A data superhero is something to be

A data superhero is something to be

A data warehousing superhero is something to be

Not all that glitters is Big Data, and Big Data has a long way to go before it can deliver anything like the same satisfying results, tangible benefits and organisational agility that a properly implemented Inmon Enterprise Data Warehouse can provide.

Therefore, I have a question for you.

Do you want to win friend and influence people in the world of data architecture and management? Do you want to do something in IT that atypically will bring kudos and credibility? Do you want to enjoy what you are doing because you are actually doing the right thing right for an appreciative audience?

Okay, this a recipe that I will now reveal, has the power to turn you into, not only a data hero, but a 4^th generation enterprise data warehousing superhero – with Big Data bells and whistles attached, and even more amazingly, it is offered for nothing, gratis, and for keeps.

Yes, you read it right. I am feeling generous, and although a rare animal, there is such a thing as a free lunch. In this instance, the free lunch takes the form of a cookbook for successful data sourcing, warehousing and provisioning, one that will turn you into a truly modern day digital superhero.

Follow the suggestions to the letter and it will be hard to fail. However, drop any magic ingredient from the mix and expect, eventually, to run out of luck – that is rhyming slang for Donald Duck, down my way. Almost as important, please apply your own criteria of good sense at every step of the way.

The craft of data  

The craft of data includes temporary-permanence in exploitation, revolution and institution.

When Sun Tzu was talking about the Art of War, he was also talking about the craft of data.

In the 21^st century the highest expression of the craft of data in an organisation, whether public, private or military, is the enterprise data warehouse.

These are some of the key rules and guidelines for ensuring that you prevail and not your adversaries. The items are necessarily terse, but should provide a sound basis for further research, thought and strategic practice.

So without further ado, let us get to the crux of the matter.

1.       This is the first piece of advice, and it's a little bit of a 'downer', but you may just thank me for it later. The business sponsor of any significant Data Warehouse initiative or iteration cannot be the CIO, CTO or any member of the IT organisation. When this unfortunately happens, and it happens far too often, you should know that this particular data warehouse project is dead before it even gets off the ground - guaranteed. If you can afford to walk away from such a project, then do so. Now for the more positive aspects.

2.       All data in the data warehouse must be subject-oriented.

3.       We must integrate all data before it enters into the data warehouse.

4.       All data in the data warehouse must be time-variant or specifically indeterminate.

5.       Data in the data warehouse must be non-volatile – within periods of explicit and implicit snapshot coverage.

6.       Data in the data warehouse is primarily used to feed into management decision making (by order of importance: strategic, tactical and then operational).

7.       We build the data warehouse iteratively and over time. We never build the data warehouse using a 'big bang' approach.

8.       We base each build iteration of the data warehouse on a specific set of well-bound departmental-oriented requirements, deliverable in a short and specific timeframe. We never try to build the data warehouse using a 'boil the ocean' approach.

9.       We never run more concurrent iterative developments in a data warehouse programme than we would in any other agile environment. This means that for a mature data warehousing setup, we run a maximum of five concurrent developments. The more immature the organisation, the less the number of concurrent iterations.

10.   We use a contemporary two-tier approach to the data-warehousing super-component. A well architected, designed and engineered third-normal form database that supports true historicity and time-variance-modelling forms the basis of the decision support database of record.

11.   We build departmental and process-centric data marts on top of the data warehouse layer, as the end-user-centric semantic-layer of the data warehouse.

12.   We use 3NF to model the data warehouse data-model. We typically use dimensional modelling to model the data mart models, although other modelling options are also valid. Target use cases will inform the decisions we make regarding the choice of data mart model.

13.   Never trust anyone who claims that we can service the strategic data needs of a complex and volatile enterprise by implementing a faux data warehouse built using a collection of conformed dimensions and facts. This approach may initially appear to work, however, this is a massive strategic, tactical and operational mistake, which will eventually involve costly reengineering, loss of valuable data, organisational disruption and dissatisfied clients.

14.   We store transaction in the data warehouse at the lowest possible level of granularity. We store transaction and fact data in the data marts at the aggregation levels appropriate to the target audience.

15.   Based on use cases and performance needs, we will accordingly aggregate data in the data marts. If, in the future, lower level data granularity is required in the data mart then we can easily provide that by reconstructing the data mart from atomic level data stored in the data warehouse.

16.   We should never second-guess business requirements. No business imperatives means no requirement. You're aiming to be a successful data superhero, keep that goal in mind. Don't be beguiled into doing the wrong things even when accosted by 'right-sounding reasons'.

17.   Data warehousing is about the permanent incremental development and redefinition of minimum viable products and a minimum viable service. Iteratively grow the data warehouse and ignore those who claim that Inmon is about 'big bang', 'bottom up' and 'boil the ocean'.

18.   Avoid pork barrel political games in data warehouse programmes. You should not use a data warehouse programme as a means to leverage a raft of other related data, operational and DevOps projects in the organisation. For example, Corporate Data Governance, Data Quality and Disaster Recovery/Business Continuity should not packed into the data warehousing programmes, at any level. Again, this is a massive strategic, tactical and operational mistake.

19.   We ensure that as a minimum that data in the data warehouse is as reliable as the data at source. Simply stated, we do not allow unnecessary entropy to effect the data in the journey from source systems to the target data warehouse or data marts.

20.   No data is 'corrected' or 'cleaned' in the data warehouse without the explicit, verifiable and express consent of the fiduciary duty holder with respect to that data. If the data warehouse is to act as a system of record then it must also hold metadata relative to any 'cleaning' that has been applied to that data, and should also hold 'before' and 'after' states of corrected data – for auditing purposes.

21.   We secure all data in the data warehouse in accordance with prevailing legislation and corporate rules and guidelines. In any conflict between corporate rule and legal jurisdiction, the current laws prevail.

22.   Ensure that competent and independent design authorities, with the support of the Data Warehouse architect, are ultimately responsible for all data-warehouse architectural, process and design decisions.

23.   Architectural and process choices govern the selection of methodology, product and partner. Always remember mens sana in corpore sano. Prejudice, speculation and opinion generally lead to very bad data-warehouse acquisition decisions, and can potentially lead to strategic, tactical and operational mistakes.

24.   Data warehousing iterations have clear top-level phases: start-up; DW management phase; analysis phase; design phase; build phase; testing phase; and, implementation phase. We complement these phases with data warehousing tracks: project management track; user track and requirements; data track; technical track; and, metadata track. This approach is used by a number of data warehousing methodologies, including the Cambriano methodology for data warehousing, information management and data integration.

25.   To conclude, I would like to iterate some of the reasons why we should follow an Inmon based approach to the building of a Data Warehouse. The Inmon approach is very much based on:

                    i.            Iteratively solving specific business challenges, iteration by iteration. This is not just a flippant excuse for spending other peoples' money. The Inmon DW is not about 'boiling the ocean', 'bottom up' or 'big bang'. Neither is it an insistence that one can build a whale by carefully configuring a collection of minnows. There's a 'little bit more' to it than that.

                   ii.            Delivering perceived and visible value within a reasonable timeframe.

                 iii.            Achieving high returns on investment.

                 iv.            Meeting or exceeding expectations.

                  v.            Meeting user requirements, first time and every time.

                 vi.            Delivering a quality data-warehouse solution on schedule, within budget, whilst effectively utilizing the resources available.

               vii.            The rational and economic need to minimize the impact that any strategic data initiative will have on operational systems and the organisation.

              viii.            The goal of maximizing information availability and analytical capabilities throughout the organisation and even to stakeholders and clients, if we so wish.

                 ix.            Designing towards maximum flexibility to ensure that we can accommodate much of the future decision support needs immediately and that we swiftly and coherently address new requirements.

Now what?

Now I've given out a wealth of valuable information and indications you may be asking 'and now what?'

This is the next step, dear budding data superhero:

1.       Take each of the items mentioned above and study them to the best of your ability. Do lots of research, and start to fit together the pieces of the jigsaw.

2.       Invent scenarios, or better still, ask other people for scenarios and hypothetical challenges, and then work through how you would go about responding to those scenarios and challenges.

3.       If you have any questions that you cannot research and answer yourself, then I will be glad to help. That is, if the request is regarding a particular aspect of data warehousing or management. Please email me your questions at martyn.jones@cambriano.es Please use one email shot per question please (e.g. if you have three questions, send three emails), so that I can prioritise the questions and manage the time I can set aside to respond to them.

The subtle evolution of Inmon's definitive Data Warehousing

What I have described are elements and requisites of a solid, coherent and cohesive approach to fourth generation Enterprise Data Warehousing, a proven approach to the provision of quality data for management decision support. The approach is the evolution of the classic Inmon approach, which has evolved over the intervening decades, thanks to Bill Inmon himself, and those who adopted and developed his approach to cohesive, coherent and comprehensive data warehousing.

Many thanks for reading

So, that's it. Many thanks for reading this piece and I sincerely hope you found it of interest.

Do keep in touch. You can connect with me via LinkedIn and you can also keep up to date with my activities on Twitter (User handle @GoodStratTweet) and on my personal blog http://www.goodstrat.com (GoodStrat.com)

I am the manager of The Big Data Contrarians group on LinkedIn. Consider joining that group, if only for the critical thinking that it could potentially provoke.

You may also be interested in some other articles I have written on the subject of Data Warehousing.

Data Warehousing explained to Big Data friends - http://goodstrat.com/2015/07/20/data-warehousing-explained-to-big-data-friends/

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

Big Data is not Data Warehousing - http://goodstrat.com/2015/03/06/consider-this-big-data-is-not-data-warehousing/

What can data warehousing do for us now - http://www.computerworld.com/article/3006473/big-data/what-can-data-warehousing-do-for-us-now.html

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

Martyn Richard Jones

Palma de Mallorca

23^rd September 2015

Stories from the Data Warehousing front-line

NB THIS IS FICTION

All characters appearing in this work are fictitious. Any resemblance to real persons, living or dead, is purely coincidental.

Data warehousing, what is she like?

Although the answers are probably obvious, and to be honest, compared to the Big Data hype-circus this is a walk in the park, I have often wondered why Data Warehousing attracts such a surfeit of lazy, socially inept and shallow-thinking chancers.

I could go on about this at length, about how I convened a meeting recently (held on the outskirts of Bornheim, a small town in Germany ) to discuss how to move rapidly forward with a new strategic data-warehousing project, and how, whilst putting aside the crass impertinence and barely-disguised arrogance of my guests, I was still amazed by the unabashed and brazen snow-job that I was subjected to.

I imagined that this was a deliberate tactic used in the craven hope that I would be overcome by the depth and breadth of their 'inside knowledge', and would consent to having my workshop hijacked and reframed

But, I wasn't having any of that. I know bluff and bluster when I see it, and as a reformed bullshitter I will not willingly accept bullshit from anyone else.

So, as their bullshit came in fast and furious I started making notes, and thinking of the most adequate response that a Project Manager could make in the circumstances, but I soon tired of note taking and was rapidly becoming irritated by a total lack of empathy and an utter lack of engagement.

Irritated as I was, I still tried putting things back on the rails. Therefore, I continued to be as engaging and constructive as one should, whilst internally suppressing the urge to ask 'what the feck is going on here?' So, I talked about lifting, shifting and dropping a legacy data warehouse and marts from one box to another - thinking that this would be the minimum that Data Warehouse experts could engage with - and the need to get estimates of the effort required to do so (you know, things like roles and number of days, Big ballpark estimate stuff). The 'Data Warehouse Architect' and I use that term loosely, went off on a tangent. Vague, fuzzy and disjointed. The architect threw in some nonsensical vagaries about the need for Master Data Management to be an integral component of any future data warehouse. I half-managed to avoid the incredulous Jeremy Paxman look of 'what on earth are you talking about?' just as the gathered augmented the assault on MDM with a call to Information Lifecycle arms. Therefore, when things were becoming even weirder, the weird turned pro, with the train kicked off the tracks, rolled down the hill, and then set on fire, by so-called professionals, passing themselves off as supine yobs, and reciting, in close harmony, "Proof of Concept! Proof of Concept! What about the f****** proof of Concept! Uh? Uh? Uh?"

Well, well, well… what a way to run a dance hall!

We were opening up all technological fronts, apart from the ones that would actually be relevant. I felt like a PG Tips chimp getting bananas and cups of tea thrown in their general direction. I was Martyn, the Project Chimp, plaintively calling out "'ere mate, do you know this is not the way to do Data Warehousing", and half-expecting a response along the lines of "you plan it, Son, we'll muddle along ". I didn't get a response, all I got was what looked like the human equivalent (if there is such a thing) of a page-fault, a glazing-over of the eyes and a rapid reboot into full-on bullshit mode.

I could go on and on about this all day, but I would rather not. Just the day in the life of a PM tasked with getting sense, sensibility, and work out of profane variations on the theme of Blackadder's stupid Prince George. "I don't need Inmon or Kimball, I know data! And… I have been to Ikea!" Sorry, that was just an example of how utterly obtuse things can get on the front-line of Data Warehousing.

So, to close, I would like to pose a question, one that goes beyond Data Warehousing and Big Data. Do people have the same or similar issues in other parts of IT or indeed in other businesses and technical related activities?

Bamberg

22nd September 2012

Many thanks for reading.

The golden age of Big Data bullshit

Without a shadow of doubt, we have entered the golden age of Big Data bullshit.

In these postmodern times of always-on, instant-gratification, and vain superficiality; where crapulence has become the new credible, mean boloney the new benign and trite the new treasure; it is hardly surprising that banality has been elevated to the status of wisdom and knowledge.  

So, in spite of what the good people at the prestigious house of Gartner have been saying, the Big Data hype-cycle is far from over. This is why I believe that we have entered what might be fairly albeit humorously described as the golden age of Big Data bullshit.

Now, I remember the renaissance of Data Warehouse boloney, and some of it was outrageous, and in many cases perpetrated by the very same companies (and in some cases, people) who are now spreading the Big Data doo-doo around, thick and fast. But although it has some parallels with the golden age of Big Data bullshit, the comparison doesn’t really do justice to either.

When I started on my first Data Warehouse projects, there were no Data Warehouse success stories in Europe, it was just all too new. Sure, I had been doing things like managing the design and build Information Centres and MIS solutions in the eighties, and the projects I was involved in and responsible for were largely successful, but they weren’t the full-Inmon DW enchilada, and sometimes these solutions became unstuck and in unpredictable ways. Later, with satisfied DW users and successful deliver of DW projects, came a slew of tangible, coherent and verifiable success stories. But it wasn’t all about success, as more than fifty percent of so called Data Warehouse projects were accidents waiting to happen. Nonetheless, there were enough tangible, coherent and verifiable Data Warehouse success stories around that the task of providing this sort of information to interested parties wasn’t turned into an onerous task of ‘inventiveness and creativity’.

Up until that point, at least on my own Data Warehousing projects, success was based on the understanding that for success to be assured the process had to be absolutely business driven, market focused and technology based.

Sometime around 1995, technology companies realised that Data Warehousing was no longer going to be a simple niche solution. So what did they do?

I’ll tell you.

All of a sudden new and massive marketing campaigns were oriented to ensure that Data Warehousing was seen primarily as technology driven, technology focused and technology based. Even if the supposed outcome was to be a large population of pleasantly surprised DW users and business stakeholders. The key to all things wonderful in DW land was to be technology. Technology, technology licenses and technology services.

So, what happened next?

Well, the massive-shift to Data Warehousing as being mainly a technical solution almost killed the fatted calf, the goose that lay the golden eggs and almost gave away all of the Data Warehousing the family silver, in one fell swoop. From those days, the world of Data Warehousing never truly recovered from that massive cluster**** - a massive and naïve act of Homeric strategic incompetence committed by those in the IT industry who should really have known better.

Big Data is like that, but worse. There is no Inmon of Big Data. There is no coherent development process. Unsurprisingly really, as there is no real equivalence at both the business or market level, and what connections there are between technologies employed in both are almost purely coincidental.

Inmon Data Warehousing was a pragmatic and business oriented solution framework looking for technologies. It was side-lined by corporations looking to maximise their hardware and license sales, and by service providers who based their models on maximising offshoring, maximising hours worked per artefact, minimising quality and by creating and selling seriously dodgy contractual agreements.

Big Data is about niche ‘roman census’ technology looking for a problem. So far, in many domains, where no suitable and obvious challenge actually exists.

So, Big Data is an entirely distinct proposition.

One way or the other, like it or hate it, so bereft is the Big Data world of success stories, beyond the usual triad of Google, Facebook and Amazon, that the leading influential pundits of the day are ‘obliged’ to eke out success stories elsewhere.

How many times have we read Big Data success stories, that…?

1.      Were actually success stories from another area, such as a success story from Data Warehousing or Business Intelligence or Statistics?

2.     Weren’t actually success stories at all, but lazy, misleading and inaccurate notions about how Big Data might be applied.

3.     Were simply taking advantage of some human tragedy or another in order to schlepp Big Data snake-oil medicine around the social media.

Sure, it's all nonsense. But it's nonsense that means that evwerything becomes much more complex, and unnecessarily so. That things are done that should not be done, that projects fail that should not fail, and that any superficial initial savings on offshoring are wiped out because deliverables are basically unusable.

So, when I hear terms such as ‘amazing’, ‘guru’ and ‘influencer’ mentioned in connection with Big Data, well, what more can I do than reach for a nice cup of tea.

That's quite enough about that for now. I hope it makes sense to you and that you can avoid any nasty surprises in the future. Whether in Big Data or Data Warehousing.

Many thanks for reading.

As always, please share your questions, views and criticisms on this piece using the comment box below. I frequently write about communication, data, information, knowledge, 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.

Finally, if you feel up to being a member of the elite Big Data Contrarians community then please send in a request to join from this LinkedIn page:

https://www.linkedin.com/groups/8338976

12 Amazing Big Data Success Stories for 2016

12 Amazing Big Data Success Stories for 2016

If this piece tickles your fancy, then please consider joining The Big Data Contrarians on LinkedIn:

https://www.linkedin.com/groups/8338976

Every year I ask myself the same question. Will there be any tangible, coherent and verifiable Big Data success stories in the coming year? Every year I come up with nothing. Nothing at all. "Sorry, no rooms at the Big Data Success Inn, as we are closed for vacations."

However, this year things are different. More positive, more alive and more fantastic.

As you can probably guess, I am well excited to be able to reach out and tell you about the twelve amazingly fab Big-Data stories that will appear during the course of 2016. The year of the incredible, startling and awesome Big Data monkey.

To this end, and as this is a magically special occasion, I have made an extra-special effort to deliver the goods, to do full justice to the task, and to go that extra Big Data kilometer for my demanding readership.

So, I gazed into Madame Frufru's crystal ball, I opened up the kimono with the Ouija spirits of Von Neumann, Babbage and Jobs, and I pushed the envelope in the vast disruptive solution-spaces habited by Ada Augusta, Audrey Tautou and Jennifer Saunders… and, I came back with the best of the best.

I only hope it was all worth the blood, the sweat and the tears.

So, here for your veritable delight and salutary entertainment, I give you the twelve remarkable Big Data success stories of 2016.

Big Data leads to massive government savings – 2016 will be the year in which right-thinking, common sense and pragmatic governments around the world will leverage Big Data to bring about a radical reduction in government expenditure. Unchained from the dogma of professionality, administrations will replace overpaid, over-educated and over-bearing statisticians, with Data Scientists who can produce 'the required numbers', a priori, and at a tenth of the cost. If this works well, as no doubt it will, other professions, such as medicine, teaching and the law enforcement agencies, will also be subjected to the Big Data treatment. Why pay a professional Doctor, Teacher or Police Officer their exorbitant fees and salaries, when a Quack Scientist, Chalky Scientist or Plod Scientist can fill their places, and for a fraction of the cost.

Big Data clamps down on gum chewing in Singapore – Radical Polymer masticating criminals are the bane of the upstanding street-walking citizens of Singapore. However, in 2016, this will change. Why? Because Big Data will be used to identify, track-down and apprehend gum-chewing, sidewalk spoiling and anti-social spearmint-breathed offenders. Yes, capital punishment for such offenses may seem harsh, but remember, if Hadoop says it is a heinous crime, especially if it's backed up by expert social media opinion, then it must be right.

Big Data solves the Climate Change conundrum – Following the amazingly successful climate talks in Paris this year, 2016 will herald in a period of fantastic adjustment in how climate change is seen, measured and addressed. No longer will Climate Change it be seen as a threat or a problem, but as a seriously good opportunity for market capitalism in general, and Big Data in particular. Measurement of temperature changes will no longer be made, but massive Big Data technologies will collect climate change opinions from global social media, and that will be our unique guide to the actual effectiveness of the fight against things 'getting too hot'. Big

Data will lead the way, and factor 10,000 sun blocker and super-mega walk-in fridge-freezers, will follow.

Big Data helps put Real Madrid back in the top tier – The BBC* might not like it, but Big Data will triumph in sport in 2016, thanks in the main to its innate ability to help Real Madrid win the Champions League, the Spanish League, and the Spanish King's Cup. Even though the mighty-whites have already been eliminated from the last of these competitions (for fielding a Big Data player who was under a match ban). Okay, so Big Data can't get it all right, but no one is perfect.

*Bale, Benzema and Cristiano.

Big Data knocks out Data Warehousing – In 2016, Big Data will finally put Data Warehousing to bed. It's been on the cards for a while now, but in 2016 it will be proven beyond any shadow of doubt that the best input into strategic, tactical and operational decision making are massive concatenations of simple word counts, done on a vast array of what people are now describing as commodity hardware. Commodity hardware, to distinguish it from the other hardware that we were using up until now, which was also confusingly termed 'commodity hardware'.

LinkedIn publishes its first ever Big Data success story – Incredible, but true. In 2016, LinkedIn will get its resident Big Data guru, data master and influencer to document a tangible, coherent and verifiable Big Data success story. It will matter not a jot that it is a knock-off plagiarism of a late nineteen-ninety Data Warehousing partial success-story, as it's the thoughts, and not the facts, that count.

Queen Brenda inaugurates the Lady Di Memorial Big Data Lake – During 2016, HRH will inaugurate the former Windermere Lake as the new Lady Di Memorial Big Data Lake. Millions of subjects will hail this as a clear success story for Big Data and for Britain. The inauguration day will be slightly marred (no pun intended) by a gushing Big Data guru being told to beggar 'orf by none other than Phil the Greek.

Big Data housing becomes an issue of significant importance to the EU – Because of the incredible speeds amazingly valuable Big Data is being created at, the EU will move to take measures to capture and more importantly store all of this new Big Data. There will exist an existential realization that none of this life-giving Big Data should be lost or compromised, or both. Chancellor Merkel has already come out strongly and offered to take much of the generated Big Data in 2016, which will be housed in both public and private premises. For example, each German household will be asked to house volumes of Big Data based on the size of the family abode, the internet bandwidth and the number of smart phones im haus. France and Spain will follow suit, but with modestly reduced quotas. The UK will spend most of 2016 trying to opt out, and will even threaten a Big Data Referendum if the onus on them to take so much Big Data is not radically reduced. So, in net, a win-win for Europe and Big Data.

The CIA will be charged with custodianship of all Big Data success stories – During 2016, together with the custodians of Fort Knox, the CIA will be charged with custodianship of all tangible, coherent and verifiable Big Data success stories, and only those who should know, and can handle the power of information, will have access to the files. This will be done to avoid information of global importance from falling into the hands of evil-doers, delinquents and busy-bodies. This is a success story because it will demonstrate once again a truly tangible, coherent and verifiable Big Data success story. The Head of the FBI was unavailable for comment.

Big Data solves world issues – For years we have struggled to see the elephants in the global room. Now with the help of Big Data, not only will we finally be able to see them, but also we will have a key component of the solution within a click of the mouse and a rapid stroke of a smartphone gesture. Yes, hunger, poverty and the refugee crisis can all be identified in 2016, thanks mainly to Big Data. What's more, if we get the political will to do so we can also think of ways of partially, or wholly, fixing those problems. Although admittedly that is a 'big ask' of Big Data, especially in one enormously hectic year, where the focus of attention will be mainly on the UEFA European Championship, the Olympics and the war on terror. Now if that isn't a Big Data success story then I really don't know what is.

Big Data success stories to top a million by the end of 2016 – Thanks to a global and socially responsible market-driven initiative to reclassify Microsoft Access and Microsoft Excel as Big Data repositories, the number of Big Data success stories for 2016 will amazingly exceed a million, and that's just in Milton Keynes.

Democratic Elections replaced by Mega-Democratic Big Data Social Media Mining – Sick and tired of having to turn out to vote every four years? Tense, nervous pre-election headaches over not being able to think, weigh or decide? Worry no more. Thanks to advanced social-media mining techniques, from 2016 the election of politicians will be decided not by you – least not in the legacy way – but by a broad interconnected raft of machine learning, sentiment analysis and other data science gizmos – guaranteed 100% democratic. This is what we have all been waiting for. The end of old fashioned and boringly DIY-democratic elections, and the heralding of a brave new world of online interactive social-media politics. Don't look at it as the trivialization of democracy, the puerility of post-modernity and the throwing away of centuries of fights for civil and human rights, look upon it as being real progress – progress with a capital pee.

On the other hand, what 2016 might really herald might just be The Golden Age of Big Data Bullshit.

Let's wait and see.

Thank you so much for reading.

Also, if you are of a mind, then please join The Big Data Contrarians on LinkedIn:

https://www.linkedin.com/groups/8338976

Big Data: And the hype played on

MARTYN RICHARD JONES

In spite of the best-efforts of Hadoop evangelists, consulting houses, and IT infrastructure and service vendors, Big Data – hailed as the greatest thing since the dawn of greatest things – is failing, and dramatically so, to produce the necessarily corresponding quantity and quality of tangible, detailed and verifiable success-stories.

So, given the dearth of Big Data success stories, why are so many in the industry still banging on with troll-like insistence about the ‘indisputable’ relevance, importance and universal applicability of Big Data?

Seen from where I stand, and I don’t think I am particularly unique in this respect, a lot of arrows are pointing towards a quieter future for Big Data, one where it has far more limited use than was once imagined, a data niche rather than a data driven all-encompassing market-based revolution. But obviously not everyone is sharing that view, at least not ostensibly so.

The fact of the matter is this. That in spite of what the good people at the prestigious house of Gartner have been saying, the Big Data hype-cycle is far from over. In fact, we have entered what might be crudely yet accurately described as the golden age of Big Data boloney.

When I survey the Big Data landscape and see companies, organisations and individuals continue to bang on the Big Data drum like as if it was going out of fashion, it’s quite embarrassing. Acutely so. Even for someone who isn’t participating, either actively or passively, in the Big Data dog and pony bestiality-show.

So, what’s happening with the Big Data in-crowd? Words were coming to my mind, words such as overcompensation, projection, doublethink, denial, delusion, social, awkward, wacky correlation and non-existent causation. But none of these were entirely satisfactory. Lacking, as they do, in conveying any sort of coherent, tangible and credible explanation of the continuing and burgeoning Big Data hype phenomena.

Then it dawned on me. Black tulips.

Industry players and Big Data pundits are inexplicably engaged in what is at best a zero-sum game, and at its worst has clear parallels with the hubris, wilful ignorance and  greed which lead to the last major global financial crisis and also has nasty parallels with the dot-com bubble.

So, the next time you are reading a gushing puff-piece on Big Data take this advice to heart. Instead of thinking about the author as a friendly adviser, think of them as an unqualified, unregistered and unregulated financial adviser who is trying to flog you something that they probably don’t understand and that they have no idea how to value – remember, all they are thinking is in their ‘commission’.

More to the point, don’t go repeating the same talking points as if they were facts you can take to the bank, when you yourself are unsure about how reliable those ‘facts’ might be.

Stated simply. There is very little which could be more suspect than an announced global revolution in Big Data, a revolution that is accompanied by a dearth of tangible, detailed and verifiable success-stories. So take good note of what is really going on.

If someone deceives you once, it’s their fault, if they deceive you twice it’s your fault. If you then go on to ‘do unto others what has been done unto you’, then expect to be rightfully derided and decried.

Be professional, be on the side of the angels, and do the right thing. In the longer run you will thank yourself for your wise choices.

Many thanks for reading.

Something funny happened on the way to the Data Lake

When one surveys the vast array of Big Data tomes and the even vaster output of the regularly amazing Big Data bullshit babblers, one thing that strikes you immediately is that Big Data isn’t explained starting with a story about the marvellous useful applications that you might imagine are generating all of this additional Big Data, but instead they almost invariably dwell on all of the data generated by social media applications of dubious business value and even more dubious social value.

Basically we are being led to believe that applications of peripheral interest, that is, peripheral in the grander scheme of things, are capable of simultaneously meeting almost all of our immediate needs and also of generating a voluminous wealth of data detritus that is somehow going to bring about an amazing economic revolution, the awesome galvanising of innovation and the creation of absolutely fabulous riches beyond the dreams of avarice.

What’s more, every day some Big Data proselyting dope has a cunning plan and pops up on the web like a drunken gopher with some extrapolating and theorising boloney about the quite unusual successes of very unusual businesses such as Facebook, Twitter and Google and then hammers square pegs into round holes in order to produce a generalised theory of awesome Big Data irksome-foolishness that will certainly mean, without a doubt, that we will see the raising of the luck and fortunes of all businesses, everywhere, if only they would buy into the Big Data religion, now.

If you don’t believe me then check it out for yourself. For example, take a look at the Big Data channel over on LinkedIn, now that it’s been apparently taken over by the master guru of astro-turfing Big Data bullshit and babble himself, it now comes with wall to wall Big Data hype, and it’s all the same, and it’s all so obviously wrong that it makes you wonder if LinkedIn is not deliberately converting itself into some species of professional laughing academy, for idiots by idiots, with no room for even the occasionally and superficially contrarian.

In fact, looking at some of the latest blunderbuss blasts of blatant bullshit I am reminded of the quote ascribed to Groucho Marx which I will makeover for modern-day consumption: “These are my Big Data principles, and if you don't like them... well, I have others.”