Existem empresas especializadas neste tipo de marketing que combinam informação pessoal para desenvolverem perfis com objectivos comerciais.
Agora, que os casos de espionagem já chegaram aos dirigentes europeus, esta questão do armazenamento da "BigData" está a levantar novamente questões sobre a sua utilização e os Direitos Individuais de Privacidade ...Os primeiros passos estão a ser desenvolvidos no Parlamento Europeu, para a formulação de uma lei afim de proteger os dados pessoais e regulamentar a sua utilização. Dados que ficam armazenados eternamente, por exemplo, no Facebook.
Entretanto talvez não saiba, mas você e o seu perfil pessoal já tem o valor comercial, no mínimo, de 1.000 euros ...
Entretanto ficam as referências de alguns livros para os interessados / atentos / alarmados ...
António Sérgio Rosa de Carvalho
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"My God, Mae thought," The Circle begins:
"It's heaven."
Mae is a twentysomething naif, journeying into the brave new
world of a vast info-tech enterprise – the Circle – which has amalgamated the
functions of Microsoft, Google, Apple, YouTube, Facebook and Twitter into a
unified corporation with seemingly beautiful ideals. Customers buy into the
Circle with a single identity, their TruYou, which grants them access to every
operation and social connection conceivable in the digital universe.
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The Circle by Dave Eggers –
review
Dave Eggers is at his fluent
best in tackling the notion of privacy in an online age
Alexander Linklater
The Observer, Saturday 12 October 2013 / http://www.theguardian.com/books/2013/oct/12/the-circle-dave-eggers-review
 |
Google’s London HQ: ‘Eggers's innovative optimism has paused
at the frontiers of social media, looking forward to an encroaching nightmare.'
Photograph: Penson/ Rex Features
|
It wasn't long ago that Dave Eggers appeared at the cutting
edge of American literature, breaking new ground with his meta-memoir, A
Heartbreaking Work of Staggering Genius, while his McSweeney's publishing
enterprises spawned magazine offshoots in online, DVD and app formats. His
pioneering literacy programmes and human rights themes have kept him as socially
engaged and culturally connected as any novelist currently writing. Yet with
his new novel, Eggers's innovative optimism appears to have paused at the
frontiers of social media, looking forward, not to a world of open potential,
but to an encroaching nightmare.
"My God, Mae thought," The Circle begins:
"It's heaven."
Mae is a twentysomething naif, journeying into the brave new
world of a vast info-tech enterprise – the Circle – which has amalgamated the
functions of Microsoft, Google, Apple, YouTube, Facebook and Twitter into a
unified corporation with seemingly beautiful ideals. Customers buy into the
Circle with a single identity, their TruYou, which grants them access to every
operation and social connection conceivable in the digital universe.
Deep within the organisation, an inner circle of bosses –
the Gang of 40 (lest readers miss the looming Maoist analogy) – fuses
technological and human rights idealism into a vision of perfect democracy,
transparency and knowledge; one with which they aim to unite private and public
spheres and perfect the operations of government.
The Orwellian references scarcely need spelling out. But the
Circle's central slogan – "All that happens must be known" – springs
less from political ideology than the kind of callow info-utopianism espoused
by Julian Assange (who gets a sardonically ambivalent mention in the novel), or
the dreams of social connectivity realised by Mark Zuckerberg (lightly
referenced in the Circle's founder, Ty Gospodinov).
Mae rises through the ultra-meritocratic ranks of the
company to emerge as its leading promotional light, devising some of the key
maxims of its credo: "Secrets are lies"; "Sharing is
caring"; "Privacy is theft".
Along the way, she is offered – and ignores – increasingly
obvious glimpses of what is wrong with all this. One night, she takes an
illicit kayak ride to an uninhabited island, momentarily liberated from her
hyper-connected world, only to be forced into a session of self-criticism
before the assembled staff of the Circle. On a Steve Jobs-style company
platform, Mae confesses to her crime – failing to stream her every private
experience for the benefit of the community.
In atonement, she turns her life into a model of relentless
visibility and her family's into a version of The Truman Show. An ex-boyfriend
provides plodding speeches about the nightmare she is fostering, to which she
responds by pursuing him through online networks as he seeks to escape to some
last, non-mediated corner of wilderness.
It's not clear whether The Circle is intended as a satire of
the present or a dystopian vision of the near future. Eggers's writing is so
fluent, his ventriloquism of tech-world dialect so light, his denouement so
enjoyably inevitable that you forgive the thin characterisation and implausibility
of what is really a clever concept novel. As soon as the novel appeared,
corners of cultural chatter-sphere lit up in typical, fissiparous mode:
commentators both "smiling" and "frowning" (to borrow
Circle terms); some happy to take the ride; others perplexed that an author as
hip as Eggers should be conservative on social media; others still mocking the
daft idea that the infinitely disputatious internet might be subsumed into any
unified programme.
It would be daft to compare today's online corporations, or
the current surveillance scare, to the real-world totalitarian forces (Nazi,
Stalinist, Maoist) evoked by Nineteen Eighty-Four. But Eggers's novel doesn't
demand to be read so weightily. Instead, it's a nicely caricatured vision of
hi-tech, soft-touch totalitarianism, a narrative thought experiment in which
it's liberal idealism (rather than the fascist or communist kind) that reaches
a final solution.
There may be some self-mockery in this. Eggers, in his 40s,
is no longer a young innovator. He may in fact represent the last generation of
major American writers to have emerged entirely from the old values of print.
The Circle may be what social media idealists look like, viewed through the
form of the traditional, sceptical novel.
June 10, 2013
Watched by the Web: Surveillance Is Reborn
By MICHIKO KAKUTANI / http://www.nytimes.com/2013/06/11/books/big-data-by-viktor-mayer-schonberger-and-kenneth-cukier.html?_r=0&pagewanted=print
Google does it. Amazon does it. Walmart does it. And, as
news reports last week made clear, the United States government does it.
Does what? Uses “big data” analysis of the swelling flood of
data that is being generated and stored about virtually every aspect of our
lives to identify patterns of behavior and make correlations and predictive
assessments.
Amazon uses customer data to give us recommendations based
on our previous purchases. Google uses our search data and other information it
collects to sell ads and to fuel a host of other services and products.
The National Security Agency, a news article in The Guardian
revealed last week, is collecting the phone records of millions of American
customers of Verizon — “indiscriminately and in bulk” and “regardless of
whether they are suspected of any wrongdoing” — under a secret court order.
Under another surveillance program called Prism, The Guardian and The
Washington Post reported, the agency has been collecting data from e-mails,
audio and video chats, photos, documents and logins, from leading Internet companies
like Microsoft, Yahoo, Google, Facebook and Apple, to track foreign targets.
Why spread such a huge net in search of a handful of
terrorist suspects? Why vacuum up data so indiscriminately? “If you’re looking
for a needle in the haystack, you need a haystack,” Jeremy Bash, chief of staff
to Leon E. Panetta, the former director of the Central Intelligence Agency and
defense secretary, said on Friday.
In “Big Data,” their illuminating and very timely book,
Viktor Mayer-Schönberger, a professor of Internet governance and regulation at
the Oxford Internet Institute at Oxford University, and Kenneth Cukier, the
data editor for The Economist, argue that the nature of surveillance has
changed.
“In the spirit of Google or Facebook,” they write, “the new
thinking is that people are the sum of their social relationships, online
interactions and connections with content. In order to fully investigate an
individual, analysts need to look at the widest possible penumbra of data that
surrounds the person — not just whom they know, but whom those people know too,
and so on.”
Mr. Cukier and Mr. Mayer-Schönberger argue that big data
analytics are revolutionizing the way we see and process the world — they even
compare its consequences to those of the Gutenberg printing press. And in this
volume they give readers a fascinating — and sometimes alarming — survey of big
data’s growing effect on just about everything: business, government, science
and medicine, privacy and even on the way we think. Notions of causality, they
say, will increasingly give way to correlation as we try to make sense of
patterns.
Data is growing incredibly fast — by one account, it is more
than doubling every two years — and the authors of this book argue that as
storage costs plummet and algorithms improve, data-crunching techniques, once
available only to spy agencies, research labs and gigantic companies, are
becoming increasingly democratized.
Big data has given birth to an array of new companies and
has helped existing companies boost customer service and find new synergies.
Before a hurricane, Walmart learned, sales of Pop-Tarts increased, along with
sales of flashlights, and so stores began stocking boxes of Pop-Tarts next to
the hurricane supplies “to make life easier for customers” while boosting
sales. UPS, the authors report, has fitted its trucks with sensors and GPS so
that it can monitor employees, optimize route itineraries and know when to
perform preventive vehicle maintenance.
Baseball teams like Billy Beane’s Oakland A’s (immortalized
in Michael Lewis’s best-seller “Moneyball”) have embraced new number-crunching
approaches to scouting players with remarkable success. The 2012 Obama campaign
used sophisticated data analysis to build a formidable political machine for
identifying supporters and getting out the vote. And New York City has used
data analytics to find new efficiencies in everything from disaster response,
to identifying stores selling bootleg cigarettes, to steering overburdened
housing inspectors directly to buildings most in need of their attention. In
the years to come, Mr. Mayer-Schönberger and Mr. Cukier contend, big data will
increasingly become “part of the solution to pressing global problems like
addressing climate change, eradicating disease and fostering good governance
and economic development.”
There is, of course, a dark side to big data, and the
authors provide an astute analysis of the dangers they foresee. Privacy has
become much more difficult to protect, especially with old strategies —
“individual notice and consent, opting out and anonymization” — losing
effectiveness or becoming completely beside the point.
“The ability to capture personal data is often built deep
into the tools we use every day, from Web sites to smartphone apps,” the
authors write. And given the myriad ways data can be reused, repurposed and
sold to other companies, it’s often impossible for users to give informed
consent to “innovative secondary uses” that haven’t even been imagined when the
data was first collected.
The second danger Mr. Cukier and Mr. Mayer-Schönberger worry
about sounds like a scenario from the sci-fi movie “Minority Report,” in which
predictions seem so accurate that people can be arrested for crimes before they
are committed. In the real near future, the authors suggest, big data analysis
(instead of the clairvoyant Pre-Cogs in that movie) may bring about a situation
“in which judgments of culpability are based on individualized predictions of
future behavior.”
Already, insurance companies and parole boards use
predictive analytics to help tabulate risk, and a growing number of places in
the United States, the authors of “Big Data” say, employ “predictive policing,”
crunching data “to select what streets, groups and individuals to subject to
extra scrutiny, simply because an algorithm pointed to them as more likely to
commit crime.”
Last week an NBC report noted that in so-called signature
drone strikes “the C.I.A. doesn’t necessarily know who it is killing”: in
signature strikes “intelligence officers and drone operators kill suspects
based on their patterns of behavior — but without positive identification.”
One problem with relying on predictions based on
probabilities of behavior, Mr. Mayer-Schönberger and Mr. Cukier argue, is that
it can negate “the very idea of the presumption of innocence.”
“If we hold people responsible for predicted future acts,
ones they may never commit,” they write, “we also deny that humans have a
capacity for moral choice.”
At the same time, they observe, big data exacerbates “a very
old problem: relying on the numbers when they are far more fallible than we
think.” They point to escalation of the Vietnam War under Robert S. McNamara
(who served as secretary of defense to Presidents John F. Kennedy and Lyndon B.
Johnson) as a case study in “data analysis gone awry”: a fierce advocate of
statistical analysis, McNamara relied on metrics like the body count to measure
the progress of the war, even though it became clear that Vietnam was more a
war of wills than of territory or numbers.
More recent failures of data analysis include the Wall
Street crash of 2008, which was accelerated by hugely complicated trading
schemes based upon mathematical algorithms. In his best-selling 2012 book, “The
Signal and the Noise,” the statistician Nate Silver, who writes the
FiveThirtyEight blog for The New York Times, pointed to failures in areas like
earthquake science, finance and biomedical research, arguing that “prediction
in the era of Big Data” has not been “going very well” (despite his own
successful forecasts in the fields of politics and baseball).
Also, as the computer scientist and musician Jaron Lanier
points out in his brilliant new book, “Who Owns the Future?,” there is a huge
difference between “scientific big data, like data about galaxy formation,
weather or flu outbreaks,” which with lots of hard work can be gathered and
mined, and “big data about people,” which, like all things human, remains
protean, contradictory and often unreliable.
To their credit, Mr. Cukier and Mr. Mayer-Schönberger
recognize the limitations of numbers. Though their book leaves the reader with
a keen appreciation of the tools that big data can provide in helping us
“quantify and understand the world,” it also warns us about falling prey to the
“dictatorship of data.”
“We must guard against overreliance on data,” they write,
“rather than repeat the error of Icarus, who adored his technical power of
flight but used it improperly and tumbled into the sea.”
Book Review: Predictive Analytics: The Power to
Predict Who Will Click, Buy, Lie or Die
Reviewed by Seewon Ryu, PhD / http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3633174/
Hospital captures daily large amounts of data about their
customers or patients, suppliers, and operations. Health insurance
organizations also have large claims data which called as big data-large pools
of data that can be captured, communicated, aggregated, stored, and analyzed
[1]. We can analyze big health data to detect signals that is useful for
patients and healthcare service management, although the data has quality
problems. It is increasingly the case that healthcare innovation and growth
could take place with predictions analyzing big data [1].
I introduce the book by referring book description and
author's interview with amazon.com.
The author makes the how and why of predictive analytics
(aka PA) understandable and captivating in various fields of big data through
the book. Although it is not targeted only for researcher or stakeholders in
healthcare area, the book would be useful for us because it is targeted from
the small to large business owner, entrepreneurs, other PAers and us common
folk who want to further understand how computerized data research is analyzed
to predict specified outcomes and scenarios.
The book breaks down predictive analytics into seven
chapters. Cause and effect charts, illustrations along with a few comics and a
glossy centerfold divulge cases of predictions in advertising, finance,
healthcare, fraud, insurance, government, employment and personal venues. Some
topics discussed explain ways to increase consumer buying, limit bank loan
defaulting or paying off, anticipate employees quitting or clients dropping
cellphone coverage along with collecting online blogs, social networking and
risk information. Each chapter includes sections of "what's
predicted" and "what's done about it" to show the correlation of
PA and gathered data.
The author explains the art of predicting has five effects
that include:
1) A little prediction goes a long way,
2) Data is always predictive,
3) Induction is reasoning from detailed facts to general
principles,
4) Ensembles compensate for limitations, and
5) Persuasion can be predictable through outcomes.
Using the predictive models of large corporations such as
Target, Hewlett-Packard, Chase Bank, Netflix and Telenor along with John
Elder's stock market techniques, Jeopardy!'s Watson computer, Kaggle's
competitions, and Obama's second term presidential campaign, we can learn the
ins and outs of predicting through collecting and interpreting simple to
complex data.
By entrusting computers to make decisions, privacy concerns
are bought up, prejudices are determined and effects are manipulated when
machine learning becomes the translated voice of data. Artificial intelligence
can often limit overlearning, crowdsourcing and correlation pitfalls, but will
it be able to always correctly interpret language, emotions and feelings of
humans as it influences, persuades and molds us?
With even the book's title been subjected to analysis and
written sometimes humorously of the writer's own experience of stolen identity
and mockery of his geekness, it is an excellent source to any reader that sees
computers overtaking and controlling our every move as we continue to be
co-dependent on them as we happily benefit from increased information and
understanding, attain higher profits and enjoy an easier lifestyle through such
a conglomerate of PA data bytes.
In the book, Siegel explains real-world examples on how
organizations are turning big data into meaningful metrics. You have been
predicted-by companies, governments, law-enforcement, hospitals and
universities. Their computers say, "I knew you were going to do that!"
These institutions are seizing upon the power to predict whether you're going
to click, buy, lie, or die.
Predicting human behavior, by analyzing accumulated health
service data not so interested until now, would fortify healthcare. Accumulated
in large part as the by-product of routine tasks, data is the unsalted,
flavorless residue deposited en masse as organizations churn away. Surprise!
This heap of refuse is a gold mine. Big data embodies an extraordinary wealth
of experience from which to learn.
Siegel assert that predictive analytics is the science that
unleashes the power of data. With this technology, the computer literally
learns from data how to predict the future behavior of individuals. Also, he
describes that perfect prediction is not possible, but even lousy predictions
can be extremely valuable.
In the book, Eric Siegel reveals the power and perils of
prediction:
What unique form of mortgage risk Chase Bank predicted
before the recession.
Predicting which people will drop out of school, cancel a
subscription or get divorced before they are even aware of it themselves.
Why early retirement decreases life expectancy and
vegetarians miss fewer flights.
Five reasons organizations predict death, including one
health insurance company.
The way United States Bank and European wireless carrier
Telenor calculate how to most strongly influence each customer.
How companies ascertain untold, private truths-how Target
figures out you're pregnant and Hewlett-Packard deduces you're about to quit
your job.
How judges and parole boards rely on crime-predicting
computers to decide who stays in prison and who goes free.
What's predicted by the BBC, Citibank, ConEd, Facebook,
Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer,
and Wikipedia.
Siegel insist that predictive analytics as a truly
omnipresent science affects everyone, every day. Although largely unseen, it
drives millions of decisions, determining who to call, mail, investigate,
incarcerate, set up on a date, or medicate.
Predictive analytics transcends human perception. The final
chapter answers the riddle: What often happens to you that cannot be witnessed,
and that you can't even be sure has happened afterward-but that can be
predicted in advance? Whether you are a consumer of it-or consumed by it-get a
handle on the power of Predictive Analytics.
Have you ever heard that those who buy diapers are more
likely to buy beer too? How about that when stapler is sold at a retailer, it
is a good indicator that a company has hired a new employee? Would you be
surprised to learn that those using their credit card to buy a drink in a
"drinking establishment" are more likely to miss their credit card
payment?
A few other surprising facts readers will find in the book
include:
Clinical researchers predict divorce with 90 percent
accuracy.
Researchers employ machine learning to predict Hollywood
blockbusters and hit songs.
Life insurance companies predict age of death to decide
whether to approve and then price a policy application.
The state of Maryland uses predictive models to detect
inmates more at risk to be perpetrators or victims of murder.
University of Phoenix predicts which students are at risk of
failing a course and then target them with intervention measures.
To fully describe what predictive analytics are and how such
data is used, Siegel uses these facts and case studies such as big box
retailer, Target, predicting which female customers will have a baby in coming
months so that they can market relevant items to the expectant parents. When
you go to the grocery store or drug store and you get coupons printed that seem
to offer just the products you may be interested in, that is another every day
use of PA that the average consumer may encounter.
Siegel writes, "I was at Walgreens a few years ago, and
upon checkout, an attractive, colorful coupon spit out of the machine. The
product it hawked, pictured for all of my fellow shoppers to see, had the
potential to mortify. It was a coupon for Beano, a medication for flatulence."
The author had developed lactose intolerance, and the drug store was
recommending products that might be compatible with his medical condition. He
describes PA as benefiting both the consumer and the organization by empowering
it "with an entirely new form of competitive armament."
Siegel spends a lot of the book going through the different
predictive models and how the data is extracted and used. This type of data
mining is being used in numerous ways, not just for consumerism. He provides
examples from family and life, healthcare, crime fighting and fraud detection,
staff and employees and human language understanding. While the book answers so
many questions about how marketers know so much about consumers, it's not just
a book for marketers. It is one of those business books that cross many
departments including sales, research and development, human resources,
customer service among others.
There are many big data in healthcare area such as health
insurance corporations, hospitals, bioinformatics research institutions, and
disease management and control institutions. PA would contribute to intervene
in utilization of healthcare service, guide new way of supply of healthcare
service, enhance lifestyle, and prolong life expectancy. The book introduces 147
examples of predictive analysis including healthcare industry. These mini-case
studies easily will inspire and broaden understanding and utilization of
predictive analytics to health professionals and scholars interested with big
data analysis.
Clinical data has issues on ownership of medical records and
also legal issues related to data access, pooling, and use [2]. These issues
may be huddle to apply predictive science with pooled big data, but we can
utilize predictive analytics effectively to the limited data.
Practitioners in hospital can use the book as a guide to
invent new way of service and business by using the amount of refuse data.
Researchers also may acquire insight on research directions to predictive
analysis. Big data aligned from various sources will give meaningful
implications to policy maker and practitioners about one's health behavior as
the case diaper.
1. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh
C, et al. Big data: the next frontier for innovation, competition, and
productivity. New York (NY): McKinsey Global Institute; 2011.
2. Goodby AW, Olsen L, McGinnis M. Institute of Medicine
(US) Roundtable on Value & Science-Driven Health Care. Clinical data as the
basic staple of health learning: creating and protecting a public good.
Washington (DC): National Academies Press; 2010. [PubMed]
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