Inspirational Data Quotes

Things get done only if the data we gather can inform and inspire those in a position to make difference.

We often hesitate to follow our intuition out of fear. Most usually, we are afraid of the changes in our own life that our actions will bring. Intuitive guidance, however, is all about change. It is energetic data ripe with the potential to influence the rest of the world. To fear change but to crave intuitive clarity is like fearing the cold, dark night while pouring water on the fire that lights your cave. An insight the size of a mustard seed is powerful enough to bring down a mountain-sized illusion that may be holding our lives together. Truth strikes without mercy. We fear our intuitions because we fear the transformational power within our revelations.

Faith doesn't rely on odds or statistical data. God only requires that we have faith; the rest is up to him.

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

Data that comes subliminally and is acted upon will look like luck or inspiration.

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.

Market research gives you enough time to learn from your competitors’ mistakes, take inspiration from their strengths, and exploit their weaknesses.

We need data and we need discernment.

It's not man versus machine; it's man with machine versus man without. Data and intuition are like horse and rider, and you don't try to outrun a horse; you ride it.

To save wildlife and wild places the traction has to come not from the regurgitation of bad-news data but from the poets, prophets, preachers, professors, and presidents who have always dared to inspire.

If we knew what is already there, there will be no need for research.

Ratios matter in Data Science. Dreams should be big and worries small.

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.

Data may disappoint, but it never lies.

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

Anthropological data clearly showed that cultures practicing religions historically had outlived nonreligious cultures. Fear of being judged by an omniscient deity always helps inspire benevolent behavior.

Historically, pandemics have forced humans to break with the past and imagine their world anew. This one is no different. It is a portal, a gateway between one world and the next. We can choose to walk through it, dragging the carcasses of our prejudice and hatred, our avarice, our data banks and dead ideas, our dead rivers and smoky skies behind us. Or we can walk through lightly, with little luggage, ready to imagine another world. And ready to fight for it.

People who stand out in work and life possess the power to get what they want and inspire others while doing so.

Sometimes, it is not you who finds good ideas when you are seeking them. Instead, good ideas find you in the most unexpected circumstances.

It is in the combination of words and visuals that the magic of understanding often happens.

It’s only because the data force us into corners that we are inspired to create the highly counterintuitive structures that form the basis for modern physics.

Recognizing that two points of data are connected is not enough. The System must ask why one point affects another.

The platforms designed to accommodate and harvest infinite data inspired infinite scroll...people were saying nothing and saying it all the time.

In Data Science if you want to help individuals, be empathetic and ask questions; that way, you can begin to understand their journey, too.

There were no laptops or handheld devices in class. Ilgauskas didn't exclude them; we did, sort of, unspokenly. Some of us could barely complete a thought without touch pads or scroll buttons, but we understood that high-speed data systems did not belong here. They were an assault on the environment, which was defined by length, width, and depth, with time drawn out, computed in heartbeats.

Tu stii ca nu-i nici o fericire sa vezi ce-i inlauntrul omului, pentru ca nici acolo, nici in afara lui lucrurile nu sunt asezate o data pentru totdeauna si se schimba pana sa apuci sa spui ce si cum.

Every day your mind will receive data both good and bad. You have the power to keep the good and delete the bad. Get rid of the negative thoughts in your mind and you will rid yourself of the negativity in you.

To summarize, these are the four critical contributions you need to bring to your team: deep knowledge (1) of your customer, (2) of the data, (3) of your business and its stakeholders, and (4) of your market and industry.

When you’re trying to communicate a big, audacious concept, it’s helpful to remember that where data falls short, a story might close the gap, and where story alone is not persuasive enough, data can make up the difference.

De fiecare data, imi aparea tot mai clar ca unicul lucru pe care doream sa-l fac in viata era sa devin scriitor si astfel mi se intarea convingerea ca singura cale pentru a reusi este aceea de a te darui, trup si suflet, numai literaturii.

they didn't examine the problem and accumulate data to figure out the best solution - they engineered the outcome they wanted from the beginning. if they didn't achieve their desired outcome, they understood it was because of a decision they made at the start of a process

These examples should be models for communication, precisely because they inspire curiosity. “How does money influence politics?” is not an especially engaging question, but “If I were running for president, how would I raise lots of money with few conditions and no scrutiny?” is much more intriguing.

In the name of speed, Morse and Vail had realized that they could save strokes by reserving the shorter sequences of dots and dashes for the most common letters. But which letters would be used most often? Little was known about the alphabet’s statistics. In search of data on the letters’ relative frequencies, Vail was inspired to visit the local newspaper office in Morristown, New Jersey, and look over the type cases. He found a stock of twelve thousand E’s, nine thousand T’s, and only two hundred Z’s. He and Morse rearranged the alphabet accordingly. They had originally used dash-dash-dot to represent T, the second most common letter; now they promoted T to a single dash, thus saving telegraph operators uncountable billions of key taps in the world to come. Long afterward, information theorists calculated that they had come within 15 percent of an optimal arrangement for telegraphing English text.

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

I eventually realized that to make a difference I had to step outside, into creation, and refocus on the roots of my passion. If an ounce of soil, a sparrow, or an acre of forest is to remain then we must all push things forward. To save wildlife and wild places the traction has to come not from the regurgitation of bad-news data but from the poets, prophets, preachers, professors, and presidents who have always dared to inspire. Heart and mind cannot be exclusive of one another in the fight to save anything. To help others understand nature is to make it breathe like some giant: a revolving, evolving, celestial being with ecosystems acting as organs and the living things within those places -- humans included -- as cells vital to its survival.

Habit Testing.” It is a process inspired by the build-measure-learn methodology championed by the lean startup movement. Habit Testing offers insights and actionable data to inform the design of habit-forming products. It helps clarify who your devotees are, what parts of your product are habit-forming (if any), and why those aspects of your product are changing user behavior.

Millions of people across the world live in a state of acute environmental crises caused by the lack of access to safe and usable water resources, because of natural disasters, socio-economic conditions, wars and conflicts. At Green the Gene, we are developing extremely simple yet highly technology and data intensive solutions tailored to address extremely specific problems faced by communities.

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

To understand this first event, you need to know that we rely on Unix and Linux machines to store the thousands of computer files that comprise all the shots of any given film. And on those machines, there is a command—/bin/rm -r -f *—that removes everything on the file system as fast as it can. Hearing that, you can probably anticipate what’s coming: Somehow, by accident, someone used this command on the drives where the Toy Story 2 files were kept. Not just some of the files, either. All of the data that made up the pictures, from objects to backgrounds, from lighting to shading, was dumped out of the system. First, Woody’s hat disappeared. Then his boots. Then he disappeared entirely. One by one, the other characters began to vanish, too: Buzz, Mr. Potato Head, Hamm, Rex. Whole sequences—poof!—were deleted from the drive. Oren

In the name of speed, Morse and Vail had realized that they could save strokes by reserving the shorter sequences of dots and dashes for the most common letters. But which letters would be used most often? Little was known about the alphabet’s statistics. In search of data on the letters’ relative frequencies, Vail was inspired to visit the local newspaper office in Morristown, New Jersey, and look over the type cases.

I like to ensure that I have music and art all around me. My personal favorite is old maps. What I love about old maps is that they are both beautiful and imperfect. These imperfections represent that some of the most talented in history were still very wrong (early cartography was very difficult). As the majority of my work is analysis and advisory, I find it a valuable reminder that my knowledge is limited. No matter how much data or insight I have, I can never fully “map out” any business. Yet, despite the incompleteness of these early cartographers, so much was learned of the world. So much done and accomplished. Therefore, these maps, or art pieces, serve as something to inspire both humility and achievement. This simple environmental factor helps my productivity and the overall quality of my work. Again, it’s like adding positive dice to my hand that are rolled each day.

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

tip for applying this learning: To get people to “fall in love” with your ideas, don’t solely rely on numbers and data. People can tune out this type of input relatively easily. But if you communicate with a story or experience, you create an emotion. Start your next meeting with a story instead of a spreadsheet. Make your audience feel as well as think. Connect emotionally with them by telling a personal anecdote that reinforces the point of your presentation. Or draw upon a nostalgic shared memory. Once you inspire emotion, your listener will be less likely to disengage, and more likely to remember and respond to your message.

Google had a built-in disadvantage in the social networking sweepstakes. It was happy to gather information about the intricate web of personal and professional connections known as the “social graph” (a term favored by Facebook’s Mark Zuckerberg) and integrate that data as signals in its search engine. But the basic premise of social networking—that a personal recommendation from a friend was more valuable than all of human wisdom, as represented by Google Search—was viewed with horror at Google. Page and Brin had started Google on the premise that the algorithm would provide the only answer. Yet there was evidence to the contrary. One day a Googler, Joe Kraus, was looking for an anniversary gift for his wife. He typed “Sixth Wedding Anniversary Gift Ideas” into Google, but beyond learning that the traditional gift involved either candy or iron, he didn’t see anything creative or inspired. So he decided to change his status message on Google Talk, a line of text seen by his contacts who used Gmail, to “Need ideas for sixth anniversary gift—candy ideas anyone?” Within a few hours, he got several amazing suggestions, including one from a colleague in Europe who pointed him to an artist and baker whose medium was cake and candy. (It turned out that Marissa Mayer was an investor in the company.) It was a sobering revelation for Kraus that sometimes your friends could trump algorithmic search.

The platforms, designed to accommodate and harvest infinite data, inspired an infinite scroll. They encouraged a cultural impulse to fill all spare time with someone else’s thoughts. The internet was a collective howl, an outlet for everyone to prove that they mattered. The full spectrum of human emotion infused social platforms. Grief, joy, anxiety, mundanity flowed. People were saying nothing, and saying it all the time. Strangers swapped confidences with other strangers in return for unaccredited psychological advice. They shared stories of private infidelities and public incontinence; photos of their bedroom interiors; photos, faded and cherished, of long-dead family members; photos of their miscarriages. People were giving themselves away at every opportunity.

Trump doesn’t happen in a country where things are going well. People give in to their baser instincts when they lose faith in the future. The pessimism and anger necessary for this situation has been building for a generation, and not all on one side. A significant number of Trump voters voted for Obama eight years ago. A lot of those were in rust-belt states that proved critical to his election. What happened there? Trump also polled 2–1 among veterans, despite his own horrific record of deferments and his insulting of every vet from John McCain to Humayun Khan. Was it possible that his rhetoric about ending “our current policy of regime change” resonated with recently returned vets? The data said yes. It may not have been decisive, but it likely was one of many factors. It was also common sense, because this was one of his main themes on the campaign trail—Trump clearly smelled those veteran votes. The Trump phenomenon was also about a political and media taboo: class. When the liberal arts grads who mostly populate the media think about class, we tend to think in terms of the heroic worker, or whatever Marx-inspired cliché they taught us in college. Because of this, most pundits scoff at class, because when they look at Trump crowds, they don’t see Norma Rae or Matewan. Instead, they see Married with Children, a bunch of tacky mall-goers who gobble up crap movies and, incidentally, hate the noble political press. Our take on Trump voters was closer to Orwell than Marx: “In reality very little was known about the proles. It was not necessary to know much.” Beyond the utility that calling everything racism had for both party establishments, it was good for that other sector, the news media.

G. Stanley Hall, a creature of his times, believed strongly that adolescence was determined – a fixed feature of human development that could be explained and accounted for in scientific fashion. To make his case, he relied on Haeckel's faulty recapitulation idea, Lombroso's faulty phrenology-inspired theories of crime, a plethora of anecdotes and one-sided interpretations of data. Given the issues, theories, standards and data-handling methods of his day, he did a superb job. But when you take away the shoddy theories, put the anecdotes in their place, and look for alternate explanations of the data, the bronze statue tumbles hard. I have no doubt that many of the street teens of Hall's time were suffering or insufferable, but it's a serious mistake to develop a timeless, universal theory of human nature around the peculiarities of the people of one's own time and place.

If Bezos took one leadership principle most to heart—which would also come to define the next half decade at Amazon—it was principal #8, “think big”: Thinking small is a self-fulfilling prophecy. Leaders create and communicate a bold direction that inspires results. They think differently and look around corners for ways to serve customers. In 2010, Amazon was a successful online retailer, a nascent cloud provider, and a pioneer in digital reading. But Bezos envisioned it as much more. His shareholder letter that year was a paean to the esoteric computer science disciplines of artificial intelligence and machine learning that Amazon was just beginning to explore. It opened by citing a list of impossibly obscure terms such as “naïve Bayesian estimators,” “gossip protocols,” and “data sharding.” Bezos wrote: “Invention is in our DNA and technology is the fundamental tool we wield to evolve and improve every aspect of the experience we provide our customers.

[THE DAILY BREATH] Blaise Pascal, the famous mathematician, once said: "To those who wish to see, God gives them sufficient light. To those who doesn't wish to see, God gives them sufficient darkness." Seeing the Truth is a choice. Listening to my words is a choice. Healing is a choice. If want scientific evidence about the existence of God, there is a wealth of data to support it. Dr. Jeffrey Long, M.D. used the best scientific techniques available today to study more than 4,000 people who had near-death experiences and found themselves face to face with our Heavenly Father. Read the book "God and the Afterlife" and you will find it. If you want scientific evidence about Jesus being the Son of God, Lee Strobel, an atheist investigative journalist discovered it. Read the book "The Case for Christ" and you will find it. If you want scientific evidence about Jesus still healing today, study the ministries of Dr. Charles Ndifon, T.L. Osborn, Kathryn Kuhlman among others, and you will find it. But most importantly, if you want to fill the emptiness within you, and experience the perfect love, mercy and forgiveness, if you want to live in the peace of our Heavenly Father, give your body, your mind and your heart to Christ. Give your life to Jesus. The empty place you feel in your heart is reserved only for the spirit of Christ and nothing from this world will fill it. Look up to heaven, behold Jesus and Live.

Your story isn’t powerful enough if all it does is lead the horse to water; it has to inspire the horse to drink, too. On social media, the only story that can achieve that goal is one told with native content. Native content amps up your story’s power. It is crafted to mimic everything that makes a platform attractive and valuable to a consumer—the aesthetics, the design, and the tone. It also offers the same value as the other content that people come to the platform to consume. Email marketing was a form of native content. It worked well during the 1990s because people were already on email; if you told your story natively and provided consumers with something they valued on that platform, you got their attention. And if you jabbed enough to put them in a purchasing mind-set, you converted. The rules are the same now that people spend their time on social media. It can’t tell you what story to tell, but it can inform you how your consumer wants to hear it, when he wants to hear it, and what will most make him want to buy from you. For example, supermarkets or fast-casual restaurants know from radio data that one of the ideal times to run an ad on the radio is around 5:00 P.M., when moms are picking up the kids and deciding what to make for dinner, and even whether they have the energy to cook. Social gives you the same kind of insight. Maybe the data tells you that you should post on Facebook early in the morning before people settle

The motor activities we take for granted—getting out of a chair and walking across a room, picking up a cup and drinking coffee,and so on—require integration of all the muscles and sensory organs working smoothly together to produce coordinated movements that we don't even have to think about. No one has ever explained how the simple code of impulses can do all that. Even more troublesome are the higher processes, such as sight—in which somehow we interpret a constantly changing scene made of innumerable bits of visual data—or the speech patterns, symbol recognition, and grammar of our languages.Heading the list of riddles is the "mind-brain problem" of consciousness, with its recognition, "I am real; I think; I am something special." Then there are abstract thought, memory, personality,creativity, and dreams. The story goes that Otto Loewi had wrestled with the problem of the synapse for a long time without result, when one night he had a dream in which the entire frog-heart experiment was revealed to him. When he awoke, he knew he'd had the dream, but he'd forgotten the details. The next night he had the same dream. This time he remembered the procedure, went to his lab in the morning, did the experiment, and solved the problem. The inspiration that seemed to banish neural electricity forever can't be explained by the theory it supported! How do you convert simple digital messages into these complex phenomena? Latter-day mechanists have simply postulated brain circuitry so intricate that we will probably never figure it out, but some scientists have said there must be other factors.

Isaac Asimov’s short story “The Fun They Had” describes a school of the future that uses advanced technology to revolutionize the educational experience, enhancing individualized learning and providing students with personalized instruction and robot teachers. Such science fiction has gone on to inspire very real innovation. In a 1984 Newsweek interview, Apple’s co-founder Steve Jobs predicted computers were going to be a bicycle for our minds, extending our capabilities, knowledge, and creativity, much the way a ten-speed amplifies our physical abilities. For decades, we have been fascinated by the idea that we can use computers to help educate people. What connects these science fiction narratives is that they all imagined computers might eventually emulate what we view as intelligence. Real-life researchers have been working for more than sixty years to make this AI vision a reality. In 1962, the checkers master Robert Nealey played the game against an IBM 7094 computer, and the computer beat him. A few years prior, in 1957, the psychologist Frank Rosenblatt created Perceptron, the first artificial neural network, a computer simulation of a collection of neurons and synapses trained to perform certain tasks. In the decades following such innovations in early AI, we had the computation power to tackle systems only as complex as the brain of an earthworm or insect. We also had limited techniques and data to train these networks. The technology has come a long way in the ensuing decades, driving some of the most common products and apps today, from the recommendation engines on movie streaming services to voice-controlled personal assistants such as Siri and Alexa. AI has gotten so good at mimicking human behavior that oftentimes we cannot distinguish between human and machine responses. Meanwhile, not only has the computation power developed enough to tackle systems approaching the complexity of the human brain, but there have been significant breakthroughs in structuring and training these neural networks.

Well before the end of the 20th century however print had lost its former dominance. This resulted in, among other things, a different kind of person getting elected as leader. One who can present himself and his programs in a polished way, as Lee Quan Yu you observed in 2000, adding, “Satellite television has allowed me to follow the American presidential campaign. I am amazed at the way media professionals can give a candidate a new image and transform him, at least superficially, into a different personality. Winning an election becomes, in large measure, a contest in packaging and advertising. Just as the benefits of the printed era were inextricable from its costs, so it is with the visual age. With screens in every home entertainment is omnipresent and boredom a rarity. More substantively, injustice visualized is more visceral than injustice described. Television played a crucial role in the American Civil rights movement, yet the costs of television are substantial, privileging emotional display over self-command, changing the kinds of people and arguments that are taken seriously in public life. The shift from print to visual culture continues with the contemporary entrenchment of the Internet and social media, which bring with them four biases that make it more difficult for leaders to develop their capabilities than in the age of print. These are immediacy, intensity, polarity, and conformity. Although the Internet makes news and data more immediately accessible than ever, this surfeit of information has hardly made us individually more knowledgeable, let alone wiser, as the cost of accessing information becomes negligible, as with the Internet, the incentives to remember it seem to weaken. While forgetting anyone fact may not matter, the systematic failure to internalize information brings about a change in perception, and a weakening of analytical ability. Facts are rarely self-explanatory; their significance and interpretation depend on context and relevance. For information to be transmuted into something approaching wisdom it must be placed within a broader context of history and experience. As a general rule, images speak at a more emotional register of intensity than do words. Television and social media rely on images that inflamed the passions, threatening to overwhelm leadership with the combination of personal and mass emotion. Social media, in particular, have encouraged users to become image conscious spin doctors. All this engenders a more populist politics that celebrates utterances perceived to be authentic over the polished sound bites of the television era, not to mention the more analytical output of print. The architects of the Internet thought of their invention as an ingenious means of connecting the world. In reality, it has also yielded a new way to divide humanity into warring tribes. Polarity and conformity rely upon, and reinforce, each other. One is shunted into a group, and then the group polices once thinking. Small wonder that on many contemporary social media platforms, users are divided into followers and influencers. There are no leaders. What are the consequences for leadership? In our present circumstances, Lee's gloomy assessment of visual media's effects is relevant. From such a process, I doubt if a Churchill or Roosevelt or a de Gaulle can emerge. It is not that changes in communications technology have made inspired leadership and deep thinking about world order impossible, but that in an age dominated by television and the Internet, thoughtful leaders must struggle against the tide.

We need to be humble enough to recognize that unforeseen things can and do happen that are nobody’s fault. A good example of this occurred during the making of Toy Story 2. Earlier, when I described the evolution of that movie, I explained that our decision to overhaul the film so late in the game led to a meltdown of our workforce. This meltdown was the big unexpected event, and our response to it became part of our mythology. But about ten months before the reboot was ordered, in the winter of 1998, we’d been hit with a series of three smaller, random events—the first of which would threaten the future of Pixar. To understand this first event, you need to know that we rely on Unix and Linux machines to store the thousands of computer files that comprise all the shots of any given film. And on those machines, there is a command—/bin/rm -r -f *—that removes everything on the file system as fast as it can. Hearing that, you can probably anticipate what’s coming: Somehow, by accident, someone used this command on the drives where the Toy Story 2 files were kept. Not just some of the files, either. All of the data that made up the pictures, from objects to backgrounds, from lighting to shading, was dumped out of the system. First, Woody’s hat disappeared. Then his boots. Then he disappeared entirely. One by one, the other characters began to vanish, too: Buzz, Mr. Potato Head, Hamm, Rex. Whole sequences—poof!—were deleted from the drive. Oren Jacobs, one of the lead technical directors on the movie, remembers watching this occur in real time. At first, he couldn’t believe what he was seeing. Then, he was frantically dialing the phone to reach systems. “Pull out the plug on the Toy Story 2 master machine!” he screamed. When the guy on the other end asked, sensibly, why, Oren screamed louder: “Please, God, just pull it out as fast as you can!” The systems guy moved quickly, but still, two years of work—90 percent of the film—had been erased in a matter of seconds. An hour later, Oren and his boss, Galyn Susman, were in my office, trying to figure out what we would do next. “Don’t worry,” we all reassured each other. “We’ll restore the data from the backup system tonight. We’ll only lose half a day of work.” But then came random event number two: The backup system, we discovered, hadn’t been working correctly. The mechanism we had in place specifically to help us recover from data failures had itself failed. Toy Story 2 was gone and, at this point, the urge to panic was quite real. To reassemble the film would have taken thirty people a solid year. I remember the meeting when, as this devastating reality began to sink in, the company’s leaders gathered in a conference room to discuss our options—of which there seemed to be none. Then, about an hour into our discussion, Galyn Susman, the movie’s supervising technical director, remembered something: “Wait,” she said. “I might have a backup on my home computer.” About six months before, Galyn had had her second baby, which required that she spend more of her time working from home. To make that process more convenient, she’d set up a system that copied the entire film database to her home computer, automatically, once a week. This—our third random event—would be our salvation. Within a minute of her epiphany, Galyn and Oren were in her Volvo, speeding to her home in San Anselmo. They got her computer, wrapped it in blankets, and placed it carefully in the backseat. Then they drove in the slow lane all the way back to the office, where the machine was, as Oren describes it, “carried into Pixar like an Egyptian pharaoh.” Thanks to Galyn’s files, Woody was back—along with the rest of the movie.

The business would do good to understand that success or failure is not final in Data Science. For this reason, the business should develop a persistent spirit.

To understand this first event, you need to know that we rely on Unix and Linux machines to store the thousands of computer files that comprise all the shots of any given film. And on those machines, there is a command—/bin/rm -r -f *—that removes everything on the file system as fast as it can. Hearing that, you can probably anticipate what’s coming: Somehow, by accident, someone used this command on the drives where the Toy Story 2 files were kept. Not just some of the files, either. All of the data that made up the pictures, from objects to backgrounds, from lighting to shading, was dumped out of the system. First, Woody’s hat disappeared. Then his boots. Then he disappeared entirely. One by one, the other characters began to vanish, too: Buzz, Mr. Potato Head, Hamm, Rex. Whole sequences—poof!—were deleted from the drive.

Data Scientists should refuse to be denied by someone else's vision of what's possible.

Virtually all organizations have legacy issues and no Data Scientist can go back and create a new beginning, but a great Data Scientist can help organizations make new endings.

When some people are choosing to work hard, not sleeping , sacrificing everything they have to fulfill their dreams. There are some who choose to interfere in their lives. Who dig in their pasts and who look for mistakes so that they can bring them down. Today if you Choose to use the same energy, skill, effort, data and resources to bring others down, or shaming them. Use it to better your life. You will go far and you will be successful just like them.

the autonomous-driving side of things, Alphabet (formerly Google), which has logged several million self-driving-car test miles, continues to lead the pack. At the end of 2016, it created a new business division, called Waymo, for its autonomous driving technology. In May 2017, Waymo and Lyft announced that they would work together on developing the technology, and later in the year, Alphabet invested $1 billion in the start-up. Others, like Cruise Automation (which GM acquired for $1 billion) and Comma.ai, which offers open-source autonomous driving technology in the same vein as Google’s Android mobile operating system, are chasing hard. Baidu, China’s leading Internet search company, has an autonomous-driving research center in Sunnyvale. Byton—backed by China’s Tencent, Foxconn, and the China Harmony New Energy auto retailer group—has an office in Mountain View, as does Didi Chuxing, the Chinese ride-sharing company in which Apple invested $1 billion. Many of these companies have taken not just inspiration but also talent from Tesla. Part of the value of an innovation cluster like Silicon Valley lies in the dispersal of intellectual labor from one node to the next. For instance, PayPal is well known in the Valley for producing a number of high performers who left the company to start, join, or invest in others. The so-called PayPal Mafia includes Reid Hoffman, who founded LinkedIn; Max Levchin, whose most recent of several start-ups is the financial services company Affirm; Peter Thiel, a Facebook board member and President Trump–supporting venture capitalist who cofounded “big data” company Palantir; Jeremy Stoppelman, who started reviews site Yelp; Keith Rabois, who was chief operating officer at Square and then joined Khosla Ventures; David Sacks, who sold Yammer to Microsoft for $1.2 billion and later became CEO at Zenefits; Jawed Karim, who cofounded YouTube; and one Elon Musk.

It normally takes about two to three months of dedicated work for a new product manager to get up to speed. This assumes you have a manager who can give you the help and access you need to gain this expertise, including lots of access to customers, access to data (and when necessary, training in the tools to access that data), access to key stakeholders, and time to learn your product and industry inside and out.

As more data become available and as the economy continues to change, the ability to ask the right questions will become even more vital. No matter how bright the light is, you won’t find your keys by searching under a lamppost if that’s not where you lost them.

For the hardest problems—the ones we really want to solve but haven’t been able to, like curing cancer—pure nature-inspired approaches are probably too uninformed to succeed, even given massive amounts of data. We can in principle learn a complete model of a cell’s metabolic networks by a combination of structure search, with or without crossover, and parameter learning via backpropagation, but there are too many bad local optima to get stuck in. We need to reason with larger chunks, assembling and reassembling them as needed and using inverse deduction to fill in the gaps. And we need our learning to be guided by the goal of optimally diagnosing cancer and finding the best drugs to cure it. Optimal learning is the Bayesians’ central goal, and they are in no doubt that they’ve figured out how to reach it. This way, please …

Passage Five: From Business Manager to Group Manager This is another leadership passage that at first glance doesn’t seem overly arduous. The assumption is that if you can run one business successfully, you can do the same with two or more businesses. The flaw in this reasoning begins with what is valued at each leadership level. A business manager values the success of his own business. A group manager values the success of other people’s businesses. This is a critical distinction because some people only derive satisfaction when they’re the ones receiving the lion’s share of the credit. As you might imagine, a group manager who doesn’t value the success of others will fail to inspire and support the performance of the business managers who report to him. Or his actions might be dictated by his frustration; he’s convinced he could operate the various businesses better than any of his managers and wishes he could be doing so. In either instance, the leadership pipeline becomes clogged with business managers who aren’t operating at peak capacity because they’re not being properly supported or their authority is being usurped. This level also requires a critical shift in four skill sets. First, group managers must become proficient at evaluating strategy for capital allocation and deployment purposes. This is a sophisticated business skill that involves learning to ask the right questions, analyze the right data, and apply the right corporate perspective to understand which strategy has the greatest probability of success and therefore should be funded. The second skill cluster involves development of business managers. As part of this development, group managers need to know which of the function managers are ready to become business managers. Coaching new business managers is also an important role for this level. The third skill set has to do with portfolio strategy. This is quite different from business strategy and demands a perceptual shift. This is the first time managers have to ask these questions: Do I have the right collection of businesses? What businesses should be added, subtracted, or changed to position us properly and ensure current and future earnings? Fourth, group managers must become astute about assessing whether they have the right core capabilities. This means avoiding wishful thinking and instead taking a hard, objective look at their range of resources and making a judgment based on analysis and experience. Leadership becomes more holistic at this level. People may master the required skills, but they won’t perform at full leadership capacity if they don’t begin to see themselves as broad-gauged executives. By broad-gauged, we mean that managers need to factor in the complexities of running multiple businesses, thinking in terms of community, industry, government,

In April 2012, The New York Times published a heart-wrenching essay by Claire Needell Hollander, a middle school English teacher in the New York City public schools. Under the headline “Teach the Books, Touch the Heart,” she began with an anecdote about teaching John Steinbeck’s Of Mice and Men. As her class read the end together out loud in class, her “toughest boy,” she wrote, “wept a little, and so did I.” A girl in the class edged out of her chair to get a closer look and asked Hollander if she was crying. “I am,” she said, “and the funny thing is I’ve read it many times.” Hollander, a reading enrichment teacher, shaped her lessons around robust literature—her classes met in small groups and talked informally about what they had read. Her students did not “read from the expected perspective,” as she described it. They concluded (not unreasonably) that Holden Caulfield “was a punk, unfairly dismissive of parents who had given him every advantage.” One student read Lady Macbeth’s soliloquies as raps. Another, having been inspired by Of Mice and Men, went on to read The Grapes of Wrath on his own and told Hollander how amazed he was that “all these people hate each other, and they’re all white.” She knew that these classes were enhancing her students’ reading levels, their understanding of the world, their souls. But she had to stop offering them to all but her highest-achieving eighth-graders. Everyone else had to take instruction specifically targeted to boost their standardized test scores. Hollander felt she had no choice. Reading scores on standardized tests in her school had gone up in the years she maintained her reading group, but not consistently enough. “Until recently, given the students’ enthusiasm for the reading groups, I was able to play down that data,” she wrote. “But last year, for the first time since I can remember, our test scores declined in relation to comparable schools in the city. Because I play a leadership role in the English department, I felt increased pressure to bring this year’s scores up. All the teachers are increasing their number of test-preparation sessions and practice tests, so I have done the same, cutting two of my three classic book groups and replacing them with a test preparation tutorial program.” Instead of Steinbeck and Shakespeare, her students read “watered-down news articles or biographies, bastardized novels, memos or brochures.” They studied vocabulary words, drilled on how to write sentences, and practiced taking multiple-choice tests. The overall impact of such instruction, Hollander said, is to “bleed our English classes dry.” So

The separation of mind and body that informs medical practice is also the dominant ideology in our culture. We do not often think of socio-economic structures and practices as determinants of illness or well-being. They are not usually “part of the equation.” Yet the scientific data is beyond dispute: socio-economic relationships have a profound influence on health. For example, although the media and the medical profession — inspired by pharmaceutical research — tirelessly promote the idea that next to hypertension and smoking, high cholesterol poses the greatest risk for heart disease, the evidence is that job strain is more important than all the other risk factors combined. Further, stress in general and job strain in particular are significant contributors both to high blood pressure and to elevated cholesterol levels. Economic relationships influence health because, most obviously, people with higher incomes are better able to afford healthier diets, living and working conditions and stress-reducing pursuits. Dennis Raphael, associate professor at the School of Health Policy and Management at York University in Toronto has recently published a study of the societal influences on heart disease in Canada and elsewhere. His conclusion: “One of the most important life conditions that determine whether individuals stay healthy or become ill is their income. In addition, the overall health of North American society may be more determined by the distribution of income among its members rather than the overall wealth of the society…. Many studies find that socioeconomic circumstances, rather than medical and lifestyle risk factors, are the main causes of cardiovascular disease, and that conditions during early life are especially important.” The element of control is the less obvious but equally important aspect of social and job status as a health factor. Since stress escalates as the sense of control diminishes, people who exercise greater control over their work and lives enjoy better health. This principle was demonstrated in the British Whitehall study showing that second-tier civil servants were at greater risk for heart disease than their superiors, despite nearly comparable incomes. Recognizing the multigenerational template for behaviour and for illness, and recognizing, too, the social influences that shape families and human lives, we dispense with the unhelpful and unscientific attitude of blame. Discarding blame leaves us free to move toward the necessary adoption of responsibility, a matter to be taken up when we come in the final chapters to consider healing.

Try to become aware of one feeling a day, keeping in mind that by doing so, you are building your data bank of self-knowledge

We've taken a small part of how our brain works - the patterns of dendrites and axons and synapses - and we've built computer architectures around them. But that's all it is - a symbolic machine inspired by the human brain. Real brains are biological pieces of meat inextricably connected to the bodies that host them and the environments they inhabit in a million essential ways. A computer is a complex tool, but it's not a brain. It requires the human operator to be its body, to be its environment, by writing its algorithm and feeding it data. If we really want to make an artificial construct that can think like we do, we have to start over with a completely different concept.

For me, Universe is an Infinite Storage Drive: No one having the access to fetch or restore the data from it. But Science is trying its best - so let it be." - T∆Nv€€π /*|*\

Inspired by the tangled webs of neurons in our brains, deep learning constructs software layers of artificial neural networks with input and output layers. Data is fed into the input layer of the network, and a result emerges from the output layer of the network. In between the input and output layers may be up to thousands of other layers, hence the name “deep” learning.

There is a wonderful story of a group of American car executives who went to Japan to see a Japanese assembly line. At the end of the line, the doors were put on the hinges, the same as in America. But something was missing. In the United States, a line worker would take a rubber mallet and tap the edges of the door to ensure that it fit perfectly. In Japan, that job didn’t seem to exist. Confused, the American auto executives asked at what point they made sure the door fit perfectly. Their Japanese guide looked at them and smiled sheepishly. “We make sure it fits when we design it.” In the Japanese auto plant, they didn’t examine the problem and accumulate data to figure out the best solution—they engineered the outcome they wanted from the beginning. If they didn’t achieve their desired outcome, they understood it was because of a decision they made at the start of the process.

This book is a compilation of interesting ideas that have strongly influenced my thoughts and I want to share them in a compressed form. That ideas can change your worldview and bring inspiration and the excitement of discovering something new. The emphasis is not on the technology because it is constantly changing. It is much more difficult to change the accompanying circumstances that affect the way technological solutions are realized. The chef did not invent salt, pepper and other spices. He just chooses good ingredients and uses them skilfully, so others can enjoy his art. If I’ve been successful, the book creates a new perspective for which the selection of ingredients is important, as well as the way they are smoothly and efficiently arranged together. In the first part of the book, we follow the natural flow needed to create the stimulating environment necessary for the survival of a modern company. It begins with challenges that corporations are facing, changes they are, more or less successfully, trying to make, and the culture they are trying to establish. After that, we discuss how to be creative, as well as what to look for in the innovation process. The book continues with a chapter that talks about importance of inclusion and purpose. This idea of inclusion – across ages, genders, geographies, cultures, sexual orientation, and all the other areas in which new ways of thinking can manifest – is essential for solving new problems as well as integral in finding new solutions to old problems. Purpose motivates people for reaching their full potential. This is The second and third parts of the book describes the areas that are important to support what is expressed in the first part. A flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage. Business Process Management (BPM) goes from task-based automation, to process-based automation, so automating a number of tasks in a process, and then to functional automation across multiple processes andeven moves towards automation at the business ecosystem level. Analytics brought us information and insight; AI turns that insight into superhuman knowledge and real-time action, unleashing new business models, new ways to build, dream, and experience the world, and new geniuses to advance humanity faster than ever before. Companies and industries are transforming our everyday experiences and the services we depend upon, from self-driving cars, to healthcare, to personal assistants. It is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Another important disruptor is the blockchain - a distributed decentralized digital ledger of transactions with the promise of liberating information and making the economy more democratic. You no longer need to trust anyone but an algorithm. It brings reliability, transparency, and security to all manner of data exchanges: financial transactions, contractual and legal agreements, changes of ownership, and certifications. A quantum computer can simulate efficiently any physical process that occurs in Nature. Potential (long-term) applications include pharmaceuticals, solar power collection, efficient power transmission, catalysts for nitrogen fixation, carbon capture, etc. Perhaps we can build quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Perhaps a quantum deep learning network can be trained more efficiently, e.g. using a smaller training set. This is still in conceptual research domain.

Mobile phone apps – 2012 Before building a QuickBooks app, I decided to try iPhone and Android apps. This my first experience entering an app store. Unfortunately, the apps failed for many reasons: User base too small: There are millions of mobile phone users, but that does not translate to millions of users for your software. There is a subsection of a user base that matters most. Too many competitors: The app stores were oversaturated. There were over a million apps, literally. There was no way to stand out from the rest. My apps became me-too apps. The Intuit app stores were just getting started at the time and there were far fewer apps. Difficult to gain entry: I tried game development, and good games are expensive to produce. You need a soundtrack and graphic designers. The cost of making an exceptional game is outrageous. There was no way I could afford it. Failed to show value: Since most apps were free, users refused to pay me. I tried in-app purchases, but most users were uninterested. I learned that businesses were a better target because I could show them how to save time. Failed to solve a problem: In my eyes, app stores were the only way to advertise my game. I failed to tap into my potential user base. Businesses have a clear data entry problem that I can fix, but consumers were too difficult to sell to. Technical issues: I submitted one app to the Windows Marketplace, and it failed 15 times. I had to wait for Apple to publish updates to my app weekly. I learned that my next plugin must receive updates in a few hours, instead of a few days. Users simply cannot wait this long for an issue to get fixed. This was the most important lesson that I learned, and it inspired me to make a cloud-based system. Different devices:

As studies have shown, there’s a difference between data, information, and knowledge. Always assess what you're gaining.

Jon Freach, director of design research at frog design, gave me three reasons why the externalization of data is critical for successful innovations: “First, the physicality of a dedicated room gives the project team a common space to work together in. Second, the room says to the organization, ‘this is important work’ and through its structure conveys an evolving narrative about what the team is learning and making. At any point in time, stakeholders can ‘read the room’ and walk away informed or inspired. The third, and possibly most useful function of a room filled with externalized data, is that it enables forced comparison of information and team dialogue to occur—two critical and often overlooked tools in a designer’s toolbox, both of which are essential to the act of sensemaking.

There is objective and subjective data in your story. It’s possible to identify, compile, and process that data to gain insight on how you can apply the science of quality management to your career.

AI can process data, but the leader possesses the heart to empathize, the ethics to judge, the spark to inspire, the intuition to innovate, and the warmth to connect.

Daniel Roth of North Coast Container has established himself as an executive leader. Through his rich resume and vast experience, he inspires growth in large organizations, focusing on predictive data analytics, manufacturing, project management, and business development. Mr. Roth is the Senior Vice President of Sales and General Manager for Stavig Industries LLC. He holds his MBA from Portland State University and applies his business and leadership acumen to embracing opportunities for himself and his clients.

Some leaders keep repeating or mentioning that there was some study conducted in Harvard or Yale that only 3 percent of people had set goals and written down these goals and they made more money than 97 percent of people! Well first of all, statements containing precise statistics, particularly ones with little empirical evidence, are often based on cooked up or questionable data. And secondly, there was no such study at either Harvard or Yale. It has just been repeated from one inspirational dude to another until it become a part of folklore. It has also been incorrectly repeated in another inspirational dude, Sir Anubhav Srivastava’s film Carve Your Destiny ten years ago. On a sidenote, I have no connections whatsoever with this gentleman. Who is he?!

We read data not to dwell into the numbers, or the failures. We dive into data to understand reasons behind the numbers and find opportunities.

INTEGRITY IS: Empirical knowledge is one of the critical ingredients of integrity, followed by truth, which is not necessarily moral, hence the evaluation of the truth. Empirical knowledge, coupled with the evaluation of truth for determination of its morality, equals factual righteousness and subsequently perpetuates integrity. Perceptions can at times influence our evaluation of truth, which may ultimately influence our identification of a subject’s morality. Our perception of what is truthful and righteous is not necessarily others interpretations. Assumption and credulity is the archenemy—the nemesis, if you will—of truth and ultimately of morality. We sometimes have an unwillingness or aversion toward researching a subject before formulating an opinion. But the more knowledge we learn about a subject, the more beneficial it will be. Understanding based on factual data will enable a more realistic and enhanced resolution of questions about a subject’s morality. Do some investigation of facts, situations, ideology, and belief systems for your empirical knowledge. As you do so, the truth shall be brought to the surface. As you analyze empirical knowledge, the truth and its morality will determine the factual righteousness of the subject, and ultimately, its integrity. Integrity Equation: Empirical knowledge + truth + morality = factual righteousness = INTEGRITY

first of which would threaten the future of Pixar. To understand this first event, you need to know that we rely on Unix and Linux machines to store the thousands of computer files that comprise all the shots of any given film. And on those machines, there is a command—/bin/rm -r -f *—that removes everything on the file system as fast as it can. Hearing that, you can probably anticipate what’s coming: Somehow, by accident, someone used this command on the drives where the Toy Story 2 files were kept. Not just some of the files, either. All of the data that made up the pictures, from objects to backgrounds, from lighting to shading, was dumped out of the system. First, Woody’s hat disappeared. Then his boots. Then he disappeared entirely. One by one, the other characters began to vanish, too: Buzz, Mr. Potato Head, Hamm, Rex. Whole sequences—poof!—were deleted from the drive.

Recommendation 7: Build human capital in government. While Moneyball uses big data and advanced statistics, the endeavor ultimately depends on people. To take one example: rapid-cycle evaluation at a service-delivery agency requires policy experts to identify plausible new approaches; program managers to implement them; technologists to create or modernize data systems to capture effects; social scientists or statisticians to analyze the data on effects; and crosscutting leaders who know how to bring these pieces together with inspiration and precision.

In a TED talk watched by over a million people, Wolfram (2010) proposes that working on mathematics has four stages: Posing a question Going from the real world to a mathematical model Performing a calculation Going from the model back to the real world, to see if the original question was answered The first stage involves asking a good question of some data or a situation—the first mathematical act that is needed in the workplace.

But then came random event number two: The backup system, we discovered, hadn’t been working correctly. The mechanism we had in place specifically to help us recover from data failures had itself failed. Toy Story 2 was gone and, at this point, the urge to panic was quite real.

The core functions of an FIU call for objectivity in decision making, the timely processing of incoming information, and strict protection of confidential data. As the exchange of information between FIUs is based in large part on trust, building an FIU that inspires trust from its counterparts is key to effective cooperation.

All problems are simply interruptions in the transmission and preservation of data, he reminded himself.

I teach not by feeding the mind with data but by kindling the mind.

Not immediately, but a decade after Mandelbrot published his physiological speculations, some theoretical biologists began to find fractal organization controlling structures all through the body. The standard "exponential" description of bronchial branching proved to be quite wrong; a fractal description turned out to fit the data. The urinary collecting system proved fractal. The biliary duct in the liver. The network of special fibers in the heart that carry pulses of electric current tot he contracting muscles. The last structure, known to heart specialists as the His-Purkinje network, inspired a particularly important line of research. Considerable work on healthy and abnormal hearts turned out to hinge on the details of how the muscle cells of the left and right pumping chambers all manage to coordinate their timing. Several chaos-minded cardiologists found that the frequency spectrum of heartbeat timing, like earthquakes and economic phenomena, followed fractal laws, and they argued that one key to understanding heartbeat timing was the fractal organization of the His-Purkinje network, a labyrinth of branching pathways organized to be self-similar on smaller and smaller scales.

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

Scientists fail all the time. We just brand it differently. We call it data.

Life is not meant to be an interstate highway. It's a winding mountain road with hills and dips, stop signs and school zones. Let friends and family be the data for your GPS satellite feed, and never forget that sometimes an unexpected detour leads to a hidden miracle.

Humans do inspiration; machines do validation.

To be data centric, is to be person centric.

Here’s the trick to significantly improving your SaaS email marketing skills—you have to become a student of it. This means you should: Start collecting great email copy, CTAs, and designs. Understand the objective behind each and every email that businesses send. Try to understand the rationale behind copy, link, and design decisions. There are great websites like Really Good Emails11, Good Email Copy12, and Good Sales Emails.com13 that you can use for your research. These sites categorize email copy and designs by types. As well as this, you should sign up to receive emails from some of the leading SaaS brands. Those include, among others: Drift MailChimp Pipedrive Shopify SurveyMonkey Trello Wistia Zapier You should also sign up to competing products and mailing lists from companies in your sector. I personally signed up to thousands of products and newsletters. It’s great for benchmarking and research. At the time of writing, I’ve already passively collected more than 60,000 emails. Obviously, don’t sign up to your competitors’ products with a business email address! I have a special email address I use for this. This account allows me to get data, understand what other organizations are doing, and find good copy ideas. For example, here’s what a search for ‘Typeform’ gives me: Figure 18.1 – Inbox Inspiration It’s not uncommon for me to sign up several times to the same product or newsletter. This allows me to see what they have learned and to track the evolution of their email marketing program. At LANDR, we created a shared document to keep track of subject lines, offers, and copy we wanted to test. Our copywriter was even going through his junk mail folder to find ideas and inspiration. There are tests we ran that were inspired by copy found in his spam folder. Some of them turned out to be really successful too—so keep your eyes open for inspiration. You can use Evernote, Paper, or any other platform to collaborate on idea generation. Alternatively, you can subscribe to paid services like Mailcharts14 or Mailody15. These services will help you track and understand your competitors’ email programs. Build processes to find and access copy and design ideas. It will help you create better emails, faster. In the next chapter we’ll get started creating our first email sequences.

Se depois de eu morrer, quiserem escrever a minha biografia, Não há nada mais simples. Tem só duas datas - a da minha nascença e a da minha morte. Entre uma e outra todos os dias são meus." - F.P.

Good teams engage directly with end users and customers every week, to better understand their customers, and to see the customer's response to their latest ideas. Bad teams think they are the customer. Good teams know that many of their favorite ideas won't end up working for customers, and even the ones that could will need several iterations to get to the point where they provide the desired outcome. Bad teams just build what's on the roadmap, and are satisfied with meeting dates and ensuring quality. Good teams understand the need for speed and how rapid iteration is the key to innovation, and they understand this speed comes from the right techniques and not forced labor. Bad teams complain they are slow because their colleagues are not working hard enough. Good teams make high‐integrity commitments after they've evaluated the request and ensured they have a viable solution that will work for the customer and the business. Bad teams complain about being a sales‐driven company. Good teams instrument their work so they can immediately understand how their product is being used and make adjustments based on the data. Bad teams consider analytics and reporting a nice to have.

even if a full plan is possible, you must not become too attached as you move through your journey. conditions change and unforeseen obstacles appear, requiring you to be flexible. during the journey a lot of learning can happen; taking in new experiences and data should inspire you to reassess your strategy so you can become more effective.

General education. The need for specialization. Foreign languages. How monographs should be read. The absolute necessity of seeking inspiration in nature. Mastery of technique. In search of original data