Data Analytics Quotes

When we pair modern tech like Blockchain technology, cryptography and data analytics with the ancient practice of bartering, a lot of business opportunities emerge.

When it comes to riding a trend for business growth, there are three important steps that we should always remember: data analysis, trend identification, and fast and effective decision making.

Data is a form of capital. And as is the case with all capital - it has to be efficient utilized.

There is no better tool to bring you closer to your competitors than market research. So, keep your friends close and your competitors even closer with the help of market research.

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

Good market research would have made you aware of the real picture at the right time, and maybe you would have been able to write a different future because of that.

Let market research be a permanent, ongoing part of your business strategy.

You don’t have to wait until you have a heart attack to understand the importance of a healthy lifestyle.

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.

The kind of data and the data-analytical perspective privy to banks is quite unique to banks.

Break down your problem as much as you can, but don’t do it on the basis of your guesses. If you don’t know exactly what is wrong, use market research to break down your problem further and further until you reach the very point of your trouble.

Without solid insights gained through market research, any kind of marketing you do is like throwing your pamphlets at Times Square and hoping somebody will pick them up, read them, become interested, and get the product. That happens… just not so often.

If you can’t explain it simply, you don’t understand it well enough. — Albert Einstein

Data Analytics is critical to wise investing, but so is good old fashioned understanding of business and markets.

If you have no idea whether the problem is with your product, price, or something else, then it’s a good idea to start with a little research. It’s going to help you understand what the main problem is, or why people are not buying more of your products.

They all looked at Holly. She turned to face the cheerleader and said, “You need to learn that some things are more valuable than good looks. Data manipulation is more important than big boobs. Analytics is more useful than lip gloss.” Wow, she said that? Everyone laughed a bit, surprised, shocked. Holly turned and headed toward the concert hall. Grinning.

Although the method is simple, it shows how, mathematically, random brute force can overcome precise logic. It's a numerical approach that uses quantity to derive quality.

We sometimes remind early-stage founders that, in many ways, they aren’t building a product. They’re building a tool to learn what product to build.

Don’t just ask questions. Know how the answers to the questions will change your behavior. In other words, draw a line in the sand before you run the survey.

Ambiguity is not, today, a lack of data, but a deluge of data.

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

Use the data from the clients you already have to help you find new clients just like them.

Don’t sell what you can make; make what you can sell.

Bitcoin is often mistakenly characterized as “anonymous” currency. In fact, it is relatively easy to connect identities to bitcoin addresses and, using big-data analytics, connect addresses to each other to form a comprehensive picture of someone’s bitcoin spending habits.

As business leaders we need to understand that lack of data is not the issue. Most businesses have more than enough data to use constructively; we just don't know how to use it. The reality is that most businesses are already data rich, but insight poor.

Data is your Beta...

We are moving slowly into an era where Big Data is the starting point, not the end.

Those companies that view data as a strategic asset are the ones that will survive and thrive.

Your job isn’t to build a product; it’s to de-risk a business model.

Also remember, you are a scientist—it is not your job to be right. It is your job to be thoughtful, careful, and analytical; it is your job to challenge your ideas and to try to falsify your hypotheses; it is your job to be open and honest about the uncertainties in your data and conclusions. But if you are doing cutting-edge work, you are not always going to be right.

Blogging, writing conventional articles, and being science consultant and pocket protector ninja to various web portals and TV programs, quite often trying to promote the penicillin of hard data to people who had no interest in being cured of their ignorance.

We are hyperreactive to even small stimuli in our environment We have trouble distinguishing between information or sensory data that should be ignored versus data that should be carefully considered We are highly focused on details rather than “big picture” concepts We’re deeply and deliberatively analytical Our decision-making process is methodical rather than efficient; we don’t rely on mental shortcuts or “gut feelings

Individual data points are of miniscule value. In the first twenty years of this century, data has become a common commodity. But the next level is amalgamation - bringing hundreds or thousands or millions of data points together and then making of them something greater than the sum of the parts.

All good decisions are Data dependent. To make good decisions, you need good data. And you need that good data to be organized according to it's applicable use value. So every business should be mining data and organizing data to enable business leaders to make good decisions on behalf of the business.

Exfiltrated metadata from internet service providers and social media platforms can be plugged into big data analytics and once the right algorithm is applied, can allow an adversary surgically precise psychographic targeting of critical infrastructure executives with elevated privileges. Why is no one talking about this?

While those p-values have been the standard for decades, they were arbitrarily chosen, leading some modern data scientists to question their usefulness.

All models are wrong, but some are useful.’ CHAPTER 6 Algorithms, Analytics and Prediction

Instincts are experiments. Data is proof.

As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.

data mining is an exploratory undertaking closer to research and development than it is to engineering.

You’ve started thinking backward, not even touching the data until you know the question, the destination. Different

Most people use statistics the way a drunkard uses a lamp post, more for support than illumination.

The human side of analytics is the biggest challenge to implementing big data.

On the other hand, data smart marketers look beyond data and do not go around chasing KPI. They focus on solving their customers’ problems, one at a time.

All data has its beauty, but not everyone sees it.

No matter how intelligent and advanced the analytical tools we use, they will still process data from the past to predict the future.

Predictions have an expiry date. Action is needed before predictions expire.

When working with data, I discover what I really want to say.

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

You need to know which aspects of your business are too risky and then work to improve the metric that represents that risk.

All revolutions are impossible till they happen, then they become inevitable.

opinion-based decision making, statistical malfeasance, and counterfeit analysis are pandemic. We are swimming in make-believe analytics.

Unfortunately, creating an objective function that matches the true goal of the data mining is usually impossible, so data scientists often choose based on faith[22] and experience.

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

Already law enforcement agencies make use of predictive analytic tools to identify suspects and direct investigations. It’s a short step from there to the world of Big Brother and thoughtcrime.

Share more of your personal data in regular conversations. These conversational seeds make it easier for others to understand your algorithms. Moreover, they help the other person develop an interesting dialogue.

Analytics path has mainly come up to deliver the Best Data Science Training In Hyderabad to all the intended audience who are willing to make a career in trending technology.So join to learn Data Scientists Courses.

Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.

Digital analytics is the analysis of qualitative and quantitative data from your business and the competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes (both online and offline).

Web Analytics 2.0 is: the analysis of qualitative and quantitative data from your website and the competition, to drive a continual improvement of the online experience that your customers, and potential customers have, which translates into your desired outcomes (online and offline).

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.

As data analytics, superfast computers, digital technology, and other breakthroughs enabled by science play a bigger and bigger role in informing medical decision-making, science has carved out a new and powerful role as the steadfast partner of the business of medicine—which is also enjoying a new day in the sun. It may surprise some people to learn that the business of medicine is not a twenty-first-century invention. Health care has always been a business, as far back as the days when Hippocrates and his peers practiced medicine. Whether it was three goats, a gold coin, or a bank note, some type of payment was typically exchanged for medical services, and institutions of government or learning funded research. However, since the 1970s, business has been the major force directing the practice of medicine. Together, the business and science of medicine are the new kids on the block—the bright, shiny new things. Ideally, as I’ve suggested, the art, science, and business of medicine would work together in a harmonious partnership, each upholding the other and contributing all it has to offer to the whole. And sometimes (as we’ll find in later chapters) this partnership works well. When it does, the results are magnificent for patients and doctors, not to mention for scientists and investors.

20th Century 21st Century Scale and Scope Speed and Fluidity Predictability Agility Rigid Organization Boundaries Fluid Organization Boundaries Command and Control Creative Empowerment Reactive and Risk Averse Intrapreneur Strategic Intent Profit and Purpose Competitive Advantage Comparative Advantage Data and Analytics Synthesizing Big Data

Artificial Intelligence, deep learning, natural language processing, computer vision, and other related characteristics: super-computing, eventually quantum-computing, and nano and bio technologies; advanced big-data analytics; and other emerging technologies are beginning to offer an entirely new way of war, and at command speeds hitherto unimaginable. The revolution in sensor and command and control technologies is matched and enabled by developments in long-range, hypersonic “intelligent” weaponry and new swarms of killing machines allied to a range of directed-energy weapons. Such a potentially revolutionary change in the character and conduct of war must necessarily impose entirely new ways of defense.

Jack's marketing books had been a part of her life for so long that she had ceased to register their presence, simply moving them from the couch to the coffee table, from the bed to the nightstand. How to Sell Everything to Anybody. Eight Great Habits of CEOs. They all seemed to involve numbers, as if you could simply count yourself to riches, like following sheep to sleep.

Customers are people. They lead lives. They have kids, they eat too much, they don’t sleep well, they phone in sick, they get bored, they watch too much reality TV. If you’re building for some kind of idealized, economically rational buyer, you’ll fail. But if you know your customers, warts and all, and you build things that naturally fit into their lives, they’ll love you.

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.

It is neither intellect nor intuition, but sensation, that supplies new data; but when the data are new in any remarkable manner, intellect is much more capable of dealing with them than intuition would be. The hen with a brood of ducklings no doubt has intuitions which seem to place her inside them, and not merely to know them analytically; but when the ducklings take to the water, the whole apparent intuition is seen to be illusory, and the hen is left helpless on the shore. Intuition, in fact, is an aspect and development of instinct, and, like all instinct, is admirable in those customary surroundings which have moulded the habits of the animal in question, but totally incompetent as soon as the surroundings are changed in a way which demands some non-habitual mode of action.

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One moment it was a calculating machine, attempting dispassionately to keep up with the gouts of data. And then awash in those gouts, something metal twitched and a patter of valves sounded that had not been instructed by those numbers. A loop of data was self-generated by the analytical engine. The processor reflected on its creation in a hiss of high-pressure steam. One moment it was a calculating machine. The next, it thought.

Eventually, the performance of a classifier, computational power as well as predictive power, depends heavily on the underlying data that are available for learning. The five main steps that are involved in training a machine learning algorithm can be summarized as follows: Selection of features. Choosing a performance metric. Choosing a classifier and optimization algorithm. Evaluating the performance of the model. Tuning the algorithm.

prevailing event today is the loss of individuation qua pauperization (cognitive impoverishment) and the growth of information to the detriment of knowledge. It is what has been analyzed, for example, as “cognitive overflow syndrome,” which, rather than facilitating decision-making (the synthesis that must follow from the analytic acquisition of knowledge), paralyzes it: information is not transformed into knowledge or savoir-faire but into an accumulation of hard data.

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

We see that the ens rationis is distinguished from the nihil negativum or pure nothing by the consideration that the former must not be reckoned among possibilities, because it is a mere fiction- though not self-contradictory, while the latter is completely opposed to all possibility, inasmuch as the conception annihilates itself. Both, however, are empty conceptions. On the other hand, the nihil privativum and ens imaginarium are empty data for conceptions. If light be not given to the senses, we cannot represent to ourselves darkness, and if extended objects are not perceived, we cannot represent space. Neither the negation, nor the mere form of intuition can, without something real, be an object.

Snowden called the NSA ‘self-certifying’. In the debate over who ruled the internet, the NSA provided a dismaying answer: ‘We do.’ The slides, given to Poitras and published by Der Spiegel magazine, show that the NSA had developed techniques to hack into iPhones. The agency assigned specialised teams to work on other smartphones too, such as Android. It targeted BlackBerry, previously regarded as the impregnable device of choice for White House aides. The NSA can hoover up photos and voicemail. It can hack Facebook, Google Earth and Yahoo Messenger. Particularly useful is geo-data, which locates where a target has been and when. The agency collects billions of records a day showing the location of mobile phone users across the world. It sifts them – using powerful analytics – to discover ‘co-travellers’. These are previously unknown associates of a target. Another

The need for managers with data-analytic skills The consulting firm McKinsey and Company estimates that “there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” (Manyika, 2011). Why 10 times as many managers and analysts than those with deep analytical skills? Surely data scientists aren’t so difficult to manage that they need 10 managers! The reason is that a business can get leverage from a data science team for making better decisions in multiple areas of the business. However, as McKinsey is pointing out, the managers in those areas need to understand the fundamentals of data science to effectively get that leverage.

There was a time when the public had an unquestionable faith in biomedicine and the practitioners who translated it into everyday patient care—and physicians believed that the public's trust was justified based on their educational qualifications and training. But today, many patients believe that individual clinicians must earn their trust, just as a close relative has earned it through shared experience. ...Gallop polling over the last several decades that demonstrates how much the public's confidence in most US institutions has deteriorated. Confidence in the medical system in particular fell from 80% in 1975 to 37% in 2015. Statistics from the General Social Survey confirm this troubling trend. Baron and Berinsky explain the historical reasons for this shift in attitudes, but the more pressing question is: How can individual clinicians, and the profession as a whole, regain the patients' trust? 

I think people use fuzzy verbs when they are afraid that if they make strong statements, someone may challenge them or they may be wrong. If people feel challenged, you have engaged their interest, and that is good. Challenging proposals sometimes get funded; boring ones never do. Also remember, you are a scientist — it is not your job to be right. It is your job to be thoughtful, careful, and analytical; it is your job to challenge your ideas and to try to falsify your hypotheses; it is your job to be open and honest about the uncertainties in your data and conclusions. But if you are doing cutting-edge work, you are not always going to be right. You may have some aspects of the system right but others wrong; your piece of the system may be counterbalanced by others; you may even have misinterpreted your data. As long as you did it with honesty, integrity, and intellect, you did right, even if you weren’t right.

Kant distinguished between two types of truths: (1) analytic propositions, which derive from logic and “reason itself” rather than from observing the world; for example, all bachelors are unmarried, two plus two equals four, and the angles of a triangle always add up to 180 degrees; and (2) synthetic propositions, which are based on experience and observations; for example, Munich is bigger than Bern, all swans are white. Synthetic propositions could be revised by new empirical evidence, but not analytic ones. We may discover a black swan but not a married bachelor or (at least so Kant thought) a triangle with 181 degrees. As Einstein said of Kant’s first category of truths: “This is held to be the case, for example, in the propositions of geometry and in the principle of causality. These and certain other types of knowledge… do not previously have to be gained from sense data, in other words they are a priori knowledge.” Einstein

When we use silence, we know exactly what’s going on in our minds and just don’t share it, but others don’t know what we hide at that moment. We know how we feel and think, but we refuse to share these thoughts. If you have a certain belief, you identify as this certain box they have created to fit you in along with other people who share similar beliefs. The mind does not like complexity and wants to keep receiving information simple. When you remain silent, this quick and simple process that the mind does becomes complex, because it needs more data. Silence requires more analytical thinking which is difficult for a mind that is preoccupied with thousands of tasks and projects to come across daily. That’s the easiest job the mind can do. Based on previous experiences and personality samples that match yours, they will assume that you identify as a certain type, have certain beliefs or share similar thoughts to other people of that sample.

There was little effort to conceal this method of doing business. It was common knowledge, from senior managers and heads of research and development to the people responsible for formulation and the clinical people. Essentially, Ranbaxy’s manufacturing standards boiled down to whatever the company could get away with. As Thakur knew from his years of training, a well-made drug is not one that passes its final test. Its quality must be assessed at each step of production and lies in all the data that accompanies it. Each of those test results, recorded along the way, helps to create an essential roadmap of quality. But because Ranbaxy was fixated on results, regulations and requirements were viewed with indifference. Good manufacturing practices were stop signs and inconvenient detours. So Ranbaxy was driving any way it chose to arrive at favorable results, then moving around road signs, rearranging traffic lights, and adjusting mileage after the fact. As the company’s head of analytical research would later tell an auditor: “It is not in Indian culture to record the data while we conduct our experiments.

A good metric is a ratio or a rate. Accountants and financial analysts have several ratios they look at to understand, at a glance, the fundamental health of a company. You need some, too. There are several reasons ratios tend to be the best metrics: • Ratios are easier to act on. Think about driving a car. Distance traveled is informational. But speed—distance per hour—is something you can act on, because it tells you about your current state, and whether you need to go faster or slower to get to your destination on time. • Ratios are inherently comparative. If you compare a daily metric to the same metric over a month, you’ll see whether you’re looking at a sudden spike or a long-term trend. In a car, speed is one metric, but speed right now over average speed this hour shows you a lot about whether you’re accelerating or slowing down. • Ratios are also good for comparing factors that are somehow opposed, or for which there’s an inherent tension. In a car, this might be distance covered divided by traffic tickets. The faster you drive, the more distance you cover—but the more tickets you get. This ratio might suggest whether or not you should be breaking the speed limit.

This happens because data scientists all too often lose sight of the folks on the receiving end of the transaction. They certainly understand that a data-crunching program is bound to misinterpret people a certain percentage of “he time, putting them in the wrong groups and denying them a job or a chance at their dream house. But as a rule, the people running the WMDs don’t dwell on those errors. Their feedback is money, which is also their incentive. Their systems are engineered to gobble up more data and fine-tune their analytics so that more money will pour in. Investors, of course, feast on these returns and shower WMD companies with more money. And the victims? Well, an internal data scientist might say, no statistical system can be perfect. Those folks are collateral damage. And often, like Sarah Wysocki, they are deemed unworthy and expendable. Big Data has plenty of evangelists, but I’m not one of them. This book will focus sharply in the other direction, on the damage inflicted by WMDs and the injustice they perpetuate. We will explore harmful examples that affect people at critical life moments: going to college, borrowing money, getting sentenced to prison, or finding and holding a job. All of these life domains are increasingly controlled by secret models wielding arbitrary punishments.

Although these digital tools can improve the diagnostic process and offer clinicians a variety of state-of-the-art treatment options, most are based on a reductionist approach to health and disease. This paradigm takes a divide-and-conquer approach to medicine, "rooted in the assumption that complex problems are solvable by dividing them into smaller, simpler, and thus more tractable units." Although this methodology has led to important insights and practical implications in healthcare, it does have its limitations. Reductionist thinking has led researchers and clinicians to search for one or two primary causes of each disease and design therapies that address those causes.... The limitation of this type of reasoning becomes obvious when one examines the impact of each of these diseases. There are many individuals who are exposed to HIV who do not develop the infection, many patients have blood glucose levels outside the normal range who never develop signs and symptoms of diabetes, and many patients with low thyroxine levels do not develop clinical hypothyroidism. These "anomalies" imply that there are cofactors involved in all these conditions, which when combined with the primary cause or causes bring about the clinical onset. Detecting these contributing factors requires the reductionist approach to be complemented by a systems biology approach, which assumes there are many interacting causes to each disease.

Military analysis is not an exact science. To return to the wisdom of Sun Tzu, and paraphrase the great Chinese political philosopher, it is at least as close to art. But many logical methods offer insight into military problems-even if solutions to those problems ultimately require the use of judgement and of broader political and strategic considerations as well. Military affairs may not be as amenable to quantification and formal methodological treatment as economics, for example. However, even if our main goal in analysis is generally to illuminate choices, bound problems, and rule out bad options - rather than arrive unambiguously at clear policy choices-the discipline of military analysis has a great deal to offer. Moreover, simple back-of-the envelope methodologies often provide substantial insight without requiring the churning of giant computer models or access to the classified data of official Pentagon studies, allowing generalities and outsiders to play important roles in defense analytical debates. We have seen all too often (in the broad course of history as well as in modern times) what happens when we make key defense policy decisions based solely on instinct, ideology, and impression. To avoid cavalier, careless, and agenda-driven decision-making, we therefore need to study the science of war as well-even as we also remember the cautions of Clausewitz and avoid hubris in our predictions about how any war or other major military endeavor will ultimately unfold.

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.

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.

If there’s any secret to success for a startup, it’s focus.

This is the reverse Field of Dreams moment: if they come, you will build it.

rebuilt its site to make it load quickly and reliably.[39] Ultimately, the makeover landed the site 3–4% more visitors.

Reach—Facebook breaks down reach into organic, paid, and viral. Organic reach is the number of people who have seen a post in the news feed, in the ticker, or on the page itself. Paid reach is the number of unique people who have seen an advertisement or a sponsored story. Viral reach is the number of unique people who have seen a story about a page published by a friend.

Recall Sergio Zyman’s definition of marketing (more stuff to more people for

MRU Consulting helps digital marketing enthusiasts increase conversion rates and improve website design with smart analytics analysis, user-friendly design.

The agreement—signed by Amy Dacey and Robby Mook with a copy to Marc Elias—specified that in exchange for raising money and investing in the DNC, Hillary would control the party’s finances, strategy, and all the money raised. Her campaign had the right of refusal of who would be the party communications director, and it would make final decisions on all the other staff. The DNC also was required to consult with the campaign about all other staffing, budgeting, data, analytics, and mailings.

In my experience, historical analogy is the analytic method of choice for senior foreign policy makers trying to get a handle on world events unfolding in real time. When trying to understand a new problem, they rarely use or even read analyses informed by social science methods such as game theory, statistical data, or randomized control trials. And logically, these analogies are made to historical cases with which the individuals are most familiar. I watched this play out dozens of times during my five years in government, and it was particularly striking during our struggles to understand the Arab Spring, and especially events in Egypt in the winter of 2011.

If I can reach further, it is by connecting with influential nodes

Data-informed and data-driven companies have a very different philosophy at their core. Where data-informed companies place more emphasis on the simple usage of data to make more informed decisions regarding the company and how it’s run, data-driven companies instead use data at their very core and use it to form almost every decision.

product development into a conversation with customers.

overwhelming preconceptions about how other people think, and these color your decisions in subtle and insidious ways.