from a business perspective then I will try and lend you some advice. If you are doing it from an IT or technology perspective, then I wish you good luck, and I hope that your Big Data initiative doesn’t turn into another tech crash-and-burn show.

Now some Big Data pros are telling us that the place to start with Big Data is with strategy. Now, I’m too polite to call this out as abject bullshit, even though it is, and will instead content myself by offering an alternative and simple approach to approaching and addressing Big Data.

My first piece of advice is this. DON’T START WITH STRATEGY!

Don’t start with Strategy

Strategy is a coherent, cohesive and executable response to a significant challenge.

Strategy is not a definition of objective, a wish list of what you are trying to achieve or aspirational goals of a nebulous nature. No, strategy is not the objective but a means of reaching that objective. Strategy is real, tangible and executable. Strategy is doing.

So what is a Big Data strategy?

If a company is looking at the Big Data options, the last place they should want to start out from is from strategy. That is as silly idea as they come. Starting with strategy on the road to formulating viable responses to significant challenges and opportunities is like saying that before we choose strategic options and a realisable strategy, then you must have a strategy in place.

Strategy is not working out what you want to achieve. That sort of thing should happen prior to any strategic work. Neither is strategy an exercise in establishing starting points, nor formulating questions nor understanding the challenges. All of this should come well before the major strategy aspects even kicks-in.

Big Data strategy is a realisable, tangible and manageable response to a significant challenge, one that depends heavily on the availability, usability and credibility of Big Data (or Very Large Data Bases) and the business value of processing that Big Data.

So, a word of advice. If you are thinking of embarking on a Big Data initiative, do not start with strategy. That is a really daft place to start.

Start with business imperatives

Start here instead. With real business imperatives. This is where you are thinking about the big and significant challenges to the business, and how, at a high level of abstraction, you could go about meeting those challenges. Here you identify your challenges and your responses, aligned to your objectives.

If you can identify business imperatives that make it absolutely necessary to include elements of Big Data, then go forward with that mandatory requirement in mind. If not, then don’t try to shoe-horn Big Data into a place where it really isn’t needed or wanted. Because if you go against the grain in this way it may well hurt you and your business, in more ways than you bargained for.

Know what you are looking for

In order to go out looking for data requirements driven by business imperatives, we really need to know what we are looking for.

What we are looking for maybe highly tangible or less so. We may have to derive the data we are looking for by refining, aggregating, enriching, filtering and cleansing. Therefore, with those and other aspects in mind, we can go out and find what we need.

How to find what you are looking for

From looking at the data requirements, you should have a good idea of potential sources of that data. Agility in this aspect is predicated on the premise that one knows the systems on the IT landscape, the business processes and all the potential sources of data – at a high level at least. So, this is not the sort of work you can do remotely with little or no knowledge of the clients business, IT setup, processes or culture.

But anyway, after you identify the sources you move on to the next step.

Check data availability

Here you discuss aspects of the data you require with the database / application platform owners to ensure that:

  1. they have the data you are looking for
  2. that quality of the data is known and data quality can be addressed
  3. that the data is relevant for what is needed
  4. that the cost of providing this data is not prohibitive
  5. that this data can be made available to you
  6. that service levels could be put in place, if and when required

So far so good. Once passed these hurdles (and don’t forget this is a super-simplification) we move in to the next.

Make proof of concepts

So, now we know:

  1. What data we need
  2. Where we can get it from
  3. How we get it
  4. What we need to do to make it usable
  5. How we need to analyse it

Therefore, we go ahead and create a proof of concept or three. Simples!

However, make sure that all prototypes are governed by these simple timeless guidelines:

  1. The proof of concept should be small enough to be doable in a reasonable time-frame. I would be rather generous for the very first pilot of its type in a company, but would set that limit at 90 days, tops.
  2. Make sure that the proof of concept is big enough to be significant. Again, ‘simple enough to be realisable’ and ‘large enough to be significant’, should go hand in hand.
  3. Arrange your proof of concept execution into sprints. So your 90 days may be made up of nine 10 day sprints.
  4. Don’t try and shoe-horn infrastructure aspects of your initiative into sprints, it just doesn’t work, and simply pisses people off.
  5. If a proof of concept looks like it will fail, then make sure it fails early. There’s nothing worse than having people insist on pushing a dead project to live the full length of its planned term. Failing early means that business doesn’t take a dim view of the pilot, and will be more open to new proof of concept initiatives.

Analyse the outcomes

You run your proof of concept. You analyse, assess and represent your outcomes. You socialise, present and interpret.

Revise your strategic outlook accordingly

When you’ve done that you are in now in a good position to estimate the usefulness of the exercise, from both a qualitative and quantitative perspective.

Did I mention technology?

I did not want to touch in specific aspects of technology in this piece, in part, because I did not consider it a central issue in the theme of things. Of course, as part of creating proofs of concepts and pilot schemes you may want to experiment with the swatch (swaith? oh for auto-correction) of technologies out there. So go ahead and evaluate ‘Big Data’ technologies, and don’t forget, the answer to every Big Data technology question isn’t an automatic ‘Hadoop’. There are other valid Big Data technology options around, such as Lustre and GPFS, or even Oracle, Teradata or EXASol. Also, remember this, if all you are working on is a prototype, a proof of concept or a pilot then you can try and negotiate a free license with any of the major DBMS vendors for that initiative. So negotiate, bargain and get the most appropriate technologies with the best deals.

That’s all folks

Finally I will leave you with three guidelines to consider:

  1. Don’t ask ‘how can I do Big Data?’ but ‘what data do we need?’
  2. You don’t need to seek out Big Data. If you really need it, and it’s available, and it’s adequate and appropriate, then you’ll be getting it soon enough.
  3. Avoid searching for a Big Data problem you don’t have, which can only be solved by Big Data technology you don’t need.

Many thanks for reading.

In subsequent blog pieces I will be sharing my views on the evolution of information management in general, and the incorporation novel and innovative techniques, technologies and methods into well architected mainstream information supply frameworks, for primarily strategic and tactical objectives.

As always, please reach out and 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 you will even consider sending me a LinkedIn invite if you feel our data interests coincide. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.

For more on this and other topics, check out some of my other posts:

Absolutely Fabulous Big Data Roles – https://www.linkedin.com/pulse/absolutely-fabulous-big-data-roles-martyn-jones?trk=prof-post

Not banking on Big Data? – https://www.linkedin.com/pulse/banking-big-data-martyn-jones?trk=prof-post

10 amazing reasons to join The Big Data Contrarians –https://www.linkedin.com/pulse/10-amazing-reasons-join-big-data-contrarians-martyn-jones?trk=prof-post

Amazing Data Warehousing with Hadoop and Big Data –https://www.linkedin.com/pulse/cloudera-kimball-dw-building-disinformation-factory-martyn-jones?trk=prof-post

The Big Data Contrarians: The Agora for Big Data dialogue –https://www.linkedin.com/pulse/big-data-contrarians-agora-dialogue-martyn-jones?trk=mp-reader-card

The Big Data Shell Game – https://www.linkedin.com/pulse/big-data-shell-game-martyn-jones?trk=mp-reader-card

Aligning Data Warehousing and Big Data –https://www.linkedin.com/pulse/aligning-data-warehousing-big-martyn-jones?trk=mp-reader-card

Big Data Luddites – https://www.linkedin.com/pulse/big-data-luddites-martyn-jones?trk=mp-reader-card

Data Warehousing Explained to Big Data Friends –https://www.linkedin.com/pulse/data-warehousing-explained-big-friends-martyn-jones?trk=mp-reader-card

Big Data, a promised land where the Big Bucks grow –https://www.linkedin.com/pulse/big-data-promised-land-where-bucks-grow-martyn-jones-6023459994031177728?trk=mp-reader-card

The Big Data Contrarians – https://www.linkedin.com/pulse/big-data-contrarians-martyn-jones?trk=mp-reader-card

Is big data really for you? Things to consider before diving in –https://www.linkedin.com/pulse/big-data-really-you-things-consider-before-diving-martyn-jones?trk=mp-reader-card

Big Data Explained to My Grandchildren – https://www.linkedin.com/pulse/big-data-explained-my-grandchildren-martyn-jones?trk=mp-reader-card

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks.