Category Archives: Business Intelligence

Postmodern Digital Stories: We’ve never seen anything like this before

23 Wednesday Aug 2017

Posted by in 4th generation Data Warehousing, business, Business Intelligence, business strategy, digital transformation, Good Strat, Good Strategy, Good Strategy Radio, IT strategy, pomo, postmodern, Social Media, Strategy

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Picture2Martyn Richard Jones

Dunny on the Wold, 23rd August 2017

The prevailing conventional wisdom is that innovative technologies and their novel use are having an effect on many peoples’ lives. Indeed, the fascination with the possibilities offered by social media, the internet and the age of data is becoming all pervasive. Of course, not all is goodness and light. Some aspects, such as Denial of Service attacks, phishing without a permit and creatively-ambiguous chimping, mark the down side of this story. Continue reading →

What Every CEO Needs to Know About Big Data

18 Wednesday Jan 2017

Posted by 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

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Martyn Richard Jones

New York City, 18th January 2017

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Blue sky for professional data architecture and management

Scope

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 →

Big Data on the Roof of the World

29 Monday Aug 2016

Posted by 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

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Mount_Everest_as_seen_from_Drukair2_PLW_editOnce 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 →

Big Data with BIG SMILES

23 Monday Nov 2015

Posted by in Big Data, Big Data Analytics, Business Intelligence, Cambriano, Data Supply Framework, Martyn Jones, Martyn Richard Jones, Methodology, Process, SMILES, Strategy, Uncategorized

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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. Continue reading →

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

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Big Data, Business Intelligence, Cambriano, Consider this, Good Strategy, Strategy

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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.

Big Data and Catfish

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, Big Data, Business Intelligence, goodstrat, Martyn Jones, Martyn Richard Jones

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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:

  • 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 World’s Best Data Quotes… Including Big Data quotes

17 Saturday Jan 2015

Posted by in Analytics, Architecture, Big Data, Business Intelligence, Consider this, Data Warehousing, statistics

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YES! IT IS ALSO ABOUT BIG DATA!

A Random walk down Data Street

If you enjoy, abhor or are simply bored with the massive surfeit of 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…

Data Sources

“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

Big Data

“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

Data Transformation

“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

Business Intelligence

“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

Data Warehousing

“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

That’s all folks!

Well, now that that’s done I can always ask for forgiveness. Not that I will of course.

 Many thanks for reading.

Martyn Jones

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

Consider this: Data Made Simple

25 Thursday Dec 2014

Posted by in Big Data, Business Intelligence, Consider this, Data Warehousing

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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
  • Enterprise Process Data
  • Enterprise Information Data

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. DataMadeSimpleDiag

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.

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The Great Information Struggle: Us and Them

14 Tuesday Oct 2014

Posted by in Architecture, Assets, business, Business Intelligence, Data Warehousing, Management, Value

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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 →

Silly Season! Data Warehousing is Hadoop is Big Data?

12 Sunday Oct 2014

Posted by in Architecture, Ask Martyn, Banking, Best principles, Big Data, Business Intelligence, Creativity, Data Warehouse, Dogma, Knowledge, Peeves

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Let’s get this baby off the ground

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.

Continue reading →