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.

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 . . .

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

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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

Data may disappoint, but it never lies.

Data has no ego and makes an excellent co-pilot.

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

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.

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.

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 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.

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.

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.

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.

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

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 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.

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 majority of schools, what's needed isn't more professional development on deconstructing standards or academic discourse or using data to drive instruction. What's needed is time, space, and attention to managing stress and cultivating resilience.

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.

when you read findings like the one above, and see that Jamal doesn’t get the job, it’s easy to shake your head at the few racist hiring managers who’ve tilted the odds against him. But the data we see in this chapter shows racism isn’t a problem of outliers. It is pervasive.

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.

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.

The good news is that these descriptive statistics give us a manageable and meaningful summary of the underlying phenomenon. That’s what this chapter is about. The bad news is that any simplification invites abuse. Descriptive statistics can be like online dating profiles: technically accurate and yet pretty darn misleading.

Database Management System [Origin: Data + Latin basus "low, mean, vile, menial, degrading, ounterfeit."] A complex set of interrelational data structures allowing data to be lost in many convenient sequences while retaining a complete record of the logical relations between the missing items. -- From The Devil's DP Dictionary

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.

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.

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.

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

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.

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.

Our mental health depends on a mechanism for editing the moment-by-moment ocean of sensory data flowing into our consciousness down to a manageable trickle of the noticed and remembered. The cannabinoid network appears to be part of that mechanism, vigilantly sifting the vast chaff of sense impression from the kernels of perception we need to remember if we’re to get through the day and get done what needs to be done.* Much depends on forgetting.

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.

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.

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.

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.

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.

Say Goodbye to Fingersticks & hello to Continuous Glucose Monitoring Systems Living with diabetes is a daily challenge, requiring individuals to closely monitor their blood glucose levels to maintain stable health. Fortunately, advancements in medical technology have revolutionized diabetes management, with one such innovation being Continuous Glucose Monitoring (CGM) systems. CGM has become a game-changer for diabetics, providing real-time data and insights that enable better control of blood sugar levels and, ultimately, a higher quality of life. In this article, we will explore the benefits of Continuous Glucose Monitoring and how it has transformed diabetes management for the better.

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.

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.

While poorhouses have been physically demolished, their legacy remains alive and well in the automated decision-making systems that encage and entrap today's poor. For all their high-tech polish, our modern systems of poverty management - automated decision-making, data mining, and predictive analysis - retain a remarkable kinship with the poorhouses of the past. Our new digital tools spring from punitive, moralistic views of poverty and create a system of high-tech containment and investigation. The digital poorhouse deters the poor from accessing public resources; polices their labor, spending, sexuality, and parenting; tries to predict their future behavior; and punishes and criminalizes those who do not comply with its dictates. In the process, it creates ever-finer moral distinctions between the 'deserving' and 'undeserving' poor, categorizations that rationalize our national failure to care for one another.

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.

But...freedom has come into the spotlight again. We find ourselves surveilled and managed to an extraordinary degree, farmed for our personal data, fed consumer goods but discouraged from speaking our minds or doing anything too disruptive in the world, and regularly reminded that racial, sexual, religious, and ideological conflict are not closed cases at all. Perhaps we are ready to talk about freedom again - and talking about it politically also means talking about it in our personal lives. This is why, when reading Sartre on freedom, Beauvoir on the subtle mechanisms of oppression, Kierkegaard on anxiety, Camus on rebellion, Heidegger on technology, or Merleau-Ponty on cognitive science, one sometimes feels one is reading the latest news. Their philosophies remain of interest, not because they are right or wrong, but because they concern life, and because they take on the two biggest human questionsL what are we? and what should we do?

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 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

The cheerleaders of the new data regime rarely acknowledge the impacts of digital decision-making on poor and working-class people. This myopia is not shared by those lower on the economic hierarchy, who often see themselves as targets rather than beneficiaries of these systems. For example, one day in early 2000, I sat talking to a young mother on welfare about her experiences with technology. When our conversation turned to EBT cards, Dorothy Allen said, “They’re great. Except [Social Services] uses them as a tracking device.” I must have looked shocked, because she explained that her caseworker routinely looked at her purchase records. Poor women are the test subjects for surveillance technology, Dorothy told me. Then she added, “You should pay attention to what happens to us. You’re next.” Dorothy’s insight was prescient. The kind of invasive electronic scrutiny she described has become commonplace across the class spectrum today. Digital tracking and decision-making systems have become routine in policing, political forecasting, marketing, credit reporting, criminal sentencing, business management, finance, and the administration of public programs. As these systems developed in sophistication and reach, I started to hear them described as forces for control, manipulation, and punishment

I will show that this spectacular increase in inequality largely reflects an unprecedented explosion of very elevated incomes from labor, a veritable separation of the top managers of large firms from the rest of the population. One possible explanation of this is that the skills and productivity of these top managers rose suddenly in relation to those of other workers. Another explanation, which to me seems more plausible and turns out to be much more consistent with the evidence, is that these top managers by and large have the power to set their own remuneration, in some cases without limit and in many cases without any clear relation to their individual productivity, which in any case is very difficult to estimate in a large organization. This phenomenon is seen mainly in the United States and to a lesser degree in Britain, and it may be possible to explain it in terms of the history of social and fiscal norms in those two countries over the past century. The tendency is less marked in other wealthy countries (such as Japan, Germany, France, and other continental European states), but the trend is in the same direction. To expect that the phenomenon will attain the same proportions elsewhere as it has done in the United States would be risky until we have subjected it to a full analysis—which unfortunately is not that simple, given the limits of the available data.

Pull approaches differ significantly from push approaches in terms of how they organize and manage resources. Push approaches are typified by "programs" - tightly scripted specifications of activities designed to be invoked by known parties in pre-determined contexts. Of course, we don't mean that all push approaches are software programs - we are using this as a broader metaphor to describe one way of organizing activities and resources. Think of thick process manuals in most enterprises or standardized curricula in most primary and secondary educational institutions, not to mention the programming of network television, and you will see that institutions heavily rely on programs of many types to deliver resources in pre-determined contexts. Pull approaches, in contrast, tend to be implemented on "platforms" designed to flexibly accommodate diverse providers and consumers of resources. These platforms are much more open-ended and designed to evolve based on the learning and changing needs of the participants. Once again, we do not mean to use platforms in the literal sense of a tangible foundation, but in a broader, metaphorical sense to describe frameworks for orchestrating a set of resources that can be configured quickly and easily to serve a broad range of needs. Think of Expedia's travel service or the emergency ward of a hospital and you will see the contrast with the hard-wired push programs.

Many aspects of the modern financial system are designed to give an impression of overwhelming urgency: the endless ‘news’ feeds, the constantly changing screens of traders, the office lights blazing late into the night, the young analysts who find themselves required to work thirty hours at a stretch. But very little that happens in the finance sector has genuine need for this constant appearance of excitement and activity. Only its most boring part—the payments system—is an essential utility on whose continuous functioning the modern economy depends. No terrible consequence would follow if the stock market closed for a week (as it did in the wake of 9/11)—or longer, or if a merger were delayed or large investment project postponed for a few weeks, or if an initial public offering happened next month rather than this. The millisecond improvement in data transmission between New York and Chicago has no significance whatever outside the absurd world of computers trading with each other. The tight coupling is simply unnecessary: the perpetual flow of ‘information’ part of a game that traders play which has no wider relevance, the excessive hours worked by many employees a tournament in which individuals compete to display their alpha qualities in return for large prizes. The traditional bank manager’s culture of long lunches and afternoons on the golf course may have yielded more useful information about business than the Bloomberg terminal. Lehman

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-evidentlynot 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. Between these core passions and scientific theory, there will always be a gap. No system of thought can contain the fullness of human experience.

I work in theoretical computer science: a field that doesn’t itself win Fields Medals (at least not yet), but that has occasions to use parts of math that have won Fields Medals. Of course, the stuff we use cutting-edge math for might itself be dismissed as “ivory tower self-indulgence.” Except then the cryptographers building the successors to Bitcoin, or the big-data or machine-learning people, turn out to want the stuff we were talking about at conferences 15 years ago—and we discover to our surprise that, just as the mathematicians gave us a higher platform to stand on, so we seem to have built a higher platform for the practitioners. The long road from Hilbert to Gödel to Turing and von Neumann to Eckert and Mauchly to Gates and Jobs is still open for traffic today. Yes, there’s plenty of math that strikes even me as boutique scholasticism: a way to signal the brilliance of the people doing it, by solving problems that require years just to understand their statements, and whose “motivations” are about 5,000 steps removed from anything Caplan or Bostrom would recognize as motivation. But where I part ways is that there’s also math that looked to me like boutique scholasticism, until Greg Kuperberg or Ketan Mulmuley or someone else finally managed to explain it to me, and I said: “ah, so that’s why Mumford or Connes or Witten cared so much about this. It seems … almost like an ordinary applied engineering question, albeit one from the year 2130 or something, being impatiently studied by people a few moves ahead of everyone else in humanity’s chess game against reality. It will be pretty sweet once the rest of the world catches up to this.

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.

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,

The US traded its manufacturing sector’s health for its entertainment industry, hoping that Police Academy sequels could take the place of the rustbelt. The US bet wrong. But like a losing gambler who keeps on doubling down, the US doesn’t know when to quit. It keeps meeting with its entertainment giants, asking how US foreign and domestic policy can preserve its business-model. Criminalize 70 million American file-sharers? Check. Turn the world’s copyright laws upside down? Check. Cream the IT industry by criminalizing attempted infringement? Check. It’ll never work. It can never work. There will always be an entertainment industry, but not one based on excluding access to published digital works. Once it’s in the world, it’ll be copied. This is why I give away digital copies of my books and make money on the printed editions: I’m not going to stop people from copying the electronic editions, so I might as well treat them as an enticement to buy the printed objects. But there is an information economy. You don’t even need a computer to participate. My barber, an avowed technophobe who rebuilds antique motorcycles and doesn’t own a PC, benefited from the information economy when I found him by googling for barbershops in my neighborhood. Teachers benefit from the information economy when they share lesson plans with their colleagues around the world by email. Doctors benefit from the information economy when they move their patient files to efficient digital formats. Insurance companies benefit from the information economy through better access to fresh data used in the preparation of actuarial tables. Marinas benefit from the information economy when office-slaves look up the weekend’s weather online and decide to skip out on Friday for a weekend’s sailing. Families of migrant workers benefit from the information economy when their sons and daughters wire cash home from a convenience store Western Union terminal. This stuff generates wealth for those who practice it. It enriches the country and improves our lives. And it can peacefully co-exist with movies, music and microcode, but not if Hollywood gets to call the shots. Where IT managers are expected to police their networks and systems for unauthorized copying – no matter what that does to productivity – they cannot co-exist. Where our operating systems are rendered inoperable by “copy protection,” they cannot co-exist. Where our educational institutions are turned into conscript enforcers for the record industry, they cannot co-exist. The information economy is all around us. The countries that embrace it will emerge as global economic superpowers. The countries that stubbornly hold to the simplistic idea that the information economy is about selling information will end up at the bottom of the pile. What country do you want to live in?

How Google Works (Schmidt, Eric) - Your Highlight on Location 3124-3150 | Added on Sunday, April 5, 2015 10:35:40 AM In late 1999, John Doerr gave a presentation at Google that changed the company, because it created a simple tool that let the founders institutionalize their “think big” ethos. John sat on our board, and his firm, Kleiner Perkins, had recently invested in the company. The topic was a form of management by objectives called OKRs (to which we referred in the previous chapter), which John had learned from former Intel CEO Andy Grove.173 There are several characteristics that set OKRs apart from their typical underpromise-and-overdeliver corporate-objective brethren. First, a good OKR marries the big-picture objective with a highly measurable key result. It’s easy to set some amorphous strategic goal (make usability better … improve team morale … get in better shape) as an objective and then, at quarter end, declare victory. But when the strategic goal is measured against a concrete goal (increase usage of features by X percent … raise employee satisfaction scores by Y percent … run a half marathon in under two hours), then things get interesting. For example, one of our platform team’s recent OKRs was to have “new WW systems serving significant traffic for XX large services with latency < YY microseconds @ ZZ% on Jupiter.”174 (Jupiter is a code name, not the location of Google’s newest data center.) There is no ambiguity with this OKR; it is very easy to measure whether or not it is accomplished. Other OKRs will call for rolling out a product across a specific number of countries, or set objectives for usage (e.g., one of the Google+ team’s recent OKRs was about the daily number of messages users would post in hangouts) or performance (e.g., median watch latency on YouTube videos). Second—and here is where thinking big comes in—a good OKR should be a stretch to achieve, and hitting 100 percent on all OKRs should be practically unattainable. If your OKRs are all green, you aren’t setting them high enough. The best OKRs are aggressive, but realistic. Under this strange arithmetic, a score of 70 percent on a well-constructed OKR is often better than 100 percent on a lesser one. Third, most everyone does them. Remember, you need everyone thinking in your venture, regardless of their position. Fourth, they are scored, but this scoring isn’t used for anything and isn’t even tracked. This lets people judge their performance honestly. Fifth, OKRs are not comprehensive; they are reserved for areas that need special focus and objectives that won’t be reached without some extra oomph. Business-as-usual stuff doesn’t need OKRs. As your venture grows, the most important OKRs shift from individuals to teams. In a small company, an individual can achieve incredible things on her own, but as the company grows it becomes harder to accomplish stretch goals without teammates. This doesn’t mean that individuals should stop doing OKRs, but rather that team OKRs become the more important means to maintain focus on the big tasks. And there’s one final benefit of an OKR-driven culture: It helps keep people from chasing competitors. Competitors are everywhere in the Internet Century, and chasing them (as we noted earlier) is the fastest path to mediocrity. If employees are focused on a well-conceived set of OKRs, then this isn’t a problem. They know where they need to go and don’t have time to worry about the competition. ==========

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,