Data Management Quotes

Yet the paradox is that scientific methodology is the product of human hands and thus cannot reach some permanent truth. We build scientific theories to organize and manipulate the world, to reduce phenomena into manageable units. Science is based on reproducibility and manufactured objectivity. As strong as that makes its ability to generate claims about matter and energy, it also makes scientific knowledge inapplicable to the existential, visceral nature of human life, which is unique and subjective and unpredictable. Science may provide the most useful way to organize empirical, reproducible data, but its power to do so is predicated on its inability to grasp the most central aspects of human life: hope, fear, love, hate, beauty, envy, honor, weakness, striving, suffering, virtue.

Software engineers are sneaky bastards when it comes to data management.

A robust cybersecurity framework is essential to protect a company's digital assets, sensitive data, and critical infrastructure.

Manager Mangione,” Ping said, “algorithmic regulation was to have been a system of governance where more exact data, collected from MEG citizens’ minds via neuralinks, would be used to organize Human life more efficiently as a CORPORATE collective. Except no one to this point in Human existence has been able to identify the mind. The CORPORATE can only receive data from the NET on behaviours which indicate feelings or intentions. I & I cannot . . .

Things nature is good at include - organizing matter in a way that is multi functional, mass customization, network adaptation to circumstance, responsive evolution, growth as a mechanism for construction, decentralization, data management and asset management. Regardless of what kind of business we are talking about, there's something vital to learn from nature.

As a business owner, you should be looking at data as a key resource to help you make more informed decisions that ultimately allow you to grow revenues and maximize profits.

And to this end they built themselves a stupendous super-computer which was so amazingly intelligent that even before its data banks had been connected up it had started from I think therefore I am and got as far as deducing the existence of rice pudding and income tax before anyone managed to turn it off.

Every business can benefit from good quality management consulting services. Consultants are able to gather, assemble and utilize data in unique ways. Consultants also have perspectives that are likely to be unique compared to the perspectives you find internal to your business.

Helping people better manage their upsetting feelings—anger, anxiety, depression, pessimism, and loneliness—is a form of disease prevention. Since the data show that the toxicity of these emotions, when chronic, is on a par with smoking cigarettes, helping people handle them better could potentially have a medical payoff as great as getting heavy smokers to quit.

Effective consulting is about gathering and presenting actionable data, and helping businesses solve problems, implement solutions, and innovate.

In the business people with expertise, experience and evidence will make more profitable decisions than people with instinct, intuition and imagination.

That the volume of information, of data, of judgements, of measurements, was too much, and there were too many people, and too many desires of too many people, and too many opinions of too many people, and too much pain from too many people, and having all of it constantly collated, collected, added and aggregated, and presented to her as if that all made it tidier and more manageable--it was too much.

Precisely because technology is now moving so fast, and parliaments and dictators alike are overwhelmed by data they cannot process quickly enough, present-day politicians are thinking on a far smaller scale than their predecessors a century ago. Consequently, in the early twenty-first century politics is bereft of grand visions. Government has become mere administration. It manages the country, but it no longer leads it.

If you believe that science provides not basis for God, then you are almost obligated to conclude that science provides no basis for meaning and, there for, life itself doesn't have any. In other words, existential claims have no weight; all knowledge is scientific knowledge. Yet the paradox is that scientific methodology is the product of human hands and thus cannot reach some permanent truth. We build scientific theories to organize and manipulate the world, to reduce phenomena into manageable units. Science is based on reproducibility and manufactured objectivity. As strong as that makes its ability to generate claims about matter and energy, it also makes scientific knowledge inapplicable to the existential, visceral nature or human life, which is unique and subjective and unpredictable. Science may provide the most useful may to organize empirical, reproducible data, but its power to do so is predicated on its inability to grasp the most central aspects of human life: hope, fear, love, hate, beauty, envy, honor, weakness, striving, suffering, virtue.

Two things happen when your objectives are too broad—you don’t achieve the right results and you lose a lot of your resources. You want to avoid both of those.

If your objectives are too broad, they can dilute your project.

Discovering a cure for a disease is one thing, but making that cure available to everyone so it can actually be used to eradicate the problem is another thing.

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

Qualitative research is about collecting and understanding non-numerical data. It’s about how humans think, perceive, or give meaning to a thing based on various stimuli.

Interviews are a qualitative form of collecting data. The reason it generates good responses is because it’s way more personal than other forms of data gathering techniques.

Interestingly, one of the biggest problems with most people’s personal management systems is that they blend a few actionable things with a large amount of data and material that has value but no action attached.

In current times, we have access to so much data. Having said that, data analysis can uncover so many hidden patterns about customer behavior and how they interact with various products.

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.

If the research is done for brand awareness and the results don't show any rosy picture, then maybe you want to share those results with your marketing head who can include more brand awareness strategies in her marketing plan.

Maintaining good accounting records is vital to the successful management of a business. It's really good to be able to assess business-specific financial data to inform decisions. So every business should invest in good accounting software like Intuit, Quicken, or Freshbooks... Or any of the many apps out there.

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.

Of course!” Jack beamed. “Software engineers are sneaky bastards when it comes to data management.

The flash opened up into something larger, an even more blasphemous notion that her brain contained too much. That the volume of information, of data, of judgments, of measurements, was too much, and there were too many people, and too many desires of too many people, and too many opinions of too many people, and too much pain from too many people, and having all of it constantly collated, collected, added and aggregated, and presented to her as if that all made it tidier and more manageable--it was too much.

Data alone has no value—it’s just masses of numbers or words.

No one keeps track of the hours we work,” said Ken Holberger. He grinned. “That’s not altruism on Data General’s part. If anybody kept track, they’d have to pay us a hell of a lot more than they do.” Yet it is a fact, not entirely lost on management consultants, that some people would rather work twelve hours a day of their own choosing than eight that are prescribed. Provided, of course, that the work is interesting. That was the main thing.

There’s no amount of data that can replace an investors ability to empathize with the entrepreneur. There’s no amount of computational capacity that can replace a genuine and instinctive understanding of business management.

The beauty in the genome is of course that it's so small. The human genome is only on the order of a gigabyte of data...which is a tiny little database. If you take the entire living biosphere, that's the assemblage of 20 million species or so that constitute all the living creatures on the planet, and you have a genome for every species the total is still about one petabyte, that's a million gigabytes - that's still very small compared with Google or the Wikipedia and it's a database that you can easily put in a small room, easily transmit from one place to another. And somehow mother nature manages to create this incredible biosphere, to create this incredibly rich environment of animals and plants with this amazingly small amount of data.

We’re creatures of habit when it comes to mobile contracts and the wires piping high-speed data into our homes. It’s a pain to deal with transfers, installations, and customer service interactions, so we shrug and keep paying a premium.

Men (women were not found to exhibit this bias) who believe that they are objective in hiring decisions are more likely to hire a male applicant than an identically described female applicant. And in organisations which are explicitly presented as meritocratic, managers favour male employees over equally qualified female employees.

You can’t manage what you can’t measure” is a maxim that is taught and believed by many in both the business and education sectors. But in fact, the phrase is ridiculous—something said by people who are unaware of how much is hidden. A large portion of what we manage can’t be measured, and not realizing this has unintended consequences. The problem comes when people think that data paints a full picture, leading them to ignore what they can’t see. Here’s my approach: Measure what you can, evaluate what you measure, and appreciate that you cannot measure the vast majority of what you do. And at least every once in a while, make time to take a step back and think about what you are doing.

Metric fixation leads to a diversion of resources away from frontline producers toward managers, administrators, and those who gather and manipulate data.

If you want to know how a manager is performing ask to see their data, really want to know... ask those that report to them.

In a digitally connected world a byte of data can boost or bite your brand

It should go without saying—but it usually doesn’t, so we’ll say it—that data is best understood by those closest to the issue, which is often not management. As a leader, it is best not to get lost in details you don’t understand, but rather trust the smart people who work for you to understand them.

Here we are, practitioners of memos:     We send e-mail and we receive it,     We copy it and forward it and save it and delete it.     We write to move the data, and                 organize the program,                 and keep people informed—     and know and control and manage.   We write and receive one-dimensional memos,         that are, at best, clear and unambiguous.     And then—in breathtaking ways—you summon us to song.

One of the main cyber-risks is to think they don’t exist. The other is to try to treat all potential risks. Fix the basics, protect first what matters for your business and be ready to react properly to pertinent threats. Think data, but also business services integrity, awareness, customer experience, compliance, and reputation.

Several talked about their “flexible hours.” “No one keeps track of the hours we work,” said Ken Holberger. He grinned. “That’s not altruism on Data General’s part. If anybody kept track, they’d have to pay us a hell of a lot more than they do.” Yet it is a fact, not entirely lost on management consultants, that some people would rather work twelve hours a day of their own choosing than eight that are prescribed. Provided, of course, that the work is interesting.

In the sixties there was proposed a “National Data Bank,” which would, theoretically, improve the government’s efficiency by allowing agencies to share information. The fact that such a system could be abused did not mean it would be, proponents said; it could be constructed in such a way as to guarantee benign use. Nonsense, said opponents, who managed to block the proposal; no matter what the intent or the safeguards, the existence of such a system would inevitably lead toward the creation of a police state.

If you didn’t already know this, the sun is going to die. When I think about the future, I don’t think about inescapable ends. But even if we solve global warming and destroy nuclear bombs and control population, ultimately the human race will annihilate itself if we stay here. Eventually, inevitably, we will no longer be able to live on Earth: we have a giant fireball clock ticking down twilight by twilight. In many ways, I think mortality is more manageable when we consider our eternal components, our genetics and otherwise that carry on after us. Still, soon enough, the books we write and the plants we grow will freeze up and rot in the darkness. But maybe there’s hope. What the universe really boils down to is whether a planet evolves a life-form intelligent enough to create technology capable of transporting and sustaining that life-form off the planet before the sun in that planet’s solar system explodes. I have a limited set of comparative data points, but I’d estimate that we’re actually doing okay at this point. We already have (intelligent) life, technology, and (primitive) space travel. And we still have some time before our sun runs out of hydrogen and goes nuclear. Yet none of that matters unless we can develop a sustainable means of living and traveling in space. Maybe we can. What I’ve concluded is that if we do reach this point, we have crossed a remarkable threshold—and will emerge into the (rare?) evolutionary status of having outlived the very life source that created us. It’s natural selection on a Universal scale. “The Origin of the Aliens,” one could say; a survival of the fittest planets. Planets capable of evolving life intelligent enough to leave before the lights go out. I suppose that without a God, NASA is my anti-nihilism. Alone and on my laptop, these ideas can humble me into apathy.

The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.

He liked the “casual” look of the basement of Westborough. “The jeans and so on.” Several talked about their “flexible hours.” “No one keeps track of the hours we work,” said Ken Holberger. He grinned. “That’s not altruism on Data General’s part. If anybody kept track, they’d have to pay us a hell of a lot more than they do.” Yet it is a fact, not entirely lost on management consultants, that some people would rather work twelve hours a day of their own choosing than eight that are prescribed. Provided, of course, that the work is interesting. That was the main thing.

The Internet of Things (IoT) devoid of comprehensive security management is tantamount to the Internet of Threats. Apply open collaborative innovation, systems thinking & zero-trust security models to design IoT ecosystems that generate and capture value in value chains of the Internet of Things.

With families, I stopped creating encyclopedias of data about all their issues and began to search instead for the member with the greatest capacity to be a leader as I have defined it. That person generally turned out to be the one who could express himself or herself with the least amount of blaming and the one who had the greatest capacity to take responsibility for his or her own emotional being and destiny. I began to coach the “leader” alone, letting the rest of the family drop out and stay home. I stopped trying to get people to “communicate” or find better ways of managing their issues. Instead, I began to concentrate on helping the leader to become better defined and to learn how to deal adroitly with the sabotage that almost invariably followed any success in this endeavor. Soon I found that the rest of the family was “in therapy” whether or not they came into my office.

For getting success in business with Data Science, predictions must be backed by timely strategic actions.

Completeness of crucial information is extremely important, as missing data is not only a cost issue but is also a massive lost opportunity issue...

Protecting data is not just about preventing unauthorized access. It also involves ensuring data integrity and availability.

Data tells you what your users are doing, not why they’re doing it. Don’t be lured away from speaking and listening to your market directly.

All models are wrong, but some are useful.” In other words, models intentionally simplify our complex world.

The very tool that had helped make NASA so consistently successful, what Diane Vaughan called “the original technical culture” in the agency’s DNA, suddenly worked perversely in a situation where the familiar brand of data did not exist. Reason without numbers was not accepted. In the face of an unfamiliar challenge, NASA managers failed to drop their familiar tools.

They somehow managed to persuade themselves that computer models constitute data. That very complicated guesses become facts. They made themselves believe they had the power to accurately model, not merely something as inconceivably complex as, say, a single zygote…but a national economy, a weather system, a planetary ecosphere, a multiplanet society—even a universe.

Anyone who knows anything about data knows that it is critical to have authentic data – data that holistically represents the truth of something, as opposed to fragments or biased portions.

A characteristic of Maxwell's work, indeed his life, was that he seemed to take everything in his stride—he was never hurried. Somehow, he and Katherine managed to go riding in the park most afternoons and, of course, they went on accumulating data on color vision, asking all new houseguests to have a go. They had installed the latest big color box near the window in an upstairs

The longer someone ignores an email before finally responding, the more relative social power that person has. Map these response times across an entire organization and you get a remarkably accurate chart of the actual social standing. The boss leaves emails unanswered for hours or days; those lower down respond within minutes. There’s an algorithm for this, a data mining method called “automated social hierarchy detection,” developed at Columbia University.8 When applied to the archive of email traffic at Enron Corporation before it folded, the method correctly identified the roles of top-level managers and their subordinates just by how long it took them to answer a given person’s emails. Intelligence agencies have been applying the same metric to suspected terrorist gangs, piecing together the chain of influence to spot the central figures.

The brain, he writes, is like Kublai Khan, the great Mongol emperor of the thirteenth century. It sits enthroned in its skull, "encased in darkness and silence," at a lofty remove from brute reality. Messengers stream in from every corner of the sensory kingdom, bringing word of distant sights, sounds, and smells. Their reports arrive at different rates, often long out of date, yet the details are all stitched together into a seamless chronology. The difference is that Kublai Khan was piecing together the past. The brain is describing the present—processing reams of disjointed data on the fly, editing everything down to an instantaneous now. How does it manage it?

The NSA may be spying on our every word, but to judge by the repeated failures of American foreign policy, nobody in Washington knows what to do with all the data. Never in history did a government know so much about what’s going on in the world – yet few empires have botched things up as clumsily as the contemporary United States. It’s like a poker player who knows what cards his opponents hold, yet somehow still manages to lose round after round.

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.

However, simplicity is a virtue when developing metrics for your platform business. Overcomplex metrics make management less effective by introducing noise, discouraging frequent analysis, and distracting from the handful of data points that are most significant.

The display, which was called 'Can Democracy Survive the Internet?' was dedicated to a 'global election management' company called Cambridge Analytica. Cambridge Analytica claimed to have gathered 5,000 data points on every American voter online: what you liked and what you shared on social media; how and where you shopped; who your friends were... They claimed to be able to take this imprint of your online self, use it to understand your deepest drives and desires, and then draw on that analysis to change your voting behaviour. The boast seemed to be backed up by success: Cambridge Analytica had worked on the victorious American presidential campaign of Donald Trump; it had also run successful campaigns for US Senator Ted Cruz (twice); and others all across Africa, Asia, the Caribbean, Latin America.

Specifying our investment decision-making criteria in algorithms and running historical data through them, or specifying our work principles in algorithms and using them to aid in management decision making, are just bigger and more complicated versions of that smart thermostat.

Using both experiments and field data, a recent study found that economic insecurity was associated with increased consumption of painkillers and produced actual physical pain and reduced pain tolerance, with the absence of control providing one mechanism explaining these results.

In this chapter I will describe the effects of the data deluge on all members of society generally and how it erodes the confidence, judgment, and decisiveness of leaders in particular. Then I will show the paradoxical side of the data deluge. Despite its anxiety-provoking effects, the proliferation of data also has an addictive quality. Leaders, healers, and parents “imbibe” data as a way of dealing with their own chronic anxiety. The pursuit of data, in almost any field, has come to resemble a form of substance abuse, accompanied by all the usual problems of addiction: self-doubt, denial, temptation, relapse, and withdrawal. Leadership training programs thus wind up in the codependent position of enablers, with publishers often in the role of “suppliers.” What does it take to get parents, healers, and managers, when they hear of the latest quick-fix fad that has just been published, to “just say no”?

As all this suggests our relationship with evidence is seldom purely a cognitive one. Vilifying menstruating women bolstering anti-Muslim stereotypes murdering innocent citizens of Salem plainly evidence is almost always invariably a political social and moral issue as well. To take a particularly stark example consider the case of Albert Speer minister of armaments and war production during the Third Reich close friend to Adolf Hitler and highest-ranking Nazi official to ever express remorse for his actions. In his memoir Inside the Third Reich Speer candidly addressed his failure to look for evidence of what was happening around him. "I did not query a friend who told him not to visit Auschwitz I did not query Himmler I did not query Hitler " he wrote. "I did not speak with personal friends. I did not investigate for I did not want to know what was happening there... for fear of discovering something which might have made me turn away from my course. I had closed my eyes." Judge William Stoughton of Salem Massachusetts became complicit in injustice and murder by accepting evidence that he should have ignored. Albert Speer became complicit by ignoring evidence he should have accepted. Together they show us some of the gravest possible consequences of mismanaging the data around us and the vital importance of learning to manage it better. It is possible to do this: like in the U.S. legal system we as individuals can develop a fairer and more consistent relationship to evidence over time. By indirection Speer himself shows us how to begin. I did not query he wrote. I did not speak. I did not investigate. I closed my eyes. This are sins of omission sins of passivity and they suggest correctly that if we want to improve our relationship with evidence we must take a more active role in how we think must in a sense take the reins of our own minds. To do this we must query and speak and investigate and open our eyes. Specifically and crucially we must learn to actively combat our inductive biases: to deliberately seek out evidence that challenges our beliefs and to take seriously such evidence when we come across it.

By definition, a tool exists to improve something you are already doing. If you're not doing formal data governance yet, or if you are doing it poorly, then casting about for a tool to help you deploy DG is a waste of time. This flies in the face of typical IT philosophy, where the tool is usually acquired first. This is a notoriously silly thing to do. However, our work always has us putting the brakes on a tool selection project. It is easy to buy a tool and install it. However, most of the time we witness new tools for data management sitting unused or poorly deployed. This is because no one has mastered the process the tool is supporting.

to make science the arbiter of metaphysics is to banish not only God from the world but also love, hate, meaning—to consider a world that is self-evidently not the world we live in. That’s not to say that if you believe in meaning, you must also believe in God. It is to say, though, that if you believe that science provides no basis for God, then you are almost obligated to conclude that science provides no basis for meaning and, therefore, life itself doesn’t have any. In other words, existential claims have no weight; all knowledge is scientific knowledge. Yet the paradox is that scientific methodology is the product of human hands and thus cannot reach some permanent truth. We build scientific theories to organize and manipulate the world, to reduce phenomena into manageable units. Science is based on reproducibility and manufactured objectivity. As strong as that makes its ability to generate claims about matter and energy, it also makes scientific knowledge inapplicable to the existential, visceral nature of human life, which is unique and subjective and unpredictable. Science may provide the most useful way to organize empirical, reproducible data, but its power to do so is predicated on its inability to grasp the most central aspects of human life: hope, fear, love, hate, beauty, envy, honor, weakness, striving, suffering, virtue.

In the same way, hybrid models of blended learning are not noticeably simpler for teachers than the existing system. On the contrary, in many cases they appear to require all the expertise of the traditional model plus new expertise in managing digital devices and in integrating data across all the supplemental online experiences in the teacher-directed rotation.

There is another issue with the largely cognitive approach to management, which we had big-time at Google. Smart, analytical people, especially ones steeped in computer science and mathematics as we were, will tend to assume that data and other empirical evidence can solve all problems. Quants or techies with this worldview tend to see the inherently messy, emotional tension that’s always present in teams of humans as inconvenient and irrational—an irritant that will surely be resolved in the course of a data-driven decision process. Of course, humans don’t always work that way. Things come up, tensions arise, and they don’t naturally go away. People do their best to avoid talking about these situations, because they’re awkward. Which makes it worse.

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Many many millions of years ago a race of hyperintelligent pandimensional beings (whose physical manifestation in their own pandimensional universe is not dissimilar to our own) got so fed up with the constant bickering about the meaning of life which used to interrupt their favorite pastime of Brockian Ultra Cricket (a curious game which involved suddenly hitting people for no readily apparent reason and then running away) that they decided to sit down and solve their problems once and for all. And to this end they built themselves a stupendous super computer which was so amazingly intelligent that even before its data banks had been connected up it had started from I think therefore I am and got as far as deducing the existence of rice pudding and income tax before anyone managed to turn it off.

New York City manages expertly, and with marvelous predictability, whatever it considers humanly important. Fax machines, computers, automated telephones and even messengers on bikes convey a million bits of data through Manhattan every day to guarantee that Wall Street brokers get their orders placed, confirmed, delivered, at the moment they demand. But leaking roofs cannot be fixed and books cannot be gotten into Morris High in time to meet the fall enrollment. Efficiency in educational provision for low-income children, as in health care and most other elementals of existence, is secreted and doled out by our municipalities as if it were a scarce resource. Like kindness, cleanliness and promptness of provision, it is not secured by gravity of need but by the cash, skin color and class status of the applicant.

High-quality and transparent data, clearly documented, timely rendered, and publicly available are the sine qua non of competent public health management. During a pandemic, reliable and comprehensive data are critical for determining the behavior of the pathogen, identifying vulnerable populations, rapidly measuring the effectiveness of interventions, mobilizing the medical community around cutting-edge disease management, and inspiring cooperation from the public. The shockingly low quality of virtually all relevant data pertinent to COVID-19, and the quackery, the obfuscation, the cherrypicking and blatant perversion would have scandalized, offended, and humiliated every prior generation of American public health officials. Too often, Dr. Fauci was at the center of these systemic deceptions. The “mistakes” were always in the same direction—inflating the risks of coronavirus and the safety and efficacy of vaccines in

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.

Reality is far too diverse, broad, elusive, ambiguous and complex for us to pin down. Even the limited empirical data we do manage to collect can only be interpreted within the framework of a subjective paradigm. It is, therefore, not really neutral. But in our desperate search for closure and reassurance we confabulate entities and explanations to construct huge edifices of assumed truths. They make up the world we actually experience; a self-woven cocoon of stories, not facts.

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

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Precisely because technology is now moving so fast, and parliaments and dictators alike are overwhelmed by data they cannot process quickly enough, present-day politicians are thinking on a far smaller scale than their predecessors a century ago. In the early twenty-first century, politics is consequently bereft of grand visions. Government has become mere administration. It manages the country, but it no longer leads it. It makes sure teachers are paid on time and sewage systems don’t overflow, but it has no idea where the country will be in twenty years.

13 core principles of managing: 1. Make a personal connection first; everything else follows. 2. There is only one team rule. (Respect 90) 3. Freedom is empowering. 4. Never hold a team meeting in your home clubhouse. 5. Do not have a fine system. 6. Wear whatever you think makes you look hot. 7. Empower your coaches. 8. But don’t allow your coaches—or veterans—to be harsh on young players. 9. Question data with feel. 10. Pregame work is excessive. 11. Keep signs simple and to a minimum. 12. A lineup card is all a manager needs in the dugout. 13. Forget “The Book.” Making the first or third out at third base is okay.

One might have thought that on learning of Quinta’s death—this woman the company doctors had professed was not going to die—the United States Radium Corporation might, at last, have softened. But one would be wrong. Berry did manage to win a settlement of $8,000 ($113,541) for Mae Canfield in the new year, but the company had a straitjacket clause attached. The only way they would pay his client any money, they said, was if Berry himself was incorporated into the deal. He was far too knowledgeable about their activities—and becoming far too skilled in court—to be left off a leash. And so Raymond Berry, legal champion, the pioneering attorney who had been the only lawyer to answer Grace’s call for help, found himself forced into signing his name to the following statement: “I agree not to be connected with, directly or indirectly, any other cases against the United States Radium Corporation, nor to render assistance to any persons in any actions against said Company, nor to furnish data or information to any such persons in matters against said Company.”37 Berry was gone. He had been a serious fighter against the firm, an irksome thorn in their side. But now, with surgical precision, they had plucked him out and banished him. They were two settlements down, but the United States Radium Corporation was winning the war.

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.

Data sliced sufficiently finely begin once again to tell stories. The top 1 percent of the income distribution—representing household incomes in excess of roughly $475,000—comprises only about 1.5 million households. If one adds up the numbers of vice presidents or above at S&P 1500 companies (perhaps 250,000), professionals in the finance sector, including in hedge funds, venture capital, private equity, investment banking, and mutual funds (perhaps 250,000), professionals working at the top five management consultancies (roughly 60,000), partners at law firms whose profits per partner exceed $400,000 (roughly 25,000), and specialist doctors (roughly 500,000), this yields perhaps 1 million people. These are surely not all one-percenters, but they are all plausibly parts of the top 1 percent, and this group might comprise half—a sizable share—of 1 percent households overall. At the very least, the people in these known and named jobs constitute a material, rather than just marginal or eccentric, part of the top 1 percent of the income distribution. They are also, of course, the people depicted in journalistic accounts of extreme jobs—the people who regularly cancel vacation plans, spend most of their time on the road, live in unfurnished luxury apartments, and generally subsume themselves in work, encountering their personal lives only occasionally, and as strangers.

The problem, however, eventually became evident: to make science the arbiter of metaphysics is to banish not only God from the world but also love, hate, meaning—to consider a world that is self-evidently not the world we live in. That’s not to say that if you believe in meaning, you must also believe in God. It is to say, though, that if you believe that science provides no basis for God, then you are almost obligated to conclude that science provides no basis for meaning and, therefore, life itself doesn’t have any. In other words, existential claims have no weight; all knowledge is scientific knowledge. Yet the paradox is that scientific methodology is the product of human hands and thus cannot reach some permanent truth. We build scientific theories to organize and manipulate the world, to reduce phenomena into manageable units. Science is based on reproducibility and manufactured objectivity. As strong as that makes its ability to generate claims about matter and energy, it also makes scientific knowledge inapplicable to the existential, visceral nature of human life, which is unique and subjective and unpredictable. Science may provide the most useful way to organize empirical, reproducible data, but its power to do so is predicated on its inability to grasp the most central aspects of human life: hope, fear, love, hate, beauty, envy, honor, weakness, striving, suffering, virtue.

Machine-learning systems generally need a goal or metric that guides them as they train themselves. Musk, who liked to manage by decreeing what metrics should be paramount, gave them their lodestar: the number of miles that cars with Tesla Full Self-Driving were able to travel without a human intervening. “I want the latest data on miles per intervention to be the starting slide at each of our meetings,” he decreed. “If we’re training AI, what do we optimize? The answer is higher miles between interventions.” He told them to make it like a video game where they could see their score every day. “Video games without a score are boring, so it will be motivating to watch each day as the miles per intervention increases.

Given the central place that technology holds in our lives, it is astonishing that technology companies have not put more resources into fixing this global problem. Advanced computer systems and artificial intelligence (AI) could play a much bigger role in shaping diagnosis and prescription. While the up-front costs of using such technology may be sizeable, the long-term benefits to the health-care system need to be factored into value assessments. We believe that AI platforms could improve on the empirical prescription approach. Physicians work long hours under stressful conditions and have to keep up to date on the latest medical research. To make this work more manageable, the health-care system encourages doctors to specialize. However, the vast majority of antibiotics are prescribed either by generalists (e.g., general practitioners or emergency physicians) or by specialists in fields other than infectious disease, largely because of the need to treat infections quickly. An AI system can process far more information than a single human, and, even more important, it can remember everything with perfect accuracy. Such a system could theoretically enable a generalist doctor to be as effective as, or even superior to, a specialist at prescribing. The system would guide doctors and patients to different treatment options, assigning each a probability of success based on real-world data. The physician could then consider which treatment was most appropriate.

Stock-exchange traders are also in danger. Most financial trading today is already being managed by computer algorithms that can process in a second more data than a human can in a year, and can react to the data much faster than a human can blink. On 23 April 2013, Syrian hackers broke into Associated Press’s official Twitter account. At 13:07 they tweeted that the White House had been attacked and President Obama was hurt. Trade algorithms that constantly monitor newsfeeds reacted in no time and began selling stocks like mad. The Dow Jones went into free fall and within sixty seconds lost 150 points, equivalent to a loss of $136 billion! At 13:10 Associated Press clarified that the tweet was a hoax. The algorithms reversed gear and by 13:13 the Dow Jones had recuperated almost all the losses.

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.

This is the thinking behind Amazon’s anticipatory shopping patent.43 Instead of customers making their own decisions, Amazon decides for them, sending what they want before they know they want it. It is, as one commentator noticed, one more step towards cutting out human agency altogether.44 Pervasive monitoring devices – smartphones, wearables, voice-enabled speakers and smart meters – allow companies to track and manage consumer behaviour. The Harvard business scholar Shoshana Zuboff quotes an unnamed chief data scientist who explains: ‘The goal of everything we do is to change people’s actual behavior at scale . . . we can capture their behaviours and identify good and bad [ones]. Then we develop “treatments” or “data pellets” that select good behaviours.’45 MIT’s Alex Pentland seems more interested in enhancing machines than human understanding. He celebrates the opportunity to deploy sensors and data in order to increase efficiency

Peopleware. A major contribution during recent years has been DeMarco and Lister's 1987 book, Peopleware: Productive Projects and Teams. Its underlying thesis is that "The major problems of our work are not so much technological as sociological in nature." It abounds with gems such as, "The manager's function is not to make people work, it is to make it possible for people to work." It deals with such mundane topics as space, furniture, team meals together. DeMarco and Lister provide real data from their Coding War Games that show stunning correlation between performances of programmers from the same organization, and between workplace characteristics and both productivity and defect levels. The top performers' space is quieter, more private, better protected against interruption, and there is more of it. . . . Does it really matter to you . . . whether quiet, space, and privacy help your current people to do better work or [alternatively] help you to attract and keep better people?[19]

One of the patterns from domain-driven design is called bounded context. Bounded contexts are used to set the logical boundaries of a domain’s solution space for better managing complexity. It’s important that teams understand which aspects, including data, they can change on their own and which are shared dependencies for which they need to coordinate with other teams to avoid breaking things. Setting boundaries helps teams and developers manage the dependencies more efficiently. The logical boundaries are typically explicit and enforced on areas with clear and higher cohesion. These domain dependencies can sit on different levels, such as specific parts of the application, processes, associated database designs, etc. The bounded context, we can conclude, is polymorphic and can be applied to many different viewpoints. Polymorphic means that the bounded context size and shape can vary based on viewpoint and surroundings. This also means you need to be explicit when using a bounded context; otherwise it remains pretty vague.

One other thing. And this really matters for readers of this book. According to official Myers–Briggs documents, the test can ‘give you an insight into what kinds of work you might enjoy and be successful doing’. So if you are, like me, classified as ‘INTJ’ (your dominant traits are being introverted, intuitive and having a preference for thinking and judging), the best-fit occupations include management consultant, IT professional and engineer.30 Would a change to one of these careers make me more fulfilled? Unlikely, according to respected US psychologist David Pittenger, because there is ‘no evidence to show a positive relation between MBTI type and success within an occupation…nor is there any data to suggest that specific types are more satisfied within specific occupations than are other types’. Then why is the MBTI so popular? Its success, he argues, is primarily due to ‘the beguiling nature of the horoscope-like summaries of personality and steady marketing’.31 Personality tests have their uses, even if they do not reveal any scientific ‘truth’ about us. If we are in a state of confusion they can be a great emotional comfort, offering a clear diagnosis of why our current job may not be right, and suggesting others that might suit us better. They also raise interesting hypotheses that aid self-reflection: until I took the MBTI, I had certainly never considered that IT could offer me a bright future (by the way, I apparently have the wrong personality type to be a writer). Yet we should be wary about relying on them as a magic pill that enables us suddenly to hit upon a dream career. That is why wise career counsellors treat such tests with caution, using them as only one of many ways of exploring who you are. Human personality does not neatly reduce into sixteen or any other definitive number of categories: we are far more complex creatures than psychometric tests can ever reveal. And as we will shortly learn, there is compelling evidence that we are much more likely to find fulfilling work by conducting career experiments in the real world than by filling out any number of questionnaires.32

In the EPJ results, there were two statistically distinguishable groups of experts. The first failed to do better than random guessing, and in their longer-range forecasts even managed to lose to the chimp. The second group beat the chimp, though not by a wide margin, and they still had plenty of reason to be humble. Indeed, they only barely beat simple algorithms like “always predict no change” or “predict the recent rate of change.” Still, however modest their foresight was, they had some. So why did one group do better than the other? It wasn’t whether they had PhDs or access to classified information. Nor was it what they thought—whether they were liberals or conservatives, optimists or pessimists. The critical factor was how they thought. One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false. Some were environmental doomsters (“We’re running out of everything”); others were cornucopian boomsters (“We can find cost-effective substitutes for everything”). Some were socialists (who favored state control of the commanding heights of the economy); others were free-market fundamentalists (who wanted to minimize regulation). As ideologically diverse as they were, they were united by the fact that their thinking was so ideological. They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions. Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong. As a result, they were unusually confident and likelier to declare things “impossible” or “certain.” Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed. They would tell us, “Just wait.” The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced. These experts gathered as much information from as many sources as they could. When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.” They talked about possibilities and probabilities, not certainties. And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds. Decades ago, the philosopher Isaiah Berlin wrote a much-acclaimed but rarely read essay that compared the styles of thinking of great authors through the ages. To organize his observations, he drew on a scrap of 2,500-year-old Greek poetry attributed to the warrior-poet Archilochus: “The fox knows many things but the hedgehog knows one big thing.” No one will ever know whether Archilochus was on the side of the fox or the hedgehog but Berlin favored foxes. I felt no need to take sides. I just liked the metaphor because it captured something deep in my data. I dubbed the Big Idea experts “hedgehogs” and the more eclectic experts “foxes.” Foxes beat hedgehogs. And the foxes didn’t just win by acting like chickens, playing it safe with 60% and 70% forecasts where hedgehogs boldly went with 90% and 100%. Foxes beat hedgehogs on both calibration and resolution. Foxes had real foresight. Hedgehogs didn’t.

2006 interview by Jim Gray, Amazon CTO Werner Vogels recalled another watershed moment: We went through a period of serious introspection and concluded that a service-oriented architecture would give us the level of isolation that would allow us to build many software components rapidly and independently. By the way, this was way before service-oriented was a buzzword. For us service orientation means encapsulating the data with the business logic that operates on the data, with the only access through a published service interface. No direct database access is allowed from outside the service, and there’s no data sharing among the services.3 That’s a lot to unpack for non–software engineers, but the basic idea is this: If multiple teams have direct access to a shared block of software code or some part of a database, they slow each other down. Whether they’re allowed to change the way the code works, change how the data are organized, or merely build something that uses the shared code or data, everybody is at risk if anybody makes a change. Managing that risk requires a lot of time spent in coordination. The solution is to encapsulate, that is, assign ownership of a given block of code or part of a database to one team. Anyone else who wants something from that walled-off area must make a well-documented service request via an API.

Under the direction of General Westmoreland, significantly himself a graduate of the Harvard Business School in which McNamara had at one time taught, the computers zestfully went to work. Fed on forms that had to be filled in by the troops, they digested data on everything from the amount of rice brought to local markets to the number of incidents that had taken place in a given region in a given period of time. They then spewed forth a mighty stream of tables and graphs which purported to measure “progress” week by week and day by day. So long as the tables looked neat, few people bothered to question the accuracy, let alone the relevance, of the data on which they were based. So long as they looked neat, too, the illusion of having a grip on the war helped prevent people from attempting to gain a real understanding of its nature. This is not to say that the Vietnam War was lost simply because the American defense establishment’s management of the conflict depended heavily on computers. Rather, it proves that there is, in war and presumably in peace as well, no field so esoteric or so intangible as to be completely beyond the reach of technology. The technology in use helps condition tactics, strategy, organization, logistics, intelligence, command, control, and communication. Now, however, we are faced with an additional reality. Not only the conduct of war, but the very framework our brains employ in order to think about it, are partly conditioned by the technical instruments at our disposal.

The 12 Principles of Permaculture Investing are: 1. Accumulate & Compound Capital: Consistently save and invest to grow your capital base over time, leveraging the power of compound interest. 2. Utilize Capital: Actively deploy your capital into productive investments that generate returns, rather than letting it sit idle. 3. Retain Maximum & Gradiented Liquidity: Maintain a balance between liquid assets (easily accessible cash) and less liquid investments, ensuring you can meet immediate needs while still investing for the long term. 4. Actively Manage Passive: While focusing on passive income sources, actively monitor and adjust your investments to optimize returns and mitigate risks. 5. Prioritize Long-Term Growth: Focus on investments that offer potential for significant growth over the long term, even if they don't provide immediate high yields. 6. Prioritize Consistent Yields: Balance your portfolio with investments that provide reliable, consistent income to support your financial needs. 7. Add Net Value to all Stakeholders: Invest in ways that benefit not only yourself but also the broader community, environment, and all parties involved. 8. Provide Authentic Data: Be transparent and honest in your financial reporting, providing accurate information to all stakeholders. 9. Collect & Utilize Authentic Data: Base your investment decisions on reliable, verified data rather than speculation or rumors. 10. Diversify Holistically: Diversify your investments across different asset classes, industries, and geographical regions to reduce risk and maximize potential returns. 11. Harvest Yields Equitably: Distribute profits fairly among all stakeholders, ensuring everyone benefits from the investment's success. 12. Reinvest Yields in Most Profitable Assets: Continuously evaluate your portfolio and reinvest profits into the most promising opportunities to further compound your growth.

The tendency to want what has been banned and therefore to presume that it is more worthwhile is not limited to such commodities as laundry soap. In fact, the tendency is not limited to commodities at all but extends to restrictions on information. In an age when the ability to acquire, store, and manage information is becoming increasingly the determinant of wealth and power, it is important to understand how we typically react to attempts to censor or otherwise constrain our access to information. Although much data exist on our reactions to various kinds of potentially censorable material—media violence, pornography, radical political rhetoric—there is surprisingly little evidence as to our reactions to the act of censoring them. Fortunately, the results of the few studies that have been done on the topic are highly consistent. Almost invariably, our response to the banning of information is a greater desire to receive that information and a more favorable attitude toward it than before the ban.112 The intriguing thing about the effects of censoring information is not that audience members want to have the information more than they did before; that seems natural. Rather, it is that they come to believe in the information more, even though they haven’t received it. For example, when University of North Carolina students learned that a speech opposing coed dorms on campus would be banned, they became more opposed to the idea of coed dorms. Thus, without ever hearing the speech, they became more sympathetic to its argument. This raises the worrisome possibility that especially clever individuals holding a weak or unpopular position can get us to agree with that position by arranging to have their message restricted. The irony is that for such people—members of fringe political groups, for example—the most effective strategy may not be to publicize their unpopular views, but to get those views officially censored and then to publicize the censorship. Perhaps the authors of this country’s Constitution were acting as much as sophisticated social psychologists as staunch civil libertarians when they wrote the remarkably permissive free-speech provision of the First Amendment. By refusing to restrain freedom of speech, they may have been attempting to minimize the chance that new political notions would win support via the irrational course of psychological reactance.

The successful individual sales producer wins by being as selfish as possible with her time. The more often the salesperson stays away from team members and distractions, puts her phone on Do Not Disturb (DND), closes her door, or chooses to work for a few hours from the local Panera Bread café, the more productive she’ll likely be. In general, top producers in sales tend to exhibit a characteristic I’ve come to describe as being selfishly productive. The seller who best blocks out the rest of the world, who maintains obsessive control of her calendar, who masters focusing solely on her own highest-value revenue-producing activities, who isn’t known for being a “team player,” and who is not interested in playing good corporate citizen or helping everyone around her, is typically a highly effective seller who ends up on top of the sales rankings. Contrary to popular opinion, being selfish is not bad at all. In fact, for an individual contributor salesperson, it is a highly desirable trait and a survival skill, particularly in today’s crazed corporate environment where everyone is looking to put meetings on your calendar and take you away from your primary responsibilities! Now let’s switch gears and look at the sales manager’s role and responsibilities. How well would it work to have a sales manager who kept her office phone on DND and declined almost every incoming call to her mobile phone? Do we want a sales manager who closes her office door, is concerned only about herself, and is for the most part inaccessible? No, of course not. The successful sales manager doesn’t win on her own; she wins through her people by helping them succeed. Think about other key sales management responsibilities: Leading team meetings. Developing talent. Encouraging hearts. Removing obstacles. Coaching others. Challenging data, false assumptions, wrong attitudes, and complacency. Pushing for more. Putting the needs of your team members ahead of your own. Hmmm. Just reading that list again reminds me why it is often so difficult to transition from being a top producer in sales into a sales management role. Aside from the word sales, there is truly almost nothing similar about the positions. And that doesn’t even begin to touch on corporate responsibilities like participating on the executive committee, dealing with human resources compliance issues, expense management, recruiting, and all the other burdens placed on the sales manager. Again,

As she explained to her students, patients often awoke from very bad illnesses or cardiac arrests, talking about how they had been floating over their bodies. “Mm-hmmm,” Norma would reply, sometimes thinking, Yeah, yeah, I know, you were on the ceiling. Such stories were recounted so frequently that they hardly jolted medical personnel. Norma at the time had mostly chalked it up to some kind of drug reaction or brain malfunction, something like that. “No, really,” said a woman who’d recently come out of a coma. “I can prove it.” The woman had been in a car accident and been pronounced dead on arrival when she was brought into the emergency room. Medical students and interns had begun working on her and managed to get her heartbeat going, but then she had coded again. They’d kept on trying, jump-starting her heart again, this time stabilizing it. She’d remained in a coma for months, unresponsive. Then one day she awoke, talking about the brilliant light and how she remembered floating over her body. Norma thought she could have been dreaming about all kinds of things in those months when she was unconscious. But the woman told them she had obsessive-compulsive disorder and had a habit of memorizing numbers. While she was floating above her body, she had read the serial number on top of the respirator machine. And she remembered it. Norma looked at the machine. It was big and clunky, and this one stood about seven feet high. There was no way to see on top of the machine without a stepladder. “Okay, what’s the number?” Another nurse took out a piece of paper to jot it down. The woman rattled off twelve digits. A few days later, the nurses called maintenance to take the ventilator machine out of the room. The woman had recovered so well, she no longer needed it. When the worker arrived, the nurses asked if he wouldn’t mind climbing to the top to see if there was a serial number up there. He gave them a puzzled look and grabbed his ladder. When he made it up there, he told them that indeed there was a serial number. The nurses looked at each other. Could he read it to them? Norma watched him brush off a layer of dust to get a better look. He read the number. It was twelve digits long: the exact number that the woman had recited. The professor would later come to find out that her patient’s story was not unique. One of Norma’s colleagues at the University of Virginia Medical Center at the time, Dr. Raymond Moody, had published a book in 1975 called Life After Life, for which he had conducted the first large-scale study of people who had been declared clinically dead and been revived, interviewing 150 people from across the country. Some had been gone for as long as twenty minutes with no brain waves or pulse. In her lectures, Norma sometimes shared pieces of his research with her own students. Since Moody had begun looking into the near-death experiences, researchers from around the world had collected data on thousands and thousands of people who had gone through them—children, the blind, and people of all belief systems and cultures—publishing the findings in medical and research journals and books. Still, no one has been able to definitively account for the common experience all of Moody’s interviewees described. The inevitable question always followed: Is there life after death? Everyone had to answer that question based on his or her own beliefs, the professor said. For some of her students, that absence of scientific evidence of an afterlife did little to change their feelings about their faith. For others,