Professional networking? Yo! BlankedOut Sucked

Martyn Richard Jones

Hello, readers.

Before my Aunt Dolly went to a better life she received a handwritten letter from her dear friend and long-time admirer Sir Arthur Streeb-Greebling, which was to be passed on to the CEO of, what he called, an interweb professional dating site. Now, she didn't actually give me a precise name, so I now find myself at a loss. So, if there's anyone out there that recognises who this might have been written for, then please let me know.

What follows is Sir Arthur's text, as relayed to my Aunt Dolly. 

Dear Mister Def Archibald Quengler,

We've never met, and we probably never will, and I don't much like the cut of your jib, but, I would like to take this opportunity to draw your attention to the demise of a once burgeoning professional dating site where decent chaps and chapesses and an assortment of pathetic likeminded individuals could share likeminded individual things. Such as pictures of cats, sexist crap, professional resumes, tips and tricks, insightful comments, 'me too' inanities, hype, boloney, mendacity, political detritus and even worse religious detritus.

Personally, I blame lack of national service and the parents… oh, and the teachers.

Anyway, I am writing to you in the tepid hope that your amazing and absolutely fabulous online concern does not fall victim to the same malaise.

You may have heard of the once significant, successful and utterly sensational BlankedOut web setup, it was a so called professional link-up site for pros, or some such dreck.

I am reliably informed that people loved BlankedOut, just a bit, and that they also hated it even more. Many people said to me "BlankedOut is the Facebook of the crass and dim-witted wannabe class, and apart from some minority exceptions, it is a gushing channel of crap and a conduit of intense mediocrity." But, not being aware of the game, I was in no position to make a judgement. I will leave that for others.

BlankedOut has been variously described as "a place where capital interests took us for a ride", and where members were generally treated as hapless schmucks, captive clowns and useful idiots, and that according to the observers on the hustings, they did it in such a way that people lapped up the ride.

My distinguished colleague Mister Bernice Hill, PhN observed that, as a role model for a Big Data and Big Data analytics company, that BlankedOut "sucked, big time".

He went on to state "It sucks from top to bottom, from left to right, and around the whole global enchilada." Bernice was tough, a hard-hearted man, and he had a way with words.

The former judge Sir James Beauchamp, also didn't hold back when he stated "From its obtuse, obnoxious and incessant promotions of sponsored rancid 'content' to its insipid, trite and fatuous love-affair with its god-damn-awful Effluence®s and fawning sycophants, BlankedOut stood as a shining internet beacon of manipulation, exploitation and hypocrisy." I seem to recall a certain Barnie Puddle as being one of the mendacious and manipulative of the Effluence®s. But, whatever, as the young people are want to say these days

So, you see. I knew bugger all about the matter of these sorts of high-class professional career-oriented pimping sites, past or present. But now, and you may call me an incurable romantic, when I look upon the history of the deceased BlankedOut community, as dead as a Norwegian blue, what I see is something that leads my thoughts to visions of a massive work of misuse, abuse and deception. 

Which is not a good omen.

Of course, alternatives to BlankedOut existed, but they were ascetically professional and did not venture much into the wild-side of vulgarity, populism and cant. They stuck to their core competences, like troopers, and trusted their clientele to be just as serious, decent and professional as they were. More fool them, what?

But not so, at poor, dead and despised BlankedOut, lying in a state of disgrace, like some sort of dead pisshead society on a pyre of burning nothing.

So, Mister Def Whiner, heed my word, don't let your business turn into yet another BlankedOut. If ever there was one, an abject lesson in snatching failure from the jaws of success.

Carpe diem, man, carpe diem!

So, I just have this to tell you, and I will say it only once. Good will to all women and men and all of that.  If you are still an admirer of what was that dreadful BlankedOut business model,,, then, bugger off and take your bloody dogs with you!  

Yours sincerely,

Sir Arthur Greeb-Streebling

Admiral of the Grand Fleet, retired

Well, nothing much to add from me. Sir Arthur seems to have said it all. Although, I would still like to know who this letter is supposed to have been written to, because try as I might, I can't track down anyone who goes by the name of Mister Def Archibald Quengler. That stated, the next time I am in Palma de Mallorca I will ask my Aunt Dolly, now that she is in a far better place and has more free-time on her hands.

Next week I will be looking at financial scams that concern greenhorns, their parentages and protectors, culture establishments, incongruous financial arrangements, the government and more importantly, the police and the judicial system.

Stay tuned.

Many thanks for reading.

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

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