Quantitative Data Quotes

Above all else show the data.

Allowing artist-illustrators to control the design and content of statistical graphics is almost like allowing typographers to control the content, style, and editing of prose.

If you are going to use the results of market research to make a big business decision, then it’s a good idea to do quantitative research rather than qualitative.

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

All so-called ‘quantitative’ data, when scrutinized, turn out to be composites of ‘qualitative’ – i.e., contextually located and indexical – interpretations produced by situated researchers, coders, government officials and others. The

One of the most convincing aspects of Stoffregen’s theory is how it finally explains why I get car sick in every seat other than the driving seat: it’s all about control. When you’re walking, you are in control of your movements. You know what’s coming. On a ship, or in a car, someone else is in control – unless you’re the driver. ‘The driver knows what the motion of the car is going to be and so the driver is able to stabilise his or herself in what we call an anticipatory fashion,’ explains Stoffregen, ‘whereas the passenger cannot know in quantitative detail what the car is going to be doing. And so their control of their own body must be compensatory. And anticipatory control is just better than compensatory control. You know, that ain’t no rocket science.

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).

Investors look at economic fundamentals; traders look at each other; ‘quants’ look at the data. Dealing on the basis of historic price series was once described as technical analysis, or chartism (and there are chartists still). These savants identify visual patterns in charts of price data, often favouring them with arresting names such as ‘head and shoulders’ or ‘double bottoms’. This is pseudo-scientific bunk, the financial equivalent of astrology. But more sophisticated quantitative methods have since proved profitable for some since the 1970s’ creation of derivative markets and the related mathematics. Profitable

A newly diagnosed person with access to the Internet is information’s incubant. Data visits like a minor god. Awake, we pass the day staring into the screen’s abyss, feeling the constriction of the quantitative, trying to learn to breathe through the bar graphs, head full of sample sizes and survival curves, eyes dimming, body reverent to math.

We never know as much as we think we do, and we are never as good as we think we are. When we forget that, the market will remind us. Approach this work with a sense of humility, realizing that however much we learn and however much we know, much more remains undiscovered.

It is not that I am a genius or exceptionally gifted, not by any means. Quite the contrary. What Happened (I shall try to explain it) is that every mind is shaped by its own experiences and memories and knowledge, and what makes it unique is the grand total and extremely personal nature of the collection of all the data that have made it what it is. Each person possesses a mind with powers that are, whether great or small, always unique, powers that belong to them alone. This renders them capable of carrying out a feat, whether grandiose or banal, that only they could have carried out. In this case, all others had failed because they had counted on the simple quantitative progression of intelligence and ingenuity, when what was required was an unspecified quantity, but of the appropriate quality, of both. My own intelligence is quite minimal, a fact that I have ascertained at great cost to myself. It has been just barely adequate to keep me afloat in the tempestuous waters of life. Yet, its quality is unique; not because I decided it would be, but rather because that is how it must be.

But psychology is passing into a less simple phase. Within a few years what one may call a microscopic psychology has arisen in Germany, carried on by experimental methods, asking of course every moment for introspective data, but eliminating their uncertainty by operating on a large scale and taking statistical means. This method taxes patience to the utmost, and could hardly have arisen in a country whose natives could be bored. Such Germans as Weber, Fechner, Vierordt, and Wundt obviously cannot ; and their success has brought into the field an array of younger experimental psychologists, bent on studying the elements of the mental life, dissecting them out from the gross results in which they are embedded, and as far as possible reducing them to quantitative scales. The simple and open method of attack having done what it can, the method of patience, starving out, and harassing to death is tried ; the Mind must submit to a regular siege, in which minute advantages gained night and day by the forces that hem her in must sum themselves up at last into her overthrow. There is little of the grand style about these new prism, pendulum, and chronograph-philosophers. They mean business, not chivalry. What generous divination, and that superiority in virtue which was thought by Cicero to give a man the best insight into nature, have failed to do, their spying and scraping, their deadly tenacity and almost diabolic cunning, will doubtless some day bring about. No general description of the methods of experimental psychology would be instructive to one unfamiliar with the instances of their application, so we will waste no words upon the attempt.

These stories of grit are one kind of data, and they complement the more systematic, quantitative studies I’ve done in places like West Point and the National Spelling Bee. Together, the research reveals the psychological assets that mature paragons of grit have in common. There are four. They counter each of the buzz-killers listed above, and they tend to develop, over the years,

Being able to spot bullshit based on data is a critical skill. Decades ago, fancy language and superfluous detail might have served a bullshitter’s needs. Today, we are accustomed to receiving information in quantitative form, but hesitant to question that information once we receive it. Quantitative evidence generally seems to carry more weight than qualitative arguments. This weight is largely undeserved—only modest skill is required to construct specious quantitative arguments. But we defer to such arguments nonetheless. Consequently, numbers offer the biggest bang for the bullshitting buck.

We can now see the continuum23 between macrohistory and microhistory. We are collecting the kinds of precise, quantitative, microhistorical measurements that typically led to the emergence of a new science…but at the scale of billions of people, and going into our second decade. So, another term for “Big Data” should be “Big History.” All data is a record of past events, sometimes the immediate past, sometimes the past of months or years ago, sometimes (in the case of Google Books or the Digital Michelangelo project) the past of decades or centuries ago. After all, what’s another word for data storage in a computer? Memory. Memory, as in the sense of human memory, and as in the sense of history.

If we look at the way an industrial producer creates new products, we see a long list of trials and errors and eventually improvement in quality at a lower cost. Urban policies and strategies, by contrast, often do not follow this logic; they are often repeated even when it is well known that they failed. For instance, policies like rent control, greenbelts, new light rail transports, among others, are constantly repeated in spite of a near consensus on their failure to achieve their objectives. A quantitative evaluation of the failure of these policies is usually well documented through special reports or academic papers; it is seldom produced internally by cities, however, and the information does not seem to reach urban decision makers. Only a systematic analysis of data through indicators allows urban policies to be improved over time and failing policies to be abandoned. But as Angus Deaton wrote: 'without data, anyone who does anything is free to claim success.

Besides increasing or decreasing the stimulation level of the environment, you can also achieve an optimal level of arousal by drinking beverages that have a direct impact on neocortical arousal.38 Alcohol, at least initially, has the effect of lowering arousal. After a couple of glasses of wine the extraverts are more likely to dip below the optimal arousal level, whereas their introverted friends, nudged closer to optimal arousal, may appear unexpectedly garrulous. Coffee, being a stimulant, has the opposite effect. After ingesting about two cups of coffee, extraverts carry out tasks more efficiently, whereas introverts perform less well. This deficit is magnified if the task they are engaged in is quantitative and if it is done under time pressure. For an introvert, an innocent couple of cups of coffee before a meeting may prove challenging, particularly if the purpose of the meeting is a rapid-fire discussion of budget projections, data analysis, or similar quantitative concerns. In the same meeting an extraverted colleague is likely to benefit from a caffeine kick that creates, in the eyes of the introverts, the illusion of competency.

A confidential report delivered in June 1965 by Abel Aganbegyan, director of the Novobirsk Institute of Economics, highlighted the difficulties. Aganbegyan noted that the growth rate of the Soviet economy was beginning to decline, just as the rival US economy seemed particularly buoyant; at the same time, some sectors of the Soviet economy - housing, agriculture, services, retail trade - remained very backward, and were failing to develop at an adequate rate. The root causes of this poor performance he saw in the enormous commitment of resources to defense (in human terms, 30-40 million people out of a working population of 100 million, he reckoned), and the 'extreme centralism and lack of democracy in economic matters' which had survived from the past. In a complex modern society, he argued, not everything could be planned, since it was impossible to foresee all possible contingencies and their potential effects. So the plan amounted to central command, and even that could not be properly implemented for lack of information and of modern data-processing equipment. 'The Central Statistical Administration ... does not have a single computer, and is not planning to acquire any,' he commented acidly. Economic administration was also impeded by excessive secrecy: 'We obtain many figures... from American journals sooner than they are released by the Central Statistical Administration.' Hence the economy suffered from inbuilt distortions: the hoarding of goods and labour to provide for unforeseen contingencies, the production of shoddy goods to fulfill planning targets expressed in crude quantitative terms, the accumulation of unused money by a public reluctant to buy substandard products, with resultant inflation and a flourishing black market.

you will see this framework at work when ethnographers employ both quantitative (e.g., surveys) and qualitative data collection (LeCompte & Schensul, 1999) and when case study researchers use both quantitative and qualitative data (Luck, Jackson, & Usher, 2006; Yin, 2009).

My hunch is that as the importance of a decision grows, the tendency to rely on quantitative analyses done by others tends to shrink. When the championship or the future of the company is on the line, managers tend to rely on their gut instincts.

Human beings are complex information consumers: they have active needs, passions, and preferences. They lead different lifestyles — some that you will never be able to empathize with unless you dive deep in qualitative and quantitative data. And that is precisely the point of persona research.

HUNTER-GATHERER BLOOD TEST NUMBERS There is some such data available today for the few remaining tribes. How the data was obtained would be an interesting story in and of itself. In any case, the numbers are almost always pristine even if the specific lifestyles are varied and rugged. Here are some various measurements obtained from a variety of tribes. Measurement Ideal value Americans aged 55 Typical hunter-gatherer value Blood pressure 110/70 145/85 110/70 Total cholesterol 150 215 125-158 mg/dl Triglycerides 70 110 73 mg/dl Glucose 75 103 72 mg/dl

In the midst of World War II, Quincy Wright, a leader in the quantitative study of war, noted that people view war from contrasting perspectives: “To some it is a plague to be eliminated; to others, a crime which ought to be punished; to still others, it is an anachronism which no longer serves any purpose. On the other hand, there are some who take a more receptive attitude toward war, and regard it as an adventure which may be interesting, an instrument which may be legitimate and appropriate, or a condition of existence for which one must be prepared” Despite the millions of people who died in that most deadly war, and despite widespread avowals for peace, war remains as a mechanism of conflict resolution. Given the prevalence of war, the importance of war, and the enormous costs it entails, one would assume that substantial efforts would have been made to comprehensively study war. However, the systematic study of war is a relatively recent phenomenon. Generally, wars have been studied as historically unique events, which are generally utilized only as analogies or examples of failed or successful policies. There has been resistance to conceptualizing wars as events that can be studied in the aggregate in ways that might reveal patterns in war or its causes. For instance, in the United States there is no governmental department of peace with funding to scientifically study ways to prevent war, unlike the millions of dollars that the government allocates to the scientific study of disease prevention. This reluctance has even been common within the peace community, where it is more common to deplore war than to systematically figure out what to do to prevent it. Consequently, many government officials and citizens have supported decisions to go to war without having done their due diligence in studying war, without fully understanding its causes and consequences. The COW Project has produced a number of interesting observations about wars. For instance, an important early finding concerned the process of starting wars. A country’s goal in going to war is usually to win. Conventional wisdom was that the probability of success could be increased by striking first. However, a study found that the rate of victory for initiators of inter-state wars (or wars between two countries) was declining: “Until 1910 about 80 percent of all interstate wars were won by the states that had initiated them. . . . In the wars from 1911 through 1965, however, only about 40 percent of the war initiators won.” A recent update of this analysis found that “pre-1900, war initiators won 73% of wars. Since 1945 the win rate is 33%.”. In civil war the probability of success for the initiators is even lower. Most rebel groups, which are generally the initiators in these wars, lose. The government wins 57 percent of the civil wars that last less than a year and 78 percent of the civil wars lasting one to five years. So, it would seem that those initiating civil and inter-state wars were not able to consistently anticipate victory. Instead, the decision to go to war frequently appears less than rational. Leaders have brought on great carnage with no guarantee of success, frequently with no clear goals, and often with no real appreciation of the war’s ultimate costs. This conclusion is not new. Studying the outbreak of the first carefully documented war, which occurred some 2,500 years ago in Greece, historian Donald Kagan concluded: “The Peloponnesian War was not caused by impersonal forces, unless anger, fear, undue optimism, stubbornness, jealousy, bad judgment and lack of foresight are impersonal forces. It was caused by men who made bad decisions in difficult circumstances.” Of course, wars may also serve leaders’ individual goals, such as gaining or retaining power. Nonetheless, the very government officials who start a war are sometimes not even sure how or why a war started.

Deep learning requires much more data than humans, but once trained on big data, it will outperform humans by far for a given task, especially in dealing with quantitative optimization (like picking an ad to maximize likelihood of purchase, or recognizing a face out of a million possible faces). While humans are limited in the number of things they can pay attention to at once, a deep-learning algorithm trained on an ocean of information will discover correlations between obscure features of the data that are too subtle or complex for we humans to comprehend, and which may not even be noticed.

And while the details of how it is implemented vary somewhat from company to company, the core elements of the method are: the creation of a cross-functional team, or a set of teams that break down the traditional silos of marketing and product development and combine talents; the use of qualitative research and quantitative data analysis to gain deep insights into user behavior and preferences; and the rapid generation and testing of ideas, and the use of rigorous metrics to evaluate—and then act on—those results.

run the backtest program again using the truncated data

retrieve the appropriate data from a broker (such as Alpaca or Interactive Brokers),

dynamic data exchange (DDE) link

adjust the historical prices by a multiplier instead of subtracting $d so that the historical daily returns will remain the same pre- and post-adjustment. This is the way Yahoo! Finance adjusts its historical data, and is the most common way.

recommend getting historical data that are already split and dividend adjusted,

Reproducibility is not a binary condition: a scientific report may not be all right or all wrong. One part of a multipart report may not replicate, while other parts do. Or a replicated result might match the original result qualitatively though not quantitatively, as was the case in many of the RPP experiments. It is often hard to classify reports as “reproducible” or “irreproducible.

Don’t Make Me Think by Steve Krug is an excellent introduction to web usability. We passionately believe it should be on the school curriculum. Designed for Use by Lukas Mathis is less entertaining than Don’t Make Me Think, but it covers more usability concepts. If this book list seems worryingly short, that’s a testament to how much ground this book covers. The Visual Display of Quantitative Information by Edward R. Tufte contains many examples of complex data shown in beautifully elegant ways. Don’t be put off by its technical-sounding title. It’s fun to read.

Are your ways of working open, accessible and transparent? Traditional approaches to quantitative data collection and analysis are misunderstood as an objective account of reality; an assumption that masks decisions made throughout the design process. A queer approach to data is also influenced by biases and assumptions; those engaged in queer data practices therefore need to describe how decisions are made, in accessible language, and its effect on the results presented. Openness about the limitations of data helps ensure that an undercount or misrepresentation of data about LGBTQ people is not used undermine political and social advances.

advantages and disadvantage of quantitative data and quantitative data,

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Third Wave technology will also change how we measure success in the classroom. What good is an annual standardized test, after all, once teachers and parents can get detailed reports with a wide range of metrics, comparing their students on a regular basis to others in their class or school or state? In this way, big data on individual students will do for education what standardized testing never quite could: bring quantitative precision to a qualitative learning process.

Quantitative data abhors emotion; qualitative data marinates in it.

What makes a good metric What vanity metrics are and how to avoid them The difference between qualitative and quantitative metrics, between exploratory and reporting metrics, between leading and lagging metrics, and between correlated and causal metrics What A/B testing is, and why multivariate testing is more common The difference between segments and cohorts

in the end, I found that the proportions obtain­ing in Colebrooke (British Orientalist, d. 1837)’s 1818 donation to the India Office Library generally held up. Out of a total of some twenty thousand manuscripts listed in these catalogs on Yoga, Nyaya­ Vaisheshika, and Vedanta philoso­phy, a mere 260 were Yoga Sutra manuscripts (in­cluding commentaries), with only thirty­ five dating from before 1823 ; 513 were manuscripts on Hatha or Tantric Yoga, manuscripts of works attributed to Ya­jnavalkya, or of the Yoga Vasistha; 9,032 were Nyaya manuscripts, and 10,320 were Vedanta manuscripts. (...) What does this quantitative analysis tell us ? For every manuscript on Yoga philosophy proper (excluding Hatha and Tantric Yoga) held in major Indian manu­script libraries and archives, there exist some forty Ve­danta manuscripts and nearly as many Nyaya­ Vaisheshika manuscripts. Manuscripts of the Yoga Sutra and its commentaries account for only one­ third of all manuscripts on Yoga philosophy, the other two­ thirds being devoted mainly to Hatha and Tantric Yoga. But it is the figure of 1.27 percent that stands out in highest relief, because it tells us that after the late sixteenth century virtually no one was copying the Yoga Sutra because no one was commissioning Yoga Sutra manuscripts, and no one was commissioning Yoga Sutra manuscripts because no one was interested in reading the Yoga Sutra. Some have argued that instruction in the Yoga Sutra was based on rote memorization or chanting : this is the position of Krishnam­acharya’s biographers as well as of a number of critical scholars. But this is pure speculation, undercut by the nineteenth­ century observations of James Ballantyne, Dayananda Saraswati, Rajendralal Mitra, Friedrich Max Müller, and others. There is no explicit record, in either the commentarial tradition itself or in the sa­cred or secular literatures of the past two thousand years, of adherents of the Yoga school memorizing, chanting, or claiming an oral transmission for their traditions. Given these data, we may conclude that Cole­brooke’s laconic, if not hostile, treatment of the Yoga Sutra undoubtedly stemmed from the fact that by his time, Patanjali’s system had become an empty signifier, with no traditional schoolmen to expound or defend it and no formal or informal outlets of instruction in its teachings. It had become a moribund tradition, an object of universal indifference. The Yoga Sutra had for all intents and purposes been lost until Colebrooke found it.

There is empirical data on relationship dynamics that states pairings are often based on a combination of attractiveness, things people have in common and other various factors, however, generally speaking, those terms are usually within a variance of the quotient of the higher individual’s measure on a quantitative scale using a divisor of two.” “What?

Now I do not think software managers have less inherent courage and firmness than chefs, nor than other engineering managers. But false scheduling to match the patron's desired date is much more common in our discipline than elsewhere in engineering. It is very difficult to make a vigorous, plausible, and job-risking defense of an estimate that is derived by no quantitative method, supported by little data, and certified chiefly by the hunches of the managers. Clearly

Similar to the value of personal data that could be obtained in the OPM breach, medical records also offer an attractive bounty to criminals looking to commit more targeted fraud or steal someone's identity.  “When someone has your clinical information, your bank account information, and your Social Security number, they can commit fraud that lasts a long time,” Pam Dixon, executive director of the World Privacy Forum, told Monitor correspondent Jaikumar Vijayan in March after the Premera Blue Cross breach. “The kind of identity theft that is on the table here is qualitatively and quantitatively different than what is typically possible when you lose your credit card or Social Security number.” What's more, it often takes longer for victims to discover that medical data has been stolen than to realize that his or her credit card is being used. Consequently, medical data theft can lead to a variety of long-term problems including damaged credit, misdiagnosed illnesses, and unwarranted medical charges. Personal data has become such a valuable commodity that it's outpacing stolen credit cards on the black markets. 

it," John Hultquist, the senior manager of cyberespionage threat intelligence at iSight, told The New York Times. Similar to the value of personal data that could be obtained in the OPM breach, medical records also offer an attractive bounty to criminals looking to commit more targeted fraud or steal someone's identity.  “When someone has your clinical information, your bank account information, and your Social Security number, they can commit fraud that lasts a long time,” Pam Dixon, executive director of the World Privacy Forum, told Monitor correspondent Jaikumar Vijayan in March after the Premera Blue Cross breach. “The kind of identity theft that is on the table here is qualitatively and quantitatively different than what is typically possible when you lose your credit card or Social Security number.

where should one focus the study of contemporary phenomena? The nomothetic social scientists were located primarily in the same five countries as the historians, and in the same way studied primarily their own countries (or at most they made comparisons among the five countries). This was to be sure socially rewarded, but in addition the nomothetic social scientists put forward a methodological argument to justify this choice. They said that the best way to avoid bias was to use quantitative data, and that such data were most likely to be located in their own countries in the immediate present. Furthermore, they argued that if we assume the existence of general laws governing social behavior, it would not matter where one studied these phenomena, since what was true in one place and at one time was true in all places at all times. Why not then study phenomena for which one had the most reliable data—that is, the most quantified and replicable data? Social scientists did have one further problem. The four disciplines together (history, economics, sociology, and political science) studied in effect only a small portion of the world. But in the nineteenth century, the five countries were imposing colonial rule on many other parts of the world, and were engaged in commerce and sometimes in warfare with still other parts of the world. It seemed important to study the rest of the world as well. Still, the rest of the world seemed somehow different, and it seemed inappropriate to use four West-oriented disciplines to study parts of the world that were not considered “modern.” As a result, two additional disciplines arose.

Correlations made by big data are likely to reinforce negative bias. Because big data often relies on historical data or at least the status quo, it can easily reproduce discrimination against disadvantaged racial and ethnic minorities. The propensity models used in many algorithms can bake in a bias against someone who lived in the zip code of a low-income neighborhood at any point in his or her life. If an algorithm used by human resources companies queries your social graph and positively weighs candidates with the most existing connections to a workforce, it makes it more difficult to break in in the first place. In effect, these algorithms can hide bias behind a curtain of code. Big data is, by its nature, soulless and uncreative. It nudges us this way and that for reasons we are not meant to understand. It strips us of our privacy and puts our mistakes, secrets, and scandals on public display. It reinforces stereotypes and historical bias. And it is largely unregulated because we need it for economic growth and because efforts to try to regulate it have tended not to work; the technologies are too far-reaching and are not built to recognize the national boundaries of our world’s 196 sovereign nation-states. Yet would it be best to try to shut down these technologies entirely if we could? No. Big data simultaneously helps solve global challenges while creating an entirely new set of challenges. It’s our best chance at feeding 9 billion people, and it will help solve the problem of linguistic division that is so old its explanation dates back to the Old Testament and the Tower of Babel. Big data technologies will enable us to discover cancerous cells at 1 percent the size of what can be detected using today’s technologies, saving tens of millions of lives. The best approach to big data might be one put forward by the Obama campaign’s chief technology officer, Michael Slaby, who said, “There’s going to be a constant mix between your qualitative experience and your quantitative experience. And at times, they’re going to be at odds with each other, and at times they’re going to be in line. And I think it’s all about the blend. It’s kind of like you have a mixing board, and you have to turn one up sometimes, and turn down the other. And you never want to be just one or the other, because if it’s just one, then you lose some of the soul.” Slaby has made an impressive career out of developing big data tools, but even he recognizes that these tools work best when governed by human judgment. The choices we make about how we manage data will be as important as the decisions about managing land during the agricultural age and managing industry during the industrial age. We have a short window of time—just a few years, I think—before a set of norms set in that will be nearly impossible to reverse. Let’s hope humans accept the responsibility for making these decisions and don’t leave it to the machines.

Tim Brown, the CEO of the international design and innovation firm IDEO, clearly acknowledges this affinity when he writes that designers need to go out and observe people’s experiences in the real world rather than rely on extensive quantitative data to develop their insights.

After you analyze each company independently, you’ll run a third analysis of the two companies combined, which will provide you with qualitative insights about the benefits of such a transaction. Once you run all the analyses, you would use the qualitative data to refine your hypothesis and then analyze the potential benefits quantitatively (e.g., estimate the magnitude of financial benefit).

Consequently, a wide variety of perspectives and frameworks have been developed for trying to understand what cities are, how they arose, how they function, and what their future is. Just within academia itself there is a dizzying array of separate departments, centers, and institutes representing a broad spectrum of alternative ways of perceiving cities: urban geography, urban economics, urban planning, urban studies, urbanomics, architectural studies, and many more, each with its own culture, paradigm, and agenda, though rarely interacting with one another. The situation is rapidly changing as new developments are being initiated, many stimulated by the advent of big data and the vision of smart cities, both somewhat naively touted as panaceas for solving all of our urban problems. But tellingly, there are as yet no explicit departments of “urban science” or “urban physics.” These represent a new frontier as the urgency to understand cities from a more scientific perspective emerges. This is the context of what I am presenting here, namely using scale as a powerful tool for opening a window onto the development of a quantitative conceptual integrated systemic framework for understanding cities.

For example, you could build many companies based on applying the cutting edge predictive analytics and data mining techniques commonly used at consumer web startups, quantitative hedge funds, etc., to less advanced industries.

What piqued my interest was a presentation your lead UI designer made about the different methods you use for data analysis, and how this helps you build a better product. I’m very quantitative, so I really want to work for such a data-driven company.

Second, as a CS major and statistics minor, I have the technical and quantitative background that you’re looking for. I haven’t done a ton of data analysis in my current role, but I have the academic background and raw skills to learn it quickly. I’ve actually started learning a bit of data analysis through some online courses. I have no doubt I’ll be able to pick up the skills you need quickly.

Mathiness: British economic journalist John Kay defines mathiness as a “use of algebraic symbols and quantitative data to give an appearance of scientific content to ideological preconceptions.” Expressing an idea in mathematical symbols instead of straightforward literary terms helps legitimize it in the minds of many people, thanks to a seeming similarity with natural science. In this respect math is basically a form of numerical rhetoric. “The American economist Paul Romer has recently written of ‘mathiness,’ by analogy with ‘truthiness,’ a term coined by American talk show host Stephen Colbert. Truthiness presents narratives which are not actually true, but consistent with the world view of the person who spins the story. It is exemplified in rightwing fabrications about European health systems – their death panels and forced euthanasia.” Paul Samuelson, for instance, trivialized economics in terms that give the outward appearance of science by being expressed mathematically, even when its assumptions are purely hypothetical (and not all realistic)and there are no quantitative statistics to illustrate its categories.

Ramakrishna Paramhans Ward, PO mangal nagar, Katni, [M.P.] 2nd Floor, Above KBZ Pay Centre, between 65 & 66 street, Manawhari Road Mandalay, Myanmar Phone +95 9972107002 Market research plays a pivotal role in shaping business strategies and facilitating growth in dynamic markets like Myanmar. As businesses navigate through the complexities of the Myanmar market landscape, the expertise and insights provided by market research agencies become invaluable. One such prominent player in the field is AMT Market Research Agency, known for its comprehensive approach and tailored solutions. This article delves into the significance of market research for businesses in Myanmar, explores the services offered by AMT, showcases success stories, analyzes emerging trends in the industry, and presents client testimonials, providing a holistic view of the market research agency in Myanmar # 1. Introduction to Market Research in Myanmar ## Understanding the Market Landscape Market research in Myanmar is like exploring a hidden gem - full of potential but requiring a keen eye to uncover the treasures within. As one of the fastest-growing economies in Southeast Asia, Myanmar presents a unique blend of traditional values and modern aspirations that make it a fascinating market to study. ## Challenges and Opportunities in Myanmar Navigating the market in Myanmar can be akin to a thrilling adventure, with challenges and opportunities around every corner. From infrastructural limitations to cultural nuances, businesses face hurdles that require insightful market research to overcome. However, with the right approach, the untapped potential of Myanmar's market can lead to significant growth and success. # 2. Overview of AMT Market Research Agency ## Background and History of AMT AMT Market Research Agency is not your average player in the market research scene. With a rich history rooted in a passion for uncovering insights and a commitment to excellence, AMT has established itself as a trusted partner for businesses looking to navigate Myanmar's complex market landscape. ## Key Differentiators of AMT What sets AMT apart from the rest of the pack? It's not just their cutting-edge methodologies or their team of expert researchers, but their genuine enthusiasm for understanding the intricacies of the Myanmar market. AMT doesn't just deliver data - they offer valuable insights that drive strategic decision-making. # 3. Importance of Market Research for Businesses in Myanmar ## Driving Informed Decision-Making In a market as dynamic as Myanmar, making informed decisions is crucial for business success. Market research provides the necessary data and insights that empower businesses to make strategic choices with confidence. With AMT by your side, you can trust that your decisions are backed by solid research and analysis. ## Mitigating Risks in a Dynamic Market The only constant in the Myanmar market is change. With shifting consumer behaviors, regulatory landscapes, and competitive pressures, businesses face a myriad of risks. Market research acts as a compass, guiding businesses through the uncertainties and helping them navigate the market with clarity and foresight. # 4. Services Offered by AMT Market Research Agency ## Quantitative Research Solutions Numbers don't lie, and neither does quantitative research. AMT offers a range of quantitative research solutions that provide businesses with statistically sound data to make informed decisions. From surveys to data analysis, AMT ensures that your business is equipped with the numbers it needs to succeed. ## Qualitative Research Approaches Sometimes, it's not just about the numbers - it's about understanding the why behind the what. Qualitative research approaches offered by AMT delve deep into consumer insights, behaviors, and motivations, providing businesses with a rich understanding of the market landscape.

AMT Market Research Agency in Myanmar stands as a pivotal player in the dynamic landscape of market research within the country. With a commitment to delivering unparalleled insights and strategic solutions, AMT Market Research has established itself as a trusted partner for businesses seeking to thrive in Myanmar's evolving market. This article delves into the core aspects of AMT Market Research, exploring its services, methodologies, and the significance of market research in Myanmar. Through case studies, client testimonials, and a glimpse into future trends, we uncover the depth and impact of AMT Market Research in driving success for businesses in Myanmar. Introduction to AMT Market Research Agency in Myanmar AMT Market Research is not your run-of-the-mill agency in Myanmar. With a knack for unraveling the mysteries of consumer behavior, they're the Sherlock Holmes of the market research world. Let's delve into what makes them tick. Background of AMT Market Research Founded with a passion for decoding the pulse of the market, AMT Market Research has been shaking up the industry in Myanmar. Their team of savvy researchers leaves no stone unturned in uncovering insights that drive business success. Mission and Vision of the Agency AMT Market Research's mission is simple yet powerful: to empower businesses with data-driven decisions that spark growth and innovation. Their vision? To be the go-to partner for companies looking to navigate the ever-evolving market landscape in Myanmar. Services Offered by AMT Market Research When it comes to services, AMT Market Research doesn't just dip their toes in the water—they dive in headfirst, armed with a treasure trove of strategic insights. Market Entry Strategy From market sizing to competitor analysis, AMT Market Research crafts bespoke market entry strategies that pave the way for success in Myanmar's dynamic business environment. Consumer Behavior Analysis Curious about what makes your target audience tick? AMT Market Research digs deep into the minds of consumers, decoding their preferences, habits, and aspirations to help you tailor your offerings with precision. Competitor Analysis In a market as competitive as Myanmar, staying ahead of the game is crucial. AMT Market Research's competitor analysis services provide a roadmap for outshining the competition and carving out your niche. Importance of Market Research Agency in Myanmar In the bustling landscape of Myanmar, market research isn't just a luxury—it's a necessity. Understanding the economic terrain and growth opportunities is key to thriving amidst the challenges that lie ahead. Economic Landscape of Myanmar Myanmar's economic landscape is a canvas of untapped potential and burgeoning opportunities. Market research serves as the compass that guides businesses through this vibrant yet complex terrain. Growth Opportunities and Challenges With growth opportunities aplenty, Myanmar beckons businesses with promises of success. However, navigating the challenges, be it regulatory hurdles or shifting consumer trends, requires a keen understanding of the market—enter AMT Market Research. Methodologies Utilized by AMT Market Research When it comes to research methodologies, AMT Market Research doesn't settle for the ordinary. Their toolbox is brimming with innovative techniques that paint a comprehensive picture of the market landscape. Quantitative Research Techniques Numbers don't lie, and neither do AMT Market Research's quantitative research techniques. From surveys to data analysis, they crunch the numbers to unearth patterns and trends that inform strategic decision-making. Qualitative Research Approaches

Ramakrishna Paramhans Ward, PO mangal nagar, Katni, [M.P.] 2nd Floor, Above KBZ Pay Centre, between 65 & 66 street, Manawhari Road Mandalay, Myanmar Phone +95 9972107002 1. Study Organizations in Myanmar: A Growing Demand for survey companies in Myanmar is a Southeast Asian nation steeped in culture and history. Over the past ten years, it has undergone rapid economic growth and modernization. This development has made an expanding market for different administrations, including statistical surveying. Businesses in Myanmar benefit greatly from the assistance of survey firms in comprehending consumer behavior, market trends, and the landscape of competition. Among the main players in this field is AMT Statistical surveying, an organization known for its complete administrations and neighborhood skill. The Role of survey companies in Myanmar Businesses wishing to establish or expand their presence in this dynamic market must conduct market research in Myanmar. Myanmar, which has a population of over 54 million people, presents significant opportunities for businesses operating in a variety of industries, including tourism, finance, consumer goods, and telecommunications. However, the market also faces unique obstacles like a diverse ethnic landscape, varying degrees of economic development across regions, and a regulatory environment that is constantly shifting. By providing insights into consumer preferences, purchasing patterns, and market dynamics, survey companies assist businesses in navigating these complexities. To get accurate and relevant data, they use a variety of methods, such as observational studies, qualitative interviews, focus groups, and quantitative surveys. Driving Overview Organizations in Myanmar A few overview organizations work in Myanmar, each offering a scope of administrations custom-made to address the issues of various clients. AMT Market Research stands out among these due to its extensive experience and thorough comprehension of the local market. AMT Statistical surveying AMT Statistical surveying is a noticeable player in Myanmar's statistical surveying industry. Surveys of customers' satisfaction, market research, brand health monitoring, and other services are all offered by the business. AMT's group of experienced scientists and examiners influence their neighborhood information and skill to convey noteworthy bits of knowledge for organizations. AMT Statistical surveying uses a blend of customary and current information assortment techniques. Depending on the research objectives and target audience, they conduct in-person interviews, telephone surveys, and online surveys. Their methodology guarantees top notch information assortment, even in remote and difficult to-arrive at areas of Myanmar. Myanmar Advertising Exploration and Advancement (MMRD) Laid out in 1992, MMRD is one of the most established statistical surveying firms in Myanmar. The organization offers an extensive variety of examination administrations, including market passage studies, contender investigation, and financial investigations. MMRD has gained notoriety for its intensive and solid exploration, making it a confided in accomplice for both neighborhood and worldwide organizations. Boondocks Myanmar Exploration Boondocks Myanmar Exploration is one more outstanding player on the lookout. The organization represents considerable authority in giving experiences into Myanmar's advancing business sector scene. Their administrations incorporate area explicit exploration, purchaser conduct studies, and effect evaluations. Wilderness Myanmar Exploration is known for its inventive philosophies and capacity to adjust to the quickly changing economic situations. Understanding Myanmar Understanding Myanmar is a somewhat new participant in the statistical surveying industry however has rapidly earned respect for its excellent exploration and client-driven approach.

It could not. Sometimes you have to be there to understand—especially when a situation is fast-moving or contains soft, hard-to-quantify details, as is typically the case on the battlefield. The Nobel laureate economist Friedrich Hayek had a phrase for the kind of awareness that is hard to capture in metrics and maps: the “knowledge of the particular circumstances of time and place.” Social scientists have long understood that statistical metrics are at their most pernicious when they are being used to control the world, rather than try to understand it. Economists tend to cite their colleague Charles Goodhart, who wrote in 1975: “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”[12] (Or, more pithily: “When a measure becomes a target, it ceases to be a good measure.”) Psychologists turn to Donald T. Campbell, who around the same time explained: “The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”[13]

They were productively adversarial, like superforecasting team discussions. Managers grilled engineers and forced them to produce data to back up their assertions. The process had worked remarkably. The space shuttle was the most complex machine ever built, and all twenty-four flights had returned safely. But on the emergency conference call, that same quantitative culture led them astray. On their engineers’ advice, McDonald and two Thiokol VPs on the call initially supported a no-launch decision. The Challenger had already been cleared, so this was an eleventh-hour reversal. When NASA officials asked Thiokol engineers exactly what temperature range was safe for flight, they recommended setting a limit at 53 degrees, the lower bound of previous experience. NASA manager Larry Mulloy was flabbergasted. He thought the shuttle was supposed to be cleared to launch from 31 to 99 degrees. A last-minute 53-degree limit was setting an entirely new technical criteria for launches. It had never been discussed, was not backed by quantitative data, and meant that suddenly winter was off-limits for space exploration. Mulloy found it frustrating; he later called it “dumb.” How had the engineers arrived at that number? “They said because they had flown at 53 degrees before,” a NASA manager reflected, “which is no reason to me. That’s tradition rather than technology.” Boisjoly was asked again for data to support his claim, “and I said I have none other than what is being presented.” With the conference call at an impasse, a Thiokol VP asked for a five-minute “offline caucus,” during which Thiokol concluded that they had no more data to provide. They returned to the call a half hour later with a new decision: proceed with launch. Their official document read, “temperature data not conclusive on predicting primary O-ring blow-by.” When conference call participants from NASA and Thiokol later spoke with investigators and gave interviews, they repeatedly brought up the “weak engineering position,” as one put it. Their statements comprised a repetitive chorus: “Unable to quantify”; “supporting data was subjective”; “hadn’t done a good technical job”; “just didn’t have enough conclusive data.” NASA was, after all, the agency that hung a framed quote in the Mission Evaluation Room: “In God We Trust, All Others Bring Data.

If you’re implementing a “take it or leave it” bluff, provide a plausible explanation for why you can’t budge off your current position. Offer a believable reason, data, or a quantitative analysis.

the core elements of the method are: the creation of a cross-functional team, or a set of teams that break down the traditional silos of marketing and product development and combine talents; the use of qualitative research and quantitative data analysis to gain deep insights into user behavior and preferences; and the rapid generation and testing of ideas, and the use of rigorous metrics to evaluate—and then act on—those results.

true Sharpe ratio is equal to or greater than 0, you need a backtest Sharpe ratio of 1 and a sample size of 681 data points (e.g., 2.71 years of daily data).

free historical intraday futures data.”)

Real-time data and quantitative benchmarks are the reason why gamers get consistently better at virtually any game they play: their performance is consistently measured and reflected back to them, with advancing progress bars, points, levels, and achievements.

To counter the effects of too-early learning, here are some things you can do: Where possible, choose schools that are developmentally sensitive in their curriculum and appropriate for your child. Some kids will do really well as big fish in small ponds. It gives them the confidence to tackle the currents without being afraid of being swept away. They get to grow strong and feel strong. So what if there are bigger fish in bigger ponds? Help your children find the right curricular environments for them. Relax and take a long view, even if no one else around you is. Most kids who learn to read at five aren’t better readers at nine than those who learn to read at six or seven. Bill remembers vividly the mild panicky feeling he and Starr had when their daughter was five years old and some of her friends were starting to read. Even though they knew that kids learn to read much easier at age seven than at age five, and that pushing academics too early was harmful and produced no lasting benefit, Bill and Starr wondered if they were jeopardizing their child’s future by letting her fall behind her peers. They briefly considered pulling her out of her nonacademic kindergarten. But they stuck to their guns and left her in a school that did not push and did not give her any homework until the fourth grade. Despite an unrushed start, she received her PhD in economics from the University of Chicago at the age of twenty-six and is a successful economist. Bill loves telling that story, not to brag (okay, just a little), but to emphasize that it is difficult to buck the tide even when you know the current is carrying you the wrong way. Remember that any gains from rushing development will wash out. Parents often tell Bill that their third grader is doing fourth- or fifth-grade math—but he never hears twenty-six-year-olds brag that they’re more successful than most twenty-eight-year-olds. Don’t go overboard on AP classes. You are doing your child no favors if you let her take more APs at the cost of her mental health and sleep. There’s a reason why kids get more out of Moby-Dick in college than in high school. When we consider the enormous differences in the maturation of their prefrontal cortex—and the associated development in their capacity for abstraction and emotional maturity—it should come as no surprise that the majority of students will understand and appreciate novels written for adults better when they’re older. The same is true for complex scientific theories and data, quantitative concepts, and historical themes, which are easier for most kids to grasp when they are college aged. This isn’t to say that some students aren’t ready for college-level courses when they’re fifteen. The problem is that when this becomes the default for most students (I’ll never get into college if I don’t have five AP classes) it’s destructive.

The ‘quantitative revolution’ in geography required the discipline to adopt an explicitly scientific approach, including numerical and statistical methods, and mathematical modelling, so ‘numeracy’ became another necessary skill. Its immediate impact was greatest on human geography as physical geographers were already using these methods. A new lexicon encompassing the language of statistics and its array of techniques entered geography as a whole. Terms such as random sampling, correlation, regression, tests of statistical significance, probability, multivariate analysis, and simulation became part both of research and undergraduate teaching. Correlation and regression are procedures to measure the strength and form, respectively, of the relationships between two or more sets of variables. Significance tests measure the confidence that can be placed in those relationships. Multivariate methods enable the analysis of many variables or factors simultaneously – an appropriate approach for many complex geographical data sets. Simulation is often linked to probability and is a set of techniques capable of extrapolating or projecting future trends.

Your dashboard serves four key roles. 1. It forces you to think strategically about the most crucial issues presently on the table that can—quickly and inexpensively—answer the all-important question, “Why won’t this work?” 2. It forces you to think rigorously about how you can examine your leaps of faith by testing hypotheses whose results can be measured quantitatively, wherever possible. Numbers are more persuasive than naïve hopes or dreams. 3. If one or more of your leaps of faith are refuted by the evidence you collect, the results displayed on your dashboard are visible and dramatic indicators of the need to alter your Plan A and move toward Plan B. 4. A dashboard is a powerful tool for convincing others—whether members of your management team, investors or others, even yourself—of the need to move from Plan A to Plan B. If your tenacity or perseverance is questioned, you can show the evidence to support the move toward Plan B. You are not being erratic or flighty. You are systematically testing hypotheses to prove or refute your leaps of faith, and you are listening to what the data tell you.

Quantitative data tell us what people are doing. Qualitative data tell us why people are doing it. The best kind of research combines the two kinds of data.

Data graphics were mainly [seen as] devices for showing the obvious to the ignorant.

Constancy of design allows the viewer to focus on changes in the data rather than on changes in graphical design.

Simple things belong in tables... Graphics can give a sense of large and complex data sets that cannot be managed in any other way.

This chart contrasts predictive and prospective thinking: Predictive Thinking Prospective Thinking Mindset Forecasting, “We expect …” Preparing, “But what if …” Goal Reduce or even discard uncertainty, fight ambiguity Live with uncertainty, embrace ambiguity, plan for set of contingencies Level of uncertainty Average High Method Extrapolating from present and past Open, imaginative Approach Categorical, assumes continuity Global, systemic, anticipates disruptive events Information inputs Quantitative, objective, known Qualitative (whether quantifiable or not), subjective, known or unknown Relationships Static, stable structures Dynamic, evolving structures Technique Established quantitative models (economics, mathematics, data) Developing scenarios using qualitative approaches (often building on megatrends) Evaluation method Numbers Criteria

This chart contrasts predictive and prospective thinking: Predictive Thinking Prospective Thinking Mindset Forecasting, “We expect …” Preparing, “But what if …” Goal Reduce or even discard uncertainty, fight ambiguity Live with uncertainty, embrace ambiguity, plan for set of contingencies Level of uncertainty Average High Method Extrapolating from present and past Open, imaginative Approach Categorical, assumes continuity Global, systemic, anticipates disruptive events Information inputs Quantitative, objective, known Qualitative (whether quantifiable or not), subjective, known or unknown Relationships Static, stable structures Dynamic, evolving structures Technique Established quantitative models (economics, mathematics, data) Developing scenarios using qualitative approaches (often building on megatrends) Evaluation method Numbers Criteria Attitude toward the future Passive or reactive (the future will be) Proactive and creative (we create or shape the future) Way of thinking Generally deduction Greater use of induction

11.2.3 Quantitative and Qualitative Data Data is often described as being either quantitative or qualitative. Quantitative data is data that can be measured, written down

Mixed methods research is a research design with philosophical assumptions as well as methods of inquiry. As a methodology, it involves philosophical assumptions that guide the direction of the collection and analysis of data and the mixture of qualitative and quantitative approaches in many phases of the research process (J. W. Cresswell & Plano Clark, 2007, p. 5).

The typical practical reasons why you might choose to mix methods include: 1. Time does not permit in-depth qualitative research 2. Time does not permit in-depth quantitative research 3. Negotiating access to participants is challenging 4. Finding a large data set is challenging

This book is primarily concerned with what I will argue is the intellectual core of any understanding of the digital humanities: the use of computational methods in humanities research and scholarship; more specifically, the use of sophisticated quantitative methods for text and data mining.

The bureaucratic method can be defined as one that (a) administers human beings as if they were things and (b) administers things in quantitative rather than qualitative terms, in order to make quantification and control easier and cheaper. The bureaucratic method is governed by statistical data: the bureaucrats base their decisions on fixed rules arrived at from statistical data, rather than on response to the living beings who stand before them; they decide issues according to what is statistically most likely to be the case, at the risk of hurting the 5 or 10 percent of those who do not fit into that pattern. Bureaucrats fear personal responsibility and seek refuge behind their rules; their security and pride lie in their loyalty to rules, not in their loyalty to the laws of the human heart.

This is why you may need to blend quantitative and qualitative analysis. Our friend John Lilly likes to distinguish between “genius-driven design” (e.g., Apple) and “data-driven design” (e.g., Google). Both approaches have their strengths and weaknesses. Data-driven design is great at optimizing products with incremental changes, but it could steer you to the top of a local hill rather than the highest peak. Genius-driven design may be the only way to build a revolutionary product, but it usually needs to be supplemented with data-driven refinement

In chess one realises that all education is ultimately self education. This idea is a timely consideration in our data driven world. Chess lends itself to structural information and quantitive analysis in a range of ways. For instance the numerical value of the pieces, databases of millions of games, computerised evaluation functions and the international rating system. However, the value of the experience of playing the game is more qualitative than quantitive. Like any competitive pursuit or sport, chess is an elaborate pretext for the production of stories. The benign conceit of rules and points and tournaments generates a narrative experience in which you are at once co-director, actor and spectator. Chess is education in the literal sense of bringing forth, and it is self education because our stories about a game emerge as we play it, as we try to achieve our goals, just as they do in real life. Chess stories are of our own making and they are often about challenges we overcame or failed to overcome. Every chess player knows the experience of encountering a vexed colleague whose desperate to share their tragic tale in which they were “completely winning!” until they screwed up and lost. And yet we also know tougher characters who recognise that taking resolute responsibility for your mistakes, no matter how painful, is the way to grow as a person and a player. As the child psychologist Bruno Bettelheim says: "we grow, we find meaning in life and security in ourselves by having understood and solved personal problems on our own, not by having them explained to us by others”.

I was in charge of decisions and marketing, and Sean was in charge of research and operations. When we were trying to identify our target customer, he spent a ton of time putting together spreadsheets comparing all the different markets we should consider. When he showed them to me and asked me what I thought, I replied, “Yoga.” Huh? “We could easily do multiple products serving people who do yoga,” I told him. “It’s an emerging trend. And I know a ton of those people; I can ask them what they want. Let’s start a yoga business.” Sean’s initial response was, “That’s not a quantitative analysis, Ryan!” I’ve never been one to overthink things—most people spend way too much time in the research period. I make decisions fast and adjust later. With our target customer identified, we made a list of possible products and chose our gateway product—a yoga mat. With that, we began the process of product development. We looked up the top-selling yoga mats on Amazon and read through the reviews; we asked questions on Facebook groups, subreddits, and Instagram influencer accounts. It didn’t take long before we had an idea of the main pain points we needed to address with our first product. I remembered Don’s advice and began looking for people to make the product. With a quick scroll and a click, we could choose between a wholesaler in China, a private label supplier out of India, or a contract manufacturer in Vietnam. For about fifty bucks, we were able to order a set of yoga mat samples that had the exact features we were looking for. It was that easy. Samples in hand, we needed to refine our product idea to make sure we were really hitting the pain points we’d identified. At that time, I’d done yoga maybe two or three times in my life, and I wasn’t nearly the right demographic for our mats anyway. That forced me to ask questions. We were targeting yoga-loving millennials, so I went where they often congregate: Starbucks. There, I did the kind of tough field work that really makes an entrepreneur sweat: asking young women questions over coffee. “Which yoga mat do you prefer? Why?” “What makes the difference between a bad yoga mat and a good one?” “What’s wrong with your current yoga mat?” “What do you think of this one? And what about this one?” Next, I headed over to local yoga studios to see how our samples stacked up against the strenuous demands of a yoga class. A few classes later, Sean and I had everything we needed to narrow down our product development. Armed with all our data, we went back to the manufacturers. From a couple yoga-clueless guys, we’d become knowledgeable enough to know not just what a good yoga mat looked like, but how it had to feel and perform. We knew what we needed our yoga mat to do. Now we just had to find the manufacturer to supply it.

Qualitative versus quantitative metrics Qualitative metrics are unstructured, anecdotal, revealing, and hard to aggregate; quantitative metrics involve numbers and statistics, and provide hard numbers but less insight. Vanity versus actionable metrics

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

Amazon’s approach to metrics embodies the Customer Obsession leadership principle. The relevance of Customer Obsession becomes evident in the company’s focus on input versus output metrics. If you look at the input metrics for Amazon, they often describe things customers care about, such as low prices, lots of available products, fast shipping, few customer service contacts, and a speedy website or app. A lot of the output metrics, such as revenue and free cash flow, are what you’d typically see in a company’s financial report. Customers don’t care about those. But as we stated at the beginning of this book, Amazon has an unshakable conviction that the long-term interests of shareowners are perfectly aligned with the interests of customers. Controllable input metrics are a quantitative (diving deep with data) and qualitative (anecdotes) way of measuring how well the organization is satisfying these customer interests so that the output metrics trend the way the company desires.

Reliability means making systems work correctly, even when faults occur. Faults can be in hardware (typically random and uncorrelated), software (bugs are typically systematic and hard to deal with), and humans (who inevitably make mistakes from time to time). Fault-tolerance techniques can hide certain types of faults from the end user. Scalability means having strategies for keeping performance good, even when load increases. In order to discuss scalability, we first need ways of describing load and performance quantitatively. We briefly looked at Twitter’s home timelines as an example of describing load, and response time percentiles as a way of measuring performance. In a scalable system, you can add processing capacity in order to remain reliable under high load. Maintainability has many facets, but in essence it’s about making life better for the engineering and operations teams who need to work with the system. Good abstractions can help reduce complexity and make the system easier to modify and adapt for new use cases. Good operability means having good visibility into the system’s health, and having effective ways of managing it. There is unfortunately no easy fix for making applications reliable, scalable, or maintainable. However, there are certain patterns and techniques that keep reappearing in different kinds of applications. In the next few chapters we will take a look at some examples of data systems and analyze how they work toward those goals.