Data Extraction Quotes

To make good business decisions, you need to be routinely extracting actionable data from the businesses processes. Analyzing the data and organizing it in alignment with the businesses goals will allow for greater clarity in making decisions.

Sometimes I would worry about my internet habits and force myself awy from the computer, to read a magazine or book. Contemporary literature offered no respite: I would find the prose cluttered with data points, tenuous historical connections, detail so finely tuned it could have only been extracted from a feverish night of search-engine queries. Aphorisms were in; authors were wired. I would pick up books that had been heavily documented on social media, only to find that the books themselves had a curatorial affect: beautiful descriptions of little substance, arranged in elegant vignettes—gestural text, the equivalent of a rumpled linen bedsheet or a bunch of dahlias placed just so. Oh, I would think, turning the page. This author is addicted to the internet, too.

With managing a business, you need to Invest in good software and or good data mining systems. Run your numbers routinely. Take a look at your revenues - when is the money typically coming in, from where, can you identify any patterns in your revenues? Then take a look at your expenses - analyze the numbers and identify patterns. Why? Because Identifying patterns and extracting actionable items from your revenue and expense data will result in the clarity you need to make good business decisions.

At the same time, we have fallen for the idea that these services are ‘free’. In reality, we pay with our data into a business model of extracting human attention.

Imagine you have a hammer. That’s machine learning. It helped you climb a grueling mountain to reach the summit. That’s machine learning’s dominance of online data. On the mountaintop you find a vast pile of nails, cheaper than anything previously imaginable. That’s the new smart sensor tech. An unbroken vista of virgin board stretches before you as far as you can see. That’s the whole dumb world. Then you learn that any time you plant a nail in a board with your machine learning hammer, you can extract value from that formerly dumb plank. That’s data monetization. What do you do? You start hammering like crazy and you never stop, unless somebody makes you stop. But there is nobody up here to make us stop. This is why the “internet of everything” is inevitable.

Industrial capitalism transformed nature’s raw materials into commodities, and surveillance capitalism lays its claims to the stuff of human nature for a new commodity invention. Now it is human nature that is scraped, torn, and taken for another century’s market project. It is obscene to suppose that this harm can be reduced to the obvious fact that users receive no fee for the raw material they supply. That critique is a feat of misdirection that would use a pricing mechanism to institutionalize and therefore legitimate the extraction of human behavior for manufacturing and sale. It ignores the key point that the essence of the exploitation here is the rendering of our lives as behavioral data for the sake of others’ improved control of us. The remarkable questions here concern the facts that our lives are rendered as behavioral data in the first place; that ignorance is a condition of this ubiquitous rendition; that decision rights vanish before one even knows that there is a decision to make; that there are consequences to this diminishment of rights that we can neither see nor foretell; that there is no exit, no voice, and no loyalty, only helplessness, resignation, and psychic numbing; and that encryption is the only positive action left to discuss when we sit around the dinner table and casually ponder how to hide from the forces that hide from us.

We do not need to plug a fiber optic cable into our brains in order to access the Internet. Not only can the human retina transmit data at an impressive rate of nearly 10 million bits per second, but it comes pre-packaged with a massive amount of dedicated wetware, the visual cortex, that is highly adapted to extracting meaning from this information torrent and to interfacing with other brain areas for further processing.

Monte Carlo is able to discover practical solutions to otherwise intractable problems because the most efficient search of an unmapped territory takes the form of a random walk. Today’s search engines, long descended from their ENIAC-era ancestors, still bear the imprint of their Monte Carlo origins: random search paths being accounted for, statistically, to accumulate increasingly accurate results. The genius of Monte Carlo—and its search-engine descendants—lies in the ability to extract meaningful solutions, in the face of overwhelming information, by recognizing that meaning resides less in the data at the end points and more in the intervening paths.

Pretty soon all the information in the world – every tiny scrap of knowledge that humans possess, every little thought we’ve ever had that’s been considered worth preserving over thousands of years – all of it will be available digitally. Every road on earth has been mapped. Every building photographed. Everywhere we humans go, whatever we buy, whatever websites we look at, we leave a digital trail as clear as slug-slime. And this data can be read, searched and analysed by computers and value extracted from it in ways we cannot even begin to conceive.

The modern world is drowning in information. We have more data than we can possibly use regarding nearly every picayune matter of society, economics, and politics. Science has contributed to this tsunami of facts and figures, but Riley's reports demonstrated that the tidal wave of minutiae is hardly unique to our time. In every age the challenge has been to move from information to knowledge. And the value of experts lies in their capacity to extract meaning from the reams of facts. Rather than being swamped by raw data, the connoisseur, craftsman, engineer, clinician, or scientist is selectively and self-consciously blind. Knowing what to ignore, recognizing what is extraneous, is the key to deriving pattern, form, and insight.

Wild animals enjoying one another and taking pleasure in their world is so immediate and so real, yet this reality is utterly absent from textbooks and academic papers about animals and ecology. There is a truth revealed here, absurd in its simplicity. This insight is not that science is wrong or bad. On the contrary: science, done well, deepens our intimacy with the world. But there is a danger in an exclusively scientific way of thinking. The forest is turned into a diagram; animals become mere mechanisms; nature's workings become clever graphs. Today's conviviality of squirrels seems a refutation of such narrowness. Nature is not a machine. These animals feel. They are alive; they are our cousins, with the shared experience kinship implies. And they appear to enjoy the sun, a phenomenon that occurs nowhere in the curriculum of modern biology. Sadly, modern science is too often unable or unwilling to visualize or feel what others experience. Certainly science's "objective" gambit can be helpful in understanding parts of nature and in freeing us from some cultural preconceptions. Our modern scientific taste for dispassion when analyzing animal behaviour formed in reaction to the Victorian naturalists and their predecessors who saw all nature as an allegory confirming their cultural values. But a gambit is just an opening move, not a coherent vision of the whole game. Science's objectivity sheds some assumptions but takes on others that, dressed up in academic rigor, can produce hubris and callousness about the world. The danger comes when we confuse the limited scope of our scientific methods with the true scope of the world. It may be useful or expedient to describe nature as a flow diagram or an animal as a machine, but such utility should not be confused with a confirmation that our limited assumptions reflect the shape of the world. Not coincidentally, the hubris of narrowly applied science serves the needs of the industrial economy. Machines are bought, sold, and discarded; joyful cousins are not. Two days ago, on Christmas Eve, the U.S. Forest Service opened to commercial logging three hundred thousand acres of old growth in the Tongass National Forest, more than a billion square-meter mandalas. Arrows moved on a flowchart, graphs of quantified timber shifted. Modern forest science integrated seamlessly with global commodity markets—language and values needed no translation. Scientific models and metaphors of machines are helpful but limited. They cannot tell us all that we need to know. What lies beyond the theories we impose on nature? This year I have tried to put down scientific tools and to listen: to come to nature without a hypothesis, without a scheme for data extraction, without a lesson plan to convey answers to students, without machines or probes. I have glimpsed how rich science is but simultaneously how limited in scope and in spirit. It is unfortunate that the practice of listening generally has no place in the formal training of scientists. In this absence science needlessly fails. We are poorer for this, and possibly more hurtful. What Christmas Eve gifts might a listening culture give its forests? What was the insight that brushed past me as the squirrels basked? It was not to turn away from science. My experience of animals is richer for knowing their stories, and science is a powerful way to deepen this understanding. Rather, I realized that all stories are partly wrapped in fiction—the fiction of simplifying assumptions, of cultural myopia and of storytellers' pride. I learned to revel in the stories but not to mistake them for the bright, ineffable nature of the world.

One day in September 2015, FBI agent Adrian Hawkins placed a call to the Democratic National Committee headquarters in Washington, D.C., and asked to speak to the person in charge of technology. He was routed to the DNC help desk, which transferred the call to Yared Tamene, a young IT specialist with The MIS Department, a consulting firm hired by the DNC. After identifying himself, Hawkins told Tamene that he had reason to believe that at least one computer on the DNC’s network was compromised. He asked if the DNC was aware of this and what it was doing. Tamene had nothing to do with cybersecurity and knew little about the subject. He was a mid-level network administrator; his basic IT duties for the DNC were to set up computer accounts for employees and be on call to deal with any problems. When he got the call, Tamene was wary. Was this a joke or, worse, a dirty trick? He asked Hawkins if he could prove he was an FBI agent, and, as Tamene later wrote in a memo, “he did not provide me with an adequate response.… At this point, I had no way of differentiating the call I received from a prank call.” Hawkins, though, was real. He was a well-regarded agent in the FBI’s cyber squad. And he was following a legitimate lead in a case that would come to affect a presidential election. Earlier in the year, U.S. cyber warriors intercepted a target list of about thirty U.S. government agencies, think tanks, and several political organizations designated for cyberattacks by a group of hackers known as APT 29. APT stood for Advanced Persistent Threat—technojargon for a sophisticated set of actors who penetrate networks, insert viruses, and extract data over prolonged periods of time.

And yet Simulmatics’ legacy endures in predictive analytics, what-if simulation, and behavioral data science: it lurks behind the screen of every device. Simulmatics, notwithstanding its own failure, helped invent the data-mad and near-totalitarian twenty-first century, in which the only knowledge that counts is prediction and, before and after the coming of the coronavirus, corporations extract wealth by way of the collection of data and the manipulation of attention and the profit of prophecy. In a final irony, Simulmatics, whose very past has been all but erased, helped invent a future obsessed with the future, and yet unable to improve it.

Our steady resistance forms cracks in the world of profit margins. It transitions us away from self-destruction. We are a thorn in the side of a world that believes it must extract to exist, a bone-deep reminder there are other ways of being…Some of us leave the land to bring our case to the financiers of the industry we oppose, to present the data and oppositional testimony the banks ostensibly have no knowledge of. In here, I feel like an exotic bird to be examined for potential danger. In here, alongside discussion of financial investments, I remind corporate heads that they drink water and breathe air. As awkward as it can be to remind a person of their own humanity, it has proven exceedingly effective to bring Indigenous rights and the voice of the land into these spaces. SACRED RESISTANCE by Tara Houska, Zhaabowekwe, Couchiching First Nation

Search engine query data is not the product of a designed statistical experiment and finding a way to meaningfully analyse such data and extract useful knowledge is a new and challenging field that would benefit from collaboration. For the 2012–13 flu season, Google made significant changes to its algorithms and started to use a relatively new mathematical technique called Elasticnet, which provides a rigorous means of selecting and reducing the number of predictors required. In 2011, Google launched a similar program for tracking Dengue fever, but they are no longer publishing predictions and, in 2015, Google Flu Trends was withdrawn. They are, however, now sharing their data with academic researchers... Google Flu Trends, one of the earlier attempts at using big data for epidemic prediction, provided useful insights to researchers who came after them... The Delphi Research Group at Carnegie Mellon University won the CDC’s challenge to ‘Predict the Flu’ in both 2014–15 and 2015–16 for the most accurate forecasters. The group successfully used data from Google, Twitter, and Wikipedia for monitoring flu outbreaks.

As planned The Three Wise Women meet at 3WW HQ for debriefing. Angelina extracted the small camera from her lapel and downloaded it onto a laptop. She then expertly digitally scanned the Polaroid into her electronic file on James. Ava had just missed Sean who had given his camcorder and photographs of himself and Patrick to Angelina. It had been digitally downloaded and formatted onto Patrick’s pc file. A back-up of all data was done on the Company server but it was heavily encrypted and written in Angelina’s own program Borrow and used her own software Gotya, so only the very best could break her code and that would take months

The ability to take data -- to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it -- that’s going to be a hugely important skill in the next decades.

The weightless rhetoric of digital technology masks a refusal to acknowledge the people and resources on which these systems depend: lithium and coltan mines, energy-guzzling data centers and server farms, suicidal workers at Apple’s Foxconn factories, and women and children in developing countries and incarcerated Americans up to their necks in toxic electronic waste.2 The swelling demand for precious metals, used in everything from video-game consoles to USB cables to batteries, has increased political instability in some regions, led to unsafe, unhealthy, and inhumane working conditions, opened up new markets for child and forced labor, and encouraged environmentally destructive extraction techniques.3 It is estimated that mining the gold necessary to produce a single cell phone—only one mineral of many required for the finished product—produces upward of 220 pounds of waste.4

It now seems clear that a capacity to learn would be an integral feature of the core design of a system intended to attain general intelligence, not something to be tacked on later as an extension or an afterthought. The same holds for the ability to deal effectively with uncertainty and probabilistic information. Some faculty for extracting useful concepts from sensory data and internal states, and for leveraging acquired concepts into flexible combinatorial representations for use in logical and intuitive reasoning, also likely belong among the core design features in a modern AI intended to attain general intelligence.

More recently, there has been growing interest in change data capture (CDC), which is the process of observing all data changes written to a database and extracting them in a form in which they can be replicated to other systems. CDC is especially interesting if changes are made available as a stream, immediately as they are written.

At the heart of the decoding problem is how to understand the vast information contained in neural signals, the challenge of what is being called "big data". For neuroscientists, big data is a means for exploring populations of neurons to discover the macroscopic signatures of dynamical systems, rather than attempting to make sense of the activity of individual neurons. Two surprising results from numerous experiments recording from neurons in different brain regions have revealed a wonderful secret of nature about the relation between the number of neurons recorded and and their dimensionality (the number of principal components required to explain a fixed percentage of variance). First, the dimensionality of the neural data is much smaller than the number of recorded neurons. Second, when dimensionality procedures are used to extract neuronal state dynamics, the resulting low-dimensional neural trajectories reveal portraits of the behavior of a dynamical system. This means that it may not be necessary to record from many more neurons within a brain region in order to accurately recover its internal state-space dynamics.

Of course, for all their counterculture pretensions, corporations like Google, Amazon, and Apple are still corporations. They seek profits, they try to maximize their monopoly power, they externalize costs, and, of course, they exploit labor. The American technology sector has externalized the cost of industrial pollution to Chinas cities, where people live in a pall of smog but no one - certainly not Apple - has to bear the cost of cleanup. Apple/Foxconn’s dreadful labor practices in China are common knowledge, and those Amazon packages with the sunny smile issue forth from warehouses that are more like Blake’s “dark satanic mills” than they are the new employment model for the internet age. The technology industry has manufactured images of the rebel hacker and hipster nerd, of products that empower individual and social change, of new ways of doing business, and now of mindful capitalism. Whatever truth might attach to any of these, the fact is that these are impressions carefully managed to get us to keep buying products and, just as importantly, to remain confident in the goodness and usefulness of the high-tech industry. We are being told these stories in the hope that we will believe them, buy into them, and feel both ip and spiritually renewed by the association. Unhappily, in this view of things, mindfulness can be extracted from a context of Buddhist meanings, values, and purposes. Meditation and mindfulness are not part of a whole way of life but only a spiritual technology, a mental app that is the same regardless of how it is used an what it is used for. Corporate mindfulness takes something that has the capacity to be oppositional - Buddhism - and redefines it. Eventually, we forget that it ever had its own meaning.

Minsky was an ardent supporter of the Cyc project, the most notorious failure in the history of AI. The goal of Cyc was to solve AI by entering into a computer all the necessary knowledge. When the project began in the 1980s, its leader, Doug Lenat, confidently predicted success within a decade. Thirty years later, Cyc continues to grow without end in sight, and commonsense reasoning still eludes it. Ironically, Lenat has belatedly embraced populating Cyc by mining the web, not because Cyc can read, but because there’s no other way. Even if by some miracle we managed to finish coding up all the necessary pieces, our troubles would be just beginning. Over the years, a number of research groups have attempted to build complete intelligent agents by putting together algorithms for vision, speech recognition, language understanding, reasoning, planning, navigation, manipulation, and so on. Without a unifying framework, these attempts soon hit an insurmountable wall of complexity: too many moving parts, too many interactions, too many bugs for poor human software engineers to cope with. Knowledge engineers believe AI is just an engineering problem, but we have not yet reached the point where engineering can take us the rest of the way. In 1962, when Kennedy gave his famous moon-shot speech, going to the moon was an engineering problem. In 1662, it wasn’t, and that’s closer to where AI is today. In industry, there’s no sign that knowledge engineering will ever be able to compete with machine learning outside of a few niche areas. Why pay experts to slowly and painfully encode knowledge into a form computers can understand, when you can extract it from data at a fraction of the cost? What about all the things the experts don’t know but you can discover from data? And when data is not available, the cost of knowledge engineering seldom exceeds the benefit. Imagine if farmers had to engineer each cornstalk in turn, instead of sowing the seeds and letting them grow: we would all starve.

In the meantime, the practical consequence of the “no free lunch” theorem is that there’s no such thing as learning without knowledge. Data alone is not enough. Starting from scratch will only get you to scratch. Machine learning is a kind of knowledge pump: we can use it to extract a lot of knowledge from data, but first we have to prime the pump.

Chapter 11 explains how a database is structured, what type of data they can contain and how to extract the variables of interest using queries;

Chasing tax cheats using normal procedures was not an option. It would take decades just to identify anything like the majority of them and centuries to prosecute them successfully; the more we caught, the more clogged up the judicial system would become. We needed a different approach. Once Danis was on board a couple of days later, together we thought of one: we would extract historical and real-time data from the banks on all transfers taking place within Greece as well as in and out of the country and commission software to compare the money flows associated with each tax file number with the tax returns of that same file number. The algorithm would be designed to flag up any instance where declared income seemed to be substantially lower than actual income. Having identified the most likely offenders in this way, we would make them an offer they could not refuse. The plan was to convene a press conference at which I would make it clear that anyone caught by the new system would be subject to 45 per cent tax, large penalties on 100 per cent of their undeclared income and criminal prosecution. But as our government sought to establish a new relationship of trust between state and citizenry, there would be an opportunity to make amends anonymously and at minimum cost. I would announce that for the next fortnight a new portal would be open on the ministry’s website on which anyone could register any previously undeclared income for the period 2000–14. Only 15 per cent of this sum would be required in tax arrears, payable via web banking or debit card. In return for payment, the taxpayer would receive an electronic receipt guaranteeing immunity from prosecution for previous non-disclosure.17 Alongside this I resolved to propose a simple deal to the finance minister of Switzerland, where so many of Greece’s tax cheats kept their untaxed money.18 In a rare example of the raw power of the European Union being used as a force for good, Switzerland had recently been forced to disclose all banking information pertaining to EU citizens by 2017. Naturally, the Swiss feared that large EU-domiciled depositors who did not want their bank balances to be reported to their country’s tax authorities might shift their money before the revelation deadline to some other jurisdiction, such as the Cayman Islands, Singapore or Panama. My proposals were thus very much in the Swiss finance minister’s interests: a 15 per cent tax rate was a relatively small price to pay for legalizing a stash and allowing it to remain in safe, conveniently located Switzerland. I would pass a law through Greece’s parliament that would allow for the taxation of money in Swiss bank accounts at this exceptionally low rate, and in return the Swiss finance minister would require all his country’s banks to send their Greek customers a friendly letter informing them that, unless they produced the electronic receipt and immunity certificate provided by my ministry’s web page, their bank account would be closed within weeks. To my great surprise and delight, my Swiss counterpart agreed to the proposal.19

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machine learning emphasizes the incremental process of self-learning and automatically detecting patterns through experience derived from exposure to data, data mining is a less autonomous technique of extracting hidden insight.

In order to construct a flawless imitation, the first step was to gather as much video data as possible with a web crawler. His ideal targets were fashionable Yoruba girls, with their brightly colored V-neck buba and iro that wrapped around their waists, hair bundled up in gele. Preferably, their videos were taken in their bedrooms with bright, stable lighting, their expressions vivid and exaggerated, so that AI could extract as many still-frame images as possible. The object data set was paired with another set of Amaka’s own face under different lighting, from multiple angles and with alternative expressions, automatically generated by his smartstream. Then, he uploaded both data sets to the cloud and got to work with a hyper-generative adversarial network. A few hours or days later, the result was a DeepMask model. By applying this “mask,” woven from algorithms, to videos, he could become the girl he had created from bits, and to the naked eye, his fake was indistinguishable from the real thing. If his Internet speed allowed, he could also swap faces in real time to spice up the fun. Of course, more fun meant more work. For real-time deception to work, he had to simultaneously translate English or Igbo into Yoruba, and use transVoice to imitate the voice of a Yoruba girl and a lip sync open-source toolkit to generate corresponding lip movement. If the person on the other end of the chat had paid for a high-quality anti-fake detector, however, the app might automatically detect anomalies in the video, marking them with red translucent square warnings

Before we proceed further, be wary that neither the null hypothesis nor the alternative hypothesis can be unequivocally proven correct within hypothesis testing. Analyzing a sample extracted from a larger population is a subset of the data, and thus, any conclusions formed about the larger population based on analyzing the sample data are considered probabilistic rather than absolute.

Varian’s casual understatement stands in counterpoint to his often-startling declarations: “Nowadays there is a computer in the middle of virtually every transaction… now that they are available these computers have several other uses.”8 He then identifies four such new uses: “data extraction and analysis,” “new contractual forms due to better monitoring,” “personalization and customization,” and “continuous experiments.

Data extraction and analysis,” Varian writes, “is what everyone is talking about when they talk about big data.

Naturally occurring processes are often informally modeled by priority queues. Single people maintain a priority queue of potential dating candidates, mentally if not explicitly. One’s impression on meeting a new person maps directly to an attractiveness or desirability score, which serves as the key field for inserting this new entry into the “little black book” priority queue data structure. Dating is the process of extracting the most desirable person from the data structure (Find-Maximum), spending an evening to evaluate them better, and then reinserting them into the priority queue with a possibly revised score.

More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills — skills that are also necessary for understanding biases in the data, and for debugging logging output from code. Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms — some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications.

Lync has its title altered. And so what sort of computer software is it now? Well, it is identified as Lync Mac Business. The particular motive for carrying this out is a need to combine the familiar experience and level of popularity from consumers associated with Lync Mac along with security regarding Lync as well as control feature set. Yet another thing which Lync has got influenced in this specific new version of Lync happens to be the transformation associated with particular graphical user interface aspects which are used in the popular program of Lync Mac. It has been chose to utilize the same icons as in Lync as an alternative to attempting to make new things. Microsoft Company furthermore included the particular call monitor screen which happens to be applied within Lync in order that consumers could preserve an active call seen inside a small display when customers happen to be focusing on yet another program. It is additionally essential to point out that absolutely no features which were obtainable in Lync are already eliminated. And you should additionally understand that Lync Mac happens to be nevertheless utilizing the foundation regarding Lync. And it is very good that the actual software is nevertheless operating on the previous foundation since it happens to be known for the security. However what helps make Lync Mac a great choice if perhaps you're searching for an immediate texting software? There are a wide range of advantages which this particular application has got and we'll have a look at a few of these. Changing from instantaneous messaging towards document sharing won't take a great deal of time. Essentially, it provides a flawless incorporation associated with the software program. An improved data transfer administration is yet another factor that you'll be in a position enjoy from this program. Network supervisors can assign bandwidth, limit people and also split video and audio streams throughout each application and control the effect of bandwidth. In case you aren't making use of Microsoft Windows operating system and prefer Lync in that case possibly you're concerned that you will not be able to utilize this particular application or it is going to possess some constraints? The reply happens to be no. As we've talked about many times currently, Lync is currently best-known as being Lync For Mac Business .There is nothing that is actually extracted from the main edition therefore the full functionality is actually offered for you. And it is certainly great to understand the fact that Lync that we should simply call Lync For Mac version is actually capable to provide you all the characteristics which you'll need. If you happen to be trying to find a fantastic application for your own organization, in that case this is the one particular you are in search of Lync For Mac which will still be acknowledged as being Lync for a long period edition is actually competent to present you with everything that is actually necessary for your organization even if you decided to not utilize Microsoft operating system. Know about more detail please visit lyncmac.com

The lessons of big data apply as much to the public sector as to commercial entities: government data’s value is latent and requires innovative analysis to unleash. But despite their special position in capturing information, governments have often been ineffective at using it. Recently the idea has gained prominence that the best way to extract the value of government data is to give the private sector and society in general access to try. There is a principle behind this as well. When the state gathers data, it does so on behalf of its citizens, and thus it ought to provide access to society (except in a limited number of cases, such as when doing so might harm national security or the privacy rights of others).

It’s not enough to analyze “Big Data” or even extract insights from them. The best sales leaders mask all that complexity and keep it simple for the front lines so sales staff like Maria can get on with what they do best — sales, not statistics.

The constant dilemma of the information age is that our ability to gather a sea of data greatly exceeds the tools and techniques available to sort, extract, and apply the information we’ve collected.

The central result in the molecular story of vision is that three different kinds of protein molecules (rhodopsins) extract our information about color. When light impinges on one of these molecules, there is a certain probability that the molecule will absorb a unit of light-a photon-and change shape. The shape changes unleash little pushes of electricity, which are the data our brains use to construct our sense of vision.

When we have specific interests or purposes, can we leverage this tsunami of multimedia to our own individual aims and for the greater good of all?

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big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more.

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email to the target-company’s employees. If just one employee clicked the email’s attachment (and all it took was one), the computer would download a webpage crammed with malware, including a “Remote Access Trojan,” known in the trade as a RAT. The RAT opened a door, allowing the intruder to roam the network, acquire the privileges of a systems administrator, and extract all the data he wanted. They did this with economic enterprises of all kinds: banks, oil and gas pipelines, waterworks, health-care data managers—sometimes to steal secrets, sometimes to steal money, sometimes for motives that couldn’t be ascertained. McAfee,

This book is a work of fiction. Actually, it is a work of fiction within a fiction, as the main characters, though real persons in a fictional world, are being depicted in a book which other fictional characters in the same world are reading. Any reference to historical events-- rather, historical events non-Marridonian, and also non-Sesternese-- real people—rather, people in our realm, not the persons I was referring to in the previous line-- or real places—places that are not Marridon, Sesterna, and any place on the Two Continents-- are used fictitiously, because this is a work of fiction, and is a fiction within a fiction, as was previously stated. All names, characters, places, and incidents are the product of the author's imagination—referring to the ultimate author, not the fictitious author who has written the book within the book-- and any resemblance to actual events, locales, persons, living, dead, or otherwise, is entirely coincidental, but any resemblance to actual persons or places in the Two Continents is intentional. Absolutely no parts of this book, text or art, may be reproduced or transmitted in any form, by any means, whether electronically or mechanically, including photocopying— “By Myrellenos, are we here in the disclaimer again? This is the third time, I believe. And there are still no cups out. Where is the teapot?” “Here, boss.” “Oh, there is tea in this story? I might be more inclined to stay and hear this one. The others were dreadful slow. I must have some tea, if I am going to be made to sit and listen to a whole book. I am not Bartleby, who can sit at his desk and flump over his tomes until he moulders.” “He’s gonna hear you, boss.” “I should say not, Rannig. He is too busy with doing the edits. He found a mistake in one of the other books about us and demanded he perform the editing this time around. The author was very good to let him do as he likes. He is missing tea, however.” --audio recording, data retrieval, cloud storage, torrent, or streaming service. If you do decide to ignore this disclaimer and print or share this book illegally, I will have Bartleby come to your house with a sample from the Marridonian legal extracts, and he will read them to you until you promise never to do anything illegal again.

The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body

When running a Python program, the interpreter spends most of its time figuring out what low-level operation to perform, and extracting the data to give to this low-level operation. Given Python’s design and flexibility, the Python interpreter always has to determine the low-level operation in a completely general way, because a variable can have any type at any time. This is known as dynamic dispatch, and for many reasons, fully general dynamic dispatch is slow.[5] For example, consider what happens when the Python runtime evaluates a + b: The interpreter inspects the Python object referred to by a for its type, which requires at least one pointer lookup at the C level. The interpreter asks the type for an implementation of the addition method, which may require one or more additional pointer lookups and internal function calls. If the method in question is found, the interpreter then has an actual function it can call, implemented either in Python or in C. The interpreter calls the addition function and passes in a and b as arguments. The addition function extracts the necessary internal data from a and b, which may require several more pointer lookups and conversions from Python types to C types. If successful, only then can it perform the actual operation that adds a and b together. The result then must be placed inside a (perhaps new) Python object and returned. Only then is the operation complete. The situation for C is very different. Because C is compiled and statically typed, the C compiler can determine at compile time what low-level operations to perform and what low-level data to pass as arguments. At runtime, a compiled C program skips nearly all steps that the Python interpreter must perform. For something like a + b with a and b both being fundamental numeric types, the compiler generates a handful of machine code instructions to load the data into registers, add them, and store the result.

At the time of the investigation, however, the data can often seem far more ambiguous. The most successful investigators reveal not just a willingness to engage with the incident, but also have the analytical skills and creative insights to extract the key lessons. Indeed, many aviation experts cite the improvement in the quality and sophistication of investigations as one of the most powerful spurs to safety in recent years.8

DECISIONS Useful: Graphical Presentation Monitor Key Indicators Effective Measurements Wisdom Knowledge The Goal: Strategic Thinking Predictive Value Experience and Judgment Automated Exception Notification Information Structured: Voluminous Grouped and Summarized Relationships Not Always Evident Raw Data: Massive Fragmented Meaningless Data EVENTS Figure 1-01. The Pyramid of KnowledgeToyota, this begins with genchi genbutsu, or gemba, which means literally “go see it for yourself. ” Taiichi Ohno, a founding father of Lean, once said, “Data is of course important in manufacturing, but I place the greatest emphasis on facts. ” 2 A direct and intuitive understanding of a situation is far more useful than mountains of data. The raw data stored in a database adds value for decision-making only if the right information is presented in the right format, to the right people, at the right time. A tall stack of printout may contain the right data, but it’s certainly not in an accessible format. Massive weekly batch printouts do not enable timely and proactive decisions. Raw data must be summarized, structured, and presented as digestible information. Once information is combined with direct experience, then the incredible human mind can extract and develop useful knowledge. Over time, as knowledge is accumulated and combined with direct experience and judgment, wisdom develops. This evolution is described by the classic pyramid of knowledge shown in Figure 1-01. BACK TO CHICAGO So what happened in Chicago? We can speculate upon several possible perspectives for why the team and its change leader were far from a true Lean system, yet they refused any help from IT providers: 1. They feared wasteful IT systems and procedures would be foisted on them.

Most mods are single-player only mods. Knowing how to install single player mods helps in installing multiplayer mods. You must first download the mod that you want. Go to a reliable website and download. If the mod that you want is missing and cannot be found, this usually means that it is discontinued.   Windows   First you will need an archive utility application, such as WinZip, WinRAR, 7-Zip, or something similar.   Locate you Minecraft application. Go to the start menu, and type “minecraft” in the search bar. Click on this option to open the folder in a new window.   Your Minecraft application data can be found within your .minecraft folder.   Back-up your Minecraft save files before installing any mod. To do this simply copy your saves folder and paste it into another folder. Copy the previous saves folder back into your .minecraft folder to restore.   Extract the mod you downloaded with WinRAR or any archive utility application.   Locate the minecraft.jar file. This file can be found in the bin folder in .minecraft.   Back-up your minecraft.jar file. Copy minecraft.jar in the same folder as the mods.   Open the minecraft.jar file with WinRAR.   Copy all the mod files into the minecraft.jar file and select "Add and replace files”   Lastly delete the folder named META-INF.

Some businesses take a unique approach to this. Footwear brand Toms, already beloved thanks to its renowned blend of “social purpose” and product, forgoes splashy celebrity marketing campaigns. Instead, they engage and elevate real customers. During the summer of 2016, Toms engaged more than 3.5 million people in a single day using what they call tribe power. The company tapped into its army of social media followers for its annual One Day Without Shoes initiative to gather millions of Love Notes on social media. However, Toms U.K. marketing manager Sheela Thandasseri explained that their tribe’s Love Notes are not relegated to one day. “Our customers create social content all the time showing them gifting Toms or wearing them on their wedding day, and they tag us because they want us to be part of it.”2 Toms uses customer experience management platform Sprinklr to aggregate interactions on Facebook, Instagram, and Twitter. Toms then engages in a deep analysis of the data generated by its tribe, learning what customers relish and dislike about its products, stores, and salespeople so they can optimize their Complete Product Experience (CPE). That is an aggressive, all-in approach that extracts as much data as possible from every customer interaction in order to see patterns and craft experiences. Your approach might differ based on factors ranging from budget limitations to privacy concerns. But I can attest that earning love does not necessarily require cutting-edge technology or huge expenditures. What it does require is a commitment to delivering the building blocks of lovability that I reviewed in the previous chapter. Lovability begins with a mindset that makes it a priority. The building blocks are feelings — hope, confidence, fun. If you stack them up over and over again, eventually you will turn those feelings into a tower of meaningful benefits for everyone with a stake in your business, including owners, investors, employees, and customers. Now let’s look more closely at those benefits and the groups they affect.

In chapter 1, Anil Maharjan explains on how you can extract the story behind your Facebook data. He shows by using Power Query along with Power BI you can extract your Facebook data easily and analyze your own story by using your Facebook data. This chapter helps you to learn about Power Query and Power BI and shows you how to use self-service BI based on your Facebook data.

That critique is a feat of misdirection that would use a pricing mechanism to institutionalize and therefore legitimate the extraction of human behavior for manufacturing and sale. It ignores the key point that the essence of the exploitation here is the rendering of our lives as behavioral data for the sake of others’ improved control of us. The remarkable questions here concern the facts that our lives are rendered as behavioral data in the first place; that ignorance is a condition of this ubiquitous rendition; that decision rights vanish before one even knows that there is a decision to make; that there are consequences to this diminishment of rights that we can neither see nor foretell; that there is no exit, no voice, and no loyalty, only helplessness, resignation, and psychic numbing; and that encryption is the only positive action left to discuss when we sit around the dinner table and casually ponder how to hide from the forces that hide from us.

Technology is grotesque, you two both understand that, right?” “Don’t patronize me,” I say. “I know it’s not perfect.” “It’s not imperfect, it’s evil.” “You sound like my sister.” “It spies on you. It mines you for data. It extracts your soul and then sells it back to you. It’s designed to make you spend money so you’re too busy shopping to notice the world is burning down. The only way I’m going to be a part of it is if we’re doing something to fundamentally change it.

In addition to trusting those who work for you by delegating work that you may truly believe only you can do, you must also understand the art of evaluating a Spartan set of data, extracting the truth, and trusting your Twinges.

Be a data sharer. That’s what experts do. In fact, that’s one of the reasons experts become experts. They understand that sharing data is the best way to move toward accuracy because it extracts insight from your listeners of the highest fidelity.

A data warehouse is an organized store of data from all over the organization, specially designed to help make management decisions. Data can be extracted from operational database to answer a particular set of queries. This data, combined with other data, can be rolled up to a consistent granularity and uploaded to a separate data store called the data warehouse. Therefore, the data warehouse is a simpler version of the operational data base, with the purpose of addressing reporting and decision-making needs only.

Identify Your Strengths With Strengths Finder 2.0 One tool that can help you remember your achievements is the ‘Strengths Finder’ "assessment. The father of Strengths Psychology, Donald O. Clifton, Ph.D, along with Tom Rath and a team of scientists at The Gallup Organization, created StrengthsFinder. You can take this assessment by purchasing the Strengths Finder 2.0 book. The value of SF 2.0 is that it helps you understand your unique strengths. Once you have this knowledge, you can review past activities and understand what these strengths enabled you to do. Here’s what I mean, in the paragraphs below, I’ve listed some of the strengths identified by my Strengths Finder assessment and accomplishments where these strengths were used. “You can see repercussions more clearly than others can.” In a prior role, I witnessed products being implemented in the sales system at breakneck speed. While quick implementation seemed good, I knew speed increased the likelihood of revenue impacting errors. I conducted an audit and uncovered a misconfigured product. While the customer had paid for the product, the revenue had never been recognized. As a result of my work, we were able to add another $7.2 million that went straight to the bottom line. “You automatically pinpoint trends, notice problems, or identify opportunities many people overlook.” At my former employer, leadership did not audit certain product manager decisions. On my own initiative, I instituted an auditing process. This led to the discovery that one product manager’s decisions cost the company more than $5M. “Because of your strengths, you can reconfigure factual information or data in ways that reveal trends, raise issues, identify opportunities, or offer solutions.” In a former position, product managers were responsible for driving revenue, yet there was no revenue reporting at the product level. After researching the issue, I found a report used to process monthly journal entries which when reconfigured, provided product managers with monthly product revenue. “You entertain ideas about the best ways to…increase productivity.” A few years back, I was trained by the former Operations Manager when I took on that role. After examining the tasks, I found I could reduce the time to perform the role by 66%. As a result, I was able to tell my Director I could take on some of the responsibilities of the two managers she had to let go. “You entertain ideas about the best ways to…solve a problem.” About twenty years ago I worked for a division where legacy systems were being replaced by a new company-wide ERP system. When I discovered no one had budgeted for training in my department, I took it upon myself to identify how to extract the data my department needed to perform its role, documented those learnings and that became the basis for a two day training class. “Sorting through lots of information rarely intimidates you. You welcome the abundance of information. Like a detective, you sort through it and identify key pieces of evidence. Following these leads, you bring the big picture into view.” I am listing these strengths to help you see the value of taking the Strengths Finder Assessment.

Data Extraction Services at Rely Services help to grow your business by providing accurate, timely, and relevant data.

The Israeli surveillance firm Cellebrite has sold its digital data extraction devices to at least 150 countries, including dictatorships such as Russia, United Arab Emirates, and Bahrain.

Internet services have made it much easier to amass huge amounts of sensitive information without meaningful consent, and to use it at massive scale without users understanding what is happening to their private data. Data as assets and power Since behavioral data is a byproduct of users interacting with a service, it is sometimes called “data exhaust”—suggesting that the data is worthless waste material. Viewed this way, behavioral and predictive analytics can be seen as a form of recycling that extracts value from data that would have otherwise been thrown away. More correct would be to view it the other way round: from an economic point of view, if targeted advertising is what pays for a service, then behavioral data about people is the service’s core asset.

The extreme consolidation in the corporate world over the past three decades has produced a playing field so rigged against consumers that pursuing the basics of life can feel like navigating a never-ending series of scams. It’s as if everyone is trying to trick us in the fine print of pages and pages of terms of service agreements they know we will never read. The black box is not just the algorithms running our communication networks—almost everything is a black box, an opaque system hiding something else. The housing market isn’t about homes; it’s about hedge funds and speculators. Universities aren’t about education; they’re about turning young people into lifelong debtors. Long-term care facilities aren’t about care; they’re about draining our elders in the last years of life and real estate plays. Many news sites aren’t about news; they’re about tricking us into clicking on autoplaying ads and advertorials that eat up the bottom half of nearly every site. Nothing is as it seems. This kind of predatory, extractive capitalism necessarily breeds mistrust and paranoia. In this context, it’s not surprising that QAnon, a conspiracy theory that tells of elites harvesting the young for their lifeblood (adrenochrome), has gone viral. Elites are sucking us dry—our money, our labor, our time, our data. So dry that large parts of our planet are beginning to spontaneously combust. The Davos elite aren’t eating our children, but they are eating our children’s futures, and that is plenty bad. QAnon believers imagine secret tunnels underneath pizza parlors and Central Park, the better to traffic children. This is fantasy, but there are tunnels—literal Shadow Lands—under some major cities, and they do house and hide the poor, the sick, the drug-dependent, the discarded. Under the flashing lights of Las Vegas, hundreds or even thousands of people really do live in a sprawling network of storm tunnels.

For a scientist, the only valid question is to decide whether the phenomenon can be studied by itself, or whether it is an instance of a deeper problem. This book attempts to illustrate, and only to illustrate, the latter approach. And my conclusion is that, through the UFO phenomenon, we have the unique opportunities to observe folklore in the making and to gather scientific material at the deepest source of human imagination. We will be the object of much contempt by future students of our civilization if we allow this material to be lost, for "tradition is a meteor which, once it falls, cannot be rekindled." If we decide to avoid extreme speculation, but make certain basic observations from the existing data, five principal facts stand out rather clearly from our analysis so far: Fact 1. There has been among the public, in all countries, since the middle of 1946, an extremely active generation of colorful rumors. They center on a considerable number of observations of unknown machines close to the ground in rural areas, the physical traces left by these machines, and their various effects on humans and animals. Fact 2. When the underlying archetypes are extracted from these rumors, the extraterrestrial myth is seen to coincide to a remarkable degree with the fairy-faith of Celtic countries, the observations of the scholars of past ages, and the widespread belief among all peoples concerning entities whose physical and psychological description place them in the same category as the present-day ufonauts. Fact 3. The entities human witnesses report to have seen, heard, and touched fall into various biological types. Among them are beings of giant stature, men indistinguishable from us, winged creatures, and various types of monsters. Most of the so-called pilots, however, are dwarfs and form two main groups: (1) dark, hairy beings – identical to the gnomes of medieval theory – with small, bright eyes and deep, rugged, "old" voices; and (2) beings – who answer the description of the sylphs of the Middle Ages or the elves of the fairy-faith – with human complexions, oversized heads, and silvery voices. All the beings have been described with and without breathing apparatus. Beings of various categories have been reported together. The overwhelming majority are humanoid. Fact 4. The entities' reported behavior is as consistently absurd as the appearance of their craft is ludicrous. In numerous instances of verbal communications with them, their assertions have been systematically misleading. This is true for all cases on record, from encounters with the Gentry in the British Isles to conversations with airship engineers during the 1897 Midwest flap and discussions with the alleged Martians in Europe, North and South America, and elsewhere. This absurd behavior has had the effect of keeping professional scientists away from the area where that activity was taking place. It has also served to give the saucer myth its religious and mystical overtones. Fact 5. The mechanism of the apparitions, in legendary, historical, and modern times, is standard and follows the model of religious miracles. Several cases, which bear the official stamp of the Catholic Church (such as those in Fatima and Guadalupe), are in fact – if one applies the deffinitions strictly – nothing more than UFO phenomena where the entity has delivered a message having to do with religious beliefs rather than with space or engineering.

Data Science is a multidisciplinary field that combines various techniques and methods to extract knowledge and insights from data. It involves the application of statistical analysis, machine learning algorithms, and computational tools to analyze and interpret complex data sets.

registered email address and went global in 2007. Twitter split off onto its own platform and went global in 2007. Airbnb was born in 2007. In 2007, VMware—the technology that enabled any operating system to work on any computer, which enabled cloud computing—went public, which is why the cloud really only took off in 2007. Hadoop software—which enabled a million computers to work together as if they were one, giving us “Big Data”—was launched in 2007. Amazon launched the Kindle e-book reader in 2007. IBM launched Watson, the world's first cognitive computer, in 2007. The essay launching Bitcoin was written in 2006. Netflix streamed its first video in 2007. IBM introduced nonsilicon materials into its microchips to extend Moore's Law in 2007. The Internet crossed one billion users in late 2006, which seems to have been a tipping point. The price of sequencing a human genome collapsed in 2007. Solar energy took off in 2007, as did a process for extracting natural gas from tight shale, called fracking. Github, the world's largest repository of open source software, was launched in 2007. Lyft, the first ride-sharing site, delivered its first passenger in 2007. Michael Dell, the founder of Dell, retired in 2005. In 2007, he decided he'd better come back to work—because in 2007, the world started to get really fast. It was a real turning point. Today, we have taken another

but the entirety of human existence. Artificial intelligence is, after all, a mirroring and mimicry machine: we feed in the cumulative words, ideas, and images that our species has managed to amass (and digitize) over its history and these programs mirror back to us something that feels uncannily lifelike. A golem world. “I’d rather see an ad for cute shoes that I am going to like than see ads for a bunch of ugly stuff I don’t want,” one student said in an early class. In our discussions, we came to call this the “cute shoes problem” because it encapsulates one of the main reasons why surveillance capitalism and the AI revolution were able to sneak up on us with so little debate. Many of us do appreciate a certain level of automated customization, especially algorithms that suggest music, books, and people who might interest us. And at first, the stakes seemed low: Is it really a big deal if we see ads and suggestions based on our interests and tastes? Or if chatbots help clear our email backlogs? Yet now we find ourselves neck-deep in a system where, as with my own real-life doppelganger, the stakes are distinctly higher. Personal data, extracted without full knowledge or understanding, is sold to third parties and can influence everything from what loans we are eligible for to what job postings we see—to whether our jobs are replaced by deep learning bots that have gotten shockingly good at impersonating us. And those helpful recommendations and eerie impersonations come from the same

A different business model was necessary if capitalist firms were to take full advantage of dwindling recording costs. This chapter argues that the new business model that eventually emerged is a powerful new type of firm: the platform.10 Often arising out of internal needs to handle data, platforms became an efficient way to monopolise, extract, analyse, and use the increasingly large amounts of data that were being recorded. Now this model has come to expand across the economy, as numerous companies incorporate platforms: powerful technology companies (Google, Facebook, and Amazon), dynamic start-ups (Uber, Airbnb), industrial leaders (GE, Siemens), and agricultural powerhouses (John Deere, Monsanto), to name just a few. What are platforms?11 At the most general level, platforms are digital infrastructures that enable two or more groups to interact.

Amazon Comprehend is a natural language processing (NLP) solution that uses machine learning to find and extract insights and relationships from documents. •​Amazon Forecast combines your historical data with other variables, such as weather, to forecast outcomes. •​Amazon Kendra is an intelligent search service powered by machine learning. •​Amazon Lex is a solution for building conversational interfaces that can understand user intent and enable humanlike interactions. •​Amazon Lookout for Metrics detects and diagnoses anomalies in business and marketing data, such as unexpected drops in sales or unusual spikes in customer churn rates. •​Amazon Personalize powers personalized recommendations using the same machine-learning technology as Amazon.com. •​Amazon Polly converts text into natural-sounding speech, enabling you to create applications that talk. •​Amazon Rekognition makes it possible to identify objects, people, text, scenes, and activities in images and videos. •​Amazon Textract automatically reads and processes scanned documents to extract text, handwriting, tables, and data. •​Amazon Transcribe converts speech to text. •​Amazon Translate uses deep-learning models to deliver accurate, natural-sounding translation.

It spies on you. It mines you for data. It extracts your soul and then sells it back to you. It’s designed to make you spend money so you’re too busy shopping to notice the world is burning down.

My students always grimace when I say the best way to understand Facebook is that it was a creation of a horny nineteen-year-old with more computing skills than social skills, and this was a way he could get to meet, in the abstract, the women he wanted to be with. Because that’s what Facebook was. It was a network that he built where people would submit pictures of themselves and he could select them at his leisure without them knowing that he was looking at them. Once you start from that understanding, Facebook’s extraction of personal data and sales to advertisers makes a lot more sense. It never has been about community. You can see how poorly they understand community with the way they moderate and run Facebook groups. It always has been about the extraction of something to satisfy the libidinal, whether it’s voyeurism or simply wanting to profit off of others.

The first of these was “data extraction and analysis,” from which we deduced the extraction imperative as one of the foundational mechanisms of surveillance capitalism.

Butterflies fluttered about him, as if they were extracting data.

Victims included U.S. state and local entities, such as state boards of elections (SBOEs), secretaries of state, and county governments, as well as individuals who worked for those entities.186 The GRU also targeted private technology firms responsible for manufacturing and administering election-related software and hardware, such as voter registration software and electronic polling stations.187 The GRU continued to target these victims through the elections in November 2016. While the investigation identified evidence that the GRU targeted these individuals and entities, the Office did not investigate further. The Office did not, for instance, obtain or examine servers or other relevant items belonging to these victims. The Office understands that the FBI, the U.S. Department of Homeland Security, and the states have separately investigated that activity. By at least the summer of 2016, GRU officers sought access to state and local computer networks by exploiting known software vulnerabilities on websites of state and local governmental entities. GRU officers, for example, targeted state and local databases of registered voters using a technique known as "SQL injection," by which malicious code was sent to the state or local website in order to run commands (such as exfiltrating the database contents).188 In one instance in approximately June 2016, the GRU compromised the computer network of the Illinois State Board of Elections by exploiting a vulnerability in the SBOE's website. The GRU then gained access to a database containing information on millions of registered Illinois voters,189 and extracted data related to thousands of U.S. voters before the malicious activity was identified.190 GRU officers [REDACTED: Investigative Technique] scanned state and local websites for vulnerabilities. For example, over a two-day period in July 2016, GRU officers [REDACTED: Investigative Technique] for vulnerabilities on websites of more than two dozen states.

after extracting the industry’s highest fees—a vertiginous 5 percent of money under management and 44 percent of the profits—the Medallion Fund was said to be up 80 percent.

As we have already seen, our minds toggle back and forth between System 1 and System 2 thinking. When we make a snap judgment, we still make all the System 2 moves—sizing up the scenario, plucking out the relevant data, joining the dots, pinpointing the best course of action—we just do it a whole lot faster. Psychologists call this “thin-slicing,” because we extract all the necessary information from a tiny sliver of experience.

Within days, an independent analysis by German security experts proved decisively that Street View’s cars were extracting unencrypted personal information from homes. Google was forced to concede that it had intercepted and stored “payload data,” personal information grabbed from unencrypted Wi-Fi transmissions. As its apologetic blog post noted, “In some instances entire emails and URLs were captured, as well as passwords.” Technical experts in Canada, France, and the Netherlands discovered that the payload data included names, telephone numbers, credit information, passwords, messages, e-mails, and chat transcripts, as well as records of online dating, pornography, browsing behavior, medical information, location data, photos, and video and audio files. They concluded that such data packets could be stitched together for a detailed profile of an identifiable person.39

Other psychological tricks we play on ourselves include the recency bias and sample size bias. The recency bias says that we weight recent information more heavily than a body of past evidence. As a result, we tend to overrate players who have done well recently even if they did poorly in the past. Sample size bias is related. The natural tendency is to extract more meaning from small samples than the data warrant. Psychologists who study these biases can teach us a great deal about why we struggle to sort out skill and luck.

The OAuth 2.0 specification is built around three types of client profiles: web applications, user agent–based applications, and native applications. Web applications are considered to be confidential clients, running on a web server: end users or resource owners access such applications via a web browser. User agent–based applications are considered to be public clients: they download the code from a web server and run it on the user agent, such as JavaScript running in the browser. These clients are incapable of protecting their credentials—the end user can see anything in the JavaScript. Native applications are also considered as public clients: these clients are under the control of the end user, and any confidential data stored in those applications can be extracted out. Android and iOS native applications are a couple of examples.

As much as you might be laser-like in your attempt to extract value, it’s critical to remember your personal and corporate values as you work with data to add value as well.

There are five ways technology can boost marketing practices: Make more informed decisions based on big data. The greatest side product of digitalization is big data. In the digital context, every customer touchpoint—transaction, call center inquiry, and email exchange—is recorded. Moreover, customers leave footprints every time they browse the Internet and post something on social media. Privacy concerns aside, those are mountains of insights to extract. With such a rich source of information, marketers can now profile the customers at a granular and individual level, allowing one-to-one marketing at scale. Predict outcomes of marketing strategies and tactics. No marketing investment is a sure bet. But the idea of calculating the return on every marketing action makes marketing more accountable. With artificial intelligence–powered analytics, it is now possible for marketers to predict the outcome before launching new products or releasing new campaigns. The predictive model aims to discover patterns from previous marketing endeavors and understand what works, and based on the learning, recommend the optimized design for future campaigns. It allows marketers to stay ahead of the curve without jeopardizing the brands from possible failures. Bring the contextual digital experience to the physical world. The tracking of Internet users enables digital marketers to provide highly contextual experiences, such as personalized landing pages, relevant ads, and custom-made content. It gives digital-native companies a significant advantage over their brick-and-mortar counterparts. Today, the connected devices and sensors—the Internet of Things—empowers businesses to bring contextual touchpoints to the physical space, leveling the playing field while facilitating seamless omnichannel experience. Sensors enable marketers to identify who is coming to the stores and provide personalized treatment. Augment frontline marketers’ capacity to deliver value. Instead of being drawn into the machine-versus-human debate, marketers can focus on building an optimized symbiosis between themselves and digital technologies. AI, along with NLP, can improve the productivity of customer-facing operations by taking over lower-value tasks and empowering frontline personnel to tailor their approach. Chatbots can handle simple, high-volume conversations with an instant response. AR and VR help companies deliver engaging products with minimum human involvement. Thus, frontline marketers can concentrate on delivering highly coveted social interactions only when they need to. Speed up marketing execution. The preferences of always-on customers constantly change, putting pressure on businesses to profit from a shorter window of opportunity. To cope with such a challenge, companies can draw inspiration from the agile practices of lean startups. These startups rely heavily on technology to perform rapid market experiments and real-time validation.

While it was possible, then, for Apple to make up ground in software very quickly, doing so in the world of data was substantially more challenging even for a company of its resources. There are no shortcuts, as data simply cannot be generated overnight. There are only two means of producing it: collecting it over time, or acquiring an entity who has done so. Unlike software, then, which is a thin shield against would-be market entrants, organizations that amass a large body of data from which to extract value for themselves or their customers are well protected against even the largest market incumbents. Every software organization today should be aggregating data, because customers are demanding it. Consider, for example, online media services such as Netflix or Pandora. Their ability to improve their recommendations to customers for movies or music depends in turn on the data they’ve collected from other customers. This data, over time, becomes far more difficult to compete with than mere software. Which likely explains why Netflix is willing to open source the majority of its software portfolio but guards the API access to its user data closely.

Empirical relationship between the bandwidth of a signal and its 10–90 rise time, as measured from a re-created ideal square wave with each harmonic added one at a time. Circles are the values extracted from the data; line is the approximation of BW = 0.35/rise time.

By at least the summer of 2016, GRU officers sought access to state and local computer networks by exploiting known software vulnerabilities on websites of state and local governmental entities. GRU officers, for example, targeted state and local databases of registered voters using a technique known as "SQL injection," by which malicious code was sent to the state or local website in order to run commands (such as exfiltrating the database contents).188 In one instance in approximately June 2016, the GRU compromised the computer network of the Illinois State Board of Elections by exploiting a vulnerability in the SBOE's website. The GRU then gained access to a database containing information on millions of registered Illinois voters,189 and extracted data related to thousands of U.S. voters before the malicious activity was identified.190

The genius of Monte Carlo—and its search-engine descendants—lies in the ability to extract meaningful solutions, in the face of overwhelming information, by recognizing that meaning resides less in the data at the end points and more in the intervening paths.

Shel Israel has been a diabetic for many years, jabbing his finger a few times every day to measure his blood sugar. Every six months he brings his glucose meter to his endocrinologist, who extracts and analyzes the data. His pharmacist recently informed him that a new California law requires him to share his data with them as well or his insurance coverage will be dropped, raising the monthly cost from about $8.25 to about $165. Who is behind this law?

The constant dilemma of the information age is that our ability to gather a sea of data greatly exceeds the tools and techniques available to sort, extract, and apply the information we’ve collected.” - Jeff Davidson, work-life balance expert, author, columnist