Mostrando entradas con la etiqueta good start. Mostrar todas las entradas
Mostrando entradas con la etiqueta good start. Mostrar todas las entradas

jueves, 17 de marzo de 2016

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.


domingo, 6 de marzo de 2016

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 marketingmultivariate 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. 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 statisticsdependence is any statistical relationship between two random variables or two sets of dataCorrelation 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 intelligencemachine learningstatistics, and database systemshttps://en.wikipedia.org/wiki/Data_mining
  13. In statistical modelingregression 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 processingtext 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 analyticsVCA) 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]optimizationnumerical 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.

martes, 22 de diciembre de 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:


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