Free Business Analytics Content –Thanks to Wikipedia – Part 1

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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.

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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.