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.

Free Business Analytics Content –Thanks to Wikipedia – Part 1

Why buy when you can get it for free?

Here is the first fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you.

1. A/B testing is a way to compare two versions of a single variable typically by testing a subject’s response to variable A against variable B, and determining which of the two variables is more effective.https://en.wikipedia.org/wiki/A/B_testing
2. Choice modelling attempts to model the decision process of an individual or segment via Revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically “utility” in economics and various related fields). https://en.wikipedia.org/wiki/Choice_modelling
3. Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself to such changing conditions. https://en.wikipedia.org/wiki/Adaptive_control
4. Multivariate Testing. In marketing, multivariate testing or multi-variable testing techniques apply statistical hypothesis testing on multi-variable systems, typically consumers on websites. Techniques of multivariate statistics are used.https://en.wikipedia.org/wiki/Multivariate_testing_in_marketing
5. In probability theory, the multi-armed bandit problem (sometimes called the K–[1] or N-armed bandit problem[2]) is a problem in which a gambler at a row of slot machines (sometimes known as “one-armed bandits”) has to decide which machines to play, how many times to play each machine and in which order to play them.https://en.wikipedia.org/wiki/Multi-armed_bandit
6. A t-test is any statistical hypothesis test in which the test statistic follows a Student’s t-distribution if the null hypothesis is supported.https://en.wikipedia.org/wiki/Student%27s_t-test
7. Visual analytics is an outgrowth of the fields of information visualization and scientific visualization that focuses on analytical reasoning facilitated by interactive visual interfaces.https://en.wikipedia.org/wiki/Visual_analytics
8. In statistics, dependence is any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence, though in common usage it most often refers to the extent to which two variables have a linear relationship with each other. Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a product and its price. https://en.wikipedia.org/wiki/Correlation_and_dependence
9. Scenario analysis is a process of analyzing possible future events by considering alternative possible outcomes (sometimes called “alternative worlds”). Thus, the scenario analysis, which is a main method of projections, does not try to show one exact picture of the future. Instead, it presents consciously several alternative future developments. https://en.wikipedia.org/wiki/Scenario_analysis
10. Forecasting is the process of making predictions of the future based on past and present data and analysis of trends.https://en.wikipedia.org/wiki/Forecasting
11. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. https://en.wikipedia.org/wiki/Time_series
12. Data mining is an interdisciplinary subfield of computer science.[1][2][3] It is the computational process of discovering patterns in largedata sets (“big data“) involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systemshttps://en.wikipedia.org/wiki/Data_mining
13. In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or ‘predictors’). https://en.wikipedia.org/wiki/Regression_analysis
14. Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning.  https://en.wikipedia.org/wiki/Text_mining
15. Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service.https://en.wikipedia.org/wiki/Sentiment_analysis
16. Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing [1] Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face.https://en.wikipedia.org/wiki/Image_analysis
17. Video content analysis (also Video content analytics, VCA) is the capability of automatically analyzing video to detect and determine temporal and spatial events.https://en.wikipedia.org/wiki/Video_content_analysis
18. Speech analytics is the process of analyzing recorded calls to gather information, brings structure to customer interactions and exposes information buried in customer contact center interactions with an enterprise. https://en.wikipedia.org/wiki/Speech_analytics
19. Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated randomsampling to obtain numerical results. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other mathematical methods. Monte Carlo methods are mainly used in three distinct problem classes:[1]optimization, numerical integration, and generating draws from a probability distribution.https://en.wikipedia.org/wiki/Monte_Carlo_method
20. Linear programming (LP; also called linear optimization) is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming (mathematical optimization).https://en.wikipedia.org/wiki/Linear_programming
21. Cohort analysis is a subset of behavioral analytics that takes the data from a given eCommerce platform, web application, or online game and rather than looking at all users as one unit, it breaks them into related groups for analysis. These related groups, or cohorts, usually share common characteristics or experiences within a defined time-span. https://en.wikipedia.org/wiki/Cohort_analysis
22. Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables calledfactors. For example, it is possible that variations in say six observed variables mainly reflect the variations in two unobserved (underlying) variables.https://en.wikipedia.org/wiki/Factor_analysis
23. Adaptive (or Artificial) Neural Networks. Like other machine learning methods – systems that learn from data – neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinaryrule-based programming, including computer vision and speech recognition.https://en.wikipedia.org/wiki/Artificial_neural_network
24. Meta Analysis. The basic tenet of a meta-analysis is that there is a common truth behind all conceptually similar scientific studies, but which has been measured with a certain error within individual studies. The aim in meta-analysis then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. In essence, all existing methods yield a weighted average from the results of the individual studies and what differs is the manner in which these weights are allocated and also the manner in which the uncertainty is computed around the point estimate thus generated. https://en.wikipedia.org/wiki/Meta-analysis

I hope you find the content useful. Of course all thanks should really go to Wikipedia and their unpaid expert contributors.

I will try and get the next part of ‘ Free Business Analytics Content’ onto Linked Pulse over the next weekend.

Many thanks for reading.

Just a few points before closing.

Firstly, please consider joining The Big Data Contrarians, here on LinkedIn:https://www.linkedin.com/groups/8338976

Secondly, keep in touch. My strategy blog is here http://www.goodstrat.comand I can be followed on Twitter at @GoodStratTweet. Please also connect on LinkedIn if you wish. If you have any follow-up questions then leave a comment or send me an email on martyn.jones@cambriano.es

Thirdly, you may be interested in other articles I have written, such as:

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

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

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.