Analyzing Data Quotes

The crucial challenge is to learn how to read critically, analyze data, and formulate ideas—and most of all to enjoy the intellectual adventure enough to be able to do them easily and often.

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.

Analyzing data from 79 men and women who wore inconspicuous devices that recorded some of their conversations over the course of four days, researchers from Washington University and the University of Arizona found a correlation between feelings of well-being and the amount of time spent talking every day. Moreover, the more substantive your conversations, the happier you're likely to be. In other words, heart-to-hearts trump small talk. (LA Times, "A lof of happy talk", March 11, 2010, A21.)

Trivia are not knowledge. Lists of facts don't comprise knowledge. Analyzing, hypothesizing, concluding from data, sharing insights, those comprise knowledge. You can't google for knowledge.

I became like the bee: intensely gathering information from as many sources as possible and analyzing the material to construct my own understanding of Muhammad’s mindset. I analyzed every piece of data, scrutinizing it for accuracy. I sought to shorten as much as possible the chains of scholarly transmission that separated me from Muhammad. Approaching Muhammad with an open mind proved transformational: making my own sense of him forged a much more meaningful personal relationship with his legacy.

Once you have analyzed data, you have to mine that data to find insights from it. At this point, you can involve your marketing or product team to work with the data analysis team.

Quantitative market research deals with collecting and analyzing numerical data to describe or predict the variables of your interest.

Perception requires imagination because the data people encounter in their lives are never complete and always equivocal. For example, most people consider that the greatest evidence of an event one can obtain is to see it with their own eyes, and in a court of law little is held in more esteem than eyewitness testimony. Yet if you asked to display for a court a video of the same quality as the unprocessed data catptured on the retina of a human eye, the judge might wonder what you were tryig to put over. For one thing, the view will have a blind spot where the optic nerve attaches to the retina. Moreover, the only part of our field of vision with good resolution is a narrow area of about 1 degree of visual angle around the retina’s center, an area the width of our thumb as it looks when held at arm’s length. Outside that region, resolution drops off sharply. To compensate, we constantly move our eyes to bring the sharper region to bear on different portions of the scene we wish to observe. And so the pattern of raw data sent to the brain is a shaky, badly pixilated picture with a hole in it. Fortunately the brain processes the data, combining input from both eyes, filling in gaps on the assumption that the visual properties of neighboring locations are similar and interpolating. The result - at least until age, injury, disease, or an excess of mai tais takes its toll - is a happy human being suffering from the compelling illusion that his or her vision is sharp and clear. We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out “picture” is clear and accurate. But is it?

In the first study, Grant and his colleagues analyzed data from one of the five biggest pizza chains in the United States. They discovered that the weekly profits of the stores managed by extroverts were 16 percent higher than the profits of those led by introverts—but only when the employees were passive types who tended to do their job without exercising initiative. Introverted leaders had the exact opposite results. When they worked with employees who actively tried to improve work procedures, their stores outperformed those led by extroverts by more than 14 percent.

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.

If you found yourself upset at some other society’s customs, Boas argued, the truly scientific thing to do was to analyze your own reaction. It was probably a good clue to the things that your own culture held dear. The best data generator was your own sense of disgust.

One major irony here is that law, which always lags behind technological innovation by at least a generation, gives substantially more protections to a communication’s content than to its metadata—and yet intelligence agencies are far more interested in the metadata—the activity records that allow them both the “big picture” ability to analyze data at scale, and the “little picture” ability to make perfect maps, chronologies, and associative synopses of an individual person’s life, from which they presume to extrapolate predictions of behavior.

The hard work, you discover over the years, is in learning to discern between correct and incorrect anxiety, between the anxiety that’s trying to warn you about a real danger and the anxiety that’s nothing more than a lying, sadistic, unrepentant bully in your head. The hard work is in learning to step back and analyze the data dispassionately.

What makes him successful is the way that he analyzes information. He is not just hunting for patterns. Instead, Bob combines his knowledge of statistics with his knowledge of basketball in order to identify meaningful relationships in the data.

Data is your Beta...

What’s it like being a writer? Mostly it’s like being a child surrounded by adults. My friends have grown-up careers. They balance spreadsheets, analyze data, negotiate deals. They build things, heal patients, teach children. Meanwhile, I’m over here saying “Let’s pretend.

Data is the blood of any organization; coming from everywhere, used everywhere, connecting all the body, transferring messages and when analyzed it reflects the whole picture of the body.

Authors are curators of experience. They filter the world's noise, and out of that noise they make the purest signal they can-out of disorder they create narrative. They administer this narrative in the form of a book, and preside, in some ineffable way, over the reading experience. Yet no matter how pure the data set that authors provide to readers-no matter how diligently prefiltered and tightly reconstructed-readers' brains will continue in their prescribed assignment: to analyze, screen, and sort. Our brains will treat a book as if it were any other of the world's many unfiltered, encrypted signals. That is, the author's book, for readers, reverts to a species of noise. We take in as much of the author's world as we can, and mix this material with our own in the alembic of our reading minds, combining them to alchemize something unique. I would propose that this is why reading "works": reading mirrors the procedure by which we acquaint ourselves with the world. It is not that our narratives necessarily tell us something true about the world (though they might), but rather that the practice of reading feels like, and is like, consciousness itself: imperfect; partial; hazy; co-creative.

We are unlikely to face a robot rebellion in the coming decades, but we might have to deal with hordes of bots that know how to press our emotional buttons better than our mother does and that use this uncanny ability to try to sell us something—be it a car, a politician, or an entire ideology. The bots could identify our deepest fears, hatreds, and cravings and use these inner leverages against us. We have already been given a foretaste of this in recent elections and referendums across the world, when hackers learned how to manipulate individual voters by analyzing data about them and exploiting their existing prejudices.

Fitbit is a company that knows the value of Shadow Testing. Founded by Eric Friedman and James Park in September 2008, Fitbit makes a small clip-on exercise and sleep data-gathering device. The Fitbit device tracks your activity levels throughout the day and night, then automatically uploads your data to the Web, where it analyzes your health, fitness, and sleep patterns. It’s a neat concept, but creating new hardware is time-consuming, expensive, and fraught with risk, so here’s what Friedman and Park did. The same day they announced the Fitbit idea to the world, they started allowing customers to preorder a Fitbit on their Web site, based on little more than a description of what the device would do and a few renderings of what the product would look like. The billing system collected names, addresses, and verified credit card numbers, but no charges were actually processed until the product was ready to ship, which gave the company an out in case their plans fell through. Orders started rolling in, and one month later, investors had the confidence to pony up $2 million dollars to make the Fitbit a reality. A year later, the first real Fitbit was shipped to customers. That’s the power of Shadow Testing.

The crucial challenge is to learn how to read critically, analyze data, and formulate ideas—and

Should you start with a hypothesis and analyze data in a way that supported it, or start with the data and sift through it for a useful hypothesis?

newish portmanteau of “anecdote” and “data,” “anecdata” refers to personal experiences or anecdotes that are treated like objectively collected and analyzed data.

Frankly, the overwhelming majority of academics have ignored the data explosion caused by the digital age. The world’s most famous sex researchers stick with the tried and true. They ask a few hundred subjects about their desires; they don’t ask sites like PornHub for their data. The world’s most famous linguists analyze individual texts; they largely ignore the patterns revealed in billions of books. The methodologies taught to graduate students in psychology, political science, and sociology have been, for the most part, untouched by the digital revolution. The broad, mostly unexplored terrain opened by the data explosion has been left to a small number of forward-thinking professors, rebellious grad students, and hobbyists. That will change.

Many doctors focus almost exclusively on processing information: they absorb medical data, analyze it, and produce a diagnosis. Nurses, in contrast, need good motor and emotional skills in order to give a painful injection, replace a bandage, or restrain a violent patient. Therefore we will probably have an AI family doctor on our smartphone decades before we have a reliable nurse robot.9 The human care industry—which takes care of the sick, the young, and the elderly—is likely to remain a human bastion for a long time. Indeed, as people live longer and have fewer children, care of the elderly will probably be one of the fastest-growing sectors in the human labor market.

What was once an anonymous medium where anyone could be anyone—where, in the words of the famous New Yorker cartoon, nobody knows you’re a dog—is now a tool for soliciting and analyzing our personal data. According to one Wall Street Journal study, the top fifty Internet sites, from CNN to Yahoo to MSN, install an average of 64 data-laden cookies and personal tracking beacons each. Search for a word like “depression” on Dictionary.com, and the site installs up to 223 tracking cookies and beacons on your computer so that other Web sites can target you with antidepressants. Share an article about cooking on ABC News, and you may be chased around the Web by ads for Teflon-coated pots. Open—even for an instant—a page listing signs that your spouse may be cheating and prepare to be haunted with DNA paternity-test ads. The new Internet doesn’t just know you’re a dog; it knows your breed and wants to sell you a bowl of premium kibble.

intellectuals who reject eternal truths and experience through the ages for the social engineering by supposed experts and their administrative state—which claim to use data, science, and empiricism to analyze, manage, and control society.

Critical thinking using root definitions is a skill set that allows an individual, by themselves, to judge and settle disputes as well as set limits on what is considered to be morally ethical and correct manners of someone who has one’s behavior in society. However, critical thinking is much more than its root definition. Critical thinking is skillfully defining, intellectualizing, analyzing, and evaluating data and information gathered from all sources and producing belief and action in rhetoric that provides clarity and consistency through evidence and reason.

All I ask is to see accurate and authentic data, analyzed from all directions—free of bias and tunnel vision—before I layer my emotions upon it. In the end, we must live with the consequences of our decisions. After all input of facts and statistical analysis, our emotions may defy reconciliation with data.

Good teams get their inspiration and product ideas from their vision and objectives, from observing customers' struggle, from analyzing the data customers generate from using their product, and from constantly seeking to apply new technology to solve real problems. Bad teams gather requirements from sales and customers.

Demonstrate ROI. In this approach, you gather and analyze data to prove that a usability change you’ve made resulted in cost savings or additional revenue (“Changing the label on this button increased sales by 0.25%”). There’s an excellent book about it: Cost-justifying Usability: An Update for the Internet Age, edited by Randolph Bias and Deborah Mayhew.

In a colorful part of the paper, he presented a fictional scenario in which he posed questions to the machine. He imagined the machine’s activity: “Over the week-end it retrieved over 10,000 documents, scanned them all for sections rich in relevant material, analyzed all the rich sections into statements in a high-order predicate calculus, and entered the statements into the data base.

asking about sampling errors and margins of error, debating if the number is rising or falling, believing, doubting, analyzing, dissecting—without taking the time to understand the first and most obvious fact: What is being measured, or counted? What definition is being used? Yet while this pitfall is common, it doesn’t seem to have acquired a name. My suggestion is “premature enumeration.

What’s more, attempting to score a teacher’s effectiveness by analyzing the test results of only twenty-five or thirty students is statistically unsound, even laughable. The numbers are far too small given all the things that could go wrong. Indeed, if we were to analyze teachers with the statistical rigor of a search engine, we’d have to test them on thousands or even millions of randomly selected students.

More often than not, at the end of the day (or a month, or a year), you realize that your initial idea was wrong, and you have to try something else. These are the moments of frustration and despair. You feel that you have wasted an enormous amount of time, with nothing to show for it. This is hard to stomach. But you can never give up. You go back to the drawing board, you analyze more data, you learn from your previous mistakes, you try to come up with a better idea. And every once in a while, suddenly, your idea starts to work. It's as if you had spent a fruitless day surfing, when you finally catch a wave: you try to hold on to it and ride it for as long as possible. At moments like this, you have to free your imagination and let the wave take you as far as it can. Even if the idea sounds totally crazy at first.

Clarify goals and gather satisfaction metrics. Determine the people and skills needed to complete a project. Set up project management tools, plans and processes. Run status meetings and gather status reports. Analyze data to identify opportunities. Identify & implement changes to improve efficiency. Manage changes that come in from the customer. Find ways to keep the project on track even when things go wrong.

In a provocative article in Wired, editor-in-chief Chris Anderson argued that huge databases render scientific theory itself obsolete. Why spend time formulating human-language hypotheses, after all, when you can quickly analyze trillions of bits of data and find the clusters and correlations? He quotes Peter Norvig, Google’s research director: “All models are wrong, and increasingly you can succeed without them.

Take healthcare, for example. Many doctors focus almost exclusively on processing information: they absorb medical data, analyze it, and produce a diagnosis. Nurses, in contrast, need good motor and emotional skills in order to give a painful injection, replace a bandage, or restrain a violent patient. Therefore we will probably have an AI family doctor on our smartphone decades before we have a reliable nurse robot.

The Swedish town of Överkalix has the most comprehensive and oldest birth, death, and crop records in the world. Their records go back generations—a remarkably rich data set. And in analyzing this data set, scientists found some fascinating correlations. There were good and bad years for the crops in Överkalix and some particularly bad years where families were forced to go hungry. But scientists discovered that when children suffered starvation between the ages of nine and twelve, their grandchildren would on average live thirty years longer. Their descendants had far lower rates of diabetes and heart disease. On the other hand, when children were well-fed during those ages, their descendants were at four times the risk for heart attacks and their life expectancy dropped. In some strange way, the trauma of starvation changed descendants’ genes to be more resilient. Healthier. More likely to survive.[5] — Clearly, it wasn’t just my ruthless nurture that had shaped me into who I was, though who knows what kind of rampant methylation savaged my epigenome during my beatings and assaults. Beyond that, every cell in my body is filled with the code of generations of trauma, of death, of birth, of migration, of history that I cannot understand. Just piecemeal moments I collected from Auntie over the years. My family tried to erase this history. But my body remembers. My work ethic. My fear of cockroaches. My hatred for the taste of dirt. These are not random attributes, a spin of the wheel. They were gifted to me with purpose, with necessity. I want to have words for what my bones know. I want to use those gifts when they serve me and understand and forgive them when they do not.

While humans lack AI’s ability to analyze huge numbers of data points at the same time, people have a unique ability to draw on experience, abstract concepts, and common sense to make decisions. By contrast, in order for deep learning to function well, the following are required: massive amounts of relevant data, a narrow domain, and a concrete objective function to optimize. If you’re short on any one of these, things may fall apart.

We think of our eyes as video cameras and our brains as blank tapes to be filled with percepts. Memory, in this flawed model, is simply rewinding the tape and playing it back in the theater of the mind. This is not at all what happens. The perceptual system, and the brain that analyzes its data, are deeply influenced by the beliefs it already holds. As a consequence, much of what passes before our eyes may be invisible to a brain focused on something else.

Elsewhere, statisticians were using similar approaches—called kernel methods—to analyze patterns in data sets. Back on Long Island, Henry Laufer was working on similar machine-learning tactics in his own research and was set to share it with Simons and others. Carmona wasn’t aware of this work. He was simply proposing using sophisticated algorithms to give Ax and Straus the framework to identify patterns in current prices that seemed similar to those in the past.

In a paper analyzing the data, Chetty and his coauthors noted two important factors that explained the uneven geographic distribution of opportunity: the prevalence of single parents and income segregation. Growing up around a lot of single moms and dads and living in a place where most of your neighbors are poor really narrows the realm of possibilities. It means that unless you have a Mamaw and Papaw to make sure you stay the course, you might never make it out.

The danger is that if we invest too much in developing AI and too little in developing human consciousness, the very sophisticated artificial intelligence of computers might only serve to empower the natural stupidity of humans. We are unlikely to face a robot rebellion in the coming decades, but we might have to deal with hordes of bots that know how to press our emotional buttons better than our mother does and that use this uncanny ability to try to sell us something—be it a car, a politician, or an entire ideology. The bots could identify our deepest fears, hatreds, and cravings and use these inner leverages against us. We have already been given a foretaste of this in recent elections and referendums across the world, when hackers learned how to manipulate individual voters by analyzing data about them and exploiting their existing prejudices.33 While science fiction thrillers are drawn to dramatic apocalypses of fire and smoke, in reality we might be facing a banal apocalypse by clicking.

Grant had a theory about which kinds of circumstances would call for introverted leadership. His hypothesis was that extroverted leaders enhance group performance when employees are passive, but that introverted leaders are more effective with proactive employees. To test his idea, he and two colleagues, professors Francesca Gino of Harvard Business School and David Hofman of the Kenan-Flagler Business School at the University of North Carolina, carried out a pair of studies of their own. In the first study, Grant and his colleagues analyzed data from one of the five biggest pizza chains in the United States. They discovered that the weekly profits of the stores managed by extroverts were 16 percent higher than the profits of those led by introverts—but only when the employees were passive types who tended to do their job without exercising initiative. Introverted leaders had the exact opposite results. When they worked with employees who actively tried to improve work procedures, their stores outperformed those led by extroverts by more than 14 percent. In

Grant and his colleagues analyzed data from one of the five biggest pizza chains in the United States. They discovered that the weekly profits of the stores managed by extroverts were 16 percent higher than the profits of those led by introverts—but only when the employees were passive types who tended to do their job without exercising initiative. Introverted leaders had the exact opposite results. When they worked with employees who actively tried to improve work procedures, their stores outperformed those led by extroverts by more than 14 percent.

The fourth article, which ran as planned on Saturday, was about BOUNDLESS INFORMANT, the NSA’s data-tracking program, and it described the reports showing that the NSA was collecting, analyzing, and storing billions of telephone calls and emails sent across the American telecommunications infrastructure. It also raised the question of whether NSA officials had lied to Congress when they had refused to answer senators about the number of domestic communications intercepted, claiming that they did not keep such records and could not assemble such data. After

The principal aim underlying this work is to render homage where homage is due, a task which I know beforehand is impossible of accomplishment. Were I to do it properly, I would have to get down on my knees and thank each blade of grass for rearing its head. What chiefly motivates me in this vain task is the fact that in general we know all too little about the influences which shape a writer’s life and work. The critic, in his pompous conceit and arrogance, distorts the true picture beyond all recognition. The author, however truthful he may think himself to be, inevitably disguises the picture. The psychologist, with his single-track view of things, only deepens the blur. As author, I do not think myself an exception to the rule. I, too, am guilty of altering, distorting and disguising the facts — if ‘facts’ there be. My conscious effort, however, has been — perhaps to a fault– in the opposite direction. I am on the side of revelation, if not always on the side of beauty, truth, wisdom, harmony and ever-evolving perfection. In this work I am throwing out fresh data, to be judged and analyzed, or accepted and enjoyed for enjoyment’s sake. Naturally I cannot write about all the books, or even all the significant ones, which I have read in the course of my life. But I do intend to go on writing about books and authors until I have exhausted the importance (for me) of this domain of reality. To have undertaken the thankless task of listing all the books I can recall ever reading gives me extreme pleasure and satisfaction. I know of no author who has been mad enough to attempt this. Perhaps my list will give rise to more confusion — but its purpose is not that. Those who know how to read a man know how to read his books.

Roy Jastram has produced a systematic study of the purchasing power of gold over the longest consistent datasets available.6 Observing English data from 1560 to 1976 to analyze the change in gold's purchasing power in terms of commodities, Jastram finds gold dropping in purchasing power during the first 140 years, but then remaining relatively stable from 1700 to 1914, when Britain went off the gold standard. For more than two centuries during which Britain primarily used gold as money, its purchasing power remained relatively constant, as did the price of wholesale commodities.

One day, Carmona had an idea. Axcom had been employing various approaches to using their pricing data to trade, including relying on breakout signals. They also used simple linear regressions, a basic forecasting tool relied upon by many investors that analyzes the relationships between two sets of data or variables under the assumption those relationships will remain linear. Plot crude-oil prices on the x-axis and the price of gasoline on the y-axis, place a straight regression line through the points on the graph, extend that line, and you usually can do a pretty good job predicting prices at the pump for a given level of oil price.

Computer cores made if liquid crystal that can re-form itself into any configuration, creating the ultimate efficiency for any particular piece of cybernetic business that needs doing, shifting from storage of data to moving it to analyzing it and the altering to a form most efficient for acting on the analysis.Hearts that can make minds, from little bits if brightness in Cowboy's skull that let him move his panzer, to large models that create working analogs of the human brain, the vast artificial intelligences that keep things moving smoothly for the Orbitals and the governments of the planet. All in miniature potential, here in the cardboard box.

Computer cores made of liquid crystal that can re-form itself into any configuration, creating the ultimate efficiency for any particular piece of cybernetic business that needs doing, shifting from storage of data to moving it to analyzing it and the altering to a form most efficient for acting on the analysis. Hearts that can make minds, from little bits if brightness in Cowboy's skull that let him move his panzer, to large models that create working analogs of the human brain, the vast artificial intelligences that keep things moving smoothly for the Orbitals and the governments of the planet. All in miniature potential, here in the cardboard box.

When Amabile analyzed the data, she came to a clear conclusion about one key factor: workers are happiest—and most motivated—when they feel that they accomplish something meaningful at work. These accomplishments do not need to be major breakthroughs: incremental but noticeable progress toward a goal was enough to make her subjects feel good. As one programmer described it, “I smashed that [computer] bug that’s been frustrating me for almost a calendar week. That may not be an event to you, but I live a very drab life, so I’m all hyped.”1 The lesson here is that managers can get the most out of their employees by helping them achieve meaningful progress every day.

Following someone covertly, either on foot or by car, costs around $175,000 per month—primarily for the salary of the agents doing the following. But if the police can place a tracker in the suspect’s car, or use a fake cell tower device to fool the suspect’s cell phone into giving up its location information, the cost drops to about $70,000 per month, because it only requires one agent. And if the police can hide a GPS receiver in the suspect’s car, suddenly the price drops to about $150 per month—mostly for the surreptitious installation of the device. Getting location information from the suspect’s cell provider is even cheaper: Sprint charges law enforcement only $30 per month. The difference is between fixed and marginal costs. If a police department performs surveillance on foot, following two people costs twice as much as following one person. But with GPS or cell phone surveillance, the cost is primarily for setting up the system. Once it is in place, the additional marginal cost of following one, ten, or a thousand more people is minimal. Or, once someone spends the money designing and building a telephone eavesdropping system that collects and analyzes all the voice calls in Afghanistan, as the NSA did to help defend US soldiers from improvised explosive devices, it’s cheap and easy to deploy that same technology against the telephone networks of other countries.

I have spent just about every day of the past four years analyzing Google data. This included a stint as a data scientist at Google, which hired me after learning about my racism research. And I continue to explore this data as an opinion writer and data journalist for the New York Times. The revelations have kept coming. Mental illness; human sexuality; child abuse; abortion; advertising; religion; health. Not exactly small topics, and this dataset, which didn’t exist a couple of decades ago, offered surprising new perspectives on all of them. Economists and other social scientists are always hunting for new sources of data, so let me be blunt: I am now convinced that Google searches are the most important dataset ever collected on the human psyche.

One may take the line that metaphorical devices are inevitable in the early stages of any science and that although we may look with amusement today upon the “essences,” “forces,” “phlogistons,” and “ethers,” of the science of yesterday, these nevertheless were essential to the historical process. It would be difficult to prove or disprove this. However, if we have learned anything about the nature of scientific thinking, if mathematical and logical researches have improved our capacity to represent and analyze empirical data, it is possible that we can avoid some of the mistakes of adolescence. Whether Freud could have done so is past demonstrating, but whether we need similar constructs in the future prosecution of a science of behavior is a question worth considering.

In their seminal work, The History of Science and Technology, Bunch and Hellemans compile a list of the 8,583 most important innovations and inventions in the history of science and technology. Physicist Jonathan Huebner17 analyzed all these events along with the years in which they happened and global population at that year, and measured the rate of occurrence of these events per year per capita since the Dark Ages. Huebner found that while the total number of innovations rose in the twentieth century, the number of innovations per capita peaked in the nineteenth century. A closer look at the innovations of the pre-1914 world lends support to Huebner's data. It is no exaggeration to say that our modern world was invented in the gold standard years preceding World War I.

In their book American Grace: How Religion Divides and Unites Us, political scientists Robert Putnam and David Campbell analyzed a variety of data sources to describe how religious and nonreligious Americans differ. Common sense would tell you that the more time and money people give to their religious groups, the less they have left over for everything else. But common sense turns out to be wrong. Putnam and Campbell found that the more frequently people attend religious services, the more generous and charitable they become across the board.58 Of course religious people give a lot to religious charities, but they also give as much as or more than secular folk to secular charities such as the American Cancer Society.59 They spend a lot of time in service to their churches and synagogues, but they also spend more time than secular folk serving in neighborhood and civic associations of all sorts. Putnam and Campbell put their findings bluntly: By many different measures religiously observant Americans are better neighbors and better citizens than secular Americans—they are more generous with their time and money, especially in helping the needy, and they are more active in community life.60 Why are religious people better neighbors and citizens? To find out, Putnam and Campbell included on one of their surveys a long list of questions about religious beliefs (e.g., “Do you believe in hell? Do you agree that we will all be called before God to answer for our sins?”) as well as questions about religious practices (e.g., “How often do you read holy scriptures? How often do you pray?”). These beliefs and practices turned out to matter very little. Whether you believe in hell, whether you pray daily, whether you are a Catholic, Protestant, Jew, or Mormon … none of these things correlated with generosity. The only thing that was reliably and powerfully associated with the moral benefits of religion was how enmeshed people were in relationships with their co-religionists. It’s the friendships and group activities, carried out within a moral matrix that emphasizes selflessness. That’s what brings out the best in people. Putnam and Campbell reject the New Atheist emphasis on belief and reach a conclusion straight out of Durkheim: “It is religious belongingness that matters for neighborliness, not religious believing.”61

Hildebrand, too, challenged the ideals of scientific naturalism by an appeal to the psychology of perception: if we attempt to analyze our mental images to discover their primary constituents, we will find them composed of sense data derived from vision and from memories of touch and movement. A sphere, for instance, appears to the eye as a flat disk; it is touch which informs us of the properties of space and form. Any attempt on the part of the artist to eliminate this knowledge is futile, for without it he would not perceive the world at all. His task is, on the contrary, to compensate for the absence of movement in his work by clarifying his image and thus conveying not only visual sensations but also those memories of touch which enable us to reconstitute the three-dimensional form in our minds.

We can assume that by now the Rasu have captured and analyzed zettabytes of government data from Namino. There’s zero chance they don’t possess the locations of every Dominion world. Why haven’t they attacked us somewhere else yet?” An uneasy silence answered Maris. Nika was reluctant to break it, but hiding from the truth did them no good. “Because the Rasu don’t fear us.” Dashiel frowned at her. “But we destroyed their entire presence in this galaxy.” “We did. And by now, they realize that we accomplished it using smoke and mirrors and are unlikely to be able to replicate the feat anytime soon. They don’t fear us, which means they can afford to take their time, methodically dismantling our civilization block by block, then planet by planet.” Lance arched an eyebrow. “Then we need to make them fear us again.

Both studies in these respected publications relied on data from the Surgisphere Corporation, an obscure Illinois-based “medical education” company that claimed to somehow control an extraordinary global database boasting access to medical information from 96,000 patients in more than 600 hospitals.87 Founded in 2008, this sketchy enterprise had eleven employees, including a middling science fiction writer and a porn star/events hostess. Surgisphere claimed to have analyzed data from six continents and hundreds of hospitals that had treated patients with HCQ or CQ in real time. Someone persuaded the Lancet and the New England Journal of Medicine to publish two Surgisphere studies in separate articles on May 1 and 22. Like the other Gates-supported studies, the Lancet article portrayed HCQ as ineffective and dangerous.

Who among us can predict the future? Who would dare to? The answer to the first question is no one, really, and the answer to the second is everyone, especially every government and business on the planet. This is what that data of ours is used for. Algorithms analyze it for patterns of established behavior in order to extrapolate behaviors to come, a type of digital prophecy that’s only slightly more accurate than analog methods like palm reading. Once you go digging into the actual technical mechanisms by which predictability is calculated, you come to understand that its science is, in fact, anti-scientific, and fatally misnamed: predictability is actually manipulation. A website that tells you that because you liked this book you might also like books by James Clapper or Michael Hayden isn’t offering an educated guess as much as a mechanism of subtle coercion.

Then there is degree of contrast: When determining contrast, the pre-attentional parts analyze incoming sensory data against the background inputs. As an example, if you are at a party where many people are talking, not only is the sound gated but the semantic meanings in the hum of conversation are also gated. Essentially, both sound and the meanings-in-the-sounds are reduced in intensity so you don’t get overwhelmed by the incoming sensory inputs. However, should you hear your name from across the crowded room, Did you hear what happened between Michael and Jenny? the gating channel that is contrasting sound meanings in the room will open more widely and allow the sensory input through. It signals the cerebral cortex to pay attention. Once signaled, the cortex, in association with other parts of the brain, uses stochastic processes to enhance the signal so that what is being said can be heard in detail.

Sound waves, regardless of their frequency or intensity, can only be detected by the Mole Fly’s acute sense of smell—it is a little known fact that the Mole Fly’s auditory receptors do not, in fact, have a corresponding center in the brain designated for the purposes of processing sensory stimuli and so, these stimuli, instead of being siphoned out as noise, bypass the filters to be translated, oddly enough, by the part of the brain that processes smell. Consequently, the Mole Fly’s brain, in its inevitable confusion, understands sound as an aroma, rendering the boundary line between the auditory and olfactory sense indistinguishable. Sounds, thus, come in a variety of scents with an intensity proportional to its frequency. Sounds of shorter wavelength, for example, are particularly pungent. What results is a species of creature that cannot conceptualize the possibility that sound and smell are separate entities, despite its ability to discriminate between the exactitudes of pitch, timbre, tone, scent, and flavor to an alarming degree of precision. Yet, despite this ability to hyper-analyze, they lack the cognitive skill to laterally link successions of either sound or smell into a meaningful context, resulting in the equivalent of a data overflow. And this may be the most defining element of the Mole Fly’s behavior: a blatant disregard for the context of perception, in favor of analyzing those remote and diminutive properties that distinguish one element from another. While sensory continuity seems logical to their visual perception, as things are subject to change from moment-to-moment, such is not the case with their olfactory sense, as delays in sensing new smells are granted a degree of normality by the brain. Thus, the Mole Fly’s olfactory-auditory complex seems to be deprived of the sensory continuity otherwise afforded in the auditory senses of other species. And so, instead of sensing aromas and sounds continuously over a period of time—for example, instead of sensing them 24-30 times per second, as would be the case with their visual perception—they tend to process changes in sound and smell much more slowly, thereby preventing them from effectively plotting the variations thereof into an array or any kind of meaningful framework that would allow the information provided by their olfactory and auditory stimuli to be lasting in their usefulness. The Mole flies, themselves, being the structurally-obsessed and compulsive creatures that they are, in all their habitual collecting, organizing, and re-organizing of found objects into mammoth installations of optimal functional value, are remarkably easy to control, especially as they are given to a rather false and arbitrary sense of hierarchy, ascribing positions—that are otherwise trivial, yet necessarily mundane if only to obscure their true purpose—with an unfathomable amount of honor, to the logical extreme that the few chosen to serve in their most esteemed ranks are imbued with a kind of obligatory arrogance that begins in the pupal stages and extends indefinitely, as they are further nurtured well into adulthood by a society that infuses its heroes of middle management with an immeasurable sense of importance—a kind of celebrity status recognized by the masses as a living embodiment of their ideals. And yet, despite this culture of celebrity worship and vicarious living, all whims and impulses fall subservient, dropping humbly to the knees—yes, Mole Flies do, in fact, have knees!—before the grace of the merciful Queen, who is, in actuality, just a puppet dictator installed by the Melic papacy, using an old recycled Damsel fly-fishing lure. The dummy is crude, but convincing, as the Mole flies treat it as they would their true-born queen.

You might think that as people get older, they spend money more freely out of the sheer desire to make the most of it before it’s truly too late. But the opposite tends to happen. In general, spending among American households declines as people age. For example, the Consumer Expenditure Survey, conducted by the Bureau of Labor Statistics, found that in 2017, average annual spending for households headed by 55-to-64-year-olds was $65,000; average spending fell to $55,000 for those between 65 and 74; and spending fell again to $42,000 for those 75 and older. This overall decline occurred despite a rise in healthcare expenses, because most other expenses, such as clothing and entertainment, were much lower. The decline in spending over time was even more acute for retirees with more than $1 million in assets, according to separate research conducted by J.P. Morgan Asset Management, which analyzed data from more than half a million of its customers.

We are living in a golden age of genetic research, with new technologies permitting the easy collection of genetic data from millions upon millions of people and the rapid development of new statistical methodologies for analyzing it. But it is not enough to just produce new genetic knowledge. As this research leaves the ivory tower and disseminates through the public, it is essential for scientists and the public to grapple with what this research means about human identity and equality. Far too often, however, this essential task of meaning-making is being abdicated to the most extreme and hate-filled voices. As Eric Turkheimer, Dick Nisbett, and I warned: If people with progressive political values, who reject claims of genetic determinism and pseudoscientific racialist speculation, abdicate their responsibility to engage with the science of human abilities and the genetics of human behavior, the field will come to be dominated by those who do not share those values.

The word “collect” has a very special definition, according to the Department of Defense. It doesn’t mean collect; it means that a person looks at, or analyzes, the data. In 2013, Director of National Intelligence James Clapper likened the NSA’s trove of accumulated data to a library. All those books are stored on the shelves, but very few are actually read. “So the task for us in the interest of preserving security and preserving civil liberties and privacy is to be as precise as we possibly can be when we go in that library and look for the books that we need to open up and actually read.” Think of that friend of yours who has thousands of books in his house. According to this ridiculous definition, the only books he can claim to have collected are the ones he’s read. This is why Clapper asserts he didn’t lie in a Senate hearing when he replied “no” to the question “Does the NSA collect any type of data at all on millions or hundreds of millions of Americans?” From the military’s perspective, it’s not surveillance until a human being looks at the data, even if algorithms developed and implemented by defense personnel or contractors have analyzed it many times over.

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.

It was common knowledge at one prominent women’s brand I worked for that the reason they didn’t have more women of color, specifically Black women, on their legacy magazine covers was because they didn’t sell as well. For a business enterprise, and a financially struggling one at that, the editorial strategy to routinely flood the covers with normatively sized straight white women was presented as necessary business, and not a deeply racist lens. But this is where I’ve encountered capitalism to be at its most damaging: it provides an all-encompassing language to code racism, heterosexism, and classism as something else—to establish distance between these deeply coursing prejudices and the unavoidable realities of running a business. This distance insulates. It establishes an alternative reality in which testimonials, diversity reports, investigations, and data analysis on representation don’t resonate because making money is the ultimate objective above all else. But that’s all the more reason why the impetus to drive profits also needs to be aligned and analyzed in endeavors against oppression. Because the drive to make money, more money, more money than your competitors, more money than you made last year, more money than projected for the following year is an enduring vehicle for suppression.

In 2000, for instance, two statisticians were hired by the YMCA—one of the nation’s largest nonprofit organizations—to use the powers of data-driven fortune-telling to make the world a healthier place. The YMCA has more than 2,600 branches in the United States, most of them gyms and community centers. About a decade ago, the organization’s leaders began worrying about how to stay competitive. They asked a social scientist and a mathematician—Bill Lazarus and Dean Abbott—for help. The two men gathered data from more than 150,000 YMCA member satisfaction surveys that had been collected over the years and started looking for patterns. At that point, the accepted wisdom among YMCA executives was that people wanted fancy exercise equipment and sparkling, modern facilities. The YMCA had spent millions of dollars building weight rooms and yoga studios. When the surveys were analyzed, however, it turned out that while a facility’s attractiveness and the availability of workout machines might have caused people to join in the first place, what got them to stay was something else. Retention, the data said, was driven by emotional factors, such as whether employees knew members’ names or said hello when they walked in. People, it turns out, often go to the gym looking for a human connection, not a treadmill. If a member made a friend at the YMCA, they were much more likely to show up for workout sessions. In other words, people who join the YMCA have certain social habits. If the YMCA satisfied them, members were happy. So if the YMCA wanted to encourage people to exercise, it needed to take advantage of patterns that already existed, and teach employees to remember visitors’ names.

I process the information slowly, piece by piece. I’m not Divergent. I’m not like Tris. I’m genetically damaged. The word “damaged” sinks inside me like it’s made of lead. I guess I always knew there was something wrong with me, but I thought it was because of my father, or my mother, and the pain they bequeathed to me like a family heirloom, handed down from generation to generation. And this means that the one good thing my father had—his Divergence—didn’t reach me. I don’t look at Tris—I can’t bear it. Instead I look at Nita. Her expression is hard, almost angry. “Matthew,” she says. “Don’t you want to take this data to your lab to analyze?” “Well, I was planning on discussing it with our subjects here,” Matthew says. “I don’t think that’s a good idea,” Tris says, sharp as a blade. Matthew says something I don’t really hear; I’m listening to the thump of my heart. He taps the screen again, and the picture of my DNA disappears, so the screen is blank, just glass. He leaves, instructing us to visit his lab if we want more information, and Tris, Nita, and I stand in the room in silence. “It’s not that big a deal,” Tris says firmly. “Okay?” “You don’t get to tell me it’s not a big deal!” I say, louder than I mean to be. Nita busies herself at the counter, making sure the containers there are lined up, though they haven’t moved since we first came in. “Yeah, I do!” Tris exclaims. “You’re the same person you were five minutes ago and four months ago and eighteen years ago! This doesn’t change anything about you.” I hear something in her words that’s right, but it’s hard to believe her right now. “So you’re telling me this affects nothing,” I say. “The truth affects nothing.” “What truth?” she says. “These people tell you there’s something wrong with your genes, and you just believe it?” “It was right there.” I gesture to the screen. “You saw it.” “I also see you,” she says fiercely, her hand closing around my arm. “And I know who you are.

Starting a little over a decade ago, Target began building a vast data warehouse that assigned every shopper an identification code—known internally as the “Guest ID number”—that kept tabs on how each person shopped. When a customer used a Target-issued credit card, handed over a frequent-buyer tag at the register, redeemed a coupon that was mailed to their house, filled out a survey, mailed in a refund, phoned the customer help line, opened an email from Target, visited Target.com, or purchased anything online, the company’s computers took note. A record of each purchase was linked to that shopper’s Guest ID number along with information on everything else they’d ever bought. Also linked to that Guest ID number was demographic information that Target collected or purchased from other firms, including the shopper’s age, whether they were married and had kids, which part of town they lived in, how long it took them to drive to the store, an estimate of how much money they earned, if they’d moved recently, which websites they visited, the credit cards they carried in their wallet, and their home and mobile phone numbers. Target can purchase data that indicates a shopper’s ethnicity, their job history, what magazines they read, if they have ever declared bankruptcy, the year they bought (or lost) their house, where they went to college or graduate school, and whether they prefer certain brands of coffee, toilet paper, cereal, or applesauce. There are data peddlers such as InfiniGraph that “listen” to shoppers’ online conversations on message boards and Internet forums, and track which products people mention favorably. A firm named Rapleaf sells information on shoppers’ political leanings, reading habits, charitable giving, the number of cars they own, and whether they prefer religious news or deals on cigarettes. Other companies analyze photos that consumers post online, cataloging if they are obese or skinny, short or tall, hairy or bald, and what kinds of products they might want to buy as a result.

A different approach was taken in 1972 by Dr. Walter Mischel, also of Stanford, who analyzed yet another characteristic among children: the ability to delay gratification. He pioneered the use of the “marshmallow test,” that is, would children prefer one marshmallow now, or the prospect of two marsh-mallows twenty minutes later? Six hundred children, aged four to six, participated in this experiment. When Mischel revisited the participants in 1988, he found that those who could delay gratification were more competent than those who could not. In 1990, another study showed a direct correlation between those who could delay gratification and SAT scores. And a study done in 2011 indicated that this characteristic continued throughout a person’s life. The results of these and other studies were eye-opening. The children who exhibited delayed gratification scored higher on almost every measure of success in life: higher-paying jobs, lower rates of drug addiction, higher test scores, higher educational attainment, better social integration, etc. But what was most intriguing was that brain scans of these individuals revealed a definite pattern. They showed a distinct difference in the way the prefrontal cortex interacted with the ventral striatum, a region involved in addiction. (This is not surprising, since the ventral striatum contains the nucleus accumbens, known as the “pleasure center.” So there seems to be a struggle here between the pleasure-seeking part of the brain and the rational part to control temptation, as we saw in Chapter 2.) This difference was no fluke. The result has been tested by many independent groups over the years, with nearly identical results. Other studies have also verified the difference in the frontal-striatal circuitry of the brain, which appears to govern delayed gratification. It seems that the one characteristic most closely correlated with success in life, which has persisted over the decades, is the ability to delay gratification. Although this is a gross simplification, what these brain scans show is that the connection between the prefrontal and parietal lobes seems to be important for mathematical and abstract thought, while the connection between the prefrontal and limbic system (involving the conscious control of our emotions and pleasure center) seems to be essential for success in life. Dr. Richard Davidson, a neuroscientist at the University of Wisconsin–Madison, concludes, “Your grades in school, your scores on the SAT, mean less for life success than your capacity to co-operate, your ability to regulate your emotions, your capacity to delay your gratification, and your capacity to focus your attention. Those skills are far more important—all the data indicate—for life success than your IQ or your grades.

To give you a sense of the sheer volume of unprocessed information that comes up the spinal cord into the thalamus, let’s consider just one aspect: vision, since many of our memories are encoded this way. There are roughly 130 million cells in the eye’s retina, called cones and rods; they process and record 100 million bits of information from the landscape at any time. This vast amount of data is then collected and sent down the optic nerve, which transports 9 million bits of information per second, and on to the thalamus. From there, the information reaches the occipital lobe, at the very back of the brain. This visual cortex, in turn, begins the arduous process of analyzing this mountain of data. The visual cortex consists of several patches at the back of the brain, each of which is designed for a specific task. They are labeled V1 to V8. Remarkably, the area called V1 is like a screen; it actually creates a pattern on the back of your brain very similar in shape and form to the original image. This image bears a striking resemblance to the original, except that the very center of your eye, the fovea, occupies a much larger area in V1 (since the fovea has the highest concentration of neurons). The image cast on V1 is therefore not a perfect replica of the landscape but is distorted, with the central region of the image taking up most of the space. Besides V1, other areas of the occipital lobe process different aspects of the image, including: •  Stereo vision. These neurons compare the images coming in from each eye. This is done in area V2. •  Distance. These neurons calculate the distance to an object, using shadows and other information from both eyes. This is done in area V3. •  Colors are processed in area V4. •  Motion. Different circuits can pick out different classes of motion, including straight-line, spiral, and expanding motion. This is done in area V5. More than thirty different neural circuits involved with vision have been identified, but there are probably many more. From the occipital lobe, the information is sent to the prefrontal cortex, where you finally “see” the image and form your short-term memory. The information is then sent to the hippocampus, which processes it and stores it for up to twenty-four hours. The memory is then chopped up and scattered among the various cortices. The point here is that vision, which we think happens effortlessly, requires billions of neurons firing in sequence, transmitting millions of bits of information per second. And remember that we have signals from five sense organs, plus emotions associated with each image. All this information is processed by the hippocampus to create a simple memory of an image. At present, no machine can match the sophistication of this process, so replicating it presents an enormous challenge for scientists who want to create an artificial hippocampus for the human brain.

What are these substances? Medicines or drugs or sacramental foods? It is easier to say what they are not. They are not narcotics, nor intoxicants, nor energizers, nor anaesthetics, nor tranquilizers. They are, rather, biochemical keys which unlock experiences shatteringly new to most Westerners. For the last two years, staff members of the Center for Research in Personality at Harvard University have engaged in systematic experiments with these substances. Our first inquiry into the biochemical expansion of consciousness has been a study of the reactions of Americans in a supportive, comfortable naturalistic setting. We have had the opportunity of participating in over one thousand individual administrations. From our observations, from interviews and reports, from analysis of questionnaire data, and from pre- and postexperimental differences in personality test results, certain conclusions have emerged. (1) These substances do alter consciousness. There is no dispute on this score. (2) It is meaningless to talk more specifically about the “effect of the drug.” Set and setting, expectation, and atmosphere account for all specificity of reaction. There is no “drug reaction” but always setting-plus-drug. (3) In talking about potentialities it is useful to consider not just the setting-plus-drug but rather the potentialities of the human cortex to create images and experiences far beyond the narrow limitations of words and concepts. Those of us on this research project spend a good share of our working hours listening to people talk about the effect and use of consciousness-altering drugs. If we substitute the words human cortex for drug we can then agree with any statement made about the potentialities—for good or evil, for helping or hurting, for loving or fearing. Potentialities of the cortex, not of the drug. The drug is just an instrument. In analyzing and interpreting the results of our studies we looked first to the conventional models of modern psychology—psychoanalytic, behavioristic—and found these concepts quite inadequate to map the richness and breadth of expanded consciousness. To understand our findings we have finally been forced back on a language and point of view quite alien to us who are trained in the traditions of mechanistic objective psychology. We have had to return again and again to the nondualistic conceptions of Eastern philosophy, a theory of mind made more explicit and familiar in our Western world by Bergson, Aldous Huxley, and Alan Watts. In the first part of this book Mr. Watts presents with beautiful clarity this theory of consciousness, which we have seen confirmed in the accounts of our research subjects—philosophers, unlettered convicts, housewives, intellectuals, alcoholics. The leap across entangling thickets of the verbal, to identify with the totality of the experienced, is a phenomenon reported over and over by these persons.

Henry, there’s something I would like to tell you, for what it’s worth, something I wish I had been told years ago. You’ve been a consultant for a long time, and you’ve dealt a great deal with top secret information. But you’re about to receive a whole slew of special clearances, maybe fifteen or twenty of them, that are higher than top secret. I’ve had a number of these myself, and I’ve known other people who have just acquired them, and I have a pretty good sense of what the effects of receiving these clearances are on a person who didn’t previously know they even existed. And the effects of reading the information that they will make available to you. First, you’ll be exhilarated by some of this new information, and by having it all—so much! incredible!—suddenly available to you. But second, almost as fast, you will feel like a fool for having studied, written, talked about these subjects, criticized and analyzed decisions made by presidents for years without having known of the existence of all this information, which presidents and others had and you didn’t, and which must have influenced their decisions in ways you couldn’t even guess. In particular, you’ll feel foolish for having literally rubbed shoulders for over a decade with some officials and consultants who did have access to all this information you didn’t know about and didn’t know they had, and you’ll be stunned that they kept that secret from you so well. You will feel like a fool, and that will last for about two weeks. Then, after you’ve started reading all this daily intelligence input and become used to using what amounts to whole libraries of hidden information, which is much more closely held than mere top secret data, you will forget there ever was a time when you didn’t have it, and you’ll be aware only of the fact that you have it now and most others don’t … and that all those other people are fools. Over a longer period of time—not too long, but a matter of two or three years—you’ll eventually become aware of the limitations of this information. There is a great deal that it doesn’t tell you, it’s often inaccurate, and it can lead you astray just as much as the New York Times can. But that takes a while to learn. In the meantime it will have become very hard for you to learn from anybody who doesn’t have these clearances. Because you’ll be thinking as you listen to them: “What would this man be telling me if he knew what I know? Would he be giving me the same advice, or would it totally change his predictions and recommendations?” And that mental exercise is so torturous that after a while you give it up and just stop listening. I’ve seen this with my superiors, my colleagues … and with myself. You will deal with a person who doesn’t have those clearances only from the point of view of what you want him to believe and what impression you want him to go away with, since you’ll have to lie carefully to him about what you know. In effect, you will have to manipulate him. You’ll give up trying to assess what he has to say. The danger is, you’ll become something like a moron. You’ll become incapable of learning from most people in the world, no matter how much experience they may have in their particular areas that may be much greater than yours.

A famous British writer is revealed to be the author of an obscure mystery novel. An immigrant is granted asylum when authorities verify he wrote anonymous articles critical of his home country. And a man is convicted of murder when he’s connected to messages painted at the crime scene. The common element in these seemingly disparate cases is “forensic linguistics”—an investigative technique that helps experts determine authorship by identifying quirks in a writer’s style. Advances in computer technology can now parse text with ever-finer accuracy. Consider the recent outing of Harry Potter author J.K. Rowling as the writer of The Cuckoo’s Calling , a crime novel she published under the pen name Robert Galbraith. England’s Sunday Times , responding to an anonymous tip that Rowling was the book’s real author, hired Duquesne University’s Patrick Juola to analyze the text of Cuckoo , using software that he had spent over a decade refining. One of Juola’s tests examined sequences of adjacent words, while another zoomed in on sequences of characters; a third test tallied the most common words, while a fourth examined the author’s preference for long or short words. Juola wound up with a linguistic fingerprint—hard data on the author’s stylistic quirks. He then ran the same tests on four other books: The Casual Vacancy , Rowling’s first post-Harry Potter novel, plus three stylistically similar crime novels by other female writers. Juola concluded that Rowling was the most likely author of The Cuckoo’s Calling , since she was the only one whose writing style showed up as the closest or second-closest match in each of the tests. After consulting an Oxford linguist and receiving a concurring opinion, the newspaper confronted Rowling, who confessed. Juola completed his analysis in about half an hour. By contrast, in the early 1960s, it had taken a team of two statisticians—using what was then a state-of-the-art, high-speed computer at MIT—three years to complete a project to reveal who wrote 12 unsigned Federalist Papers. Robert Leonard, who heads the forensic linguistics program at Hofstra University, has also made a career out of determining authorship. Certified to serve as an expert witness in 13 states, he has presented evidence in cases such as that of Christopher Coleman, who was arrested in 2009 for murdering his family in Waterloo, Illinois. Leonard testified that Coleman’s writing style matched threats spray-painted at his family’s home (photo, left). Coleman was convicted and is serving a life sentence. Since forensic linguists deal in probabilities, not certainties, it is all the more essential to further refine this field of study, experts say. “There have been cases where it was my impression that the evidence on which people were freed or convicted was iffy in one way or another,” says Edward Finegan, president of the International Association of Forensic Linguists. Vanderbilt law professor Edward Cheng, an expert on the reliability of forensic evidence, says that linguistic analysis is best used when only a handful of people could have written a given text. As forensic linguistics continues to make headlines, criminals may realize the importance of choosing their words carefully. And some worry that software also can be used to obscure distinctive written styles. “Anything that you can identify to analyze,” says Juola, “I can identify and try to hide.

The popular social network Twitter is handing over to data scientists at the Massachusetts Institute of Technology’s famed Media Lab every message ever tweeted. It’s part of a five-year $10 million program to develop new ways to understand and use social networks. The school is setting up a Laboratory for Social Machines,where researchers will work on methods for understanding public opinion through the messages we post online. The lab will be able to analyze all new Twitter messages in real time, as well as the company’s archive of all previous tweets.

We need to take out the trash.” As it happens, I have no intention of actually analyzing that data. Nor am I proposing to my son that we take a family outing to the trash bin. In many situations, people use the word we when they mean you. It serves as a polite form to order others around.

Trying to analyze a situation without enough data was like looking at a photograph of a ball in flight and trying to gauge its direction. Is it going up, down, sideways? Is it about to collide with a baseball bat? Is it moving at all, or is something on the blind side holding it in place? A single frame didn't mean a thing. Patterns were based on data. With enough datapoints, you could predict just about anything.

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In another case, a McKinsey team went in to evaluate expansion opportunities for a division of a manufacturing company. After a few weeks of gathering and analyzing data, the team realized that what the division needed was not expansion; it was closure or sell-off.

What was once an anonymous medium where anyone could be anyone,” Eli Pariser wrote in 2011, “is now a tool for soliciting and analyzing our personal data.

To this end, the Commission capitalized on the complaint analysis system, the big data of civil complaints, and based on the past and recent complains, monitored the complaint trends and analyzed and predicted the trends in a comprehensive and statistical way.

Furthermore, the ACRC will make the system into a government-wide complaint data hub by connecting with local governments to share complaint information, in order to expand the range of analysis covering not only complaints on government agencies but also complaints in daily lives and regional complaints. In addition, the Commission will actively participate in realizing Government 3.0 by sharing the analyzed information, such as top complaints and rising keywords, on the website

(Data from the under-construction Square Kilometer Array telescope is expected to be collected at the rate of 10 PB/hour. Data from Facebook is estimated to accumulate at the rate of less than 1 PB/day.)

Data science teams need direct access to both raw data and decision-makers, and based on our analysis, they need a diversity of skills to make best use of that access.

At the start of a McKinsey-ite’s career, most of his time is spent gathering data, whether from one of the Firm’s libraries, from McKinsey’s many databases, or from the Internet. Gathering, filtering, and analyzing data is the skill exercised most by new associates. As a result, McKinsey-ites have learned a number of tricks for jump-starting their research. You can use these tricks to find the answers to your business problem too.

Whether it’s anthropology or sociology or geography, social scientists are often asked – no, required – early in their careers, to choose between humanistic and scientific approaches to the subject matter of their discipline and between collecting and analyzing qualitative or quantitative data. Even worse, they are taught to equate science with quantitative data and quantitative analysis and humanism with qualitative data and qualitative analysis. This denies the grand tradition of qualitative approaches in all of science, from astronomy to zoology. When Galileo first trained his then-brand-new telescope on the moon, he noticed what he called lighter and darker areas. The large dark spots had, Galileo said, been seen from time immemorial and so he said, “These I shall call the ‘large’ or ‘ancient’ spots.” He also wrote that the moon was “not smooth, uniform, and precisely spherical” as commonly believed, but “uneven, rough, and full of cavities and prominences,” much like the Earth. No more qualitative description was ever penned

Each business process is represented by a dimensional model that consists of a fact table containing the event's numeric measurements surrounded by a halo of dimension tables that contain the textual context that was true at the moment the event occurred. This characteristic star-like structure is often called a star join, a term dating back to the earliest days of relational databases. Figure 1.5 Fact and dimension tables in a dimensional model. The first thing to notice about the dimensional schema is its simplicity and symmetry. Obviously, business users benefit from the simplicity because the data is easier to understand and navigate. The charm of the design in Figure 1.5 is that it is highly recognizable to business users. We have observed literally hundreds of instances in which users immediately agree that the dimensional model is their business. Furthermore, the reduced number of tables and use of meaningful business descriptors make it easy to navigate and less likely that mistakes will occur. The simplicity of a dimensional model also has performance benefits. Database optimizers process these simple schemas with fewer joins more efficiently. A database engine can make strong assumptions about first constraining the heavily indexed dimension tables, and then attacking the fact table all at once with the Cartesian product of the dimension table keys satisfying the user's constraints. Amazingly, using this approach, the optimizer can evaluate arbitrary n-way joins to a fact table in a single pass through the fact table's index. Finally, dimensional models are gracefully extensible to accommodate change. The predictable framework of a dimensional model withstands unexpected changes in user behavior. Every dimension is equivalent; all dimensions are symmetrically-equal entry points into the fact table. The dimensional model has no built-in bias regarding expected query patterns. There are no preferences for the business questions asked this month versus the questions asked next month. You certainly don't want to adjust schemas if business users suggest new ways to analyze their business.

The structure of de Prony’s computing office cannot be easily seen in Smith’s example. His computing staff had two distinct classes of workers. The larger of these was a staff of nearly ninety computers. These workers were quite different from Smith’s pin makers or even from the computers at the British Nautical Almanac and the Connaissance des Temps. Many of de Prony’s computers were former servants or wig dressers, who had lost their jobs when the Revolution rendered the elegant styles of Louis XVI unfashionable or even treasonous.35 They were not trained in mathematics and held no special interest in science. De Prony reported that most of them “had no knowledge of arithmetic beyond the two first rules [of addition and subtraction].”36 They were little different from manual workers and could not discern whether they were computing trigonometric functions, logarithms, or the orbit of Halley’s comet. One labor historian has described them as intellectual machines, “grasping and releasing a single piece of ‘data’ over and over again.”37 The second class of workers prepared instructions for the computation and oversaw the actual calculations. De Prony had no special title for this group of workers, but subsequent computing organizations came to use the term “planning committee” or merely “planners,” as they were the ones who actually planned the calculations. There were eight planners in de Prony’s organization. Most of them were experienced computers who had worked for either the Bureau du Cadastre or the Paris Observatory. A few had made interesting contributions to mathematical theory, but the majority had dealt only with the problems of practical mathematics.38 They took the basic equations for the trigonometric functions and reduced them to the fundamental operations of addition and subtraction. From this reduction, they prepared worksheets for the computers. Unlike Nevil Maskelyne’s worksheets, which gave general equations to the computers, these sheets identified every operation of the calculation and left nothing for the workers to interpret. Each step of the calculation was followed by a blank space for the computers to fill with a number. Each table required hundreds of these sheets, all identical except for a single unique starting value at the top of the page. Once the computers had completed their sheets, they returned their results to the planners. The planners assembled the tables and checked the final values. The task of checking the results was a substantial burden in itself. The group did not double-compute, as that would have obviously doubled the workload. Instead the planners checked the final values by taking differences between adjacent values in order to identify miscalculated numbers. This procedure, known as “differencing,” was an important innovation for human computers. As one observer noted, differencing removed the “necessity of repeating, or even of examining, the whole of the work done by the [computing] section.”39 The entire operation was overseen by a handful of accomplished scientists, who “had little or nothing to do with the actual numerical work.” This group included some of France’s most accomplished mathematicians, such as Adrien-Marie Legendre (1752–1833) and Lazare-Nicolas-Marguerite Carnot (1753–1823).40 These scientists researched the appropriate formulas for the calculations and identified potential problems. Each formula was an approximation, as no trigonometric function can be written as an exact combination of additions and subtractions. The mathematicians analyzed the quality of the approximations and verified that all the formulas produced values adequately close to the true values of the trigonometric functions.

researchers who analyzed the data from four large research studies that had followed thousands of people from birth to adulthood calculated that when corrected for such variables as age and gender and weight, an inch of height is worth $789 a year in salary.

As Pole’s computer program crawled through the data, he was able to identify about twenty-five different products that, when analyzed together, allowed him to, in a sense, peer inside a woman’s womb. Most important, he could guess what trimester she was in—and estimate her due date—so Target could send her coupons when she was on the brink of making new purchases. By the time Pole was done, his program could assign almost any regular shopper a “pregnancy prediction” score.

open coding; development of concepts; grouping concepts into categories; formation of a theory. In the open coding stage, we analyze the text and identify any interesting phenomena in the data. Normally each unique phenomenon is given a distinctive name or code. The procedure and methods for identifying coding items are discussed in section 11.5.2. In the second stage, collections of codes that describe similar contents are grouped together to form higher level “concepts.” In the third stage, broader groups of similar concepts are identified to form “categories” and there is a detailed interpretation of each category. In this process, we are constantly searching for and refining the conceptual construct that may explain the relationship between the concepts and categories (Glaser, 1978). In the last stage, theory formulation, we aim at creating inferential and predictive statements about the phenomena recorded in the data.

Founded in 2011, ToyTalk already produces popular animated conversational apps — among them the Winston Show and SpeakaZoo — that encourage young children to engage in complex dialogue with a menagerie of make-believe characters. Now the company’s technology, originally designed for two-dimensional characters on-screen, is poised to power tangible playthings that children hold in their hands. This fall, Mattel plans to introduce Hello Barbie, a Wi-Fi enabled version of the iconic doll, which uses ToyTalk’s system to analyze a child’s speech and produce relevant responses. “She’s a huge character with an enormous back story,” Mr. Jacob says of Barbie. “We hope that when she’s ready, she will have thousands and thousands of things to say and you can speak to her for hours and hours.” [Video: Hello Barbie is World's First Interactive Barbie Doll Watch on YouTube.] It was probably inevitable that the so-called Internet of Things — those Web-connected thermostats and bathroom scales and coffee makers and whatnot — would beget the Internet of Toys. And just like Web-connected consumer gizmos that can amass details about their owners and transmit that data for remote analysis, Internet-connected toys hold out the tantalizing promise of personalized services and the risk of privacy perils.

A boss might give instructions and bark orders, a consultant would analyze data and give advice, but a coach would use curiosity to ask, listen and draw out the best from people.

As these companies had expanded their operations in the wake of the Industrial Revolution, they’d found it necessary to collect, store, and analyze ever larger amounts of data—on their customers, their finances, their employees, their inventories, and so on. Electrification allowed the companies to grow larger still, further expanding the information they had to process. This intellectual work became as important, and often as arduous, as the physical labor of manufacturing products and delivering services. Hollerith’s

In analytics, it’s more important for individuals to be able to formulate problems well, to prototype solutions quickly, to make reasonable assumptions in the face of ill-structured problems, to design experiments that represent good investments, and to analyze results.

WHILE I THINK the reasons for postmortems are compelling, I know that most people still resist them. So I want to share some techniques that can help managers get the most out of them. First of all, vary the way you conduct them. By definition, postmortems are supposed to be about lessons learned, so if you repeat the same format, you tend to uncover the same lessons, which isn’t much help to anyone. Even if you come up with a format that works well in one instance, people will know what to expect the next time, and they will game the process. I’ve noticed what might be called a “law of subverting successful approaches,” by which I mean once you’ve hit on something that works, don’t expect it to work again, because attendees will know how to manipulate it the second time around. So try “mid-mortems” or narrow the focus of your postmortem to special topics. At Pixar, we have had groups give courses to others on their approaches. We have occasionally formed task forces to address problems that span several films. Our first task force dramatically altered the way we thought about scheduling. The second one was an utter fiasco. The third one led to a profound change at Pixar, which I’ll discuss in the final chapter. Next, remain aware that, no matter how much you urge them otherwise, your people will be afraid to be critical in such an overt manner. One technique I’ve used to soften the process is to ask everyone in the room to make two lists: the top five things that they would do again and the top five things that they wouldn’t do again. People find it easier to be candid if they balance the negative with the positive, and a good facilitator can make it easier for that balance to be struck. Finally, make use of data. Because we’re a creative organization, people tend to assume that much of what we do can’t be measured or analyzed. That’s wrong. Many of our processes involve activities and deliverables that can be quantified. We keep track of the rates at which things happen, how often something has to be reworked, how long something actually took versus how long we estimated it would take, whether a piece of work was completely finished or not when it was sent to another department, and so on. I like data because it is neutral—there are no value judgments, only facts. That allows people to discuss the issues raised by data less emotionally than they might an anecdotal experience.

Using the IBM data, and later incorporating the research of other social scientists, he came up with six measures to help define and analyze the values, attitudes and behavioral impulses of any group — from something as small as the population of a single office or factory to something as large as a nation. These include individualism versus collectivism, indulgence versus restraint, power distance (a group’s acceptance or rejection of hierarchy) and, perhaps most important for Airbnb, uncertainty avoidance.