29 Tuesday Mar 2016
Posted in 4th generation Data Warehousing, All Data, Big Data, Big Data 7s, Big Data Analytics, business strategy, Cambriano, Consider this, dark data, data architecture, Data governance, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Good Strategy, IT strategy, pig data, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians
Tags
Behavioural Economics, Big Data, Good Strategy, goodstrat, Information Technology, IT Strategy, Martyn Jones, Martyn Richard Jones, Strategy
Big Data is all pervasive, all seeing and all knowing.
Everyone is doing Big Data, and if they aren’t then they will. It’s inevitable.
Big Data will revolutionise the worlds of data, decision making and business.
Am I right, or am I right?
15 Tuesday Mar 2016
Tags
Analytics, Behavioural Economics, Business Enablement, business strategy, Consider this, Martyn Jones, Martyn Richard Jones, Organisational Autism, Strategy
Martyn Richard Jones
When you have read this, if indeed you read it all, will I have failed to convey the essence of what I am trying to get at? Will a confusion of entropy win the battle? Will the wheel of fortune turn in my favour, or will I fail to connect and communicate effectively?
Let’s give it a spin and see what happens.
First, hold this thought. Bertholt Brecht never wrote “Latest smartphone, yacht in Nice and apartment block in Manhattan, then ethics” what he wrote was “Grub first, then ethics.”
We’ve never had it so good, right? We are living in an always-on, always-available and consumer-centric world of immediacy, simplicity and conformity replete with a surfeit of adult toys, legal drugs and other me-too brain-cell-killing distractions.
We are relatively comfy in our abusive environmental broth, as life moves at an ever-increasing pace, bombarded as we are with a burgeoning plethora of knowledge, information and data, which we occasionally try to ingest, process, remember and even reuse.
In many ways, it’s almost inevitable that we are increasingly superficial, contradictory and imprecise in our readings and interpretations of emotional states, interactions and motivations.
This malaise or sanguinity (depending on our personal perspective) extends to what we now term ‘content’, whether that content is found in a film, a newspaper, a magazine, social media or scrawled on a napkin. Continue reading
09 Tuesday Dec 2014
Posted in Big Data, Marty does
Clive: Yeah, well, you had to, didn’t you? You had to stand up for what you stood for, didn’t you? I mean, the only time I remember a similar occasion was, I was in, errm… I was at Spurs, Tottenham Hotspurs.
Derek: Yeah.
Clive: I was watching a game against Arsenal, and this bloke come up to me and said, “Hello”.
Derek: Oh no…
Derek and Clive – This Bloke Came Up to Me
Many people come up to me in the street and ask me what Big Data is. It has happened so many times in the past that I am convinced that it might just happen to you as well.
The first time a complete stranger came up to me in public and said “Hello, will you tell me what this Big Data lark is all about then?” I was lost for words. Later that day I read a book and adopted a strategy.
So, in the spirit of seasonal goodwill to all men and women, I have put together this blog piece that hopefully can be used in such situational encounters.
What is big data?
Big Data can be characterised by the 4+3+2+1 Vs. Which, in my book, is more than enough to bring up-to-speed an average John or Jane that one meets on the street, and who wish to be informed of such matters.
These 4+3+2+1 individualities herein described are designed to help one understand the harnessing of the synergies of Big Data awareness and to purposefully empower the breaking down of the entrance barriers to the understanding of cross-organisational silo-integration.
In layperson’s terms this a series of landmarks and pointers in the analytics space used to frame and guide the didactic aspects of Big Data.
It’s a Big Data cheat sheet. (Yes, I do know)
The fundamental Vs of the Big Data canon are these:
So, let me now explain what each of these characteristics mean to those who might know and for those who might want to know.
Vagueness – A very good place to start on our journey into the discovery of Big Data is with vagueness. Although, to be honest, if I was going on such a journey, and had a choice, I would start from somewhere else.
So, how does vagueness define Big Data? This is perhaps the trickiest of questions to address, given the vast panorama that is cast before this incredibly complex yet easily graspable concept. But let me state this, and let there be no mistake about it. At this point in time, what makes Big Data vague is also what makes Big Data specific, explicit and certain. That is to say, in order to ‘come to an understanding’ of Big Data, it is necessary to completely embrace the dialectic of knowing the unknowable. So belief is an absolute essential element – belief and data, that is.
I sincerely hope that I haven’t laboured the points too much, as that is very easy to do. But, in order to comprehend Big Data, in all its magnificent vastness, it is imperative that we understand, reconcile and internalise ambiguity, polysemy and especially vagueness.
Vagueness is a starting point, an end-point and a journey, and it will give us a basis from which we can push the envelope with respect to the other key characteristics of Big Data.
Volume – If there ever was a time to “pump up the volume”, we have it here with Big Data.
Big, voluminous, gorgeously rotund and infinite. Big Data is called Big Data because there is a lovely, roly-poly, likeable never-ending load of it. Its volumes can be measured in zeta-bytes, which you can be assured, is a helluva lot of data.
The name for a ginormous volume of ‘things’ was chosen to honour the massive talent of that great acting diva, Ms Catherine Zeta Jones, of the USA’s very own Spartacus Family, and to pay tribute to her magnificent efforts in leading the campaign to put Wales back on the map. So, you should know very well that there will always be a Big Data welcome for the Big Data ‘believers’ who venture down the valleys.
Big Data is proving Yoda wrong. Size does matter and Star Wars is for wimps
Variety – What constitutes variety in Big Data is a matter of intense debate, leading to some minor difficulties in defining what exactly the sense of the term is supposed to be. So, this is a curiously polemic aspect for sure.
But, as they might say down my way, “variety is the spice of life, innit”. This is what makes Big Data so special. So appealing.
Because before Big Data there was absolutely no variety in anything, at all. We lived in a bland world, bereft of detail, nuance and diversity. Nothing could be measured, analysed or explained, because we lacked Big Data. We were ignorant. So ignorant and stupid that we couldn’t see the sense of putting the diapers next to the beer, or of offering three for the price of two.
This now should be plainly obvious to anyone. But there are none so blind as those who will not see the Big Data.
Fortunately, today this is no longer the case if we don’t want it to be, and thanks to Big Data we have a veritable sensorial explosion. No longer is IT just a couple of symbols scribbled in crayon on someone’s school notebook. IT (and consequently humanity itself) has suddenly been expanded to include the perceptions of sight, hearing, taste, smell and touch, not to mention temperature, kinesthic sense, pain and balance.
Virility – Move over Smart Data, the new kid on the block is Big Data.
If Big Data were described in the manner of a religious text, it would be accompanied by a never ending narrative of begets.
So, what does that mean?
Simply stated, Big Data creates itself, in and of itself. The more Big Data you have, the more Big Data gets created. It’s like a self-fulfilling prophecy in 360 degree, high-definition, poly-faceted and all-encompassing knowing. The sort of thing that governments would pay an arm and a leg to get their mitts on.
But, we are getting a little ahead of ourselves here. So now I will backtrack.
We’ve all heard the expression ‘Big data, little feet’, or something along those lines. But what does it actually mean?
It’s understandably important when it comes to Big Data to speak in riddles, to be creative with euphemisms and to gild the lily.
Put it this way, if Big Data was a ‘ride’ that could be ‘pimped’, MTV style, then Big Data would be an all singing and dancing Nightrider, fully loaded, bells and whistles, with go fast stripes, flashing LED lights and ultra-shiny alloy ‘dubs’. Big Data has become the bling of IT.
As the ace yachtsman, MIG flying, master of relational data business might have put it (or not) “You’ve got 99 problem and the data ain’t one”. I happen to agree, even if the meaning is somewhat obscure.
So, just hold this thought for now: Big Data will expand to fill the whole of the known universe, so you’d better buy plenty of disk storage now, whilst you can afford them.
Velocity – Velocity is of the essence. Velocity kills the competition. More velocity, less haste.
We demand that service is ‘velocious’. ‘Everything’ must be ‘now’, or it’s too late.
This means we need to be able to handle Big Data at velocity – at the speed of need.
Big Data is so big, so squishy, so slippery and so fast that it can go from real-time input to real-time output without touching the sides. Which in and of itself is just absolutely fabulous. Moreover, the heat that this process generates could light up the whole of the Big Apple, and you would still have some left over to power a plethora of Ozzy Osbourne concerts. (And yes, Sir, I know my informal grammar sucks, and that… I’m using… incomplete sentences… but, this is a blog piece). But I digress,
Charles Babbage once stated (or maybe it was more than once) that “whenever the work is itself light, it becomes necessary, in order to economize time, to increase the velocity.”
But remember, we are dealing with mega-velocity here, so don’t drink and drive the Big Data Steamship, Star-ship or Mustang.
Hark! Did I hear you ask: “No drink, not even beer?” To which I might sensibly reply “Hell, no! Not even water”. So, be forewarned, forearmed and forward thinking.
Vendible – If you can sell it, and sell it as Big Data, then it ‘is’ Big Data. If you can’t, then it’s not. The saleability of Big Data proves its existence. The very existence of client’s for Big Data demonstrates conclusively that it is tangible – at least in market terms, and it’s the market that rules.
So, what are the vendible aspects of Big Data?
For some people, Big Data is like a crock of fertilizer. The ideal formula for nurturing and growing responses to significant challenges.
For other people, Big Data is the next big bandwagon of which to jump.
Then there are those who see the magic dollar signs in the glittering prize of Big Data success.
Big Data is both palpable and incorporeal, it cannot be touched, yet it can touch.
Big Data is both transient and enduring, it is like a moveable and yet unspecified feast.
It is a game-changing and strategy-energising shape-shifter.
It has the power to remould itself into a ‘potentiator’ of corporate riches, as a cure for all the important human ailments and afflictions, and as a solver of the most pressing issues facing humankind today.
More importantly it can drive whole new markets of supply and demand.
Demand for hardware, demand for software, demand for ‘appliances’, demand for implementation services and ‘instant experts’, and demand for litigation and legal services.
It can also be used to mobilise armies of commentators, industry analysts, publicists, punters, writers, bloggers, gurus, futurologists, conference organisers, conference speakers, educators, customer relationship managers, salespeople, marketers and admen.
Indeed. It can be confidently stated that never have the words, ‘mark it up, and sell it on’ been as apt as in this age of Big Data.
Vaticination – Edmund Burke is down on record as stating that “you can never plan the future by the past”. Now Burke may have been a clever person when it came to many things, but he wasn’t exactly a whiz when it came to Big Data.
There are people in the world who are in no doubt that Big Data provides the sort of visionary and predictive powers only previously obtainable through ritual sacrifice, magic potions and the casting of spells. Others are highly critical of the understatement implicit in this belief.
For many, Big Data will make the Oracle of Delphi look like a mere call centre.
This is why the power of vaticination plays a characteristically important role in the world of Big Data.
If it weren’t for Big Data’s unique set of prophetic value-propositions we may as well have gone back to being cave dwelling hunter-gatherers.
Voracity – This is based on the quasi-rationalist argument that Big Data is big and it has an omnipresent and insatiable self-fulfilling desire.
Big Data comes with an attendant requirement for hardware, even if it is a whole load of consumer hardware tacked together in a magnificent and miraculous mesh of magic.
Big Data can be characterised by voracity, but this comes hand in hand with the ‘ventripotent’ IT industry.
Unfairly in my view, some people claim that Big Data satisfies the fetishist appetites and whims of the rapacious, greedy and insatiable. I would disagree. I would argue that Big Data is for people who just ‘like a lot’.
Although, I do generally ascribe to the view of Ms Piggy that one should never eat more than one can lift.
But beware, treat the leviathan with a lot of caution. Big Data is potentially so voracious that it may attain the clout, control and the capability to eat itself, alive.
Veracity – The eminence of the data being captured for Big Data handling can vary significantly. The quality or lack of quality of the data naturally has the potential to impact the accuracy of analysis using that data.
Before Big Data arrived on the scene we knew nothing about Data Quality or data verification. This is why ETL and Data Cleansing tools lacked the power to effectively quality check and verify data, to ensure that any erroneous or anomalous data was rejected or flagged.
But now, with the sophistication of tools such as ‘grep’ and ‘awk’ at our disposal, we have the power in our hands to ensure nothing ‘dodgy’ gets into the analytical mix.
We are now able to sequentially clean, map and reduce datasets at will.
I can well imagine why a company like Oracle would be kicking themselves now for not designing and implementing a method of being able to distribute data across multiple channels and controllers, and of providing the capability of running queries “split and distributed across parallel nodes and processed in parallel”, and of then constructing a result set. Okay, they had these and other features in their products from Oracle 7.3 onwards, but it was not Big Data, was it? And anyway, this section is about veracity, it is not about MapReduce, Oracle RDBMS or of the history of advances in relational database technology.
Vanity – To paraphrase Max Beerbohm, ‘to say that data is vain means merely that it is pleased with the effect it produces on other people. Conceited data is satisfied with the effect it produces on itself’.
In my opinion, to fully grasp the underlying and profound meaning of Big Data, it is essential for us to understand the difference between vanity and conceit. Max Counsell claimed that “Vanity is the flatterer of the soul”. Goethe characterised vanity as being “a desire for personal glory”. After an incident with an Anarchist (presumable a Big Data Anarchist), Blackadder remarked to Baldrick that “The criminal’s vanity always makes them make one tiny but fatal mistake. Theirs was to have their entire conspiracy printed and published in plain manuscript”.
So that ends the brief rundown of the defining characteristics of Big Data.
So, to summarise. That, which has passed before, necessarily divulges both the upside and downside of Big Data. By reaching out, opening up the kimono and relating the 4+3Vs we are disclosing that which cannot be disclosed, exhibiting the absence of essential essence, and thereby opening up the entire field, discipline, profession, science and art to examination, questioning and ridicule.
Finally, I hope that as we move forward, in time and space, onwards and upwards to greater, bigger and better data, that we do not forget the fundamental lessons of life. Especially the “laugh at nonsense” bit.
Thanks for reading.
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 a LinkedIn invite. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.
For more on the topic, check out my other recent posts:
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
15 Saturday Nov 2014
Posted in Ask Martyn, Data governance, information
The management and architecture of Information Assets
For more than two decades I have tried to convey the importance of treating information and knowledge as potential assets.
Around the world, the response has usually been mixed.
It is understandable that there is frequent reluctance to accept that information might have real value.
After all, we are frequently unaware of what really constitutes an asset and what a liability is.
And we are frequently reluctant to accept puerile arguments that all information has value, whether it does or not, or whether it is an asset or not.
In some organisations where the attitude has been more positive I have managed to take things to the next level.
In many of these case it is business people who will identify the significant challenges, the greatest opportunities and the primary and secondary benefits that might be accrued from treating knowledge and information as if might be an asset.
When we get to this point then it is time to get serious about what actually needs to be done.
This is the time when we start thinking strategically.
As part of this phase we start by exploring what can actually be done in terms of the key aspects and features of a knowledge organisation, and get to some common ground on understanding through constructive discourse.
We address what the organisation needs to think about in order to improve the management and architecture of information assets and
So, the purpose of this (and subsequent follow up blog pieces), is to provide a hugely simplified version of the Cambriano method for the management and architecture of Information Assets.
As an aside, Cambriano Energy is the management consulting company founded by Martyn Jones, and which is also used as a vehicle for promoting the Cambriano Knowledge Asset Management (KAM) approach.
Subsequently, in this blog I will outline a key organisational feature of this method. The Knowledge Asset Management Organisation (KAMO).
I will discuss the drivers for having a KAMO, the justification for this approach, and the benefits that might be accruable from choosing this avenue.
As part of this blog series I will also discuss how the KAM approach relates to reality and how KAM challenges, configures and compliments the vital field of Infonomics.
But before continuing I would like to get a feel for what people would like to see in such articles, especially with regards to the lines of business people are in, and their hands-on-experiences with the issues of information management and treating information as an asset, something with potential value that would need to be managed.
What do you think? What would you like to see addressed? What do you think the opportunities, problems and challenges are?
Until next time…
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
26 Sunday Oct 2014
Posted in Analytics, Architecture, Ask Martyn, awareness, Big Data, BS, deceit, governance
24 Friday Oct 2014
Posted in Ask Martyn, information
Tags
Behavioural Economics, Business, business strategy, corporate assets, Corporate IT, Information Technology, Offshoring, Outsourcing, Strategy
I wanted to call this piece ‘A random drive down Camino Real’.
But that is an ‘in joke’ and no one would get it.
So instead I called in ‘Developing and Aligning IT Strategy’. Continue reading
18 Saturday Oct 2014
Posted in Ask Martyn, awareness
Tags
advertising, apple, Behavioural Economics, consumerism, crass, creepy, cults, fetishism, hype, sects, style
Steve Jobs was a great entrepreneur.
Clearly he was.
Jobs turned a dismal maker of a massive range of gadgets into a powerful and highly-focussed technology fashion and PR business.
The stylistic touches in Apple products carry the elegant and crispy palate of bourgeois minimalism, a fragrant bouquet of exclusivity and a delightful after-taste of subdued superiority. Continue reading
12 Sunday Oct 2014
Posted in Architecture, Ask Martyn, Banking, Best principles, Big Data, Business Intelligence, Creativity, Data Warehouse, Dogma, Knowledge, Peeves
Tags
Banking, Behavioural Economics, Big Data, Bill Inmon, business intelligence, data integration, Data Marts, Demagogism, Dogma, enterprise data warehousing, hadoop, Information and Technology, information management
This weekend I read a piece on the Information Management website by Steve Miller with the title of Big Data vs. the Data Warehouse. It’s an old piece, from March 2014.
It was in response to a piece penned by Bill Inmon, titled Big Data or Data Warehouse? Turbocharge Your Porsche – Buy an Elephant, in which he singled out for criticism the ad campaign of a big-data and Hadoop promoter.
11 Saturday Oct 2014
Posted in Management, project management
Tags
Behavioural Economics, Commercial IT, IT business, IT Strategy, Organisational Autism, project management, Risk Management
Peter Drucker once stated that “There is nothing so useless as doing efficiently that which should not be done at all”.
That is one of the guiding principles in my professional role as strategist, leader and coach.
I work in business and IT.
With engineers, administrators, managers and executives.
I occasionally read blogs and forum posts related to my areas of interests.
A question appeared on a popular forum for Project Managers.
It asked, when it comes to successful Project Management, “what is more important, the right people or the right process?”
You get a lot of questions like that in IT.
It’s probably the same for other jobs.
A lot of the replies to the question were terse, mind-numbing and vacuous.
Other replies read like concatenations of fortune cookie quotes based on someone’s idealistic and flawed notion of management.
There were answers in favour of people over process, process over people and others that put “right process” and “right people” on an equal footing.
I didn’t get the impression that people were addressing the question from a position of knowledge and experience.
No one asked any questions.
No even the hint of one.
Though the obvious questions were there, staring at them in the face.
But no one asked.
Everyone assumed that there was a common understanding about what “right”, “right people” and “right process” mean in a project context.
Because people didn’t ask the obvious questions, they couldn’t move on to the more subtle and substantial questions.
They couldn’t move upstream or downstream.
Wherever they stood their position was untenable.
They didn’t have the social skills, the creativity or the intelligence to step back from the question.
They were stuck in the trivial, the hackneyed and the simplistic.
They answered with clichés, vagaries and baloney.
So what we had, was a long-life thread of ill-informed responses to a vague question.
It was if you’d asked a group of unthinking patriots what was better for the country, “the right people” or “the right political system”.
But it goes deeper than that.
Politicians who are reduced to talking about rights and wrongs, without being able to pony up any rational explanations, are quite rightly derided for being shallow and removed.
In IT we think it’s a sign of considered professionalism.
But regurgitating motivational slogans that are well passed their use by date is not professionalism.
The unquestioning subservience to trite, populist and unrealistic management dogma is not professionalism.
Acting as if project management were some bizarre super-hero Hollweird invention is not professionalism.
Needing to break everything down into right and wrong, good and bad, black or white, etc. is the height of arrogant superciliousness.
What is worse than arrogance or ignorance, is when they go hand in hand.
It’s just not on.
If IT was an army, it wouldn’t be the professional modern army of today. But an army lead by well-meaning, socially inept and multiply-challenged incompetents. The sort of army that would march a battalion of the “right people” to their certain death, or the sort of people who would see instrumental reason as being the “right process”.
“Lions lead by donkeys”.
Students of European history – say from 1934 to 1945 – might make the connections.
If you can’t define what you mean by “right”, you may as well be discussing the sex of angels.
If some people can’t even ask the obvious questions, then what the feck are they doing managing projects?
Never mind, life is too short to fret the inadequacies and excesses of IT.
As Lucius Seneca was want to say “A physician is not angry at the intemperance of a mad patient, nor does he take it ill to be railed at by a man in fever. Just so should a wise man treat all mankind, as a physician does his patient, and look upon them only as sick and extravagant”.
10 Friday Oct 2014
Posted in Data Warehouse, market forces, nine competitive forces, Strategy
Tags
Behavioural Economics, BI, Business, business analysis, Business Management, business strategy, Challenges, corporate assets, Creativity, Crisis, Data Warehouse, Dogma, Goal Setting, Information Technology, marketforces, operationalwareness, Strategy
Let’s now take a brief look at my ‘nine competitive dimensions’ model.
This model will be familiar to some who will readily connect with the inclusion of government as an environmental dimension.
What does Big Data have to do with Robitussin?