18 Wednesday Jan 2017
Posted in 4th generation Data Warehousing, All Data, Big Data, Big Data 7s, Big Data Analytics, business, Business Intelligence, business strategy, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, pig data, The Amazing Big Data Challenge, The Big Data Contrarians
Martyn Richard Jones
New York City, 18th January 2017
Blue sky for professional data architecture and management
I’ll make this short, sweetish and to the point.
These are the arguments that CEOs need to know about Big Data.
It was written for CEOs and those who provide independent advice to CEOs.
If you are someone who wants to be prepared for CEOs who are armed with the realities of Big Data, then maybe this is for you too. Continue reading
29 Monday Aug 2016
Posted in 4th generation Data Warehousing, All Data, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, business, Business Intelligence, business strategy, dark data, data architecture, Data governance, Data Lake, data management, good start, Good Strat, Good Strategy, goodstart, goodstartegy, goodstrat, Martyn does, Martyn Jones, Martyn Richard Jones
Once upon a time, there was a mountain known as Peak 15. Very little was known about it. Then in 1852, surveyors found it was the highest in the world, and they named it Everest.
As with other significant challenges that we can identify in life, many people have been driven by a passionate desire to conquer peaks all around the world. This is just one illustration of those of us who can identify their significant challenges and rise to them. This sharp focus, determination and courage turns ordinary citizens into people who are invariably on a mission. People who know what they want. Continue reading
23 Monday Nov 2015
Having got your attention I would like to introduce you to a pragmatic, real-world and business centric approach to Big Data and Big Data Analytics. When I say that this is the best approach to Big Data you are ever likely to find in the whole universe and in your entire life, I am still significantly understating the magnificent utility, timeliness and the here-and-now facets of the approach.
Now with the introduction done and dusted with, and the virtues of the BIG SMILES approach exalted, it should come as no surprise that this eminently sensible, highly rational and thoroughly reasonable methodical and no-nonsense technique has been applied successfully in more than 500 business-oriented situations.
Best of all, this amazing Big Data approach is free of charge and with no-strings attached – you don’t even have to buy my book. Now, isn’t that amazing?
Let’s start with the basics. The BIG in BIG SMILES refers to Business Insight Gains. This refers to the focus of SMILES. Simples, right? Now what does SMILES refer to:
Fig. The process chain of Big Data SMILES
SMILES is also an acronym, and it refers to the six major components of the SMILES Big Data approach (as illustrated above). Or, more precisely the various phases of the approach. Namely:
Let’s take a look at each of those aspects of Big Data SMILES in a little more detail.
It makes sound business sense to start any business initiative with a compelling business reason. If you don’t have one then don’t start, it’s as easy as that.
Fig. Start SMILES
Now, having identified your significant business challenge you should ask the following questions:
When compiling your view of the significant business challenge, you could look for example at questions along the lines of:
Don’t forget, asking great questions about significant business challenges will lead to even more questions, which is where you will want to highlight the new questions that could quite possibly be answered, wholly or partially, using Big Data. Also, don´t confuse great questions with complex questions, the idea is not to impress the audience but to identify and address the challenge.
Another important thing to point out in relation to the SMILES approach is that you should never, ever, in no way shape or form, try and boil the ocean. That is, do not try and implement and leverage Big Data analytics in your organisation in big leaps and bounds. Start out with baby steps, and treat it like a game of tennis. Win points, games, and sets and beat challenges and bad decisions one-step at a time.
Finally, make sure each iteration of SMILES starts with an objective that is big enough to be significant yet small enough to be doable in a reasonably short-time scale, one preferably made up of sprints of no more than 5 to 10 days.
Here we are looking at a process of discover and insight; conceptualisation and creativity; deign and innovation; and, prototyping expertise and domain capabilities.
Fig. Model SMILES
This phase has three parts:
Using Co-operative Prototype Ideation, Concretisation and Realisation (a unique rapid development feature of SMILES), you may now choose to develop options and models to various stages of maturity and extensiveness.
Through a cycle of hypothesise and test you may arrive at the model most suited to your needs. If however, no such model is forthcoming or the best model simply is not good enough or promising enough then do not proceed to the next step. Make sure you have a good reason to progress and inertia is definitely not a good a reason, and neither is ‘because we have to do something’.
In this phase, you develop the chosen model through to the stage where it is ready ‘productised’ in the following prototype implementation phase.
In addition, as part of this phase, you will be looking at which technology options might provide the best fit with your requirements. Common technological options are typically associated in one way or another with the Hadoop ecosphere, so your problem may be adequately addressed using technology such as Hive, Pig, Bash, Spark or Python, or indeed with products such as MapR, Neo4j or EXASol.
In this phase, you take your well thought out proof of concept prototype and turn it into a business ready and production hardened product.
What is involved in turning a Big Data solution prototype into a product?
Here we are focusing on tools, build competence and teamwork; product piloting and development support; and, the realities of production and support.
Also, as a closing message for this phase description, please note that the implementation phase also includes the active participation of the development sponsors and target user group, as should every phase up to this point, and all of the follow on phases.
It’s been designed, prototyped, built and productised. Now what?
Well, here comes the moment of truth.
In this phase SMILES can help Big Data teams quickly react to changing business and market needs by capturing and managing new and changing requirements, and by constantly monitoring feedback. The framework can be totally integrated into true agile development processes especially where requirements are extremely dynamic and free-flowing but nonetheless must be managed at a complete Big Data product level.
An assessment of the value of the Big Data initiative should be made periodically. However, there should be at least four event-pegged must-do valuations carried out during the life-time of the project products.
Fig. Evaluate SMILES
These are the activities that generally put the smiles in SMILES.
Whatever happens with your initiative you must never fail to socialise the outcomes, for as kitsch, cute or painful the exercise may appear to you or anyone else.
Put it this way. You’ve gone to all the effort and trouble of making a Big Data initiative work, and it’s working well, people like it and it’s delivering value. So, what else is there to do? It’s a success, so you shout it from the rooftops, tell all your colleagues and peers, put the news on the intranet and in the company house magazine, and organise a party.
If your project is killed off early or late, or simply fails to deliver value, and sometimes this just happens, then hold a wake, in the Celtic manner, and learn all the lessons that are worth shaking a stick at and make them part of your corporate data management story and knowledge base.
When entering the new dynamic world of Big Data and Big Data Analytics and to ensure that SMILES delivers the kind of success that others enjoy, you must take ensure that the 9 golden rules of SMILES are also followed. These are the golden rules for beginners.
Fig. The 9 golden rules of SMILES
When using SMILES keep these 9 golden nuggets of rules in mind, and you won’t go far wrong.
Try to make sure that your funding aligns with the phases of SMILES.
However, split your funding requests into three parts.
This ensures an optimum allocation of resources and provides additional executive and project management safeguards and options, and helps to ensure alignment of contractual assurances and obligations with committed and planned budgets.
Testing is an integral element of every phase of the SMILES approach. For brevity the approach has not been detailed in this document, but the philosophy is essentially one of test early and test often. Under normal circumstances the SMILES approach does not require a User Acceptance Testing phase, and if we are in shops that do require this testing then the UAT becomes a mere bureaucratic formality.
Some people may be surprised that the Big Data SMILES approach does not start with a strategy. In my view, the idea that strategy can begin without the need for identifying a significant challenge is to get things wrong on two counts: i. It’s not the way to go about strategy, and ii. It’s not the way to go about Big Data.
The BIG SMILES approach is not in itself a strategy, it is a guide, reference and framework for those who wish to develop a specific Big Data oriented strategy for addressing a significant business challenge. This is where it is powerful, useful and relevant. It’s a roadmap, a cookbook and a method to understand issues, formulate questions, and provide adequate, appropriate and timely responses, whilst separating the wheat from the chaff, the core from the periphery, and the important from the inconsequential.
Lastly, if you or your Big Data analysis, design development team haven’t done so already, I suggest that you take a look at the Cambriano Information Supply Framework, which provides a solid-basis upon which to architect and design Big Data oriented solutions.
That’s all from me for now. Have fun and enjoy the Big Data journey.
Thank you for reading.
If you would like to know more about SMILES, the Information Supply Framework, Core Statistics, Core Data Sourcing, Data Governors, the Analytics Data Store or 4th generation Enterprise Data Warehousing then please drop me an email or visit:
On a lighter note, readers may also be interested in joining The Big Data Contrarians, the friendliest, most relevant and massively irreverent Big Data community on the whole of the entire world wide web. So, if you think you are up for it then we can be found on LinkedIn at this address:
11 Wednesday Nov 2015
Tags
All Data, Analytics, aspiring tendencies in IM, Big Data, cambriano, Martyn Jones, The Big Data Contrarians
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 encounter in your entire life’. However, many people hesitate before joining us.
I ‘kinda’ know what some people might be thinking. Are they Big Data anarchists? Are they pro or anti-establishment data stirrers? Will membership of The Big Data Contrarians put me on some sort of McCarthyite death-wish list? Will my data look small in this group? Will I ever work again? Will I ever be able to sing like Elvis?
Don’t worry, I assure you that none of the previously mentioned ‘potential sticking points’ have anything to do with The Big Data Contrarians; isn’t that right, Comrade Leon?
Therefore, if you think that this could be the group for you, but are hesitating in your decision, then hesitate no further, and join today. If you find that you do not like it, then you can always leave. It’s not the Hotel California.
So, onwards and upwards.
In order to celebrate the 500 members of the community, I decided to talk a little bit about one of the memes doing the rounds of the Big Data hype circus. “Hello big boy is that big data you have in your pen-drive, or are you just happy to visualise me?” If you like, dress it up, take it out on the town and call it a Rantasaurus Rex. If you use the material mentioned here you will win friends, influence people and break the ice at parties – guaranteed! Now, if that is not a deal that cannot be refused, I don’t know what is.
Many people who are ‘bigging up’ Big Data but without talking the tangible, refer to the massive volumes of data that have been created in the past few years as palpable evidence of a great, growing and massively beneficial phenomenon. With the absence of more reliable evidence, we have to suffer the continual exhumation, resuscitation and recycling of Google’s Eric Schmidt musings on the issue of the data explosion. For example, I seem to vaguely recall a Mister Whatshisname or Mister Whatever, stating on this very same self-publication platform, that in 2010:
<<Eric Schmidt, executive chairman of Google, tells a conference that as much data is now being created every two days, as was created from the beginning of human civilization to the year 2003.>>
Now, this isn´t the first or the last time that Eric has been mentioned in connection with this reference and the context of the massive data explosion. Indeed, if I had a Ben Franklin for every time that Eric Schmidt has been used to justify the Big Data hoopla, then I’d have quite a few greenbacks by now.
Now, call me old fashioned and even cynical, but I do not see what the data generation explosion has to do with anything other than the explosion of the creation of data. It’s not a revolution, it’s an increase in output, and may not be any more of a innate benefit to humankind and the planet, than massive landfills filled with trillions of plastic bags, some of them even with “save the planet” and “we’re all in this together” printed on them in toxic inks.
But, what will future generations say about that, and the passing Big Data phenomenon? “Ah, they went a bit crazy with the old Big Data lark, those old timers did, but at least they made throwaway packaging that would last a trillion years. That’s foresight, that is.”
However, I like the latest line of attack used by those who really are over-egging the Big Data and Big Data analytics hype, it’s a pseudo-intellectual rendition of “if Big Data is misunderstood – and YOU clearly don’t understand it – then it’s not because of Big Data, but of your own wilful ignorance. You choose not to believe in Big Data, because you are anchored in the past. That’s where you are your kind are! Yesterday’s men! Oh, yes! You and people like Franklin and his outdated theories of gravity, and that Marx, and his archaic explanations of whatever, or whomever and her outdated theories of all that jazz. Yesterday’s token men and women, entrenched in your passé European logic and ensconced in your modernist liberal studies Bleh! Bleh! Dee! Bleh! Bleh!”
How can one reply to that? Thank you so much Professor for the exposition of that brilliant counter argument. A strong, solid and coherent counter argument to the vain, naïve and self-interested question of “Can we have some tangible, detailed and verifiable Big Data Analytics success stories or case studies, please?”
I find that after asking all of these questions, and yet not getting many or any reasonable or tangible answers, that trying to get a straight answer to a straight question on Big Data and Big Data Analytics is like trying to pin Jell-O to an elephant.
I have issues with the claims that more data is necessarily better data, and therefore massive amounts of data means massive amounts of data benefits; so, I would just like to wrap things with a quote from alternative comedian Stewart Lee.
“The eighteenth-century polymath Thomas Young was the last person to have read all the books published in his lifetime. That means that he would’ve read all the Shakespeare and all the Greek and Roman classics and all the theology and all the philosophy and all the science. But the same man today, a man who had read all the books published today, would’ve had to have read all Dan Brown’s novels, two volumes of Chris Moyles’ autobiography, The World According to Clarkson by Jeremy Clarkson, The World according to Clarkson II by Jeremy Clarkson, The World according to Clarkson III by Jeremy Clarkson… his mind would be awash with bad metaphors and unsustainable, reactionary opinion; one long anecdote about the time that Comedy Dave put pound coins in the urinal. In short, the man who had read everything published today would be more stupid than a man who had read nothing. That’s not a good state of affairs.”
Which is pretty much what I think about all of the gratuitous and promiscuous hype surrounding Big Data and Big Data Analytics. There is so much rich data that still has not been exploited, and so much more significant data that we could be capturing (much of it of more humanitarian, social and scientific value than commercial) yet we are now cajoled into dedicating significant efforts and resources on harvesting commercial junk data of potentially marginal or no value.
Many thanks for reading.
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.
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:
Stuff a really great Data Architect should know – https://www.linkedin.com/pulse/stuff-really-great-data-architect-should-know-martyn-jones
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.
11 Wednesday Nov 2015
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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:
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:
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.
17 Saturday Jan 2015
Tags
Analytics, aspiring tendencies in IM, Big Data, business intelligence, Core Statistics, enterprise data warehousing, Quotes
If you enjoy, abhor or are simply bored with the hype surrounding Big Data, Data Warehousing and Analytics, then you might just hate these less than faithful quotes as well.
If you enjoy one or two of the quotes, well, then that’s an acceptable bonus too.
So, to begin at the beginning…
“My data sources are unreliable, but their information is fascinating.” – Ashleigh Brilliant
“I give no data sources, because it is indifferent to me whether what data I have sourced has already been sourced before me by another.” – Ludwig Wittgenstein
“In the kitchen of a great Data Warehouse, the data source chef is a soloist.” – Fernand Point
“It is better to be hated for what data sources you have than to be loved for what data sources you do not have.” – André Gide
“In England, there are sixty different types of Data Warehouse and only one data source.” – Attributed to Voltaire
“It is a capital mistake to theorize before one has data sources. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” – Arthur Conan Doyle, Sherlock Holmes
“From such a gentle thing, from such a source of all data, my every pain is born.” –Michelangelo
“Noise free data is a source of great strength.” – Lao Tzu
“In three words I can sum up everything I’ve learned about data: it goes on.” – Robert Frost
“Data enrichment improves a mighty fine data source” – Anonymous
“Junk food, empty calories and carbs are the Big Data of the masses” – Karl Marx
“We live, I regret to say, in an age of Big Data hype.” – Oscar Wilde
“We are not rich by the Big Data we possess but by what Big Data we can do without.” – Immanuel Kant
“He who has Big Data hype on his side has no need of proof.” – Theodor Adorno
“The religion of Big Data sets itself the goal of fulfilling man’s unattainable desires, but for that very reason ignores her attainable needs.” – Ludwig Feuerbach
“The flesh endures the storms of the present alone; the mind in our social network interactions, those of the past and future as well as the present. Big Data is a covetousness of the mind.” – Thomas Hobbes
“Big Data is negative and dialectical, because it resolves the determinations of the understanding of things into nothings.” – Georg Wilhelm Friedrich Hegel
“I am trapped in this Big Data, and there is nothing I can do about it.” – Dudley Moore
“And remember, never take the ruby case off your iPad for a moment, or you will be at the mercy of the Big Data Witch of the West.” – The Wizard of Oz
“Imagine there’s no Big Data…” – John Lennon
“Analysis does not transform data.” – Jiddu Krishnamurtu
“I live in a data landscape, which every single day of my life is enriching data.” – Daniel Day-Lewis
“Data opportunities multiply as the data is transformed” – Sun Tzu
“He who integrates data badly is lost.” – Theodor Adorno
“Today we transform the data; tomorrow, the whole enchilada” – Leon Trotsky
“Well, it’s all about the ETL law of the transformation of data quantity into data quality, and vice versa. Innit!” – Friedrich Engels
“The management consultants have only interpreted the business data, in various ways. The point, however, is to transform it.” – Karl Marx
“Hey! What’s going down here in the Hollyweird of data?” – Joe McCarthy
“The Big Data alchemists in their transformational search for gold discovered much data of greater value.” – Arthur Schopenhauer
“That Schopenhauer yolk was a bit of an old Big Data ‘procurer’ wasn’t he now Rodge?” – Pádraig Judas O’Leprosy
“The trouble with the world is that the cocksure have Big Data and that Data Science and Business Intelligence are all sexed up.” – Bertrand Russell
If people never did silly things no Business Intelligence would ever get done.” – Ludwig Wittgenstein
“The best Business Intelligence user is intelligent, well-educated and a little drunk.” –Alben W Barkley
“The Master said, “If your conduct is determined solely by considerations of Business Intelligence and profit you will arouse great resentment.” ― Confucius
“That’s cricket, Harry, you get these sort of things in Business Intelligence” – Frank Bruno
“Business Intelligence without ambition is a bird without wings.” – Salvador Dali
“I would prefer a Business Intelligence hell to a Big Data paradise.” – Blaise Pascal
“Many much-learned business men have no Business Intelligence.” – Democritus
“We should not only use the brains we have, but all that we can borrow.” – Woodrow Wilson
“The reason we have Business Intelligence is so we don’t have to think all the time” –Homer Simpson
“The study of Data Warehousing, like the Nile, begins in Inmon and ends in magnificence.” – Charles Caleb Colton
“Big Data wins games, but Data Warehousing wins championships.” – Michael Jordan
“Big Data is no substitute for Data Warehousing.” – Frank Herbert
“It’s in me blood, Clive, without Data Warehousing I’d be nothing,” – Alan Latchley
“The trouble with the world is that the cocksure have Big Data and that Data Science is all sexed up.” – Bertrand Russell
If people never did silly things no Business Intelligence would ever get done.” – Ludwig Wittgenstein
“The best Business Intelligence user is intelligent, well-educated and a little drunk.” –Alben W Barkley
“You can catch all the whales in the ocean and stack them together and they still do not make a minnow.” – Ralph Wiggum
“Well, the smarter I practice Inmon Data Warehousing, the luckier I get.” – Gary Player
“Well, I’ve cleaned up facts and dimensions in a star-schema ‘data warehouse’. That was pretty terrible. But I can’t complain because I’m sure other people have done worse.” – Cee Lo Green
“You can give a person a bowl of Big Data Gruel and feed them for a day, or teach them Inmon Data Warehousing and feed them for a lifetime.” – Proverb
“A Data Warehouse is like a tea bag; you never know how strong it is until you are in hot water.” – Eleanor Roosevelt
” οἶδα δ᾽ ἐγὼ ψάμμου τ᾽ ἀριθμὸν καὶ μέτρα θαλάσσης, καὶ κωφοῦ συνίημι, καὶ οὐ φωνεῦντος ἀκούω. ὀδμή μ᾽ ἐς φρένας ἦλθε κραταιρίνοιο χελώνης ἑψομένης ἐν χαλκῷ ἅμ᾽ ἀρνείοισι κρέεσσιν, ᾗ χαλκὸς μὲν ὑπέστρωται, χαλκὸν δ᾽ ἐπιέσται.” – An Oracle to Croesus of Lydia
Well, now that that’s done I can always ask for forgiveness. Not that I will of course.
Founder and CEO, Cambriano Energy
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
25 Thursday Dec 2014
Posted in Big Data, Business Intelligence, Consider this, Data Warehousing
Tags
I have worked in data architecture and management for three decades, I have become a recognised expert in my field, and as a result I have become almost oblivious to the fads, fancies and fashions that pass through IT. However, being an expert in a field also means that from time to time we are oblivious to the difficulties that some people may have when trying to understand issues and concepts that we simply take for granted – because, one simply knows. This is the case with data.
With the polemic and resourcefulness surrounding buzz-words such as Big Data, Cloud and the Internet of Things, one may be forgiven for assuming that there has been a massive inflection point in the generation, variety, understanding and use of data. Now, this isn’t strictly accurate, as, outside of the handful of speculative and high-visibility projects of social media and networking, data collators and indexers, search engines and online volume ad-sellers, there has been scant publicised evidence of significant ‘data revolutions’ elsewhere.
Things haven’t changed significantly simply because a handful of companies are making money with other people’s data, rather than in the more traditional organisation, where their own data is one of the most important assets. So what’s really out there in the world of data? First, let’s look at some broad-brush classes of data, namely:
Enterprise Operational Data – This is data that is used in applications that support the day to day running of an organisation’s operations. Typical data items in this space are sales transactions, purchase transactions, product information, client and contact information. Enterprise Operational Data may also include complexly structured data, such as contracts and other business documents. Applications in this space may include production control, logistics and stock control, as well as purchase order, supply chain management, management accounting and human resource modules.
Enterprise Process Data – This is measurement and management data collected to show how the operational systems are performing. In the past the recording of events went down to the level of a completed transaction – with a start and an end and nothing in between, and as transactions were kept as simple as possible, to maximize performance and throughput and minimise the risk of failure, very little process data was captured. Now, especially with the advent of Business Process Management and Web Logs, we collect a whole array of transaction and process performance data that was never previously captured.
Enterprise Information Data – This is primarily data which is collected from internal and external data sources, the most significant source being typically Enterprise Operational Data. Other sources for this aspect of data include Enterprise Process Data and data provided by 3rd party data providers. In this data space we find Enterprise Data Warehousing, Operational Data Stores, Data Marts and Special Project Data Stores. Applications in this space include support for strategic and tactical decision making, formal statistical analysis, speculative ad-hoc analysis, data mining, business intelligence and reporting (also called Management Information reporting), and qualitative as well as quantitative analysis.
As we can see, there are interdependencies, synergies between each of these broad areas of data generation and use. Of course, data in each of these areas can be augmented and enriched by new sources of data, whether that data is richer market data, competitive data or weather data. Now, this is a very simplistic view of data, but for the purposes here it is both coherent and cohesive. 
Fig. 1 – Data Made SImple
In the above diagram I have identified an area labelled as ‘Data Transitioning’. This is usually referred to as ETL (Extract, Transform and Load), although in more and more cases, instead of extracting data directly from source systems (Enterprise Operational Data Management) we are capturing data sent via enterprise message queueing, this drip feed approach in most cases allows us to maximise the time available for data loading and analysis.
Another important point to note is that although Enterprise Information Data Management has been associated with Relational Database platforms, such as Oracle, Teradata and DB/2, in this IT domain we are also using a wide range of ‘databases’, from the humble ‘flat file’ to powerful column oriented database engines, such as EXASOL, Teradata and Vertica to provide information and analysis to business stakeholders. As more and more ‘exotic’ data formats and sources are incorporated into our Enterprise Information Data platforms, we will also witness the evolution of tools, technologies and techniques to meet those new requirements.
Last of all, there will be no revolution in data management, because in most cases data architectures are built around sound data engineering principles, constrained and governed by the limitations of mainstream hardware, the operating systems that bring them to life and the competencies of those charged with designing, building or using them.
In subsequent blog pieces I will be sharing my views on the evolution of information management in general, and the incorporation of ‘Ad hoc Speculative-Predictive Analytics’ into well architected mainstream information supply frameworks for primarily strategic and tactical objectives.
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
14 Tuesday Oct 2014
Posted in Architecture, Assets, business, Business Intelligence, Data Warehousing, Management, Value
Tags
Big Data, Business, business intelligence, Data Marts, enterprise data warehousing, hadoop, information management, knowledge, knowledge management, Risk Management
In the dim and distant past most organisations struggled along with what they euphemistically referred to as Information Systems.
They were Information Systems with no overall design, no elements of management and no architecture.
Information Systems were built to show how the company had been performing in the immediate past, and that was it. Continue reading
12 Sunday Oct 2014
Posted in Architecture, Ask Martyn, Banking, Best principles, Big Data, Business Intelligence, Creativity, Data Warehouse, Dogma, Knowledge, Peeves
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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.
10 Friday Oct 2014
Posted in Big Data, Business Intelligence, Data Warehouse, Management
Sometimes I get asked in to support the manager of a failing project.
Most of the time I get to take over a failing project, with or without the previous manager’s involvement.
Occasionally I am called in to act as a Project Manager for clients who have brought in Systems Integrators and Management Consultants to do most of the project work.
