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For Today’s Graduate, Just One Word: Statistics

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  1. nvha

    nvha MBA family

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    Google and the flu: how big data will help us make gigantic mistakes

    As Google's attempt to predict the spread of flu by using search terms shows, lots of data can cause plenty of confusion


    John Naughton

    The Observer, Saturday 5 April 2014 18.30 BST



    Google's use of search terms to predict the spread of flu led to massive overestimates. Photograph: PHOTOTAKE Inc/Alamy


    A concept of enduring utility rarely emerges from the market research business, but the Gartner hype cycle is an exception that proves the rule. It is a graph that describes the life cycle of a technological innovation in five phases. First, there's the "trigger" that kicks off the feverish excitement and leads to a rapid escalation in public interest, which eventually leads to a "peak of inflated expectations" (phase two), after which there's a steep decline as further experimentation reveals that the innovation fails to deliver on the original – extravagant – claims that were made for it. The curve then bottoms out in a "trough of disillusionment" (phase three), after which there's a slow but steady rise in interest (the "slope of enlightenment" – phase four) as companies discover applications that really do work. The final phase is the "plateau of productivity" – the phase where useful applications of the idea finally become mainstream. The time between phases one and five varies between technologies and can be several decades long.

    As the "big data" bandwagon gathers steam, it's appropriate to ask where it currently sits on the hype cycle. The answer depends on which domain of application we're talking about. If it's the application of large-scale data analytics for commercial purposes, then many of the big corporations, especially the internet giants, are already into phase four. The same holds if the domain consists of the data-intensive sciences such as genomics, astrophysics and particle physics: the torrents of data being generated in these fields lie far beyond the processing capabilities of mere humans.

    But the big data evangelists have wider horizons than science and business: they see the technology as a tool for increasing our understanding of society and human behaviour and for improving public policy-making. After all, if your shtick is "evidence-based policy-making", then the more evidence you have, the better. And since big data can provide tons of evidence, what's not to like?

    So where on the hype cycle do societal applications of big data technology currently sit? The answer is phase one, the rapid ascent to the peak of inflated expectations, that period when people believe every positive rumour they hear and are deaf to sceptics and critics.

    It's largely Google's fault. Four years ago, its researchers caused a storm by revealing (in a paper published in Nature) that web searches by Google users provided better and more timely information about the spread of influenza in the United States than did the data-gathering methods of the US government's Centres for Disease Control and Prevention. This paper triggered a frenzy of speculation about other possible public policy applications of massive-scale data analytics.

    As the economist Tim Harford puts it: "Not only was Google Flu Trends quick, accurate and cheap, it was theory-free. Google's engineers didn't bother to develop a hypothesis about what search terms – 'flu symptoms' or 'pharmacies near me' – might be correlated with the spread of the disease itself. The Google team just took their top 50m search terms and let the algorithms do the work."

    Thus was triggered the hype cycle. If Google could do this for flu, surely it could be done for lots of other societal issues. And maybe it can. But in this particular case, the enthusiasm turned out to be premature. Nature recently reported that Google Flu Trends had gone astray. "After reliably providing a swift and accurate account of flu outbreaks for several winters," reports Harford, "the theory-free, data-rich model had lost its nose for where flu was going. Google's model pointed to a severe outbreak, but when the slow-and-steady data from the [US government centre] arrived, they showed that Google's estimates of the spread of flu-like illnesses were overstated by almost a factor of two."

    So what went wrong? Simply this: Google doesn't know anything about the causes of flu. It just knows about correlations between search terms and outbreaks. But as every GCSE student knows, correlation is quite different from causation. And causation is the only basis we have for real understanding.

    Big data enthusiasts seem remarkably untroubled by this. In many cases, they say, knowing that two things are correlated is all you need to know. And indeed in commerce that may be reasonable. I buy stuff both for myself and my kids on Amazon, for example, which leads the company to conclude that I will be tempted not only by Hugh Trevor-Roper's letters but also by new releases of hot rap artists. This is daft, but does no harm. Applying the kind of data analytics that produces such absurdities to public policy, however, would not be funny. But it's where the more rabid big data evangelists want to take us. We should tell them to get lost.


    http://www.theguardian.com/technology/2014/apr/05/google-flu-big-data-help-make-gigantic-mistakes
     
  2. nvha

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    Eight (No, Nine!) Problems With Big Data


    By GARY MARCUS and ERNEST DAVISAPRIL 6, 2014


    BIG data is suddenly everywhere. Everyone seems to be collecting it, analyzing it, making money from it and celebrating (or fearing) its powers. Whether we’re talking about analyzing zillions of Google search queries to predict flu outbreaks, or zillions of phone records to detect signs of terrorist activity, or zillions of airline stats to find the best time to buy plane tickets, big data is on the case. By combining the power of modern computing with the plentiful data of the digital era, it promises to solve virtually any problem — crime, public health, the evolution of grammar, the perils of dating — just by crunching the numbers.

    Or so its champions allege. “In the next two decades,” the journalist Patrick Tucker writes in the latest big data manifesto, “The Naked Future,” “we will be able to predict huge areas of the future with far greater accuracy than ever before in human history, including events long thought to be beyond the realm of human inference.” Statistical correlations have never sounded so good.

    Is big data really all it’s cracked up to be? There is no doubt that big data is a valuable tool that has already had a critical impact in certain areas. For instance, almost every successful artificial intelligence computer program in the last 20 years, from Google’s search engine to the I.B.M. “Jeopardy!” champion Watson, has involved the substantial crunching of large bodies of data. But precisely because of its newfound popularity and growing use, we need to be levelheaded about what big data can — and can’t — do.

    The first thing to note is that although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, it never tells us which correlations are meaningful. A big data analysis might reveal, for instance, that from 2006 to 2011 the United States murder rate was well correlated with the market share of Internet Explorer: Both went down sharply. But it’s hard to imagine there is any causal relationship between the two. Likewise, from 1998 to 2007 the number of new cases of autism diagnosed was extremely well correlated with sales of organic food (both went up sharply), but identifying the correlation won’t by itself tell us whether diet has anything to do with autism.



    Second, big data can work well as an adjunct to scientific inquiry but rarely succeeds as a wholesale replacement. Molecular biologists, for example, would very much like to be able to infer the three-dimensional structure of proteins from their underlying DNA sequence, and scientists working on the problem use big data as one tool among many. But no scientist thinks you can solve this problem by crunching data alone, no matter how powerful the statistical analysis; you will always need to start with an analysis that relies on an understanding of physics and biochemistry.

    Third, many tools that are based on big data can be easily gamed. For example, big data programs for grading student essays often rely on measures like sentence length and word sophistication, which are found to correlate well with the scores given by human graders. But once students figure out how such a program works, they start writing long sentences and using obscure words, rather than learning how to actually formulate and write clear, coherent text. Even Google’s celebrated search engine, rightly seen as a big data success story, is not immune to “Google bombing” and “spamdexing,” wily techniques for artificially elevating website search placement.

    Fourth, even when the results of a big data analysis aren’t intentionally gamed, they often turn out to be less robust than they initially seem. Consider Google Flu Trends, once the poster child for big data. In 2009, Google reported — to considerable fanfare — that by analyzing flu-related search queries, it had been able to detect the spread of the flu as accurately and more quickly than the Centers for Disease Control and Prevention. A few years later, though, Google Flu Trends began to falter; for the last two years it has made more bad predictions than good ones.

    As a recent article in the journal Science explained, one major contributing cause of the failures of Google Flu Trends may have been that the Google search engine itself constantly changes, such that patterns in data collected at one time do not necessarily apply to data collected at another time. As the statistician Kaiser Fung has noted, collections of big data that rely on web hits often merge data that was collected in different ways and with different purposes — sometimes to ill effect. It can be risky to draw conclusions from data sets of this kind.

    A fifth concern might be called the echo-chamber effect, which also stems from the fact that much of big data comes from the web. Whenever the source of information for a big data analysis is itself a product of big data, opportunities for vicious cycles abound. Consider translation programs like Google Translate, which draw on many pairs of parallel texts from different languages — for example, the same Wikipedia entry in two different languages — to discern the patterns of translation between those languages. This is a perfectly reasonable strategy, except for the fact that with some of the less common languages, many of the Wikipedia articles themselves may have been written using Google Translate. In those cases, any initial errors in Google Translate infect Wikipedia, which is fed back into Google Translate, reinforcing the error.

    A sixth worry is the risk of too many correlations. If you look 100 times for correlations between two variables, you risk finding, purely by chance, about five bogus correlations that appear statistically significant — even though there is no actual meaningful connection between the variables. Absent careful supervision, the magnitudes of big data can greatly amplify such errors.

    Seventh, big data is prone to giving scientific-sounding solutions to hopelessly imprecise questions. In the past few months, for instance, there have been two separate attempts to rank people in terms of their “historical importance” or “cultural contributions,” based on data drawn from Wikipedia. One is the book “Who’s Bigger? Where Historical Figures Really Rank,” by the computer scientist Steven Skiena and the engineer Charles Ward. The other is an M.I.T. Media Lab project called Pantheon.

    Both efforts get many things right — Jesus, Lincoln and Shakespeare were surely important people — but both also make some egregious errors. “Who’s Bigger?” claims that Francis Scott Key was the 19th most important poet in history; Pantheon has claimed that Nostradamus was the 20th most important writer in history, well ahead of Jane Austen (78th) and George Eliot (380th). Worse, both projects suggest a misleading degree of scientific precision with evaluations that are inherently vague, or even meaningless. Big data can reduce anything to a single number, but you shouldn’t be fooled by the appearance of exactitude.

    FINALLY, big data is at its best when analyzing things that are extremely common, but often falls short when analyzing things that are less common. For instance, programs that use big data to deal with text, such as search engines and translation programs, often rely heavily on something called trigrams: sequences of three words in a row (like “in a row”). Reliable statistical information can be compiled about common trigrams, precisely because they appear frequently. But no existing body of data will ever be large enough to include all the trigrams that people might use, because of the continuing inventiveness of language.

    To select an example more or less at random, a book review that the actor Rob Lowe recently wrote for this newspaper contained nine trigrams such as “dumbed-down escapist fare” that had never before appeared anywhere in all the petabytes of text indexed by Google. To witness the limitations that big data can have with novelty, Google-translate “dumbed-down escapist fare” into German and then back into English: out comes the incoherent “scaled-flight fare.” That is a long way from what Mr. Lowe intended — and from big data’s aspirations for translation.

    Wait, we almost forgot one last problem: the hype. Champions of big data promote it as a revolutionary advance. But even the examples that people give of the successes of big data, like Google Flu Trends, though useful, are small potatoes in the larger scheme of things. They are far less important than the great innovations of the 19th and 20th centuries, like antibiotics, automobiles and the airplane.

    Big data is here to stay, as it should be. But let’s be realistic: It’s an important resource for anyone analyzing data, not a silver bullet.



    Gary Marcus is a professor of psychology at New York University and an editor of the forthcoming book “The Future of the Brain.” Ernest Davis is a professor of computer science at New York University.
     
    Last edited: Apr 9, 2014
  3. nvha

    nvha MBA family

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    Redlining for the 21st Century

    Companies can now use data to constrict which options they offer to certain consumers—and at what prices.

    Bill DavidowMar 5 2014, 11:19 AM ET

    Redlining map of Philadelphia from the 1930s (Wikimedia Commons)

    Using personal information gathered about you on the Internet to provide you with better choice is very different from using the same information to control your behavior. The former is a service to the consumer. The latter is exploitation. I call the use of big data to exploit consumers “personal redlining.”

    The term “redlining,” which first emerged in the 1950s, referred to the practice of denying service or charging more for products to particular groups based on race, sex, or where they lived. The Fair Housing Act of 1968 made redlining based on race, religion, sex, and the like illegal in mortgage lending.

    Personal redlining is not about using big data in clever ways to influence choice as has been discussed in a recent Atlantic article by Rebecca J. Rosen. It is about using big data to dictate choice. When companies engage in personal redlining they use big data to learn everything possible about you as an individual and then decide what information, products, and services you should have—and at what price. It is about limiting options and pressuring customers to select one of those options.




    If you are provided with too much information, it becomes impossible to find the information you want. Publications such as The Atlantic filter the information they publish to provide their large reader population with the articles they believe will interest them. (It’s called “editing.”) Personal redlining using big data can also be used to provide you with relevant choices and make it easier for you to find what you want.

    Businesses would like customers to believe that they use big data only to add value to the consumer experience. But the behavior of many businesses demonstrates a deep interest in customer control.

    Here are some of the techniques businesses will have at their disposal. When a consumer applies for automobile or homeowner insurance or a credit card, companies will be able to make a pretty good guess as to the type of risk pool they should assign the consumer to. The higher-risk consumers will never be informed about or offered the best deals. Their choices will be limited.

    State Farm is currently offering a discount to customers through a program called Drive Safe & Save. The insurer offers discounts to customers who use services such as Ford’s Sync or General Motors’ OnStar, which, among other things, read your odometer remotely so that customers no longer have to fuss with tracking how many miles they drive to earn insurer discounts. How convenient!

    State Farm makes it seem that it’s only your mileage that matters but imagine the potential for the company once it has remote access to your car. It will know how fast you drive on the freeway even if you don't get a ticket. It will know when and where you drive. What if you drive on routes where there are frequent accidents? Or what if you park your car in high-crime areas?

    By personal redlining, State Farm will have the ability to offer you your own personalized insurance rate.

    Health insurers, now that health care reform is in effect, cannot refuse you based on prior condition. But they can use other creative techniques. If a company can make a pretty good guess that you are a high-risk customer, it can give you a lousy Internet experience. It can deluge you with questions and long, hard-to-navigate forms, and randomly drop Internet connections and slow the page response. The goal will be to drive you to another company. As journalist Phil Mattera recently pointed out, “These companies have always found ways to increase profits at the expense of coverage, and they always will.”

    By analyzing credit-card transactions, credit-card companies can discover customers who frequently return merchandise for credit. Internet retailers could discourage those customers by increasing the charges for shipping.

    With access to enough data, airlines can make a pretty good guess as to whether a customer is morbidly obese. They could decide to charge more for a seat, or indicate that nearly full flights have no space.

    Of course, companies can also choose to do none of the above. I’m sure that many will instead use the Internet to provide consumers with better choices; they will use big data because they believe the best way to create a profitable business is to provide customers with the best possible service. But others will not. They will use big data to manage information flow, charge customers they decide are less desirable higher prices and make doing business with them less, not more, convenient. They will be the pioneers of personal redlining.

    http://www.theatlantic.com/business/archive/2014/03/redlining-for-the-21st-century/284235/
     
  4. nvha

    nvha MBA family

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    The Power of 'Thick' Data

    Businesses need to know how a product or service fits into the emotional lives of their customers

    By Christian Madsbjerg and

    Mikkel B. Rasmussen

    March 21, 2014 7:15 p.m. ET

    Ben Wiseman


    At its core, all business is about making bets on human behavior. Which product is most likely to sell, what employee is most likely to succeed, what price is a customer willing to pay? Companies that excel at making these bets tend to thrive in the marketplace.

    So it's no wonder that the latest fad in the business world is Big Data—massive data sets sifted by powerful analytical tools. Big Data can be an extraordinary tool, helping to gather new information about our behavior and preferences. What it can't explain is why we do what we do.

    In fact, companies that rely too much on the numbers, graphs and factoids of Big Data risk insulating themselves from the rich, qualitative reality of their customers' everyday lives. They can lose the ability to imagine and intuit how the world—and their own businesses—might be evolving. By outsourcing our thinking to Big Data, our ability to make sense of the world by careful observation begins to wither, just as you miss the feel and texture of a new city by navigating it only with the help of a GPS.

    Successful companies and executives work to understand the emotional, even visceral context in which people encounter their product or service, and they are able to adapt when circumstances change. They are able to use what we like to call Thick Data.

    Consider the story of Lego. In 2004, the Danish firm was hemorrhaging a million dollars a day. It had lost touch with its customers and was on the brink of collapse.

    At the time, there was a clear idea within Lego about what the answer might be. Modern kids, the company thought, were seeking "instant traction" in their play experience. They wanted toys that they could pick up and play with immediately, not ones that required meticulous assembly, brick by brick, as with classic Legos.

    Working on this assumption, Lego started developing new action figures and other concepts, but Jørgen Vig Knudstorp, the firm's newly appointed CEO, had a nagging feeling that the ideas were wrong. He decided that he needed to start over and understand, more fundamentally, how and why kids play. He engaged our firm to do research with Lego users across five global cities. We were sent to play with kids—not in focus groups but in the context of their real lives.

    After collecting countless hours of video, thousands of photos and journal entries, and hundreds of artifacts of the play experience, Lego meticulously coded everything and looked for patterns across geography and age. Slowly, a pattern emerged from all corners of the data.

    Not every child wants to be a Lego builder, but those who do, the company discovered, are passionate about the play experience: They want to achieve mastery, and they want to understand where they fit in the hierarchy of Lego skills. Lego's team arrived at a moment of clarity: They needed to "go back to the brick."







    Lego analyzed data and arrived at a moment of clarity: They needed to "go back to the brick." David McLain/Aurora Photos/Corbis

    Today Lego is again a successful company. The turnaround has many reasons, including the recent success of "The Lego Movie," but one of them is certainly a deeper understanding of the play experience.

    Since its founding in 1954, Coloplast, a medical technology company based in Denmark, had grown by double digits every year. Suddenly, in 2008, the company missed its sales targets four times in a single year. Long the global leader in the niche market for stoma bags for personal hygiene after colon surgery, Coloplast found that its products were losing market share to the competition.

    Coloplast's research and development team was focused on solving the problem of "leakage" in the stoma bags. Countless studies showed that people who experienced leakage would lose trust in the product and change to something else. The general assumption across the industry was that better adhesives equaled less leakage.

    For years, the engineers at Coloplast had made incremental improvements to their products, adding new features or improving the adhesive. But it was no longer enough. To get a better sense of what the company could do to provide a superior product, they decided to immerse themselves in the world of the customer. Over the course of several months, our firm worked with them to collect, sort and analyze troves of Thick Data about their customers' world.

    As the executives at Coloplast worked their way through the videos, photos and firsthand impressions, they could see the actual bodies of their customers and how their products worked in relationship to them. As one executive told us, "I could pick up the photographs and the diaries, page through them, feel them. This data has a completely different texture to it."

    What dawned on the Coloplast team was that the adhesive wasn't the problem. Rather, what caused the dreaded leakages was a lack of fit to the patients' diverse and changing bodies. Many patients gained or lost a lot of weight following their surgery or developed scar tissue that made it difficult to keep the stoma bags in place.

    Coloplast used this insight to create three different product categories for body types. Not only did this help stop the leakage problem, it gave the company a clear perspective and direction for future innovation.

    Finally, consider the case of Samsung's 005930.SE +1.23% TV business. In the early 2000s, Samsung's TVs looked like every other TV on the shelf. Samsung executives could sense that they were missing opportunities to stand out, but they didn't understand how to excite consumers. They needed to ask a bigger question about human behavior in a cultural context: "What does the TV mean in the modern household?"

    We worked with Samsung to answer that question. Through hundreds of hours of interviews, videos and other artifacts, we helped the company identify crucial patterns. One interview subject said that he hid the television in a corner because he didn't like the way it looked; another said he wanted his TV to communicate "timelessness," like his meticulously crafted chair. To most people, we discovered, TVs aren't electronics. They are furniture.

    From this essential insight, the Samsung team was able to develop a completely redesigned TV with an aesthetic closer to that of modernist furniture than clunky technology. They hid speakers and other eyesores. They changed the way TVs were sold, marketed and serviced. The resulting TV—now a piece of furniture—was a perfect marriage of form and function.

    Working with Thick Data isn't straightforward, but the alternative is to outsource these complex business challenges to machines. Even with the magnificent computational power now at our disposal, sometimes there is no alternative to sitting with problems, stewing in them and struggling through them with the help of careful, patient human observation.

    —Mr. Madsbjerg and Mr. Rasmussen are the authors of "The Moment of Clarity: Using the Human Sciences to Solve Your Toughest Business Problems," from which this essay is adapted. Their consulting firm is ReD Associates.

    http://online.wsj.com/news/article_...4579449254114659882-lMyQjAxMTA0MDIwMjEyNDIyWj
     
  5. nvha

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    Big data: are we making a big mistake?

    By Tim Harford


    Big data is a vague term for a massive phenomenon that has rapidly become an obsession with entrepreneurs, scientists, governments and the media

    Five years ago, a team of researchers from Google announced a remarkable achievement in one of the world’s top scientific journals, Nature. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of influenza across the US. What’s more, they could do it more quickly than the Centers for Disease Control and Prevention (CDC). Google’s tracking had only a day’s delay, compared with the week or more it took for the CDC to assemble a picture based on reports from doctors’ surgeries. Google was faster because it was tracking the outbreak by finding a correlation between what people searched for online and whether they had flu symptoms.

    Not only was “Google Flu Trends” quick, accurate and cheap, it was theory-free. Google’s engineers didn’t bother to develop a hypothesis about what search terms – “flu symptoms” or “pharmacies near me” – might be correlated with the spread of the disease itself. The Google team just took their top 50 million search terms and let the algorithms do the work.

    The success of Google Flu Trends became emblematic of the hot new trend in business, technology and science: “Big Data”. What, excited journalists asked, can science learn from Google?

    As with so many buzzwords, “big data” is a vague term, often thrown around by people with something to sell. Some emphasise the sheer scale of the data sets that now exist – the Large Hadron Collider’s computers, for example, store 15 petabytes a year of data, equivalent to about 15,000 years’ worth of your favourite music.

    But the “big data” that interests many companies is what we might call “found data”, the digital exhaust of web searches, credit card payments and mobiles pinging the nearest phone mast. Google Flu Trends was built on found data and it’s this sort of data that ­interests me here. Such data sets can be even bigger than the LHC data – Facebook’s is – but just as noteworthy is the fact that they are cheap to collect relative to their size, they are a messy collage of datapoints collected for disparate purposes and they can be updated in real time. As our communication, leisure and commerce have moved to the internet and the internet has moved into our phones, our cars and even our glasses, life can be recorded and quantified in a way that would have been hard to imagine just a decade ago.

    Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.

    Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.”

    Found data underpin the new internet economy as companies such as Google, Facebook and Amazon seek new ways to understand our lives through our data exhaust. Since Edward Snowden’s leaks about the scale and scope of US electronic surveillance it has become apparent that security services are just as fascinated with what they might learn from our data exhaust, too.

    Consultants urge the data-naive to wise up to the potential of big data. A recent report from the McKinsey Global Institute reckoned that the US healthcare system could save $300bn a year – $1,000 per American – through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes.

    But while big data promise much to scientists, entrepreneurs and governments, they are doomed to disappoint us if we ignore some very familiar statistical lessons.

    “There are a lot of small data problems that occur in big data,” says Spiegelhalter. “They don’t disappear because you’ve got lots of the stuff. They get worse.”

    . . .

    Four years after the original Nature paper was published, Nature News had sad tidings to convey: the latest flu outbreak had claimed an unexpected victim: Google Flu Trends. After reliably providing a swift and accurate account of flu outbreaks for several winters, the theory-free, data-rich model had lost its nose for where flu was going. Google’s model pointed to a severe outbreak but when the slow-and-steady data from the CDC arrived, they showed that Google’s estimates of the spread of flu-like illnesses were overstated by almost a factor of two.

    The problem was that Google did not know – could not begin to know – what linked the search terms with the spread of flu. Google’s engineers weren’t trying to figure out what caused what. They were merely finding statistical patterns in the data. They cared about ­correlation rather than causation. This is common in big data analysis. Figuring out what causes what is hard (impossible, some say). Figuring out what is correlated with what is much cheaper and easier. That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, “causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning”.

    But a theory-free analysis of mere correlations is inevitably fragile. If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down. One explanation of the Flu Trends failure is that the news was full of scary stories about flu in December 2012 and that these stories provoked internet searches by people who were healthy. Another possible explanation is that Google’s own search algorithm moved the goalposts when it began automatically suggesting diagnoses when people entered medical symptoms.


    Google Flu Trends will bounce back, recalibrated with fresh data – and rightly so. There are many reasons to be excited about the broader opportunities offered to us by the ease with which we can gather and analyse vast data sets. But unless we learn the lessons of this episode, we will find ourselves repeating it.

    Statisticians have spent the past 200 years figuring out what traps lie in wait when we try to understand the world through data. The data are bigger, faster and cheaper these days – but we must not pretend that the traps have all been made safe. They have not.

    . . .

    In 1936, the Republican Alfred Landon stood for election against President Franklin Delano Roosevelt. The respected magazine, The Literary Digest, shouldered the responsibility of forecasting the result. It conducted a postal opinion poll of astonishing ambition, with the aim of reaching 10 million people, a quarter of the electorate. The deluge of mailed-in replies can hardly be imagined but the Digest seemed to be relishing the scale of the task. In late August it reported, “Next week, the first answers from these ten million will begin the incoming tide of marked ballots, to be triple-checked, verified, five-times cross-classified and totalled.”

    After tabulating an astonishing 2.4 million returns as they flowed in over two months, The Literary Digest announced its conclusions: Landon would win by a convincing 55 per cent to 41 per cent, with a few voters favouring a third candidate.

    The election delivered a very different result: Roosevelt crushed Landon by 61 per cent to 37 per cent. To add to The Literary Digest’s agony, a far smaller survey conducted by the opinion poll pioneer George Gallup came much closer to the final vote, forecasting a comfortable victory for Roosevelt. Mr Gallup understood something that The Literary Digest did not. When it comes to data, size isn’t everything.

    Opinion polls are based on samples of the voting population at large. This means that opinion pollsters need to deal with two issues: sample error and sample bias.

    Sample error reflects the risk that, purely by chance, a randomly chosen sample of opinions does not reflect the true views of the population. The “margin of error” reported in opinion polls reflects this risk and the larger the sample, the smaller the margin of error. A thousand interviews is a large enough sample for many purposes and Mr Gallup is reported to have conducted 3,000 interviews.

    But if 3,000 interviews were good, why weren’t 2.4 million far better? The answer is that sampling error has a far more dangerous friend: sampling bias. Sampling error is when a randomly chosen sample doesn’t reflect the underlying population purely by chance; sampling bias is when the sample isn’t randomly chosen at all. George Gallup took pains to find an unbiased sample because he knew that was far more important than finding a big one.

    The Literary Digest, in its quest for a bigger data set, fumbled the question of a biased sample. It mailed out forms to people on a list it had compiled from automobile registrations and telephone directories – a sample that, at least in 1936, was disproportionately prosperous. To compound the problem, Landon supporters turned out to be more likely to mail back their answers. The combination of those two biases was enough to doom The Literary Digest’s poll. For each person George Gallup’s pollsters interviewed, The Literary Digest received 800 responses. All that gave them for their pains was a very precise estimate of the wrong answer.

    The big data craze threatens to be The Literary Digest all over again. Because found data sets are so messy, it can be hard to figure out what biases lurk inside them – and because they are so large, some analysts seem to have decided the sampling problem isn’t worth worrying about. It is.

    Professor Viktor Mayer-Schönberger of Oxford’s Internet Institute, co-author of Big Data, told me that his favoured definition of a big data set is one where “N = All” – where we no longer have to sample, but we have the entire background population. Returning officers do not estimate an election result with a representative tally: they count the votes – all the votes. And when “N = All” there is indeed no issue of sampling bias because the sample includes everyone.

    But is “N = All” really a good description of most of the found data sets we are considering? Probably not. “I would challenge the notion that one could ever have all the data,” says Patrick Wolfe, a computer scientist and professor of statistics at University College London.

    An example is Twitter. It is in principle possible to record and analyse every message on Twitter and use it to draw conclusions about the public mood. (In practice, most researchers use a subset of that vast “fire hose” of data.) But while we can look at all the tweets, Twitter users are not representative of the population as a whole. (According to the Pew Research Internet Project, in 2013, US-based Twitter users were disproportionately young, urban or suburban, and black.)

    There must always be a question about who and what is missing, especially with a messy pile of found data. Kaiser Fung, a data analyst and author of Numbersense, warns against simply assuming we have everything that matters. “N = All is often an assumption rather than a fact about the data,” he says.

    Consider Boston’s Street Bump smartphone app, which uses a phone’s accelerometer to detect potholes without the need for city workers to patrol the streets. As citizens of Boston download the app and drive around, their phones automatically notify City Hall of the need to repair the road surface. Solving the technical challenges involved has produced, rather beautifully, an informative data exhaust that addresses a problem in a way that would have been inconceivable a few years ago. The City of Boston proudly proclaims that the “data provides the City with real-time in­formation it uses to fix problems and plan long term investments.”

    Yet what Street Bump really produces, left to its own devices, is a map of potholes that systematically favours young, affluent areas where more people own smartphones. Street Bump offers us “N = All” in the sense that every bump from every enabled phone can be recorded. That is not the same thing as recording every pothole. As Microsoft researcher Kate Crawford points out, found data contain systematic biases and it takes careful thought to spot and correct for those biases. Big data sets can seem comprehensive but the “N = All” is often a seductive illusion.

    . . .

    Who cares about causation or sampling bias, though, when there is money to be made? Corporations around the world must be salivating as they contemplate the uncanny success of the US discount department store Target, as famously reported by Charles Duhigg in The New York Times in 2012. Duhigg explained that Target has collected so much data on its customers, and is so skilled at analysing that data, that its insight into consumers can seem like magic.

    Duhigg’s killer anecdote was of the man who stormed into a Target near Minneapolis and complained to the manager that the company was sending coupons for baby clothes and maternity wear to his teenage daughter. The manager apologised profusely and later called to apologise again – only to be told that the teenager was indeed pregnant. Her father hadn’t realised. Target, after analysing her purchases of unscented wipes and magnesium supplements, had.

    Statistical sorcery? There is a more mundane explanation.

    “There’s a huge false positive issue,” says Kaiser Fung, who has spent years developing similar approaches for retailers and advertisers. What Fung means is that we didn’t get to hear the countless stories about all the women who received coupons for babywear but who weren’t pregnant.

    Hearing the anecdote, it’s easy to assume that Target’s algorithms are infallible – that everybody receiving coupons for onesies and wet wipes is pregnant. This is vanishingly unlikely. Indeed, it could be that pregnant women receive such offers merely because everybody on Target’s mailing list receives such offers. We should not buy the idea that Target employs mind-readers before considering how many misses attend each hit.

    In Charles Duhigg’s account, Target mixes in random offers, such as coupons for wine glasses, because pregnant customers would feel spooked if they realised how intimately the company’s computers understood them.

    Fung has another explanation: Target mixes up its offers not because it would be weird to send an all-baby coupon-book to a woman who was pregnant but because the company knows that many of those coupon books will be sent to women who aren’t pregnant after all.

    None of this suggests that such data analysis is worthless: it may be highly profitable. Even a modest increase in the accuracy of targeted special offers would be a prize worth winning. But profitability should not be conflated with omniscience.

    . . .

    In 2005, John Ioannidis, an epidemiologist, published a research paper with the self-explanatory title, “Why Most Published Research Findings Are False”. The paper became famous as a provocative diagnosis of a serious issue. One of the key ideas behind Ioannidis’s work is what statisticians call the “multiple-comparisons problem”.

    It is routine, when examining a pattern in data, to ask whether such a pattern might have emerged by chance. If it is unlikely that the observed pattern could have emerged at random, we call that pattern “statistically significant”.

    The multiple-comparisons problem arises when a researcher looks at many possible patterns. Consider a randomised trial in which vitamins are given to some primary schoolchildren and placebos are given to others. Do the vitamins work? That all depends on what we mean by “work”. The researchers could look at the children’s height, weight, prevalence of tooth decay, classroom behaviour, test scores, even (after waiting) prison record or earnings at the age of 25. Then there are combinations to check: do the vitamins have an effect on the poorer kids, the richer kids, the boys, the girls? Test enough different correlations and fluke results will drown out the real discoveries.

    There are various ways to deal with this but the problem is more serious in large data sets, because there are vastly more possible comparisons than there are data points to compare. Without careful analysis, the ratio of genuine patterns to spurious patterns – of signal to noise – quickly tends to zero.

    Worse still, one of the antidotes to the ­multiple-comparisons problem is transparency, allowing other researchers to figure out how many hypotheses were tested and how many contrary results are languishing in desk drawers because they just didn’t seem interesting enough to publish. Yet found data sets are rarely transparent. Amazon and Google, Facebook and Twitter, Target and Tesco – these companies aren’t about to share their data with you or anyone else.

    New, large, cheap data sets and powerful ­analytical tools will pay dividends – nobody doubts that. And there are a few cases in which analysis of very large data sets has worked miracles. David Spiegelhalter of Cambridge points to Google Translate, which operates by statistically analysing hundreds of millions of documents that have been translated by humans and looking for patterns it can copy. This is an example of what computer scientists call “machine learning”, and it can deliver astonishing results with no preprogrammed grammatical rules. Google Translate is as close to theory-free, data-driven algorithmic black box as we have – and it is, says Spiegelhalter, “an amazing achievement”. That achievement is built on the clever processing of enormous data sets.

    But big data do not solve the problem that has obsessed statisticians and scientists for centuries: the problem of insight, of inferring what is going on, and figuring out how we might intervene to change a system for the better.

    “We have a new resource here,” says Professor David Hand of Imperial College London. “But nobody wants ‘data’. What they want are the answers.”

    To use big data to produce such answers will require large strides in statistical methods.

    “It’s the wild west right now,” says Patrick Wolfe of UCL. “People who are clever and driven will twist and turn and use every tool to get sense out of these data sets, and that’s cool. But we’re flying a little bit blind at the moment.”

    Statisticians are scrambling to develop new methods to seize the opportunity of big data. Such new methods are essential but they will work by building on the old statistical lessons, not by ignoring them.

    Recall big data’s four articles of faith. Uncanny accuracy is easy to overrate if we simply ignore false positives, as with Target’s pregnancy predictor. The claim that causation has been “knocked off its pedestal” is fine if we are making predictions in a stable environment but not if the world is changing (as with Flu Trends) or if we ourselves hope to change it. The promise that “N = All”, and therefore that sampling bias does not matter, is simply not true in most cases that count. As for the idea that “with enough data, the numbers speak for themselves” – that seems hopelessly naive in data sets where spurious patterns vastly outnumber genuine discoveries.

    “Big data” has arrived, but big insights have not. The challenge now is to solve new problems and gain new answers – without making the same old statistical mistakes on a grander scale than ever.

    -------------------------------------------

    Tim Harford’s latest book is ‘The Undercover Economist Strikes Back’. To comment on this article please post below, or email magazineletters@ft.com

    http://www.ft.com/cms/s/2/21a6e7d8-b479-11e3-a09a-00144feabdc0.html#axzz2yP0y9nsJ
     
  6. nvha

    nvha MBA family

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    http://proxyweb.com.es/browse.php/V...a/9BBZEJL_/2B9CPcMd/5HgQ9pgw/0_2BvACI/N0/b13/

    Singapore: Nghề IT vẫn luôn “hot”!
    Tháng Sáu 13, 2014 – 12:34 chiều

    Cho dù kinh tế toàn cầu và khu vực trong năm nay còn nhiều khó khăn, các doanh nghiệp tại nhiều nước châu Á – Thái Bình Dương vẫn cần dựa vào công nghệ để tăng cường năng suất và hướng đến sáng tạo trong kinh doanh.

    Đó là dự báo của công ty chuyên nghiên cứu công nghệ thông tin (CNTT) mang tên Garner có trụ sở tại Singapore.

    Theo công ty này, chi tiêu công nghệ trong khu vực châu Á – Thái Bình Dương trong năm nay sẽ lên đến 767 tỷ đô la, tăng 5,5% so với năm 2013.

    Điều này kéo theo nhu cầu nhân lực trong ngành CNTT sẽ tăng và các công ty săn đầu người lại càng thêm bận rộn.

    Data_ScientistNhu cầu nhân lực trong ngành công nghệ thông tin vẫn luôn cao

    Tại Singapore, chuyên viên CNTT có thể đòi tăng lương từ 7-12% khi chuyển sang chỗ làm mới và ba công việc được xem là “hot” nhất là phát triển ứng dụng di động (mobile application developer), khoa học dữ liệu (data scientist) và quản lý dự án CNTT.

    Với tỷ lệ thâm nhập di động hơn 150%, các doanh nghiệp tại Singapore phải không ngừng đón đầu những con sóng cạnh tranh bằng cách phát triển các ứng dụng đi động để thiết lập vị thế trên thương trường và tạo ra giá trị cộng thêm cho khách hàng.

    Đặc biệt, các định chế tài chính – ngân hàng liên tục cập nhật các ứng dụng di động để cung cấp sự tiện lợi và an toàn tối đa cho khách hàng.

    Như vậy, nhu cầu tuyển dụng nhà phát triển ứng dụng di động sẽ luôn cao.

    Tuy nhiên, nhà phát triển ứng dụng di động muốn thành công phải trang bị những kỹ năng lập trình cao và cần theo kịp những thay đổi chóng mặt về công nghệ.

    Người này cũng cần hiểu biết những vấn đề về phù hợp quy chế luật lệ và an ninh công nghệ khi phát triển những ứng dụng cho ngân hàng và các doanh nghiệp trong những lĩnh vực gặp thách thức ngày càng tăng trong an ninh công nghệ.

    Theo các nhà quan sát, một nhà phát triển ứng dụng di động bậc trung (mid-level) tại Singapore có thể kiếm mỗi tháng từ 6.000-8000 đô la Singapore (SGD).

    Báo cáo Việc làm Thế giới năm 2013-2014 của công ty tuyển dụng Ransdtad cho biết 33% tố chức và doanh nghiệp tại Singapore sử dụng những “dữ liệu lớn” (big data) để phục vụ cho việc đưa ra những quyết định về chiến lược nhân tài của mình.

    Trong bối cảnh đó, thị trường lao động cần có những chuyên gia mà thuật ngữ tiếng Anh gọi là “data scientist” (tạm dịch là “nhà khoa học dữ liệu”) để hỗ trợ việc xử lý dữ liệu nội bộ hay bên ngoài tổ chức hay doanh nghiệp.

    Theo ước tính của Gartner, trong năm 2015 trên toàn thế giới sẽ có thêm 4,4 triệu công việc về CNTT để phục vụ cho việc sử dụng dữ liệu.

    Được tạp chí Harvard Business Review xem là công việc hấp dẫn nhất của thế kỷ 21, công việc của nhà khoa học dữ liệu là kiểm tra, so sánh đối chiếu, sắp xếp, phân loại và chú giải dữ liệu.

    Những bộ dữ liệu mà họ phát triển sẽ được sử dụng để định hướng việc ra quyết định và dự đoán kết quả hoạt động kinh doanh.

    Ví dụ như nhờ những dữ liệu này mà doanh nghiệp có thể biết được một chiến dịch tiếp thị và bán hàng của mình có hiệu quả hay không, thị trường nào tiềm năng nhất và thời điểm nào trong năm thích hợp nhất để tung ra sản phẩm hay dịch vụ.

    Nhà khoa học dữ liệu đương nhiên phải giỏi toán, có óc phân tích và tư duy phản biện.

    Tuy nhiên, họ cũng phải có thêm những kỹ năng mềm như khả năng giao tiếp và trình bày hiệu quả để truyền đạt kết quả dữ liệu có ý nghĩa phục vụ kinh doanh cho khách hàng.

    Tùy vào kinh nghiệm và thâm niên, lương tháng của một nhà khoa học dữ liệu tại Singapore dao động từ 6.000 SGD đến 15.000 SGD.

    Cuối cùng, phải kể đến công việc của nhà quản lý dự án CNTT chịu trách nhiệm đảm bảo việc hoàn thành một dự án đúng tiến độ và trong ngân sách đặt ra.

    Ngoài những kỹ năng về tổ chức, nhà quản lý dự án CNTT còn phải có phong thái hết sức linh hoạt với tư duy nhanh nhạy để xử lý những tình huống bất ngờ và thay đổi thứ tự ưu tiên trong dự án.

    Tại Singapore, nhà quản lý dự án phải làm việc với những thành phần cá nhân và tổ chức đa dạng có quyền lợi và trách nhiệm trong dự án, do đó những kỹ năng giao tiếp hay dịch vụ khách hàng là bắt buộc.

    Lương đặc thù của một nhà quản lý dự án CNTT có thể từ 7.000 SGD đến 12.000 SGD.

    LÊ HỮU HUY (*)

    (*) Giám đốc Công ty Tư vấn Vietnam Global Network, Singapore

    Nguồn: Vietnam Business Centre, Singapore

    Viết xong tại Singapore ngày 3 tháng 3 năm 2014
     
  7. nvha

    nvha MBA family

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    http://www.nytimes.com/2014/12/26/b...n-valley-into-the-data-age.html?ref=education
    M.B.A. Programs Start to Follow Silicon Valley Into the Data Age
    By STEVE LOHRDEC. 25, 2014

    Greg Pass, the former chief technology officer of Twitter, put the matter succinctly. The M.B.A., he observed, is “a challenged brand.”

    That’s because the degree suggests a person steeped in finance and corporate strategy rather than in the digital-age arts of speed and constant experimentation — and in skills like A/B testing, rapid prototyping and data-driven decision making, the bread and butter of Silicon Valley.
    Those skills are not just for high-tech start-ups. They are required now in every industry. And leading business schools are struggling to keep pace.

    Mr. Pass is on the faculty at Cornell Tech in New York, where an innovative new program brings M.B.A. candidates and graduate students in computer science together. Meanwhile, across the country, colleges are adding new courses in statistics, data science and A/B testing, which often involves testing different web page designs to see which attracts more traffic.

    Business plan competitions have become common. Students’ ideas usually have a digital component — websites, smartphone apps or sensor data — and prizes are up to $100,000 or more. Innovation and entrepreneurship centers have proliferated. Dual-degree programs, with a science or engineering degree added to an M.B.A., are increasing.
    The program at Cornell Tech brings M.B.A. candidates and graduate students in computer science together. Credit Richard Perry/The New York Times

    Graduate business schools have picked up the digital ethos of experimentation and new ventures. At the Stanford Graduate School of Business, 150 elective courses are offered; 28 percent of those did not exist last year.
    “We’re responding to the best practices we see in the outside world like A/B testing and working with massive data sets,” said Garth Saloner, dean of the Stanford business school. “We’re adapting.”

    So are the students. Once, students who had professional experience with computer programming were rare at business schools. Today, David B. Yoffie, a professor at Harvard Business School, estimates that a third or more of the 900 students there have experience as programmers, and far more of them have undergraduate degrees in the so-called STEM disciplines — science, technology, engineering or mathematics. “There’s been an extraordinary change in the talent pool,” Mr. Yoffie said.
    Yet lately, many talented young people who might have gone to business school in the past are looking elsewhere. Applications to business graduate schools fell by 1 percent in 2013, the most recent statistics, reports the Council of Graduate Schools. By contrast, applications for computer science and mathematics graduate programs increased by 11 percent.
    “Business schools are a legacy industry that is trying to adapt to a digital world,” said Douglas M. Stayman, associate dean at Cornell Tech.

    Mr. Stayman describes the school’s M.B.A. program in New York as a start-up of its own, unencumbered by tradition. “Our starting assumption here is that a new kind of education is needed for managing in a digital economy, where speed and integration have to occur at a different level than in the industrial economy,” he said.
    Cornell Tech, a partnership with Technion-Israel Institute of Technology, began with a relative handful of computer science students in 2013. The long-range goal of the new “applied sciences” school is to have 2,000 graduate students by 2043. But this is the first year for the M.B.A. program, in which 39 business graduate students share a third of their curriculum with 34 graduate students in computer science.

    Their joint work includes projects for New York businesses including banks, hedge funds, larger technology companies and start-ups. They work in small teams, and typically design and write software programs for the companies. The emphasis is on making things rather than planning.

    On Tuesday afternoons, the students gather for “studio” sessions, where they sit in circles of chairs, give progress reports, discuss problems and get critiqued by faculty and outsiders. Until 2017, when it begins moving to its campus being built on Roosevelt Island, Cornell Tech resides in one of Google’s buildings in downtown Manhattan.
    The aim of Cornell Tech is to train what its faculty calls “entrepreneurial, technology product managers,” which are needed across industry as digital technology spreads.

    The students selected are not typical of M.B.A. classes. About 75 percent have STEM undergraduate degrees, Mr. Stayman said, while the comparable share is about one third at Cornell University’s Johnson Graduate School of Management in Ithaca, N.Y.

    Amanda Emmanuel, 28, is fairly representative of the business students at Cornell Tech. At Carleton University in Canada, she majored in computing and design, and she had summer internships as a software engineer for General Dynamics. But she also started her own fashion business, which used her software algorithms to create figure-flattering clothing designs.
    Ms. Emmanuel came to Cornell Tech to burnish her business skills in a technology-rich environment. The Cornell program, which costs $93,000, lasts one year instead of the traditional two-year M.B.A. curriculum — another plus for Ms. Emmanuel.
    “Saying you’re going to be out for two years can be a big drawback,” she said. “Some of the companies for the target jobs we’re looking for didn’t exist two years ago.”

    Mr. Pass holds the title of chief entrepreneurial officer at Cornell Tech, and he leads studio sessions, critiques projects and acts as a coach. At Twitter, Mr. Pass was part of a huge commercial success and, afterward, he says he felt “realistic guilt,” holding a winning ticket in the lottery of start-up capitalism.

    A graduate of Cornell, where he majored in computer science, Mr. Pass, 39, said he wanted to “give something back to the community.” Something he figured he could provide was perspective, including his belief in the need for a “balanced culture,” especially in tech companies and start-ups.

    Engineers may be the most valued asset in business today, but the engineering mentality has its weaknesses, Mr. Pass noted. Engineers, he said, tend to be problem solvers, one step at a time, solving the problem in front of them.
    “But the major business issue, especially for entrepreneurs, is often that problems are not known, need to be discovered or defined in a new way,” Mr. Pass said. “You need a more integrated, broader view of things.”
    The M.B.A. program, he said, is trying to nurture people with those wider horizons, technical know-how and quick business reflexes — “a new pivot on graduate education,” as he put it.

    A version of this article appears in print on December 26, 2014, on page B1 of the New York edition with the headline: M.B.A. Programs Start to Follow Silicon Valley Into the Data Age
     
  8. nvha

    nvha MBA family

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    http://www.informationweek.com/big-...sters-degrees-20-top-programs/d/d-id/1108042?

    ( These one-year and two-year graduate programs are just what's needed to close the big-data talent gap. Read on to find a school that fits your ambitions and background. )

    http://www.mastersindatascience.org/schools/23-great-schools-with-masters-programs-in-data-science/

    ( 23 Great Schools with Master’s Programs in Data Science

    Looking to freshen your résumé and improve your earning potential? You are in exactly the right place at exactly the right time. A 2011 McKinsey report estimates there will be 140,000 to 190,000 unfilled positions of U.S. data analytics experts by 2018. In response, universities are scrambling to improve their existing degree programs and create entirely new offerings.

    We’ve listed 23 of these programs in order of the state they’re located in. Many of these choices can also be found in the helpful list compiled by Information Week and Data Informed’s Map of University Programs.)


    http://www.valuecolleges.com/rankings/best-big-data-graduate-programs-2016/

    Top 50 Best Value Big Data Graduate Programs of 2016


    Top 50 Best Value Big Data Graduate Programs of 2016Data Analytics is the technical term, but everyone knows it a Big Data. Actually, “everyone” isn’t quite true – Big Data has been called “the fastest-growing job market you’ve never heard of” by the Globe and Mail, and “the Next Frontier” by McKinsey Global Institute. In other words, not many people know what it means, no one knows exactly where it’s going, and there are definitely not enough people doing it. It’s an exciting time, with projected employee shortages estimated well into the millions, so getting into the field now means making a career path where few have trod and no one is likely to get in your way.

    Big Data involves the collection, sorting, analysis, and communication of data, the kind that businesses depend on for finding their customers, banks need for keeping track of investments, and government needs to provide services and protect citizens. With so much data massing in computer systems all over the world, human workers are still needed to make sense of it all. That’s where the data analyst comes in.

    Of course, with any new field, colleges and universities are catching up. There more than a few different names, including Analytics, Data Science, Business Analytics, and every possible combination of those words, and they’re offered by all kinds of departments, from engineering and computer science to business and marketing. What matters, more than the name, is that the program find the right balance between technical computer skills, business and marketing knowledge, and statistical analysis. Most programs are interdisciplinary, because it takes the right combination of experts to teach so many different areas.

    Something else usually comes along with emerging fields, too – unscrupulous, disreputable diploma mills serving up worthless degrees that won’t really prepare students for their professional work or the job market. That’s why Value Colleges has prepared the Top 50 Residential Big Data Graduate Programs of 2016 (VC has ranked the Top 50 Online Big Data Programs separately). Value Colleges selects only regionally accredited colleges and universities with proven reputations, ranked according to our specific formula, taking three metrics into account:

    Payscale’s 2015-16 College Salary Report (based at Master’s level)
    US News & World Reports rankings
    Actual Cost (reported from institution website)
    The Value Colleges methodology makes sure that every program we rank will provide a trustworthy balance of educational quality with affordability and job marketability, making every college and university on our list a best value.
     
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    So sánh hai mô hình computer tranh cử của Trump (đúng!) và Clinton (sai!): Trump’s Data Team Saw a Different America—and They Were Right (Bloomberg 10-11-16) -- Clinton’s data-driven campaign relied heavily on an algorithm named Ada. What didn’t she see? (WP 9-11-16) - Hai bài thật hay (nhát là khi đặt chúng bên cạnh nhau như trên viet-studies!) ◄


    http://www.bloomberg.com/news/artic...m-saw-a-different-america-and-they-were-right

    Trump’s Data Team Saw a Different America—and They Were Right

    The president-elect’s analysts picked up disturbances others weren’t seeing—the beginning of the storm that would deliver Trump to the White House.

    Joshua Green

    Sasha Issenberg @sissenberg

    November 10, 2016 — 5:00 PM ICT

    How Team Trump Used Data to Win


    Don't Miss Out — Follow Bloomberg On



    A First Look at Trump's Agenda


    Nobody saw it coming. Not the media. Certainly not Hillary Clinton. Not even Donald Trump’s team of data scientists, holed up in their San Antonio headquarters 1,800 miles from Trump Tower, were predicting this outcome. But the scientists picked up disturbances—like falling pressure before a hurricane—that others weren’t seeing. It was the beginning of the storm that would deliver Trump to the White House.
    Flash back three weeks, to Oct. 18. The Trump campaign’s internal election simulator, the “Battleground Optimizer Path to Victory,” showed Trump with a 7.8 percent chance of winning. That’s because his own model had him trailing in most of the states that would decide the election, including the pivotal state of Florida—but only by a small margin. And in some states, such as Virginia, he was winning, even though no public poll agreed.
    Included in the new issue of Bloomberg Businessweek, Nov. 14-20, 2016, which features two covers on Trump’s American Revolution. Subscribe now.
    Included in the new issue of Bloomberg Businessweek, Nov. 14-20, 2016, which features two covers on Trump’s American Revolution. Subscribe now. (l-r) Photographers: M. Scott Brauer for Bloomberg Businessweek; Jonno Rattman for Bloomberg Businessweek
    Trump’s numbers were different, because his analysts, like Trump himself, were forecasting a fundamentally different electorate than other pollsters and almost all of the media: older, whiter, more rural, more populist. And much angrier at what they perceive to be an overclass of entitled elites. In the next three weeks, Trump channeled this anger on the stump, at times seeming almost unhinged.
    “A vote for Hillary is a vote to surrender our government to public corruption, graft, and cronyism that threatens the survival of our constitutional system itself,” Trump told an Arizona crowd on Oct. 29. “What makes us exceptional is that we are a nation of laws and that we are all equal under those laws. Hillary’s corruption shreds the principle on which our nation was founded.”
    His hyperbole and crassness drew broad condemnation from the media and political elite, who interpreted his anger as an acknowledgment that he was about to lose. But rather than alienate his gathering army, Trump’s antipathy fed their resolve.
    He had an unwitting ally. “Hillary Clinton was the perfect foil for Trump’s message,” says Steve Bannon, his campaign chief executive officer. “From her e-mail server, to her lavishly paid speeches to Wall Street bankers, to her FBI problems, she represented everything that middle-class Americans had had enough of.”
    Trump’s analysts had detected this upsurge in the electorate even before FBI Director James Comey delivered his Oct. 28 letter to Congress announcing that he was reopening his investigation into Clinton’s e-mails. But the news of the investigation accelerated the shift of a largely hidden rural mass of voters toward Trump.
    Inside his campaign, Trump’s analysts became convinced that even their own models didn’t sufficiently account for the strength of these voters. “In the last week before the election, we undertook a big exercise to reweight all of our polling, because we thought that who [pollsters] were sampling from was the wrong idea of who the electorate was going to turn out to be this cycle,” says Matt Oczkowski, the head of product at London firm Cambridge Analytica and team leader on Trump’s campaign. “If he was going to win this election, it was going to be because of a Brexit-style mentality and a different demographic trend than other people were seeing.”
    Trump’s team chose to focus on this electorate, partly because it was the only possible path for them. But after Comey, that movement of older, whiter voters became newly evident. It’s what led Trump’s campaign to broaden the electoral map in the final two weeks and send the candidate into states such as Pennsylvania, Wisconsin, and Michigan that no one else believed he could win (with the exception of liberal filmmaker Michael Moore, who deemed them “Brexit states”). Even on the eve of the election Trump’s models predicted only a 30 percent likelihood of victory.
    The message Trump delivered to those voters was radically different from anything they would hear from an ordinary Republican: a bracing screed that implicated the entire global power structure—the banks, the government, the media, the guardians of secular culture—in a dark web of moral and intellectual corruption. And Trump insisted that he alone could fix it.
    “This is not the French Revolution. They destroyed the basic institutions of their society and changed their form of government. What Trump represents is a restoration—a restoration of true American capitalism”
    In doing so, Trump knit together a worldview, frequently propounded by Bannon, that the U.S. was on the cusp of joining the right-wing populist uprisings that have swept across Europe. It was Trump who featured Nigel Farage, the champion of the United Kingdom’s Brexit campaign, at a Mississippi stadium rally and Trump who became the American embodiment of that sentiment. “It was basically the game plan from the very first day I arrived,” says Bannon.
    Trump’s election represents a jarring realignment of American politics. It delivered a rebuke to GOP leaders such as House Speaker Paul Ryan, even as it cast Democrats into the wilderness. It could render large swaths of the GOP agenda inoperative. But we really don’t know yet.
    Long before election night, Trump’s data operatives, in particular those contracted from Cambridge Analytica, understood that his voters were different. And to better understand how they differed from Ryan-style Republicans, they set off to study them.
    The firm called these Trump supporters “disenfranchised new Republicans”: younger than traditional party loyalists and less likely to live in metropolitan areas. They share Bannon’s populist spirit and care more than other Republicans about three big issues: law and order, immigration, and wages.
    They also harbored a deep contempt for the reigning political establishment in both parties, along with a desire to return the country to happier times. Trump was the key that fit in this lock. “Trump is fundamentally a populist,” says Bannon. “He’s the leader of a populist uprising. But he’s also an enormously successful entrepreneur who succeeded in real estate, media, and branding.” The voters who elected Trump, he says, wish to partake in this story of American success but not destroy the American system of government. “This is not the French Revolution,” says Bannon. “They destroyed the basic institutions of their society and changed their form of government. What Trump represents is a restoration—a restoration of true American capitalism and a revolution against state-sponsored socialism. Elites have taken all the upside for themselves and pushed the downside to the working- and middle-class Americans.”
    According to Cambridge’s analysis, these Trump backers subordinate the standard conservative Republican priorities, especially social and cultural issues such as abortion and guns, which Trump largely ignored during the campaign, and cutting Social Security and Medicare spending, which he vowed to preserve. Trump got elected by outlining a worldview that reflects these priorities—even though many of them are sharply at odds with those of Ryan and the Republican leaders that Trump has displaced.
    Trump’s primary challenge as president, since he’ll need congressional support, will be to synthesize his brand of populist Republicanism with the diminished, yet still powerful, version espoused by leaders like Ryan and Senate Majority Leader Mitch McConnell. One way to do this may be with a kind of free-market capitalism to which many conservatives pay lip service but rarely do much to bring about. “Those elites [Trump rails against] are represented in Washington by a bevy of lobbyists,” says Bannon. “Crony capitalism has gotten out of control. Trump saw this. The American people saw this. And they have risen up to smash it. Ordinary people want to make sure we have an evenhanded system that’s transparent and accountable and takes their interests into mind. And they want to share in the rewards.”
    Throughout October, the surge of early ballots from these voters grew so strong that Oczkowski’s team members decided to reweight their surveys to account for the possibility that the electorate might look much different than even they had imagined. Part of it, Oczkowski concedes, was wishful thinking—an attempt to conjure up an electorate that would favor his candidate just enough to illuminate a plausible path to victory. “Older white voters were returning early ballots at an enormous clip,” he says. “So either older people were voting early because of enthusiasm or this was a trend that would carry through to election night.”
    After adjusting their models in late October, Trump’s numbers immediately shot up across the Rust Belt—2 points in Michigan, 2.5 points in Pennsylvania. Suddenly, the Battleground Optimizer showed a way to win.
    The trend did, indeed, materialize at the polls. Trump made some of his biggest gains over Mitt Romney’s performance in small Midwestern counties, which allowed him to sweep Rust Belt states that hadn’t voted Republican since the 1980s. And on election night, in the critical state of Florida—the key to any Trump path to 270 electoral votes—the rural vote spiked 10 percentage points higher than the campaign’s optimistic scenarios had assumed. Taken as a whole, Trump’s electoral map represents a powerful, largely rural backlash against a country where wealth and power have increasingly accrued to the cities.
    “No one could have anticipated the exceptional concentration of wealth and talent in just a handful of urban centers. Class is etched into location, and the way location defines your economic opportunities—but the brew is combustible,” says Richard Florida, a professor at the University of Toronto’s Rotman School of Management and author of The Rise of the Creative Class. “It’s really the bypassing of a way of life, and they know it. And no one is standing up for them.”

    Photographer: Christopher Goodney/Bloomberg
    Trump did. And in doing so, he appealed to some people who were once Democrats or descended from New Deal Democratic families. In Rust Belt strongholds like Mahoning County, Ohio, these voters would explain that Trump alone seemed to register their complaints in a political world that was otherwise deaf to their concerns. “These were disenfranchised voters who no party has spoken to for several elections,” says Oczkowski.
    Back in May, speaking to Bloomberg Businessweek about how he intended to remake the Republican Party, Trump laid out precisely the message that would activate these voters in November. “Five, 10 years from now—[it will be a] different party,” he said. “You’re going to have a worker’s party. A party of people that haven’t had a real wage increase in 18 years, that are angry. What I want to do, I think cutting Social Security is a big mistake for the Republican Party. And I know it’s a big part of the budget. Cutting it the wrong way is a big mistake, and even cutting it [at all].”
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    Having sent Trump to the White House, this newly activated coalition of white rural voters will now expect him to deliver on their priorities. Trump is, of course, a wildly unlikely tribune for rural America. He’ll be the first president since Richard Nixon to live in a high-rise when elected. His closest tie to the agricultural economy is Trump Winery. And the economic policy he espoused most vigorously was his desire for more infrastructure spending, which he illustrated in strikingly metropolitan (and luxurious) terms. “You land at LaGuardia, you land at Kennedy, LAX, and you come in from Dubai, China—you see these incredible airports, and you land, we’ve become a third-world country,” he said this fall.
    It remains a mystery how Trump will govern. No president in living memory has as little political experience or has put forward fewer details of the policies he intends to pursue. But it’s possible to see in Trump’s coalition of voters and the issues they care about the broad contours of a new Republican politics that’s more populist, more rural in its character (and less beholden to Wall Street), and oriented toward a class of Americans—not all of them conservatives or even Republicans—whose concerns weren’t addressed by the Democratic and Republican parties that both crumbled on Nov. 8.


    Clinton’s data-driven campaign relied heavily on an algorithm named Ada. What didn’t she see?

    By John Wagner November 9

    Hillary Clinton at a rally in Raleigh, N.C., on the eve of the election. (Melina Mara/The Washington Post)

    https://www.washingtonpost.com/news...on-an-algorithm-named-ada-what-didnt-she-see/


    Inside Hillary Clinton's campaign, she was known as Ada. Like the candidate herself, she had a penchant for secrecy and a private server. As blame gets parceled out Wednesday for the Democrat's stunning loss to Republican President-elect Donald Trump, Ada is likely to get a lot of second-guessing.

    Ada is a complex computer algorithm that the campaign was prepared to publicly unveil after the election as its invisible guiding hand. Named for a female 19th-century mathematician — Ada, Countess of Lovelace — the algorithm was said to play a role in virtually every strategic decision Clinton aides made, including where and when to deploy the candidate and her battalion of surrogates and where to air television ads — as well as when it was safe to stay dark.

    The campaign's deployment of other resources — including county-level campaign offices and the staging of high-profile concerts with stars like Jay Z and Beyoncé — was largely dependent on Ada's work, as well.

    [Trump’s White House win promises to reshape U.S. political landscape]

    While the Clinton campaign's reliance on analytics became well known, the particulars of Ada's work were kept under tight wraps, according to aides. The algorithm operated on a separate computer server than the rest of the Clinton operation as a security precaution, and only a few senior aides were able to access it.

    According to aides, a raft of polling numbers, public and private, were fed into the algorithm, as well as ground-level voter data meticulously collected by the campaign. Once early voting began, those numbers were factored in, too.

    What Ada did, based on all that data, aides said, was run 400,000 simulations a day of what the race against Trump might look like. A report that was spit out would give campaign manager Robby Mook and others a detailed picture of which battleground states were most likely to tip the race in one direction or another — and guide decisions about where to spend time and deploy resources.

    The use of analytics by campaigns was hardly unprecedented. But Clinton aides were convinced their work, which was far more sophisticated than anything employed by President Obama or GOP nominee Mitt Romney in 2012, gave them a big strategic advantage over Trump.


    So where did Ada go wrong?

    About some things, she was apparently right. Aides say Pennsylvania was pegged as an extremely important state early on, which explains why Clinton was such a frequent visitor and chose to hold her penultimate rally in Philadelphia on Monday night.

    But it appears that the importance of other states Clinton would lose — including Michigan and Wisconsin — never became fully apparent or that it was too late once it did.

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    Obama's final fiery moments on the campaign trail Play Video3:29
    President Obama brought his tried-and-true campaign slogans and humor-filled attacks on Donald Trump to Hillary Clinton's defense in the final days of the presidential campaign. (Jenny Starrs/The Washington Post)
    Clinton made several visits to Michigan during the general election, but it wasn't until the final days that she, Obama and her husband made such a concerted effort.

    As for Wisconsin: Clinton didn't make any appearances there at all.

    Like much of the political establishment Ada appeared to underestimate the power of rural voters in Rust Belt states.

    Clearly, there were things neither she nor a human could foresee — like a pair of bombshell letters sent by the FBI about Clinton's email server. But in coming days and weeks, expect a debate on how heavily campaigns should rely on data, particularly in a year like this one in which so many conventional rules of politics were cast aside.
     
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    Video :

    http://www.realclearpolitics.com/vi...s_data_that_led_to_victory_on_kelly_file.html


    Trump Digital Director Brad Parscale Explains Data That Led To Victory on 'Kelly File'
    372 Shares
    Posted By Ian Schwartz
    On Date November 16, 2016


    Brad Parscale, Trump campaign digital director, explains the data he used for the campaign to target voters and eventually lead to a victory.

    From Tuesday's broadcast of The Kelly File on the FOX News Channel:

    MEGYN KELLY, FOX NEWS: I mean, you're interesting too. But I want to bring you this guy. An insider's take now on how Mr. Trump actually won. The man behind the digital operation now credited with helping find and turn out the voters who made the difference.

    Here now in a "Kelly File" exclusive, Brad Parscale, the former digital director of the Trump campaign and president of Giles-Parscale. Did I say that right?

    BRAD PARSCALE, DONALD TRUMP'S DIGITAL DIRECTOR: Yes. Perfectly.

    KELLY: Great to see you, Brad. This is fascinating. Fascinating.

    PARSCALE: Thank you.

    KELLY: You are the one person who actually knows the answer to this question.

    PARSCALE: Yes, the data.

    KELLY: How did Trump win?

    PARSCALE: Well, I think first you have to say it started with leadership. And that was with Jared Kushner and also Trump's genius coming down to allow us to put a date operation to place --

    KELLY: Jared Kushner is married to Ivanka.

    PARSCALE: Jared Kushner is Ivanka Trump husband. Jared was instrumental in being kind of an overlay in kind of bringing Trump's genius down to the all the different parts of leadership. You know, Steve Bannon was there. It's kind of that strategy person. But the data operation right from the middle and I think for the first time in history, the data operation ran everything from TV buying to where we were on the ground to all of the different operations. And so, and having that data right there, we could start to where the persuadable targets are, GOTV (PH), everything we needed to know --

    KELLY: How? How? I mean, like, when you first saw, whoa, we're in play in Michigan --

    PARSCALE: Yes.

    KELLY: What showed you that?

    PARSCALE: Well so, I think there's a good example from both Pennsylvania and Michigan. We played in some other spots also as I started to see data and started to track it. We were making thousands of live calls, web tracking, web different surveys and it was building and it's building what's called models and universes. What we can start to see is, we're in play in Pennsylvania and play in Michigan. Let's buy in these areas. Let's buy these DMAs. Let's buy these voter targets. We started to see that move our direction. And by the Friday before the election, I had predicted that we were going to win 305 electoral results.

    KELLY: Come on!

    PARSCALE: Yes. The ABEV (ph) results coming in which is -- ballots in early voting was showing the data that where we were hitting targets and where we've wanted to see the voters turn out were showing up for us.

    KELLY: Was the reaction by the others was what?

    PARSCALE: Yes. I think there was a thing that stay around Brad's office, he seems really happy and giddy.

    (LAUGHTER)

    KELLY: You feel good when you leave Brad's office for some reason.

    PARSCALE: Yes. So, the data is, the data doesn't lie. And that is the beauty of our data. I had some great data scientists, we have teams of them putting that data in a way that could be consumed so we could understand where we need to target people.

    KELLY: But here's the interesting thing about brad. He says some political operative. You're from country Kansas, as you put it.

    PARSCALE: Born outside in Topeka, Kansas.

    KELLY: So what gave you the skills to, you know, get a man elected president?

    PARSCALE: Well, I think some of that is just blessing. And, you know, I came out of college in the early '90s. That was a great time to exit school and get a job in the dot-com world and get educated. I had to finance in business degree but at that point there was no one with degrees in internet marketing and I was -- to spend that. And I spent 15 years building the company that I started with this $500.

    KELLY: And Jared and Ivanka first hired you, right? Back in 2010 -- Trump Organization.

    PARSCALE: Ivanka and Eric Trump hired me for the real estate website.

    KELLY: Okay.

    PARSCALE: And once I got the real state website then I started to work my way through the Trump.

    KELLY: So how closely connected with the family have you been?

    PARSCALE: I think at this point, I have a very good relationship with them. I mean, they value hard work, they value loyalty, they value success.

    KELLY: Results. Right.

    PARSCALE: And results. And that's what I wanted to bring.

    KELLY: So, where were you on election night?

    PARSCALE: I was in Trump Tower and eventually up in the apartment.

    KELLY: In front of your computer?

    PARSCALE: I was in front of my computer.

    KELLY: What did you then know before others that we got this?

    PARSCALE: Friday, I was 95 percent sure and by Sunday, I was about even more. And my Tuesday morning, I got more nervous Tuesday morning because I knew so much, I just had to wait.

    KELLY: Did you know about Wisconsin?

    PARSCALE: My one flip mistake was Wisconsin and Colorado. That's my 305 or 306. However as you can see our media buys from where we bought them in Pennsylvania and a different ways we're doing, we had a good strategy with the data.

    KELLY: Well, why do you think -- do you know why she lost and he won? I mean, other than strategy, do you know what it was that turned it or?

    PARSCALE: Well, I think that, you know, that's a good -- Steve Bannon for a strategy question as well but I think change. I mean, I think --

    KELLY: But was there an event like the Comey announcement or the changed the number --

    PARSCALE: Well, what's funny about that announcements was, in the numbers, I was actually flying with Mr. Trump that night. I showed him plenty of the numbers before that announcement that we were already coming. Those undecideds are moving our way.

    KELLY: Uh-hm.

    PARSCALE: People in this country were ready for change, they are ready for something new. They were already moving that way.

    KELLY: Uh-hm.

    PARSCALE: And I think those have been continued to have an --

    KELLY: What about the "Access Hollywood" tape? Did they move the other way? Was there --

    PARSCALE: Well, I think here's the thing. All campaigns have ebb and flows along the way, right? Ups and downs. I mean, the progress that reassess your data, remove and build new universes that now we have new targets. So, you move, you're in the bag, you move the people in and out.

    KELLY: This is incredible. So now what are they going to do with you? Right? Because is it true that you worked for Ted Cruz for a little while?

    PARSCALE: No, I did not work for Ted Cruz.

    KELLY: Okay. So --

    PARSCALE: This is my first ever campaign.

    KELLY: What does President-Elect Trump do with you?

    PARSCALE: Well, I think that that's a President-Elect Trump question.

    KELLY: Do you want to work for the administration?

    PARSCALE: No. I never was a Politico. You know, that wasn't my goal. My goal is to be a megaphone for people, for businesses, for candidates, for who that is.

    KELLY: I mean, but you're a Republican.

    PARSCALE: I'm a Republican.

    KELLY: They're writing your name down in every Republican county in America right now.

    PARSCALE: And I think that Science that was missed in the previous campaigns was to take the digital, and mix TV, ground game, door knocking, all of those people, even budget. Jared and I oversaw where the budget data was.

    KELLY: You shouldn't give all of this away. You should hold some of it inside so you can make more money doing it for others.

    PARSCALE: Well, just saying it isn't as easy as doing it.

    KELLY: All right.

    PARSCALE: But sometimes, you have to say, so people will know that you can do it.

    KELLY: Fascinating. Brad, thank you so much.

    PARSCALE: Congratulations on your book.

    KELLY: Thank you very much. Congratulations on your win.

    PARSCALE: Thank you very much.

    KELLY: Amazingly done, right?

    "Project Alamo": Lessons From Inside Trump's SA-Based Digital Nerve Center
    Posted By Michael Barajas on Thu, Oct 27, 2016 at 3:45 pm
    http://www.sacurrent.com/the-daily/...m-inside-trumps-sa-based-digital-nerve-center

    In a new interview with Bloomberg Businessweek, SA tech entrepreneur Brad Parscale says he's "like family" with the Trumps - SCREENSHOT, TWITTER @BRADPARSCALE

    For this week's cover story, Bloomberg Businessweek was granted remarkable–and exclusive–access inside Donald Trump's digital nerve center, which is run by San Antonio web designer Brad Parscale, one half of the local digital marketing firm Giles-Parscale.

    Parscale, a political newbie who won Trump's love after building some web pages for the family's businesses and charities over the past several years, reportedly started working on the Trump-for-president team even before Mr. Orange officially announced his run. This summer, Parscale emerged as the Trump campaign's digital director–a feat that in and of itself seems notable when you consider how many top campaign personnel Trump jettisoned from his team during his rise to the GOP presidential nomination.


    Bloomberg's piece busts wide open the black box that has been the Trump campaign's digital strategy. Here are a few takeaways from the magazine's fascinating peek inside Trump's digital team–and a hometown boy's role within it:

    To the Trumps, Parscale Is "Like Family"

    One reason for Trump’s affinity for Parscale? Apparently, he's pretty cheap.


    In fact, it seems Parscale owes much of his current success within the Trump machine to him low-balling bids to build Trump-affiliated websites. It started with a random solicitation Parscale received by Trump International Realty in 2010, and soon enough Parscale was building sites for the Trump Winery and the Eric Trump Foundation. Bloomberg reports that when Trump launched a presidential exploratory committee, he tapped Parscale because he could build him a website on the cheap–just $1,500.

    And even though the Trump campaign has run a massive amount of cash through Parscale's San Antonio firm (about $50 million at last count), Bloomberg implies that he's kept quite little of it for himself, passing most of that money onto online ad networks at very little markup. In fact, he was so cheap that the campaign worried it might have to count Parscale's work as an in-kind contribution.

    And that's all because, as Parscale told Bloomberg, "I was willing to do it like family."

    Trump doesn't want a lot of you to vote

    Bloomberg quotes a senior (and anonymous) official within Trump's digital team saying the campaign has no less than "three major voter suppression operations underway," aimed at white liberals, young women and African American voters.

    Parscale told Bloomberg that part of dissuading voters from showing up to the polls includes bombarding Facebook with so-called "dark posts," or non-public posts whose viewership the campaign controls so that, in Parscale's words, "only the people we want to see it, see it." That includes, according to Bloomberg, a South Park-style animation of Hilary Clinton delivering her infamous 1996 "super predator" remarks, targeted messaging around Miami's Little Haiti neighborhood to drum up discontent around the Clinton Foundation's controversial record in Haiti, and giving Bill Clinton accusers a megaphone.

    As Bloomberg notes, the strategy could very well backfire and undermine GOP efforts to draw in those very same voting blocks.

    Trump knows he's losing

    Despite Trump's poll-bashing rhetoric, there's a lot polling happening inside the Trump campaign, according to Bloomberg, including $100,000 spent every week on surveys and detailed, daily simulations of the election. It all apparently echoes what we already know from other public polling: Trump will almost certainly not win this thing.

    But that doesn't mean Trump plans on letting the ambitious digital operation Parscale built for him go to waste. Using a database the team built, dubbed "Project Alamo," and other data siphoned from the Republican National Committee and the London-based Cambridge Analytica (which just so happened to push for "Brexit"), Bloomberg reports that Trump's digital team is spending some $70 million to "cultivate a universe of millions of fervent Trump supporters, many of them reached through Facebook."

    And since Trump built the whole apparatus with his own campaign funds, he owns it all. Which means, even if he tanks at the polls in two weeks, he could turn around and sell the data to other campaigns, use it to foment a larger fringe political movement, or even make "Project Alamo" the basis for a new Right-Wing media empire – you know, like Trump TV.

    “We knew how valuable this would be from the outset,” Parscale told Bloomberg. “We own the future of the Republican Party.”
     
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    Inside the Trump Bunker, With Days to Go
    Win or lose, the Republican candidate and his inner circle have built a direct marketing operation that could power a TV network—or finish off the GOP.

    JoshuaGreen- Sasha Issenberg Bloomberg Businessweek October 27, 2016 — 5:00 PM ICT

    http://www.bloomberg.com/news/articles/2016-10-27/inside-the-trump-bunker-with-12-days-to-go

    Inside the Trump Campaign’s Data Bunker



    On Oct. 19, as the third and final presidential debate gets going in Las Vegas, Donald Trump’s Facebook and Twitter feeds are being manned by Brad Parscale, a San Antonio marketing entrepreneur, whose buzz cut and long narrow beard make him look like a mixed martial arts fighter. His Trump tie has been paired with a dark Zegna suit. A lapel pin issued by the Secret Service signals his status. He’s equipped with a dashboard of 400 prewritten Trump tweets. “Command center,” he says, nodding at his laptop.
    Parscale

    Parscale Photographer: Alex Welsh for Bloomberg Businessweek

    Parscale is one of the few within Trump’s crew entrusted to tweet on his behalf. He’s sitting at a long table in a double-wide trailer behind the debate arena, cheek to jowl with his fellow Trump staffers and Reince Priebus, chairman of the Republican National Committee. The charged atmosphere and rows of technicians staring raptly at giant TVs and computer screens call to mind NASA on launch day. On the wall, a poster of Julian Assange reads: “Dear Hillary, I miss reading your classified emails.”

    10:02 p.m.: Trump, onstage, criticizes Hillary Clinton for accepting foreign money. “Fire it off!” Parscale barks. Instantly, a new Trump tweet appears: “Crooked @HillaryClinton’s foundation is a CRIMINAL ENTERPRISE. Time to #DrainTheSwamp!”

    10:04 p.m.: Trump blames Clinton for $6 billion that went missing during her tenure at the State Department (actually a bookkeeping error). “Hit that hard,” shouts Jason Miller, Trump’s senior communications adviser. Parscale already has: “Crooked’s top aides were MIRED in massive conflicts of interest at the State Dept. WE MUST #DrainTheSwamp.”

    10:09 p.m.: Trump deploys a carefully rehearsed WikiLeaks attack: “Podesta said some horrible things about you—and he was right.” The trailer erupts. “There it is!” someone shouts. “Push that,” Parscale commands. Within seconds, Trump’s roiling social mediasphere is bestowed with a curated Clinton burn from their leader: “Bernie Sanders on HRC: Bad Judgement [sic]. John Podesta on HRC: Bad Instincts #BigLeagueTruth.”

    When the debate wraps, Parscale leaps up, open laptop still in hand, and bolts from the trailer with Priebus and the rest of the senior staff to congratulate Trump as he comes off the stage. In the wings, Parscale joins Steve Bannon, Trump’s Machiavelli and campaign chairman, on leave from Breitbart News Network; Dan Scavino Jr., his social media director; and a clutch of Trump children and their spouses, including Trump’s son-in-law, Jared Kushner, whom Parscale considers nearly a brother. Up on stage, Trump had been visibly upset, snapping at Clinton (“nasty woman”) and tearing a page from his notebook. But a moment later, when he emerges from a dark corridor with a phalanx of Secret Service agents, he’s thronged by his worshipful band of advisers, quasi-celebrities, and hangers-on. Parscale, tweeting as he walks, nearly misses him. Trump leans over to whisper into Bannon’s ear, and a Secret Service officer ushers Trump, Bannon, and Parscale toward a row of black SUVs. A moment later, they’re gone. Trump reclaims possession of his virtual self.

    Parscale, now tweeting from his own account, celebrates the night’s haul: “HUGE 24hrs of online donations for @realDonaldTrump. 125,000+ unique donors grossing over $9,000,000! Thank you America! #MAGA.”


    Featured in Bloomberg Businessweek, Oct. 31-Nov. 6, 2016. Subscribe now. Photographer: Caroline Tompkins for Bloomberg Businessweek

    Almost every public and private metric suggests Trump is headed for a loss, possibly an epic one. His frustrated demeanor on the campaign trail suggests he knows it. Yet even as he nears the end of his presidential run, his team is sowing the seeds of a new enterprise with a direct marketing effort that they insist could still shock the world on Election Day.

    Beginning last November, then ramping up in earnest when Trump became the Republican nominee, Kushner quietly built a sprawling digital fundraising database and social media campaign that’s become the locus of his father-in-law’s presidential bid. Trump’s top advisers won’t concede the possibility of defeat, but they’re candid about the value of what they’ve built even after the returns come in—and about Trump’s desire for influence regardless of outcome. “Trump is a builder,” says Bannon, in a rare interview. “And what he’s built is the underlying apparatus for a political movement that’s going to propel us to victory on Nov. 8 and dominate Republican politics after that.”

    If Trump wants to strengthen his hold on his base, then his apocalyptic rhetoric on the stump begins to make more sense. Lately he’s sounded less like a candidate seeking to persuade moderates and swing voters and more like the far-right populist leaders who’ve risen throughout Europe. Most Republican Party officials ardently hope he’ll go away quietly if he loses. But given all that his campaign—and Kushner’s group especially—has been doing behind the scenes, it looks likelier that Trump and his lieutenants will stick around. They may emerge as a new media enterprise, an outsider political movement, or perhaps some combination of the two: an American UK Independence Party (UKIP) that will wage war on the Republican Party—or, rather, intensify the war that Trump and Bannon have already begun.

    To outsiders, the Trump campaign often appears to be powered by little more than the candidate’s impulses and Twitter feed. But after Trump locked down the GOP nomination by winning Indiana’s primary, Kushner tapped Parscale, a political novice who built web pages for the Trump family’s business and charities, to begin an ambitious digital operation fashioned around a database they named Project Alamo. With Trump atop the GOP ticket, Kushner was eager to grow fast. “When we won the nomination, we decided we were going to do digital fundraising and really ramp this thing up to the next level,” says a senior official. Kushner, this official continued, “reached out to some Silicon Valley people who are kind of covert Trump fans and experts in digital marketing. They taught us about scaling. There’s really not that much of a difference between politics and regular marketing.”

    When Bannon joined the campaign in August, Project Alamo’s data began shaping even more of Trump’s political and travel strategy—and especially his fundraising. Trump himself was an avid pupil. Parscale would sit with him on the plane to share the latest data on his mushrooming audience and the $230 million they’ve funneled into his campaign coffers. Today, housed across from a La-Z-Boy Furniture Gallery along Interstate 410 in San Antonio, the digital nerve center of Trump’s operation encompasses more than 100 people, from European data scientists to gun-toting elderly call-center volunteers. They labor in offices lined with Trump iconography and Trump-focused inspirational quotes from Sheriff Joe Arpaio and evangelical leader Jerry Falwell Jr. Until now, Trump has kept this operation hidden from public view. But he granted Bloomberg Businessweek exclusive access to the people, the strategy, the ads, and a large part of the data that brought him to this point and will determine how the final two weeks of the campaign unfold.
    “We have three major voter suppression operations under way”

    Several things jump out. Despite Trump’s claim that he doesn’t believe the polls, his San Antonio research team spends $100,000 a week on surveys (apart from polls commissioned out of Trump Tower) and has sophisticated models that run daily simulations of the election. The results mirror those of the more reliable public forecasters—in other words, Trump’s staff knows he’s losing. Badly. “Nate Silver’s results have been similar to ours,” says Parscale, referring to the polling analyst and his predictions at FiveThirtyEight, “except they lag by a week or two because he’s relying on public polls.” The campaign knows who it must reach and is still executing its strategy despite the public turmoil: It’s identified 13.5 million voters in 16 battleground states whom it considers persuadable, although the number of voters shrinks daily as they make up their minds.
    Trump’s team also knows where its fate will be decided. It’s built a model, the “Battleground Optimizer Path to Victory,” to weight and rank the states that the data team believes are most critical to amassing the 270 electoral votes Trump needs to win the White House. On Oct. 18 they rank as follows: Florida (“If we don’t win, we’re cooked,” says an official), Ohio, Pennsylvania, North Carolina, and Georgia.

    Trump believes he possesses hidden strength that may only materialize at the ballot box. At rallies, he’s begun speculating that the election will be like “Brexit times five,” implying that he’ll upend expectations much as the Brexit vote shocked experts who didn’t believe a majority of Britons would vote to leave the European Union. Trump’s data scientists, including some from the London firm Cambridge Analytica who worked on the “Leave” side of the Brexit initiative, think they’ve identified a small, fluctuating group of people who are reluctant to admit their support for Trump and may be throwing off public polls.

    Still, Trump’s reality is plain: He needs a miracle. Back in May, newly anointed, he told Bloomberg Businessweek he would harness “the movement” to challenge Clinton in states Republicans haven’t carried in years: New York, New Jersey, Oregon, Connecticut, California. “I’m going to do phenomenally,” he predicted. Yet neither Trump’s campaign nor the RNC has prioritized registering and mobilizing the 47 million eligible white voters without college degrees who are Trump’s most obvious source of new votes, as FiveThirtyEight analyst David Wasserman noted.

    To compensate for this, Trump’s campaign has devised another strategy, which, not surprisingly, is negative. Instead of expanding the electorate, Bannon and his team are trying to shrink it. “We have three major voter suppression operations under way,” says a senior official. They’re aimed at three groups Clinton needs to win overwhelmingly: idealistic white liberals, young women, and African Americans. Trump’s invocation at the debate of Clinton’s WikiLeaks e-mails and support for the Trans-Pacific Partnership was designed to turn off Sanders supporters. The parade of women who say they were sexually assaulted by Bill Clinton and harassed or threatened by Hillary is meant to undermine her appeal to young women. And her 1996 suggestion that some African American males are “super predators” is the basis of a below-the-radar effort to discourage infrequent black voters from showing up at the polls—particularly in Florida.


    Campaign staff in Trump Tower. Photographer: Alex Welsh for Bloomberg Businessweek


    On Oct. 24, Trump’s team began placing spots on select African American radio stations. In San Antonio, a young staffer showed off a South Park-style animation he’d created of Clinton delivering the “super predator” line (using audio from her original 1996 sound bite), as cartoon text popped up around her: “Hillary Thinks African Americans are Super Predators.” The animation will be delivered to certain African American voters through Facebook “dark posts”—nonpublic posts whose viewership the campaign controls so that, as Parscale puts it, “only the people we want to see it, see it.” The aim is to depress Clinton’s vote total. “We know because we’ve modeled this,” says the official. “It will dramatically affect her ability to turn these people out.”
    The Trump team’s effort to discourage young women by rolling out Clinton accusers and drive down black turnout in Miami’s Little Haiti neighborhood with targeted messages about the Clinton Foundation’s controversial operations in Haiti is an odd gambit. Campaigns spend millions on data science to understand their own potential supporters—to whom they’re likely already credible messengers—but here Trump is speaking to his opponent’s. Furthermore, there’s no scientific basis for thinking this ploy will convince these voters to stay home. It could just as easily end up motivating them.

    Regardless of whether this works or backfires, setting back GOP efforts to attract women and minorities even further, Trump won’t come away from the presidential election empty-handed. Although his operation lags previous campaigns in many areas (its ground game, television ad buys, money raised from large donors), it’s excelled at one thing: building an audience. Powered by Project Alamo and data supplied by the RNC and Cambridge Analytica, his team is spending $70 million a month, much of it to cultivate a universe of millions of fervent Trump supporters, many of them reached through Facebook. By Election Day, the campaign expects to have captured 12 million to 14 million e-mail addresses and contact information (including credit card numbers) for 2.5 million small-dollar donors, who together will have ponied up almost $275 million. “I wouldn’t have come aboard, even for Trump, if I hadn’t known they were building this massive Facebook and data engine,” says Bannon. “Facebook is what propelled Breitbart to a massive audience. We know its power.”

    Since Trump paid to build this audience with his own campaign funds, he alone will own it after Nov. 8 and can deploy it to whatever purpose he chooses. He can sell access to other campaigns or use it as the basis for a 2020 presidential run. It could become the audience for a Trump TV network. As Bannon puts it: “Trump is an entrepreneur.”

    Whatever Trump decides, this group will influence Republican politics going forward. These voters, whom Cambridge Analytica has categorized as “disenfranchised new Republicans,” are younger, more populist and rural—and also angry, active, and fiercely loyal to Trump. Capturing their loyalty was the campaign’s goal all along. It’s why, even if Trump loses, his team thinks it’s smarter than political professionals. “We knew how valuable this would be from the outset,” says Parscale. “We own the future of the Republican Party.”


    Like so many Trump die-hards, Parscale, 40, is an up-from-nothing striver who won a place in the Trump firmament by dint of his willingness to serve the family’s needs—and then, when those needs turned to presidential campaigning, wound up inhabiting a position of remarkable authority. He oversees the campaign’s media budget and supervises a large staff of employees and contractors, a greater number than report for duty each day at Trump Tower headquarters. “My loyalty is to the family,” he says. “Donald Trump says ‘Jump’; I say, ‘How high?’ Then I give him my opinion of where I should jump to, and he says, ‘Go do it.’ ”

    Parscale was born in a small town outside Topeka, Kan., a self-described “rural jock” whose size—6-foot-8, 240 pounds—won him a basketball scholarship to the University of Texas at San Antonio. When injuries derailed his playing career, his interest turned to business. “The day I graduated, I skipped the ceremony to go straight to California for the dot-com boom,” he says. It was 1999. He became a sales manager for a video streaming company, taught himself programming, and eventually bought some of the company’s intellectual property, in digital video and 3D animation, and struck out on his own. But after the dot-com crash, his company failed, he got divorced, and by 2002 he was back in San Antonio, broke and unemployed.

    Parscale and his colleagues in his Trump Tower office. Photographer: Alex Welsh for Bloomberg Businessweek

    He hustled consulting gigs, going door to door and cold-calling local businesses. “My first year, I tapped on shoulders in a bookstore to get my first customers, people who were buying web books, and asked if they needed help,” he says. One day in 2010, the phone rang. It was Kathy Kaye, the new head of Trump International Realty. “She said, ‘Would you like to bid on building the Trump website?’ ” Parscale recalls. “I said yeah. I bid $10,000 on the first website. I think they were shocked how cheap it was. Next thing I know, I’m talking to Ivanka. So they signed a contract with me, and I wrote the website by myself. I told ’em I’d give all the money back if they didn’t like it.”

    The Trumps liked it. He eventually built sites for Trump Winery and the Eric Trump Foundation. When Trump launched a presidential exploratory committee, he knew who could build a website for him on the cheap: Parscale charged $1,500.

    By then he’d partnered with a local designer and expanded into a design and marketing agency, Giles-Parscale. Trump’s own approach to self-promotion, reinforced by Kushner’s advice, was at odds with the highly targeted logic of the web. “If you’re running a burger shop, you have to let people know that your burgers are good and get them into your shop to buy them,” says a source close to the candidate. “It’s pretty similar with voting: You have to find out what people want and then convince them why your product is the right one.”

    A poll map. Photographer: Alex Welsh for Bloomberg Businessweek

    Trump’s digital operation was focused primarily on tracking down the people who already liked his burgers and getting them to buy more. Parscale began toying with a list of registered voters acquired from a nonpartisan database vendor to learn more about who Trump’s backers were. Because the campaign hadn’t cultivated his supporters as donors or volunteers, most of what it knew about them came from requests for tickets to his rallies. After a March event in Chicago devolved into a melee, Parscale decided to stop relying on the ticketing service Eventbrite and build his own tool to accept RSVPs. He says he coded the program himself in two days so eventgoers would have to confirm via mobile phone. The added layer would weed out fraudulent requests placing tickets in protesters’ hands—and also collect supporters’ phone numbers.
    Parscale was given a small budget to expand Trump’s base and decided to spend it all on Facebook. He developed rudimentary models, matching voters to their Facebook profiles and relying on that network’s “Lookalike Audiences” to expand his pool of targets. He ultimately placed $2 million in ads across several states, all from his laptop at home, then used the social network’s built-in “brand-lift” survey tool to gauge the effectiveness of his videos, which featured infographic-style explainers about his policy proposals or Trump speaking to the camera. “I always wonder why people in politics act like this stuff is so mystical,” Parscale says. “It’s the same shit we use in commercial, just has fancier names.”

    As Kushner, who shares his father-in-law’s disdain for political professionals, became more active in the campaign’s operations, Parscale emerged from among dozens of vendors into a unique role. “Once Jared found Brad,” says a campaign official, “we were able to avoid building a big team and ran a lot of our back end through his office in San Antonio.”

    After Trump won the Indiana primary, vanquishing his remaining rivals, Parscale had to integrate his do-it-yourself operation with two established players who would jostle for primacy as supplier of Trump’s data. The first was Cambridge Analytica, on whose board Bannon sits. Among its investors is the hedge fund titan Robert Mercer and his daughter, Rebekah, who were about to become some of the largest donors to the Trump cause. Locations for the candidate’s rallies, long the centerpiece of his media-centric candidacy, are guided by a Cambridge Analytica ranking of the places in a state with the largest clusters of persuadable voters. The other was the Republican National Committee, to which Trump relinquished control over many of its tactical decisions. “I told him he’s going to want to use the RNC once he’s the nominee,” says Newt Gingrich. “Reince has built a real system, and it can be very valuable to him.”

    “That willingness to embrace what the RNC built allowed them to harness that movement”
    Soon after Trump secured the nomination, a team from the RNC flew to San Antonio to meet Parscale at his favorite Mexican restaurant and discuss what party officials began describing as “the merger.” Priebus boasted then of having put “more than $100 million into data and infrastructure” since Mitt Romney’s 2012 loss. More than 10 percent of that cash went solely to beefing up the RNC’s e-mail list, which now has a dedicated department of a dozen people managing a list of more than 6 million supporters. To win access to them, Trump negotiated a partnership. The party’s online fundraising specialists would use his name and keep 80 percent of the revenue, while Trump’s campaign would get the remainder. “This is exactly what the party needed the RNC to do—building assets and infrastructure and the nominee gets to benefit from it,” says Chief Digital Officer Gerrit Lansing.

    Trump’s team, which hadn’t actively raised money during the primaries, was unprepared. “I was put in the position of ‘We need to start fundraising tomorrow,’ ” says Parscale. That turn was so hasty that when, in late June, Trump sent out his first e-mail solicitation, it ended up in recipients’ spam folders 60 percent of the time. Typically marketers in that situation would have begun quietly blasting less important messages from a new server to familiarize spam filters with the sender’s address. Parscale shrugs off the ensuing criticism from technologists. “Should I have set up an e-mail server a month earlier? Possibly,” he says. “We also raised $40 million in two weeks. Woo-hoo, spam rating.”

    Parscale was building his own list of Trump supporters, beyond the RNC’s reach. Cambridge Analytica’s statistical models isolated likely supporters whom Parscale bombarded with ads on Facebook, while the campaign bought up e-mail lists from the likes of Gingrich and Tea Party groups to prospect for others. Some of the ads linked directly to a payment page, others—with buttons marked “Stand with Trump” or “Support Trump”—to a sign-up page that asked for a name, address, and online contact information. While his team at Giles-Parscale designed the ads, Parscale invited a variety of companies to set up shop in San Antonio to help determine which social media ads were most effective. Those companies test ad variations against one another—the campaign has ultimately generated 100,000 distinct pieces of creative content—and then roll out the strongest performers to broader audiences. At the same time, Parscale made the vendors, tech companies with names such as Sprinklr and Kenshoo, compete Apprentice-style; those whose algorithms fared worst in drumming up donors lost their contracts. Each time Parscale returned to San Antonio from Trump Tower, he would find that some vendors had been booted from their offices.

    Parscale’s department not only paid for itself but also was the largest source of campaign revenue. That endeared it to a candidate stingy with other parts of the budget. When Trump fired his campaign manager, Corey Lewandowski, Parscale’s responsibilities grew, then further still when Lewandowski’s replacement, Paul Manafort, flamed out. In June, Parscale, whose prior political experience was a Bexar County tax assessor’s race (his client lost), became Trump’s digital director and, in many ways, the linchpin of his unusual run.
    By the time Bannon became chief executive officer, Parscale had balanced the competition between the RNC and Cambridge Analytica, with different sources of data being tapped for the campaign’s fundraising appeals, persuasive communication, and get-out-the-vote contacts. “I’m the only one that hasn’t gained from any of this,” he says pointedly about the data rivalry.

    In June, Parscale granted his first national interview, to Wired, to preemptively explain why the Federal Election Commission was about to report that an unknown agency in San Antonio was the Trump campaign’s largest vendor. In August, Giles-Parscale handled $9 million in business from Trump’s campaign; two months later, the company’s total haul had cleared $50 million, most of it money passing through to online ad networks at little markup. Parscale was delivering his services at such a discount that Kushner even worried that the agency’s efforts might have to be classified as an in-kind contribution. “Jared’s a big part of what gave me my power and ability to do what I’ve been doing,” says Parscale, who sees himself as more than just a staffer. “Because you know what I was willing to do? I was willing to do it like family.”

    There are signs that Trump’s presidential run has dealt a serious blow to his brand. His inflammatory comments about Mexican “rapists” and demeaning comments about women triggered a flood of busted deals and lost partnerships. Macy’s stopped making Trump-branded menswear, Serta halted its line of mattresses emblazoned with his logo, and celebrity chefs fled his new luxury hotel in Washington. Booking websites show that visits to Trump-branded hotels are down. Win or lose, Trump’s future may well lie in capitalizing on the intense, if limited, political support he has cultivated over the past year.

    According to a source close to Trump, the idea of a Trump TV network originated during the Republican primaries as a threat Kushner issued to Roger Ailes when Trump’s inner circle was unhappy with the tenor of Fox News’s coverage. The warring factions eventually reconciled. But Trump became enamored by the power of his draw after five media companies expressed interest. “One thing Jared always tells Donald is that if the New York Times and cable news mattered, he would be at 1 percent in the polls,” says the source. “Trump supporters really don’t have a media outlet where they feel they’re represented—CNN has gone fully against Trump, MSNBC is assumed to be against Trump, and Fox is somewhere in the middle. What we found is that our people have organized incredibly well on the web. Reddit literally had to change their rules because it was becoming all Trump. Growing the digital footprint has really allowed us to take his message directly to the people.”

    It’s not clear how much of this digital audience will remain in Trump’s thrall if he loses. But the number should be substantial. “Trump will get 40 percent of the vote, and half that number at least will buy into his claim that the election was rigged and stolen from him,” says Steve Schmidt, John McCain’s 2008 presidential campaign chief and an outspoken Trump critic. “That is more than enough people to support a multibillion-dollar media business and a powerful presence in American politics.”

    Kushner and Bannon at a Trump rally in Canton, Ohio, on Sept. 14. Photographer: Alex Welsh for Bloomberg Businessweek

    Digital strategists typically value contact lists at $3 to $8 per e-mail, which would price Trump’s list of supporters anywhere from $36 million to $112 million. The Trump enterprise could benefit from it in any number of ways. The easiest move would be for Trump to partner with Bannon’s global Breitbart News Network, which already has a grip on the rising generation of populist Republicans. Along with a new venture, Trump would gain a platform from which to carry on his movement, built upon the millions of names housed in Project Alamo. “This is the pipe that makes the connection between Trump and the people,” says Bannon. “He has an apparatus that connects him to an ever-expanding audience of followers.”

    As it happens, this cross-pollination of right-wing populist media and politics is already occurring overseas—and Trump’s influence on it is unmistakable. In early October, the editor-in-chief of Breitbart London, Raheem Kassam, a former adviser to Nigel Farage, announced he would run for leader of UKIP. His slogan: “Make UKIP Great Again.”


    The final ignominy for a Republican Party brought low by Trump is that its own digital efforts may undermine its future. The data operation in which Priebus and the RNC invested so heavily has fed into Project Alamo, helping Parscale build Trump’s base. “They brought to the table this movement and people who were willing to donate and activate, and we brought to the table a four-year investment and said we can process that for you,” says Sean Spicer, the RNC’s chief strategist. “That willingness to embrace what the RNC built allowed them to harness that movement.”

    If the election results cause the party to fracture, Trump will be better positioned than the RNC to reach this mass of voters because he’ll own the list himself—and Priebus, after all he’s endured, will become just the latest to invest with Trump and wind up poorer for the experience.


    Report: Donald Trump's 'Project Alamo' in S.A. is working to 'suppress' voters
    By Kelsey Bradshaw Updated 12:35 pm, Thursday, October 27, 2016



    Republican presidential candidate Donald Trump speaks at Saint Anselm College Monday, June 13, 2016, in Manchester, N.H. The Trump campaign has paid San Antonio firm Giles-Parscale more than $1.9 million since May 2015 for website development and digital consulting. Photo: Jim Cole
    Photo: Jim Cole /Associated Press

    Republican presidential candidate Donald Trump speaks at Saint Anselm College Monday, June 13, 2016, in Manchester, N.H. The Trump campaign has paid San Antonio firm Giles-Parscale more than $1.9 million since May 2015 for website development and digital consulting.

    With just a three point lead in Texas, Donald Trump's campaign team here, called "Project Alamo," has turned to voter suppression tactics in an effort to give the Republican nominee an uptick in votes on Election Day, according to a new Bloomberg Businessweek article.
    Bloomberg reports the research team in San Antonio, which spends $100,000 on surveys weekly, along with Trump's national campaign effort, are targeting three voter groups: women, white liberals and African Americans.



    "We have three major voter suppression operations under way," a senior official with the campaign told Businessweek.

    RELATED: Everything you need to know to vote in San Antonio in the 2016 General Election
    The Trump camp is hoping those who have accused former President Bill Clinton of sexual assault will deter women from voting, Bussinessweek reported. On Oct. 24, the Trump campaign began running ads on African American radio stations in an attempt to discourage the group from voting.
    In San Antonio, campaign staffers are working on suppression tactics as well, campaign officials told Bloomberg.

    One staffer made an animation of Hillary Clinton, the Democratic nominee for president, saying African Americans are "super predators." The animation will be published on Facebook in "dark posts" that allow "only the people we want to see it, see it," a campaign official told


    "It will dramatically affect her ability to turn these people out," a campaign official told Businessweek.

    “Project Alamo” has some wondering if the Republican candidate has studied up on Texas history— the state fell during the Battle of the Alamo.
    On Thursday, Trump tweeted that he had received “a lot of call-ins about vote flipping at the voting booths in Texas.”


    “People are not happy. BIG lines. What is going on?” he tweeted.
    Counties across Texas have seen record-busting voter turnout since early voting began Monday. In Bexar County alone, 40,186 people voted, marking the third consecutive day of record participation in a presidential contest. Between Monday and Tuesday, 74,000 registered voters cast ballots.

    On Twitter Thursday, one said the name “Project Alamo” was “profoundly offensive,” while others predicted Trump will lose in November with a project name involving one of the biggest defeats in Texas history. Another suggested the name meant, “stop the Mexican invasion,” coinciding with Trump’s plan to build a wall along the U.S.-Mexico border.
    kbradshaw@express-news.net

    http://www.mysanantonio.com/news/lo...-has-a-Project-Alamo-here-in-San-10417170.php
     
    Last edited: Nov 20, 2016
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    Exclusive Interview: How Jared Kushner Won Trump The White House
    Steven Bertoni ,
    FORBES STAFF
    [​IMG]

    I cover the Forbes Under 30 franchise, technology and entrepreneurs.
    This story appears in the December 20, 2016 issue of Forbes
    Jared Kushner (Jamel Toppin for Forbes)


    It’s been one week since Donald Trump pulled off the biggest upset in modern political history, and his headquarters at Trump Tower in New York City is a 58-story, onyx-glassed lightning rod. Barricades, TV trucks and protesters frame a fortified Fifth Avenue. Armies of journalists and selfie-seeking tourists stalk Trump Tower’s pink marble lobby, hoping to snap the next political power player who steps into view. Twenty-six floors up, in the same building where washed-up celebrities once battled for Trump’s blessing on The Apprentice, the president-elect is choosing his Cabinet, and this contest contains all the twists and turns of his old reality show.
    Winners will emerge shortly. But today’s focus is on the biggest loser: New Jersey governor Chris Christie, who has just been fired from his role leading the transition, along with most of the people associated with him. The episode is being characterized as a “knife fight” that ends in a “Stalinesque purge.”
    The most compelling figure in this intrigue, however, wasn’t in Trump Tower. Jared Kushner was three blocks south, high up in his own skyscraper, at 666 Fifth Avenue, where he oversees his family’s Kushner Companies real estate empire. Trump’s son-in-law, dressed in an impeccably tailored gray suit, sitting on a brown leather couch in his impeccably neat office, displays the impeccably polite manners that won the 35-year-old a dizzying number of influential friends even before he had gained the ear, and trust, of the new leader of the free world.

    “Six months ago Governor Christie and I decided this election was much bigger than any differences we may have had in the past, and we worked very well together,” he says with a shrug. “The media has speculated on a lot of different things, and since I don’t talk to the press, they go as they go, but I was not behind pushing out him or his people.”
    The speculation was well-founded, given the story’s Shakespearean twist: As a U.S. attorney in 2005, Christie jailed Kushner’s father on tax evasion, election fraud and witness tampering charges. Revenge theories aside, the buzz around Kushner was directional and indicative. A year ago he had zero experience in politics and about as much interest in it. Suddenly he sits at its global center. Whether he plunged the dagger into Christie–Trump insiders insist the Bridgegate scandal did him in–is less important than the fact that he easily could have. And that power comes well-earned.

    Kushner almost never speaks publicly–his chats with FORBES mark the first time he has talked about the Trump campaign or his role in it–but interviews with him and a dozen people around him and the Trump camp lead to an inescapable fact: The quiet, enigmatic young mogul delivered the presidency to the most fame-hungry, bombastic candidate in American history.
    “It’s hard to overstate and hard to summarize Jared’s role in the campaign,” says billionaire Peter Thiel, the only significant Silicon Valley figure to publicly back Trump. “If Trump was the CEO, Jared was effectively the chief operating officer.”
    “Jared Kushner is the biggest surprise of the 2016 election,” adds Eric Schmidt, the former CEO of Google, who helped design the Clinton campaign’s technology system. “Best I can tell, he actually ran the campaign and did it with essentially no resources.”

    No resources at the beginning, perhaps. Underfunded throughout, for sure. But by running the Trump campaign–notably, its secret data operation–like a Silicon Valley startup, Kushner eventually tipped the states that swung the election. And he did so in manner that will change the way future elections will be won and lost. President Obama had unprecedented success in targeting, organizing and motivating voters. But a lot has changed in eight years. Specifically social media. Clinton did borrow from Obama’s playbook but also leaned on traditional media. The Trump campaign, meanwhile, delved into message tailoring, sentiment manipulation and machine learning. The traditional campaign is dead, another victim of the unfiltered democracy of the Web–and Kushner, more than anyone not named Donald Trump, killed it.
    That achievement, coupled with the personal trust Trump has in him, uniquely positions Kushner to be a power broker of the highest order for at least four years. “Every president I’ve ever known has one or two people he intuitively and structurally trusts,” says former secretary of state Henry Kissinger, who has known Trump socially for decades and is currently advising the president-elect on foreign policy issues. “I think Jared might be that person.”
    JARED KUSHNER’S ASCENT from Ivanka Trump’s little-known husband to Donald Trump’s campaign savior happened gradually. In the early days of the scrappy campaign, it was all hands on deck, with Kushner helping research policy positions on tax and trade. But as the campaign gained steam, other players began using him as a trusted conduit to an erratic candidate. “I helped facilitate a lot of relationships that wouldn’t have happened otherwise,” Kushner says, adding that people felt safe speaking with him, without risk of leaks. “People were being told in Washington that if they did any work for the Trump campaign, they would never be able to work in Republican politics again. I hired a great tax-policy expert who joined under two conditions: We couldn’t tell anybody he worked for the campaign, and he was going to charge us double.”
    Kushner’s role expanded as the Trump ticket gained traction–so did his enthusiasm. Kushner went all-in with Trump last November after seeing his father-in-law pack a raucous arena in Springfield, Illinois, on a Monday night. “People really saw hope in his message,” he says. “They wanted the things that wouldn’t have been obvious to a lot of people I would meet in the New York media world, the Upper East Side or at Robin Hood [Foundation] dinners.” And so this Harvard-educated child of privilege put on a bright-red Make American Great Again hat and rolled up his sleeves.

    A power vacuum awaited him at Trump Tower. When FORBES visited the Trump campaign floor in the skyscraper a few weeks before Kushner’s Springfield epiphany, there was literally nothing there. No people–and no desks or chairs or computers awaiting the arrival of staffers. Just campaign manager Corey Lewandowski, spokesperson Hope Hicks and a strategy that centered on Trump making headline-grabbing statements, often by calling in to television shows, supplemented by a rally once or twice a week to provide the appearance of a traditional campaign. It was the epitome of the super-light startup: to see how little they could spend and still get the results they wanted.
    Kushner stepped up to turn it into an actual campaign operation. Soon he was assembling a speech and policy team, handling Trump’s schedule and managing the finances. “Donald kept saying, ‘I don’t want people getting rich off the campaign, and I want to make sure we are watching every dollar just like we would do in business.’”
    That structure provided a baseline, though still a blip compared with Hillary Clinton’s state-by-state machine. The decision that won Trump the presidency started on the return trip from that Springfield rally last November aboard his private 757, dubbed Trump Force One. Chatting over McDonald’s Filet-O-Fish sandwiches, Trump and Kushner talked about how the campaign was underutilizing social media. The candidate, in turn, asked his son-in-law to take over his Facebook initiatives.
    Despite his itchy Twitter finger, Trump is a Luddite. He reportedly gets his news from print and television, and his version of e-mail is to handwrite a note that his assistant will scan and attach. Among those in his close circle, Kushner was the natural pick to create a modern campaign. Yes, like Trump he’s primarily a real estate guy, but he had invested more broadly, including in media (in 2006 he bought the New York Observer) and digital commerce (he helped launch Cadre, an online marketplace for big real estate deals). More important, he knew the right crowd: co-investors in Cadre include Thiel and Alibaba’s Jack Ma–and Kushner’s younger brother, Josh, a formidable venture capitalist who also cofounded the $2.7 billion insurance unicorn Oscar Health.

    Jared Kushner: The FORBES cover story

    “I called some of my friends from Silicon Valley, some of the best digital marketers in the world, and asked how you scale this stuff,” Kushner says. “They gave me their subcontractors.”
    At first Kushner dabbled, engaging in what amounted to a beta test using Trump merchandise. “I called somebody who works for one of the technology companies that I work with, and I had them give me a tutorial on how to use Facebook micro-targeting,” Kushner says. Synched with Trump’s blunt, simple messaging, it worked. The Trump campaign went from selling $8,000 worth of hats and other items a day to $80,000, generating revenue, expanding the number of human billboards–and proving a concept. In another test, Kushner spent $160,000 to promote a series of low-tech policy videos of Trump talking straight into the camera that collectively generated more than 74 million views.
    By June the GOP nomination secured, Kushner took over all data-driven efforts. Within three weeks, in a nondescript building outside San Antonio, he had built what would become a 100-person data hub designed to unify fundraising, messaging and targeting. Run by Brad Parscale, who had previously built small websites for the Trump Organization, this secret back office would drive every strategic decision during the final months of the campaign. “Our best people were mostly the ones who volunteered for me pro bono,” Kushner says. “People from the business world, people from nontraditional backgrounds.”
    Kushner structured the operation with a focus on maximizing the return for every dollar spent. “We played Moneyball, asking ourselves which states will get the best ROI for the electoral vote,” Kushner says. “I asked, How can we get Trump’s message to that consumer for the least amount of cost?” FEC filings through mid-October indicate the Trump campaign spent roughly half as much as the Clinton campaign did.

    Kushner and his father-in-law Donald Trump, America’s President-Elect. (Photo: Taylor Hill/Getty Images)

    Just as Trump’s unorthodox style allowed him to win the Republican nomination while spending far less than his more traditional opponents, Kushner’s lack of political experience became an advantage. Unschooled in traditional campaigning, he was able to look at the business of politics the way so many Silicon Valley entrepreneurs have sized up other bloated industries.
    Television and online advertising? Small and smaller. Twitter and Facebook would fuel the campaign, as key tools for not only spreading Trump’s message but also targeting potential supporters, scraping massive amounts of constituent data and sensing shifts in sentiment in real time.
    “We weren’t afraid to make changes. We weren’t afraid to fail. We tried to do things very cheaply, very quickly. And if it wasn’t working, we would kill it quickly,” Kushner says. “It meant making quick decisions, fixing things that were broken and scaling things that worked.”

    This wasn’t a completely raw startup. Kushner’s crew was able to tap into the Republican National Committee’s data machine, and it hired targeting partners like Cambridge Analytica to map voter universes and identify which parts of the Trump platform mattered most: trade, immigration or change. Tools like Deep Root drove the scaled-back TV ad spending by identifying shows popular with specific voter blocks in specific regions–say, NCIS for anti-ObamaCare voters or The Walking Dead for people worried about immigration. Kushner built a custom geo-location tool that plotted the location density of about 20 voter types over a live Google Maps interface.
    Soon the data operation dictated every campaign decision: travel, fundraising, advertising, rally locations–even the topics of the speeches. “He put all the different pieces together,” Parscale says. “And what’s funny is the outside world was so obsessed about this little piece or that, they didn’t pick up that it was all being orchestrated so well.”
    For fundraising they turned to machine learning, installing digital marketing companies on a trading floor to make them compete for business. Ineffective ads were killed in minutes, while successful ones scaled. The campaign was sending more than 100,000 uniquely tweaked ads to targeted voters each day. In the end, the richest person ever elected president, whose fundraising effort was rightly ridiculed at the beginning of the year, raised more than $250 million in four months–mostly from small donors.
    As the election barreled toward its finale, Kushner’s system, with its high margins and up-to-the-minute voter data, provided both ample cash and the insight on where to spend it. When the campaign registered the fact that momentum in Michigan and Pennsylvania was turning Trump’s way, Kushner unleashed tailored TV ads, last-minute rallies and thousands of volunteers to knock on doors and make phone calls.

    And until the final days of the campaign, he did all this without anyone on the outside knowing about it. For those who can’t understand how Hillary Clinton could win the popular vote by at least 2 million yet lose handily in the electoral college, perhaps this provides some clarity. If the campaign’s overarching sentiment was fear and anger, the deciding factor at the end was data and entrepreneurship.

    “Jared understood the online world in a way the traditional media folks didn’t. He managed to assemble a presidential campaign on a shoestring using new technology and won. That’s a big deal,” says Schmidt, the Google billionaire. “Remember all those articles about how they had no money, no people, organizational structure? Well, they won, and Jared ran it.”

    CONTROLLED, UNDERSTATED and calm, Jared Kushner couldn’t be more different from his father-in-law in personality and style. Take Twitter. While Trump’s impulsive tweeting to his 15.5 million followers reportedly forced his staff to withhold his phone during parts of the campaign, Kushner–who has had a verified account since April 2009–has never posted a single tweet.

    And whereas Trump’s office is wall-to-wall Donald, a memorabilia-stuffed shrine to ego, the headquarters for the Kushner Companies is sparse and sober. A leather-bound copy of Jewish teachings, the Pirkei Avot, sits on a wooden pedestal in the reception room, and identical silver mezuzahs adorn the side of each office door. The only decoration in his large, terraced boardroom is an oil painting of his grandparents, Holocaust survivors who immigrated to the U.S. after World War II. But enter Kushner’s corner office and you see–under a painting with the words “Don’t Panic” over a canvas of New York Observer pages–two critical commonalities that unite the pair: columns of real estate deal trophies and framed photos of Ivanka. If you are looking for a consistent ideology from either Kushner or Trump, it can be summarized in a word: family.

    Kushner and his wife, businesswoman Ivanka Trump. (Photo: Mark Wilson/Getty Images)

    Jared and Ivanka met at a business lunch and started dating in 2007. During the courtship Kushner had met Donald only a few times in passing when, sensing the relationship was getting serious, he asked Trump for a meeting. Over lunch at the Trump Grill (which Trump briefly made a household name with his infamous taco bowl tweet), they discussed the couple’s future. “I said, ‘Ivanka and I are getting serious, and we’re starting to go down that path,’” Kushner says and laughs.
    “He said, ‘You’d better be serious on this.’”

    “Jared and my father initially bonded over a combination of me and real estate,” Ivanka Trump says in her Trump Tower offices as dark-suited Secret Service agents stand watch in the halls. “There’s a lot of parallels between Jared as a developer and my father in the early years of his development career.”
    Like Trump, Kushner grew up outside Manhattan: New Jersey in Kushner’s case, versus Trump’s Queens. Also like Trump, Kushner is the son of a man who created a real estate empire in his local market–Charles Kushner eventually controlled 25,000 apartments across the Northeast–and steeped his children in the family business. “My father never really believed in summer camp, so we’d come with him to the office,” Kushner says. “We’d go look at jobs, work on construction sites. It taught us real work.” Raised with three siblings in an observant Jewish home in Livingston, New Jersey, Kushner went to a private Jewish high school and then to Harvard (a 2006 book about college admissions would later single out Kushner as a prime example of an alumnus’ child getting preferential treatment; administrators quoted within that work later challenged its accuracy, calling it “distorted” and “false”). Next came New York University, for a joint J.D. and M.B.A.

    His father was a huge supporter of Democrats, giving $1 million to the Democratic National Committee in 2002 and $90,000 to Hillary Clinton’s Senate run in 2000, and Jared largely followed suit, with more than $60,000 to Democratic committees and $11,000 to Clinton. During grad school Kushner interned for Manhattan’s longtime district attorney, Robert Morgenthau, before a family scandal upended his life. In 2004 Charles Kushner pleaded guilty to tax evasion, illegal campaign contributions and witness tampering. The latter charge brought national tabloid attention. Angry that his brother-in-law was talking to prosecutors, Charles had paid a prostitute to entrap him–a tryst that he secretly taped and then mailed to his sister.

    Just 24, Jared, as the elder son, suddenly found himself charged with keeping the family together. He saw his mother most days and flew to Alabama to visit his father in prison on most weekends. He also developed a deeper bond with his brother, Josh, who had just started Harvard when the scandal broke. Says Josh, who considers Jared his best friend: “He is the person that I turn to for guidance and support no matter the circumstance.”

    “The whole thing taught me not to worry about the things you can’t control,” Kushner says. “You can control how you react and can try to make things happen as you want them to. I focus on doing my best to ensure the outcomes. And when it doesn’t go my way I have to work harder the next time.”
    That applied to the family business, too, which Kushner now led. To start fresh, he took aim at Manhattan, just as Trump did 40 years before, determined to play in America’s most lucrative and competitive real estate market.

    The timing couldn’t have been worse. His first big purchase as CEO of the Kushner Companies, 666 Fifth, for a record-breaking $1.8 billion, closed in 2007–just in time for the financial crisis. Rents fell, leases broke, funding vanished. To stay solvent, Kushner sold 49% of the building’s retail space to the Carlyle Group and others for $525 million and seemingly restructured every loan agreement possible, showing a willingness to pay more down the road for room to breathe in the short term. In the end he avoided the kind of bankruptcy maneuvers that Trump pulled in the 1990s and weathered the storm.

    Kushner had learned a lesson. Rather than chase top-dollar, blue-chip addresses around New York, he would try to ride up with cooler, up-and-coming neighborhoods, which he has done to the tune of $14 billion worth of acquisitions and developments, in places like Manhattan’s SoHo and East Village and Brooklyn’s Dumbo. “Jared brings a youthful perspective, an innovative mind-set, to a very traditional industry that’s comprised of predominantly 70-year-old men,” Ivanka Trump says. He has also pushed into resurgent areas–Astoria, Queens, and Journal Square in Jersey City–that were once the stomping grounds of Fred Trump and Charles Kushner, respectively.
    PART OF THE REASON Jared Kushner has engendered such public interest, besides the power he suddenly wields and the curiosity generated by his near-invisible media presence, is the paradoxes that he represents.

    He brought the Silicon Valley ethos, which values openness and inclusiveness, to a campaign that promised closed borders, trade protection and religious exclusion. He is the scion of prodigious Democratic donors yet steered a Republican presidential campaign. A grandson of Holocaust survivors who serves a man who has advocated a ban on war refugees. A fact-driven lawyer whose chosen candidate called global warming a hoax, linked vaccines to autism and challenged President Obama’s citizenship. A media mogul in a campaign stoked by fake news. A devout Jew advising a president-elect embraced by the alt-right and supported by the KKK.
    Kushner’s answers to these conflicts come down to one core conviction–his unflagging faith in Donald Trump. A faith that, ironically, given his role in the campaign, he defends with the “data” he’s accumulated about the man over a decade-plus relationship.

    “If I know somebody and everyone else says that this person’s a terrible person,” he says, “I’m not going to start thinking that this person’s a terrible person or disassociating myself, when my empirical data and experience is a lot more informed than many of the people casting these judgments. What would that say about me if I changed my view based on what other people think, as opposed to the facts that I actually know for myself?”
    Regarding Trump’s worldview: “I don’t think it’s very controversial in an election to become the president of the United States to say that your position is to put America first and to be nationalist as opposed to a globalist.”
    As for Trump’s endless stream of statements that insulted and threatened Muslims, Mexicans, women, prisoners of war and U.S. generals, among others? “I just know a lot of the things that people try to attack him with are just not true or overblown or exaggerations. I know his character. I know who he is, and I obviously would not have supported him if I thought otherwise. If the country gives him a chance, they’ll find he won’t tolerate hateful rhetoric or behavior.”
    On his political affiliation, he defines himself thus: “To be determined. I haven’t made a decision. Things are still evolving as they go.” He adds: “There’s some aspects of the Democrat Party that didn’t speak to me, and there are some aspects of the Republican Party that didn’t speak to me. People in the political world try to put you into different buckets based on what exists. I think Trump’s creating his own bucket–a blend of what works and eliminating what doesn’t work.” (Though in using the GOP-favored pejorative “Democrat Party” over the traditional “Democratic Party,” Kushner gives a hint about the contents of his bucket.)
    The allegations of anti-Semitism hit closer to home. In July, Trump tweeted a graphic of Hillary Clinton against a background of dollar bills and a six-pointed star that contained the words “most corrupt candidate ever,” an image that had allegedly originated on a white supremacist message board. Dana Schwartz, a reporter for Kushner’s Observer, wrote a widely read piece for the paper’s site urging her boss, given the prominence he places on his faith and family, to denounce the tweet. Kushner responded with an opinion piece that defended Trump using the same old line: that he knows Trump. “If even the slightest infraction against what the speech police have deemed correct speech is instantly shouted down with taunts of ‘racist,’ then what is left to condemn the actual racists?”
    Kushner insists today that there will be no hate element in the Trump Administration, starting at the top. “You can’t not be a racist for 69 years, then all of a sudden become a racist, right?” he says. “You can’t not be an anti-Semite for 69 years and all of a sudden become an anti-Semite because you’re running.”
    His reaction to fringe elements, like the KKK and the white nationalist alt-right, who have embraced Trump? “Trump has disavowed their support 25 times. He’s renounced hatred, he’s renounced bigotry, and he’s renounced racism. I don’t know if he could ever denounce them enough for some people.” He then paraphrases a quote he attributes to Ronald Reagan: “Just because they support me doesn’t mean that I support them.”
    Kushner’s support extends to Steve Bannon, Trump’s strategic advisor, who had been accused by his ex-wife of making anti-Semitic comments (he denies it) and whose website, Breitbart, has often published articles that dog-whistle racist, anti-Semitic sentiments. “Do you hold me accountable for every single thing that the Observer’ s ever written, like they came from me?” Kushner says. “All I know about Steve is my experience working with him. He’s an incredible Zionist and loves Israel. He was one of the leaders in the anti-divestiture campaign. And what I’ve seen from working together with him was somebody who did not fit the description that people are pushing on him. I choose to judge him based on my experience and seeing the job he’s done, as opposed to what other people are saying about him.”
    And that seems to reflect how Kushner feels about friends upset by his role in electing someone who offends their values, to the point where, before the election, several wrote to him in fits of pique. “I call it an exfoliation. Anyone who was willing to change a friendship or not do business because of who somebody supports in politics is not somebody who has a lot of character.
    “People are very fickle,” he adds. “You have to find what you believe in, challenge your truths. And if you believe in something, even if it’s unpopular, you have to push with it.”

    MANY OF THOSE fickle friends are likely to return now that Kushner, after masterminding Trump’s stunning victory, has the ear of the future president. What he will do with that power is anyone’s guess.
    For now, Kushner plays coy: “There’s a lot of people who have been asking me to get involved in a more official capacity. I just have to think about what that means for my family, for my business and make sure it’d be the right thing for a multitude of reasons.”
    It’s unlikely that he can hold a formal position in the Trump White House. Nepotism laws established after President Kennedy made brother Bobby attorney general bar the president from giving government roles to relatives–including in-laws. Reports have stated that the administration is exploring every legal angle to get Kushner into the West Wing–including adding him as an unpaid advisor, though even that may be covered by the law, which was written to ensure fealty to the Constitution rather than the individual.
    But it may be a moot point. With or without a government title or a $170,000 federal salary, there’s no law that bans a president from seeking counsel from whomever he wants. It’s clear America’s tech and entrepreneurial leaders, who heavily backed Clinton and collectively denounced Trump, will use Kushner as a go-between and that Trump will lean on him just as heavily.

    “I assume he’ll be in the White House throughout the entire presidency,” says News Corp. billionaire Rupert Murdoch. “For the next four or eight years he’ll be a strong voice, maybe even the strongest after the vice president.”

    http://vnexpress.net/tin-tuc/the-gi...iup-trump-len-dinh-cao-quyen-luc-3503688.html
     
  14. nvha

    nvha MBA family

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    KẾT QUẢ SÁNG NAY 28/11, CNN CÔNG BỐ :

    CNN Breaking News <CNNBreakingNews@mail.cnn.com>
    To no-reply@siteservices.cnn.com Today at 3:06 AM

    President-elect Donald Trump won Michigan's 16 electoral votes, state officials say, bringing the final electoral tally to Trump 306, Hillary Clinton 232.
    Clinton currently leads the popular vote by more than 2 million, and that margin is expected to grow.

    http://edition.cnn.com/election/results


    CON SỐ NÀY PHÙ HỢP VỚI NHẬN ĐỊNH CỦA PARSCALE TRONGABÀI BÁO Ở PHÍA TRÊN NGÀY 16/11

    " PARSCALE: Friday, I was 95 percent sure and by Sunday, I was about even more. And my Tuesday morning, I got more nervous Tuesday morning because I knew so much, I just had to wait.

    KELLY: Did you know about Wisconsin?

    PARSCALE: My one flip mistake was Wisconsin and Colorado. That's my 305 or 306. However as you can see our media buys from where we bought them in Pennsylvania and a different ways we're doing, we had a good strategy with the data. "
     
  15. nvha

    nvha MBA family

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    http://sohoa.vnexpress.net/tin-tuc/...ung-facebook-de-thang-cu-the-nao-3505713.html

    https://www.cnet.com/news/twitter-f...illary-clinton-president-republican-democrat/

    http://www.theverge.com/2016/11/13/13619148/trump-facebook-twitter-helped-win

    http://www.businessinsider.com/mark...-feel-hopeful-and-powerful-about-the-future-4

    https://www.wired.com/2016/11/facebook-won-trump-election-not-just-fake-news/

    http://www.business2community.com/g...-presidency-hoax-01698836#LYW7JFIqTQoZ6Cgw.97

    http://www.forbes.com/sites/stevenb...shner-won-trump-the-white-house/#1decd39f2f50


    For Trump son-in-law and confidant Jared Kushner, a long history of fierce loyalty
    By Shawn Boburg November 27

    Jared Kushner is President-elect Donald Trump's son-in-law but he's also one of his key advisers. Here's a closer look at the man who may play a central role in Trump's administration. (Deirdra O'Regan/The Washington Post)

    Jared Kushner was just an undergrad at Harvard when politicians began receiving big-dollar campaign donations bearing his name.

    In reality, the money was sent by his father, a powerful New Jersey real estate mogul. Over the course of several years, Charles Kushner pumped half a million dollars into political campaigns, avoiding legal limits by attributing checks to family members and business associates without their knowledge. The scheme sent the elder Kushner to prison and engulfed the family in scandal.
    It was a defining and pivotal episode for Jared Kushner, now 35, who is poised to become one of the most trusted advisers to his father-in-law, President-elect Donald Trump.

    The donation scandal provides a glimpse of the privilege and influence that marked Kushner’s upbringing in a prominent family. But friends say it also reveals in Kushner a fundamental trait that Trump prizes and has strengthened their bond: unflinching loyalty.

    Far from seeing his father’s actions as a betrayal, the younger Kushner flew to Alabama almost every Sunday to visit his father during his 14 months behind bars. He took the helm of the family business. And he publicly insisted that his father was unfairly prosecuted.

    “Jared is a devoted son in an almost old-world sense of respect and duty and devotion,” said former New Jersey governor Jim McGreevey (D), who counted Charles Kushner as his biggest donor until McGreevey resigned in 2004 amid a sex scandal.

    The same dynamic — this time between Kushner and Trump — played out on the campaign trail, when Kushner, an Orthodox Jew, publicly defended his father-in-law against claims that his rhetoric was fueling anti-Semitism and racism. And it seems likely to carry over into the White House, where Kushner is expected to play the role of informal gatekeeper and confidant to the president and may be entrusted with the enormous task of trying to broker an end to conflict in the Middle East.

    Kushner married into a family that, much like his own, keeps its business in the bloodline. He and Ivanka Trump were introduced at a business lunch, and ever since they got married, they have been trusted advisers to her father.

    During the campaign, Kushner and the elder Trump complemented each other with contrasting styles, according to multiple people who have observed them over the past year. Trump was brash and confrontational while Kushner was soft-spoken and discreet; Trump focused his energy on traditional media and rallies while Kushner worked with digital strategists to build a data operation.

    Kushner would often sit with Trump on the president-elect’s private plane, a Boeing 757 outfitted with cream leather couches, gold seat buckles and a big-screen television. He would quietly turn to Trump as they both read between stops — Trump rifling through a pile of printed articles, Kushner on his laptop or phone. They’d keep an eye on Fox News Channel, with Kushner whispering tidbits of information and the latest about the family.

    Jared Kushner and his wife, Ivanka Trump, celebrate at the New York Hilton Midtown in the early morning hours of Nov. 9, after news of Donald Trump’s presidential victory. Kushner and Trump have contrasting demeanors. (Chip Somodevilla/Getty Images)

    Kushner carved out a portfolio of sorts on foreign policy, with particular interest in the Middle East and Israel, and helped to shape Trump’s speech in March to the American Israel Public Affairs Committee, a well-received address during which Trump stuck to his prepared remarks.
    Trump told the New York Times last week that, once he is in the White House, Kushner would probably keep his role as an informal counselor and envoy to the Middle East, where Kushner already has close relationships with people close to Israeli Prime Minister Benjamin Netanyahu.
    Several Trump associates have said that Kushner will be a chief of staff in all but name, with wide-ranging — if sometimes hard-to-quantify — influence and a voice equal to incoming Chief of Staff Reince Priebus and chief White House strategist Stephen K. Bannon.
    Privilege and proximity to power

    Like his ascent in the family business, and perhaps even his Ivy League education, Kushner’s influence on the future president is partly a by-product of his proximity to power.
    Few families in the Northeast enjoyed more political wattage than the Kushners in the late 1990s and early 2000s. Jared Kushner’s grandparents, Holocaust survivors, had laid roots in New Jersey and started a family business in construction. Charles Kushner grew the company to encompass office buildings and thousands of condos and apartments. The Kushners gave millions to political, charitable and pro-Israel causes.

    As a teenager, Jared Kushner became accustomed to seeing national leaders pay their respects to his father.

    In 1997, when Kushner was just 16, then-President Bill Clinton made a stop at the corporate headquarters of the family business, lavishing praise on the Kushners during a speech. To mark the moment, the Kushners gave Clinton a shofar — a ram’s-horn musical instrument used in Jewish religious ceremonies.

    A year later, as Jared Kushner was starting to fill out college applications, his father pledged $2.5 million to Harvard, to be paid in $250,000 yearly installments, according to a book, “The Price of Admission: How America’s Ruling Class Buys Its Way into Elite Colleges,”by journalist Daniel Golden. Jared’s test scores were below Ivy League standards, Golden wrote, citing an unnamed official at the yeshiva high school in northern New Jersey that Jared attended. But he had powerful people vouch for him.

    Then-Sen. Edward Kennedy (D-Mass.) made a call to the Harvard admissions staff on Kushner’s behalf — at the urging of a Democratic senator from New Jersey, Frank Lautenberg, who had received more than $100,000 in donations from Charles Kushner, according to the book.
    Jared Kushner was admitted.

    Risa Heller, a spokeswoman for Kushner Companies, said the suggestion that Jared Kushner’s acceptance was connected to his father’s gift to the school “is and always has been false.”
    “Jared Kushner was an honors student in high school, played on the hockey, basketball and debate teams. He graduated from Harvard with Honors,” she said in a statement.
    Kushner’s parents, she said, have donated more than $100 million to universities, hospitals and other charitable causes, she said.

    Rabbi Hirschy Zarchi, founder of Chabad House at Harvard, who met Jared his freshman year and became close to the Kushner family, said that as a student Jared was devoted to his family and his Orthodox Jewish faith and had the mature bearing of a graduate student. He made the trip home to New Jersey to celebrate the smallest family milestones and celebrations.
    “His exceptional respect, devotion and love for his family always came across,” Zarchi said.

    A family embroiled in scandal

    That loyalty appears to have been tested as the Kushner family became embroiled in scandal.
    By the time Jared finished his studies at Harvard, nearly $90,000 had been donated to state and federal campaigns in his name, records show, almost entirely to Democrats. The giving spree pulled Jared into the crosshairs of the Federal Election Commission.
    Just before he began his senior year in 2002, a letter from the FEC addressed to the younger Kushner arrived at his New Jersey home, a 7,300-square-foot mansion in a wealthy suburban neighborhood. In the letter, federal regulators wrote that Jared Kushner appeared to have broken campaign-finance laws by contributing more than they allowed.

    Jared Kushner, who was later cleared when the donations were found to have come from his father, declined to comment on anything related to the investigation involving his father.
    Records also show that Kushner was among 15 people, whose names had appeared on checks for campaign contributions signed by his father, who were issued subpoenas by the FEC after initially not answering questions about donations.

    Although Kushner eventually cooperated with FEC investigators, it is not clear if he did so with prosecutors in the subsequent criminal investigation. In any event, the scandal does not appear to have damaged his relationship with his father.

    That was not true of other family members, including an uncle who had been cooperating with federal prosecutors; Charles Kushner apparently did not take lightly to the betrayal, records show. He paid a prostitute $10,000 to seduce his brother-in-law in a hotel room set up with hidden cameras to record the rendezvous. He later instructed a private detective to mail the tape to his sister as a warning — he wanted it to arrive at her house shortly before a family party, records show.

    Instead, she took the tape to the FBI, leading to Kushner’s arrest.
    Kushner learned of the arrest when his father called him on a July morning. Jared was on his way to an internship in the Manhattan District Attorney’s Office, he told New York magazine in a 2009 interview. In the interview, he sounded more angry that the tape had been deemed illegal than he was about his father’s role in producing it.

    “They’re going to arrest me today,” Charles Kushner told him.
    “For what?” Jared Kushner recalled asking. “Is it because of the tape? I thought your lawyers knew about that. I thought it’s not illegal.”
    “Apparently they’re saying that it is,” his father said.

    Charles Kushner decided not to fight. He pleaded guilty to making false statements to the FEC, witness tampering and tax evasion stemming from $6 million in political contributions and gifts mischaracterized as business expenses. Among the allegations were that he paid for an unnamed individual’s private school tuition out of company accounts and declared them charitable contributions on his tax returns, according to court documents.

    He was sentenced to two years in prison. His son stood by him, visiting most weekends and insisting, as he still does, that his father’s prosecution was unjust.

    The flip side of loyalty

    If Charles Kushner taught his son deep loyalty, he may also have taught him its flip side, revenge — at the very least modeling that behavior with his decision to target his brother-in-law.
    For Jared Kushner, the evidence of whether he absorbed the lesson lies in his actions toward Chris Christie, the hard-charging federal prosecutor — and future governor of New Jersey, 2016 Republican presidential candidate and endorser of Donald Trump for president — who put the elder Kushner behind bars.

    A former law-enforcement source familiar with the nearly two-year criminal investigation who spoke on the condition of anonymity to discuss sensitive internal deliberations, said Christie, a Republican, took an unusual interest in the Kushner probe. “He was very hands-on in that case,” said the person, who described Christie as privately “gleeful” at the outcome. In court filings, Christie’s office described Charles Kushner’s actions as “evil.”
    But the network of politicians Kushner had cultivated also whispered about another possibility: that Christie had targeted a major Democratic donor for political reasons.
    “I think a lot of people would say his prosecutions were political in nature,” former New Jersey governor Richard Codey, a longtime Kushner ally who is now a Democratic state senator, said of Christie.

    A spokesman for Christie told The Washington Post that the 135 corruption convictions he won as U.S. attorney in New Jersey were “not because of politics, but because all the individuals he charged were guilty.”

    Yet the arrest changed Jared Kushner’s career path: He no longer wanted to be a prosecutor.
    “Seeing my father’s situation, I felt what happened was obviously unjust in terms of the way they pursued him,” he told the Real Deal in 2014. “I just never wanted to be on the other side of that and cause pain to the families I was doing that to, whether right or wrong.”
    Instead, Jared Kushner took the family business across the Hudson River into Manhattan with audacious real estate acquisitions, selling his family’s portfolio of apartments in New Jersey and, in 2007, buying an office tower on Fifth Avenue, about three blocks south of Trump Tower.

    He also purchased the New York Observer, a Manhattan newspaper. Ross Barkan, a reporter there from 2013 to 2016, left the paper, he said in an interview, because the line between the Trump campaign and the paper’s editorial decisions had become “fuzzy.”

    For instance, Barkan said, the Observer published two stories that appeared to target Trump’s enemies at the time — one taking on New York Attorney General Eric Schneiderman after he sued Trump University, and another critical of Sen. Marco Rubio (Fla.) during the Republican primaries.
    Kushner also made a business decision that would give him a toehold in the world of New Jersey politics that Christie was about to inhabit. He bought a successful political gossip website called PolitickerNJ.com that was run by an anonymous blogger. Later, when Christie was running for governor in 2009, he suggested that Kushner was using the website to damage him.
    “It’s a Kushner-owned enterprise,” Christie said. “And I don’t think I’ll be getting Charles Kushner’s family’s vote come November.”

    Christie became governor of New Jersey. He was an early favorite for the Republican presidential nomination this year until Trump’s remarkable ascent. Christie dropped out and supported Trump, putting him in a position to get a key role in a Trump administration. But Kushner now was in a position to influence the fate of the man who had put his father behind bars.

    Speculation has swirled that Kushner helped convince Trump not to pick Christie to be his vice president. Friends said privately that Kushner was smart enough not to have made his argument a personal one. The residual damage from a Christie scandal that became known as Bridgegate was enough reason, they said.

    At it turned out, the anonymous blogger whose website Kushner had acquired was at the center of the scandal. The Christie administration had recruited David Wildstein away from Kushner’s website for a job as an executive at the Port Authority of New York and New Jersey, an agency that runs the region’s bridges and airports.

    Wildstein and two other Christie aides were convicted this year of closing lanes to the George Washington Bridge in an act of political revenge. The mayor of a town at the foot of the bridge had not endorsed Christie, and the lane closures choked the town with crippling traffic.
    Back on Dec. 7, 2013, the day after Wildstein resigned from the Port Authority amid growing evidence that he had ordered the lane closures, Kushner got in touch with him. In an email obtained by The Post, Kushner drew a parallel between Wildstein and his father, who had also resigned as a Port Authority commissioner in 2003 as questions began to percolate about Kushner’s campaign contributions.

    “Just wanted you to know that I am thinking of you and wishing the best. For what it’s worth, I thought the move you pulled was kind of badass,” Kushner wrote.
    Heller, the Kushner Companies spokeswoman, said this week that the message was a “poorly worded way of Jared trying to cheer up an old friend.”

    Alice Crites and Robert Costa contributed to this reporting.

    https://www.washingtonpost.com/poli...3ebab6bcdd3_story.html?utm_term=.44eaaf95b790
     
    Last edited: Dec 2, 2016
  16. nvha

    nvha MBA family

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    Kỳ I: Big Data đã giúp Trump chiến thắng trong cuộc Bầu cử Mỹ
    13/12/2016 15:02 GMT+7

    Tờ tạp chí "Das Magazin" của Thụy sĩ xuất bản bằng tiếng Đức một cuộc điều tra về cách thức mà các nhà khoa học về dữ liệu (data scientist) kết hợp các công ty phân tích dữ liệu lớn (data analytics) sử dụng công nghệ quảng cáo tùy biến theo cá nhân trên Facebook gây ảnh hưởng đến cuộc bầu cử tổng thống Hoa Kỳ. Nhiều nhà báo trên thế giới đã gọi bài báo điều tra này là “text of the year” (bài viết của năm) về tác dụng của bigdata vào đời sống trong đó chúng ta có thể thấy các công nghệ mới nhất về dữ liệu lớn (Big Data), khoa học hành vi, và các phần mềm gián điệp đang len lỏi vàođời sống hàng ngày của mỗi người chúng ta. Phần lớn bài biết dưới đây rút ra từ bài báo: “Ich habe nur gezeigt, dass es die Bombe gibt” đăng ngày 3 tháng 12 năm 2016 trên Das Magazin.

    Ngày 9 tháng 11 năm 2016, theo như Das Magazin thì một quả bom đã phát nổ: Donald Trump đã được bầu làm tổng thống Mỹ, bất chấp dự báo của các nhà xã hội học.

    Cũng ngày hôm đó, một công ty nhỏ chưa ai nghe tên ở London đã gửi đi thông cáo báo chí: “chúng tôi lấy làm kinh ngạc vì phương pháp truyền thông dựa trên dữ liệu có tính cách mạng của mình đã góp phần đáng kế vào chiến thắng của Donald Trump”. Thông cáo được ký bởi một người tên là Alexander Nix, 41 tuổi, người Anh và lãnh đạo công ty Cambridge Analytica. Phương pháp cách mạng về truyền thông dựa trên dữ liệu (revolutionary approach to data-driven communications) này sử dụng kết quả công trình nghiên cứu của một nhà khoa học 34 tuổi tên là Michal Kosinski, chuyên gia hàng đầu trong lĩnh vực psychometrics – ngành tâm lý học dựa trên phân tích dữ liệu.


    Từ dữ liệu (data) đến phân tích dữ liệu (data analytics) và dữ liệu lớn (Big Data) thành những từ thời thượng (buzzword) nhưng Big Data nguy hiểm đến mức nào?

    Dữ liệu lớn là một thuật ngữ dựa trên một khái niệm rằng tất cả những gì ta làm, trên mạng hay offline, đều để lại dấu vết số. Mua hàng bằng thẻ tín dụng, tìm đường trên Google, dạo chơi với điện thoại trong túi, dùng thiết bị đeo (wearable device) để theo dõi sức khỏe, mức độ tập luyện đến việc nhấn like trên mạng xã hội: tất cả đều được lưu lại dưới dạng những dữ liệu và dữ liệu này rất lớn, khổng lồ theo thời gian. Một thời gian dài không ai hình dung có thể sử dụng các dữ liệu ấy làm gì. Cũng không ai biết Big Data sẽ là gì đối với nhân loại, hiểm họa lớn hay thành tựu vĩ đại? Nhưng từ 9/11 chúng ta đã biết câu trả lời. Đằng sau chiến dịch tranh cử của Trump trên mạng, và đằng sau chiến dịch ủng hộ Brexit là cùng một công ty chuyên nghiên cứu Big Data: Cambridge Analytica (CA) dưới sự lãnh đạo của giám đốc AlexanderNix. Tuy nhiên, dữ liệu chỉ là dữ liệu nếu nó không được phân tích và được mô hình hóa. Có rất nhiều mô hình để phân tích dữ liệu nhưng công ty CA đã sử dụng phương pháp đo nhân cách (psychometrics), đôi khi gọi là đồ hình nhân cách (psychography) do Michal Kosinski,nhà khoa học hàng đầu về dữ liệu và là phó giám đốc Trung tâm đo nhân cách (Psychometrics Centre) thuộc trường Đại học Cambridge.

    Trong tâm lý học hiện đại, để đo nhân cách thì phổ biến nhất là dùng phương pháp OCEAN (từ chữ cái đầu của 5 chiều đo). Vào những năm 1980, các nhà tâm lý học đã chứng minh là mỗi người có thể được đo bằng 5 chiều. Đó là Big Five: độ mở đối với trải nghiệm (Openness), sự ý thức (Conscientiousness), sự hướng ngoại (Extraversion), sự dễ chịu (Agreeableness) và sự nhạy cảm (Neuroticism). Trên cơ sở những số đo ấy có thể hiểu chính xác bạn là ai, bạn có mong muốn và nỗi sợ hãi nào, và bạn sẽ hành xử như thế nào. Trở ngại chính là việc thu thập dữ liệu vì để hiểu được một người, cần phải điền bảng hỏi khổng lồ và cần thu thập dữ liệu rất lớn. Sự phát triển của khoa học dữ liệu và dữ liệu lớn đã góp phần giải quyết thành công những trở ngại mà phương pháp OCEAN đặt ra.

    Tại Trung tâm đo nhân cách, Kosinski và các cộng sự đã phát triển một ứng dụng trên facebook là MyPersonality trong đó người dùng trả lời các bảng các câu hỏi để biết nhân cách của mình và họ đã thu thập được dữ liệu của hàng triệu người dùng Facebook.

    Kosinski và nhóm nghiên cứu các hành động của họ trên Facebook như like và re-post, và giới tính, độ tuổi và nơi ở. Qua đó nhóm thu được các mối tương quan (correlation). Từ kỹ thuật phân tích các dữ liệu trên mạng có thể cho ra những kết luận bất ngờ. Ví dụ, nếu một người đàn ông là fan của page mỹ phẩm MAC, thì khả năng lớn là đồng tính; Ngược lại, anh ta rất nam tính nếu là fan của ban nhạc hip hop Wu-Tang Clan ở New York. Fan của Lady Gaga khả năng lớn là người hướng ngoại, còn kẻ hay like các post mang tính triết lý thì hướng nội.


    Công ty Cambridge Analytica đã phát triển một giải pháp toàn diện cho phép biết tính cách của mỗi công dân Mỹ, những người có quyền bỏ phiếu.
    Công trình nghiên cứu của Kosinski không chỉ cho phép lập chân dung tâm lý của người dùng, mà còn cho phép tìm kiếm những người có chân dung cần thiết. Ví dụ như có thể tìm những ông bố lo lắng, những kẻ hướng nội giận dữ, hay những người ngả theo đảng Dân chủ nhưng còn lưỡng lự bỏ phiếu. Về bản chất, đó là hệ thống tìm kiếm con người với những đặc tính cần tìm hiểu.

    Vào năm 2014, một công ty quan tâm đến phương pháp của Kosinski đề nghị thực hiện một dự án sử dụng psychometrics để phân tích 10 triệu người dùng Mỹ trên Facebook nhưng phân tích người dùng làm gì và tên công ty thì không nói viện cớ bảo mật thông tin. Lúc đầu Kosinski đồng ý nhưng rồi anh lại trì hoãn. Cuối cùng, tên công ty được tiết lộ là SCL (Strategic Communications Laboratories). Và trên website của công ty giới thiệu: “chúng tôi là công ty toàn cầu chuyên về quản lý các chiến dịch tranh cử”. Công ty SCL này là công ty mẹ của công ty Cambridge Analytica, công ty thực hiện chiến dịch online cho Brexit và Trump.

    Tháng 11 năm 2015 lãnh tụ phái cấp tiến ủng hộ Brexit Nigel Farage tuyên bố là website của của ông ta bắt đầu làm việc với một công ty chuyên về Big Data, chính là CA. Năng lực cốt lõi của công ty này là tiếp thị chính trị (political marketing) kiểu mới, còn được gọi là microtargeting, trên nền tảng phương pháp OCEAN.

    (Còn nữa)

    Đào Trung Thành
    Kỳ II: Big Data nguy hiểm tới mức nào?


    http://vietnamnet.vn/vn/cong-nghe/u...hang-trong-cuoc-bau-cu-my-the-nao-346184.html

    Kỳ II: Big Data nguy hiểm tới mức nào?
    14/12/2016 08:00 GMT+7

    Tháng 6 năm 2016, Trump đã thuê các chuyên gia Cambridge Analytica, nhiều người ở Washington cười cợt. Họ chắc chắn rằng Trump sẽ không bao giờ có thể hợp tác với các chuyên gia tư vấn nước ngoài của mình một cách hiệu quả. Tuy nhiên, họ đã sai.



    Khác với Omama được xem là Tổng thống của mạng xã hội thì D. Trump vẫn được người ta cười nhạo về việc khả năng rất kém của ông trong việc sử dụng các công cụ công nghệ thông tin, mạng xã hội. Trợ lý của Trump tiết lộ, thậm chí ông còn không dùng email. Bản thân cô trợ lý này đã dạy ông dùng điện thoại, và từ đó ông dùng nó để đổ dòng suy nghĩ của mình lên Twitter.

    Công ty Cambridge Analytica đã phát triển một giải pháp toàn diện cho phép biết tính cách của mỗi công dân Mỹ, những người có quyền bỏ phiếu. Giải pháp tiếp thị chính trị tuyệt vời của Cambridge Analytica dựa trên ba chiến thuật chính:

    • Phân tích hành vi (Behavioral analysis) theo mô hình OCEAN.

    • Nghiên cứu cẩn thận Big Data.

    • Quảng cáo nhắm mục tiêu (Targeted advertising).

    Quảng cáo nhắm mục tiêu có nghĩa là quảng cáo cá nhân hóa, được xây dựng theo tính cách của từng cá thể người dùng dựa trên nhân tính của họ xác định từ mô hình OCEAN.

    Như vậy, bản thân Big Data chỉ là một nguồn thông tin lớn, một mỏ vàng cần được khai thác nhưng khái thác thế nào hiệu quả lại là một vấn đề khác. Cần có một mô hình phân tích hành vi tiên tiến. Và khi biết một người có tính cách gì và đang lưỡng lự trong việc bỏ phiếu thì cần phải dùng thông điệp thế nào cho phù hợp.


    Bản chất đầy mâu thuẫn của Trump, tính phi nguyên tắc và hệ quả là số lượng lớn các loại thông điệp khác nhau bỗng trở nên hữu ích cho ông ta: mỗi cử tri nhận được một thông điệp riêng. “Trump hành xử như một thuật toán cơ hội lý tưởng, hoàn toàn chỉ dựa trên phản ứng của công chúng” – nhà toán học Cathy O’Neil nhận xét vào tháng tám. Vào ngày tranh luận thứ ba giữa Trump và Clinton, đội của Trump đã gửi vào mạng xã hội (chủ yếu là Facebook) hơn 175 nghìn thông điệp. Chúng chỉ khác nhau ở những chi tiết rất nhỏ, nhằm phù hợp nhất với tâm lý của người nhận cụ thể: tiêu đề, tiêu đề phụ, màu nền, ảnh và video. Cách làm tỉ mỉ như vậy khiến cho thông điệp nhận được sự đồng cảm của những nhóm cư dân nhỏ nhất, như Nix giải thích cho Das Magazine: “Bằng cách đó chúng tôi có thể vươn đến tận làng, khu phố hay ngôi nhà cần thiết, thậm chí là từng người”.

    Chiến thắng của ứng cử viên Cộng hòa Donald Trump trước đối thủ đến từ Đảng Dân chủ Hillary Clinton được cho là một chiến thắng ít tốn kém và nhiều hiệu quả nhất. Theo hãng tin Reuters, Trump tiêu tốn dưới 5 USD cho mỗi lá phiếu bầu cho ông, thấp hơn nhiều so với chi phí của bà Clinton.

    Theo số liệu mới nhất từ Ủy ban Bầu cử Liên bang, Trump đã huy động được tổng số 270 triệu USD kể từ khi bắt đầu chiến dịch tranh cử vào tháng 6/2015. Trong khi Hillary Clinton huy động được 521 triệu USD, gấp đôi khoản tiền mà Trump huy động được. Nhưng 237 triệu chi phí cho truyền hình, 53 triệu cho nhân viên và các tình nguyện viên hỗ trợ tranh cử, một khoản tiền lớn cho các báo, đài, các phương tiện truyền thống. Đó cũng là lý giải tại sao khi xem các phương tiện truyền thống như báo, đài thì có vẻ Hillary được đánh giá cao hơn Trump.

    Một hãng Phân tích dữ liệu lớn khác là mediaQuant đánh giá các chương trình truyền thông thông tin cho cuộc bầu cử 2016 của Trump mang lại một giá trị định lượng tương đương 5 tỷ USD cho ông này, so với bà Clinton là 3.5 tỷ hay tỉ lệ 58% so với 42%. Thực tế ông Trump đã chiếm được 279 phiếu bầu đại cử tri so với 228 phiếu bầu của bà Hillary, tỷ lệ 52% so với 48%.

    Khó có thể nói xã hội Mỹ bị tác động đến mức nào bởi các chuyên gia của Trump tại một thời điểm cụ thể, vì họ không sử dụng các kênh trung ương mà dùng mạng xã hội và truyền hình cáp.

    Từ tháng 7/2016 các tình nguyện viên của Trump đã nhận được app cho phép biết được thiên kiến chính trị và loại nhân cách của cư dân nhà này hay nhà khác. Theo đó, những tình nguyện viên – tuyên truyền viên điều chỉnh hội thoại của mình với người dân. Phản hồi của người dân lại được họ ghi ngược vào app đó, và dữ liệu chuyển thẳng về trung tâm phân tích của CA.

    Công ty xác định ra 32 loại tính cách tâm lý của dân Mỹ, tập trung ở 17 bang. Và như Kosinski đã phát hiện, rằng đàn ông thích mỹ phẩm MAC thì hầu như chắc chắn là đồng tính, CA chứng minh rằng những kẻ trung thành với ô tô Mỹ hẳn nhiên là ngả theo Trump. Hơn nữa, những phát kiến như vậy giúp bản thân Trump hiểu những thông điệp nào dùng ở đâu thì tốt nhất. Quyết định của đại bản doanh về việc tập trung vào Michigan và Wisconsin vào những tuần cuối cùng là dựa trên phân tích dữ liệu.

    Ngoài chiếm thắng Nigel Farage ở Brexit và của Trump trong cuộc Bầu cử Mỹ 2016 thì người chiến thắng là công ty CA với khoản thù lao 15 triệu USD nhận được từ chiến dịch của Trump. Marion Maréchal-Le Pen, một nhân vật cấp tiến đồng thời là cháu của thủ lĩnh đảng “Mặt trận dân tộc” Pháp cũng đã mừng vui loan báo về quan hệ hợp tác với hãng. Theo Nix, công ty đang được rất nhiều khách hàng trên thế giới quan tâm, có cả từ Thụy sỹ và Đức.

    Nhu cầu phân tích và khai thác những nguồn dữ liệu lớn và phức tạp trong các hoạt động của con người và các tổ chức trong những năm gần đây đang trở nên cấp bách. Sở dĩ vậy vì ta đang có nhiều dữ liệu quanh mình hơn bao giờ hết và nếu dùng được chúng sẽ đưa ra được các quyết định đúng đắn hơn, những hiểu biết chính xác, những khám phá quan trọng. Khoa học phân tích dữ liệu (data science or data analytics) gần đây trở thành một lĩnh vực sôi động của công nghệ thông tin, có ảnh hưởng sâu sắc tới mọi lĩnh vực hoạt động của con người, đặc biệt trong kinh doanh.

    Theo nghiên cứu của các nhà kinh tế, đến năm 2018, Mỹ sẽ cần 140.000 đến 190.000 người có kỹ năng phân tích chuyên sâu cũng như 1,5 triệu nhà quản lý và phân tích trong lĩnh vực “dữ liệu lớn” (Big Data).Nắm chắc và biết sử dụng khoa học phân tích dữ liệu chính là chìa khoá của công việc và thành công trong những thập kỷ tới đây, như ý kiến nêu trong Harvard Business Review: “Khoa học dữ liệu là công việc hấp dẫn nhất trong thế kỷ 21” (“Data scientist: the sexiest job of the 21st century”).

    Đào Trung Thành
     
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    https://www.nytimes.com/2016/11/15/...edge-fund-stars-data-crunching-computers.html

    The Next Generation of Hedge Fund Stars: Data-Crunching Computers
    By ALEXANDRA STEVENSONNOV. 14, 2016

    photo:
    https://static01.nyt.com/images/2016/11/15/your-money/15DB-Quant/15DB-Quant-master768.jpg
    Credit Christina Chung


    Every five minutes a satellite captures images of China’s biggest cities from space. Thousands of miles away in California, a computer looks at the shadows of the buildings in the images and draws a conclusion: China’s real estate boom is slowing.

    Traders at BlackRock, the money management giant, then use the data to help choose whether to buy or sell the stocks of Chinese developers. “The machine is able to deal with some of the very complex decisions,” said Jeff Shen, co-chief investment officer at Scientific Active Equity, BlackRock’s quantitative trading, or quant, arm in San Francisco.

    The future star of the hedge fund industry is not the next William A. Ackman, Carl C. Icahn or George Soros. Rather, it is a computer like the one at Scientific Active Equity, which sifts through data like satellite images from China every day.

    Math whizzes have long dominated the hedge fund universe, but until recently, only a handful of well-known firms like Renaissance Technologies, the D. E. Shaw Group and AQR Capital Management used mathematical models and computers to plot out trading techniques. And other than the occasional blowup, as when Long-Term Capital Management went bust in spectacular fashion in 1998 after its models failed to factor in the possibility of a Russian government debt default, the world of quantitative trading has remained out of the limelight.


    Now, as the financial world faces dismal returns and investor criticism over high fees, hedge fund managers are turning to computers to make decisions that used to be left to humans about which stocks to buy and sell, for example. Celebrity investors like Mr. Ackman are slowly being replaced by teams of Ph.D. holders who develop mathematical equations for trading and systems to scrape huge sets of data for patterns.

    For instance, the billionaire investor Paul Tudor Jones, who runs the Tudor Investment Corporation, needed to make changes after investors pulled more than $2 billion from his firm, which now manages $10.6 billion. So he cut staff and brought in mathematicians and scientists to build up an analytical team. Other hedge funds have made similar moves.

    “We’re seeing a kind of bifurcation among hedge funds, with some moving towards more quant-driven or automated style, while others are turning towards a more ‘long-only’ model, where they are judged on longer-term investment performance,” said Craig Coben, global head of equity capital markets at Bank of America Merrill Lynch.

    Big institutional investors are also diverting more money to the hedge fund firms that use computer-driven hedge fund strategies.

    While the hedge fund industry in recent months has suffered the biggest quarterly outflow since the financial crisis, investors continue to allocate money to hedge funds that use computer-driven strategies. Investors have put $7.9 billion into quantitative hedge funds this year, and the universe of hedge funds devoted to these strategies has more than doubled, to $900 billion from $408 billion seven years ago, according to Hedge Fund Research.

    More broadly, money flowing out of the hedge fund industry as a whole comes at a time when performance has been disappointing. The Hedge Fund Research Composite Index, the broadest gauge of hedge fund performance, has lagged the Standard & Poor’s 500-stock index this year, gaining 3.56 percent through the end of October compared with the index’s 4 percent gain over the same period, accounting for reinvested dividends.


    “Frankly, we expect to see assets move from human managers to machine managers,” Tony James, chief operating officer of Blackstone, told investors earlier this year. The Blackstone Alternative Asset Management arm, which manages $70 billion in hedge fund investments, is a big investor in quant-related hedge fund firms and has put billions of dollars toward these firms in recent years. The division now has $10 billion invested in quant-dedicated hedge fund firms, according to one person with direct knowledge of the firm; it has not publicly released the number.

    Some industry observers warn that hedge funds building out new quant arms may simply be trying to capture investor money that is flowing into the strategy. But veterans in the quant world see the trend as an indication that the industry is finally catching up to other industries in which technology has disrupted businesses.

    “The portfolio investment industry has been relatively late to adopting technology,” said Philippe Jordan, the president of Capital Fund Management, a 25-year-old quant hedge fund firm that manages $6.9 billion. “Finance is deeply conservative in nature,” he added.

    Capital Fund Management has 160 employees, including 40 scientists, most of whom hold Ph.D.s in physics; 75 employees are focused on information technology, 20 of which are in data management. Like other types of hedge funds, the firm has a research department. The only difference is that at Capital Fund Management, the analysts who conduct research approach the work more like academics, and ideas are peer-reviewed.

    With more investor money going toward firms that build models to trade on, there is some concern that these models will begin to look similar, potentially resulting in overcrowding. That could be a problem if there is a sudden event that drives everyone to start selling at the same time, something that happened during the “quant crunch” in the summer of 2007. Over one week in August, AQR Capital Management, D. E. Shaw and Renaissance Technologies were all hit with huge losses as the housing market began to show signs of collapse. With similar models and huge positions, the losses each firm suffered were amplified.

    Mr. Shen at BlackRock thinks there are fewer risks this time around. “The diversity of data allows people to do a lot of different things,” he said.

    Back in his San Francisco office, employees are using computers to create models for parsing the scripts from corporate quarterly financial earnings calls. At times, these computers are thinking faster than those who are using them.

    “The machines are certainly doing more and more, so humans should worry there is a human replacement factor,” Mr. Shen said.

    “But,” he added, “ultimately I do think it is the human who creates the machine and these techniques.”

    A version of this article appears in print on November 15, 2016, on Page F6 of the New York edition with the headline: Rise of Computers.
     

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