Data Insights Quotes

Cognitive robotics can integrate information from pre-operation medical records with real-time operating metrics to guide and enhance the precision of physicians’ instruments. By processing data from genuine surgical experiences, they’re able to provide new and improved insights and techniques. These kinds of improvements can improve patient outcomes and boost trust in AI throughout the surgery. Robotics can lead to a 21% reduction in length of stay.

Pilots used to fly planes manually, but now they operate a dashboard with the help of computers. This has made flying safer and improved the industry. Healthcare can benefit from the same type of approach, with physicians practicing medicine with the help of data, dashboards, and AI. This will improve the quality of care they provide and make their jobs easier and more efficient

When we exclude half of humanity from the production of knowledge we lose out on potentially transformative insights.

Many doctors are drawn to this profession (psychology) because they have an innate deficiency of insight into the motives, feelings and thoughts of others, a deficiency they hope to remedy by ingesting masses of data.

In the absence of data, we will always make up stories. In fact, the need to make up a story, especially when we are hurt, is part of our most primitive survival wiring. Mean making is in our biology, and our default is often to come up with a story that makes sense, feels familiar, and offers us insight into how best to self-protect.

It is well and good to opine or theorize about a subject, as humankind is wont to do, but when moral posturing is replaced by an honest assessment of the data, the result is often a new, surprising insight.

When moral posturing is replaced by an honest assessment of the data, the result is often a new, surprising insight.

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

Trivia are not knowledge. Lists of facts don't comprise knowledge. Analyzing, hypothesizing, concluding from data, sharing insights, those comprise knowledge. You can't google for knowledge.

Artificial intelligence is defined as the branch of science and technology that is concerned with the study of software and hardware to provide machines with the ability to learn insights from data and the environment, and the ability to adapt to changing situations with increasing precision, accuracy, and speed.

The insights derived from data can be invaluable as a feedback loop to decision-making, but should never be confused with being a proxy for the future, a predictor of the future, nor the future itself.

We are using data as a way to identify large scale patterns and narratives and then use that insight exclusively in service of the businesses in our network.

Once you have analyzed data, you have to mine that data to find insights from it. At this point, you can involve your marketing or product team to work with the data analysis team.

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.

All the data you have collected is of no use if you don’t know how to gain insights from it, how to make profitable decisions with the help of this, and how to put your data into action.

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.

Quantum Machine Learning is defined as the branch of science and technology that is concerned with the application of quantum mechanical phenomena such as superposition, entanglement and tunneling for designing software and hardware to provide machines the ability to learn insights and patterns from data and the environment, and the ability to adapt automatically to changing situations with high precision, accuracy and speed. 

The purpose of mining data is to deduce insights and solve real world business problems.

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.

Big Data: by itself doesn't yield insights...information doesn't serve us...without knowing why....unless something transformative is brought to these data sets (to create) understanding Gathering data is easy, understanding why is hard.

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.

The other buzzword that epitomizes a bias toward substitution is “big data.” Today’s companies have an insatiable appetite for data, mistakenly believing that more data always creates more value. But big data is usually dumb data. Computers can find patterns that elude humans, but they don’t know how to compare patterns from different sources or how to interpret complex behaviors. Actionable insights can only come from a human analyst (or the kind of generalized artificial intelligence that exists only in science fiction).

Data may disappoint, but it never lies.

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

Few data sets don’t provide the opportunity to develop new insights. Conversely, few data sets are so imbued with novelty that you can’t use them to tell a boring and uninsightful story.

Embracing data-driven decisions allows organizations to uncover valuable insights, adapt to changing market conditions, and stay ahead of the beat, ultimately increasing their chances of success and growth.

Companies should monitor key performance indicators (KPIs) because they provide critical insights into the health and success of the business. Regular KPI tracking helps companies make data-driven decisions, identify areas for improvement, and stay aligned with their strategic goals, ultimately contributing to sustained growth and profitability.

lone piece of small data is almost never meaningful enough to build a case or create a hypothesis, but blended with other insights and observations gathered from around the world, the data eventually comes together to create a solution that forms the foundation of a future brand or business. My

Such is the strange situation in which modern philosophy finds itself. No former age was ever in such a favourable position with regard to the sources of our knowledge of human nature. Psychology, ethnology, anthropology, and history have amassed an astoundingly rich and constantly increasing body of facts. Our technical instruments for observation and experimentation have been immensely improved, and our analyses have become sharper and more penetrating. We appear, nonetheless, not yet to have found a method for the mastery and organization of this material. When compared with our own abundance the past may seem very poor. But our wealth of facts is not necessarily a wealth of thoughts. Unless we succeed in finding a clue of Ariadne to lead us out of this labyrinth, we can have no real insight into the general character of human culture; we shall remain lost in a mass of disconnected and disintegrated data which seem to lack all conceptual unity.

Big Data allows us to meaningfully zoom in on small segments of a dataset to gain new insights on who we are.

for all the valuable insights big data provides, the Web remains a curated, idealized version of who we really are.

In the future, an executive will be supported by an abundance of computation power and powerful AI.

For others, however, the significance of anxiety in disclosing a fundamental insight into human existence is grasped. At this point their consciences will never allow them to return to a contented absorption in particular entities. Any such attempt to do so will be felt deep down as a betrayal of their truer instincts. Those things which previously were experienced with full satisfaction will now seem shallow, hollow, and somehow meaningless. We come to understand with greater and greater clarity that absorption int the world of things provides no refuge, and one ceases to center one's hope in them. At this critical juncture of human existence two basic alternatives remain: either to dismiss existence in general and man's existence in particular as essentially futile and absurd, or to place one's hope in the actualization of a greater purpose or meaning that is not immediately evident within the realm of empirical data.

Data science unlocks the power of data to provide insights, knowledge, and action. Whether you're in business, academia, or government, data science enables informed decision-making and helps solve complex challenges.

The mind prefers order to disorder, and even imposes imagined order on random data it encounters. Consider for instance the ancient Greeks, who imagined animal shapes in the stars rather than seeing them as mere random dots.

Agricultural commodity traders, on the other hand, buy from thousands of individual farmers. That makes the traders’ job harder, but it also provides an opportunity: dealing with so many farmers gives the largest traders valuable information. Long before the concept of ‘big data’ became popular, the agricultural traders were putting it to work, aggregating information from thousands of farmers to get a real-time insight into the state of the markets.

Two months in Shanghai, and what does she have to show for herself? She had been full of plans on the plane ride over, had studied her phrase book as if cramming for an exam, had been determined to refine her computational model with a new set of data, expecting insights and breakthroughs, plotting notes for a new article. Only the time has trickled away so quickly. She has meandered through the days chatting with James instead of gathering data. At night, she has gone out to dinners and bars. [James'] Chinese has not improved; her computational model has barely been touched. She does not know what she has been doing with herself, and now an airplane six days away is waiting for her.

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.

Despite all their surface diversity, most jokes and funny incidents have the following logical structure: Typically you lead the listener along a garden path of expectation, slowly building up tension. At the very end, you introduce an unexpected twist that entails a complete reinterpretation of all the preceding data, and moreover, it's critical that the new interpretation, though wholly unexpected, makes as much "sense" of the entire set of facts as did the originally "expected" interpretation. In this regard, jokes have much in common with scientific creativity, with what Thomas Kuhn calls a "paradigm shift" in response to a single "anomaly." (It's probably not coincidence that many of the most creative scientists have a great sense of humor.) Of course, the anomaly in the joke is the traditional punch line and the joke is "funny" only if the listener gets the punch line by seeing in a flash of insight how a completely new interpretation of the same set of facts can incorporate the anomalous ending. The longer and more tortuous the garden path of expectation, the "funnier" the punch line when finally delivered.

Every computing device today has five basic components: (1) the integrated circuits that do the computing; (2) the memory units that store and retrieve information; (3) the networking systems that enable communications within and across computers; (4) the software applications that enable different computers to perform myriad tasks individually and collectively; and (5) the sensors—cameras and other miniature devices that can detect movement, language, light, heat, moisture, and sound and transform any of them into digitized data that can be mined for insights.

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.

What data did you notice about the week, what stood out for you? What were your emotional reactions to the week? What made you happy? Where were you challenged? Where were you frustrated? What were your insights? What did you learn? What one or two things will you do based on this week?

Within the intelligence community the realization is that Open Source Intelligence is very, very valuable across the whole spectrum of messaging through news, sentiment and measurement,’ Kaplan says. ‘Combined with Human Intelligence and Signal Intelligence, it’s a really powerful predictive tool.

Are you high, Wes Bennett?” "I'll answer that if you answer this: Were you playing Beyoncé on the piano last night?" Her green eyes went wide, and her mouth dropped open. "You heard me?" "The windows were open," I said, shrugging like it was the first time I'd ever heard her, "and I was having a smoke out back. So was it 'Halo'?" "You smoke?" She was looking at me like I was a puzzle, like she couldn't figure me out. "No. Was it?" The crinkle in her forehead grew somehow. "Yes. So ... do you or don't you?" "Like Beyonce? Fucking love her." She rolled her eyes. "Why do I even bother trying to have a conversation with you?" "Because you're fascinated and want to know more." She snorted. "Because you find me wildly attractive and need some insight into my soul?" "Try again." "Because you want to reconcile the data you've entered into your diary about me with the real-life, actual facts?" "So you are high.

More information means less ignorance and a greater chance of rational and better decisions and not those based on illusions, hope, preconceived notions or perceptions. The danger from so much data—there is no definition of what is optimum—is that there are chances of overanalysis or falling into a conspiracy theory trap.

The modern world is drowning in information. We have more data than we can possibly use regarding nearly every picayune matter of society, economics, and politics. Science has contributed to this tsunami of facts and figures, but Riley's reports demonstrated that the tidal wave of minutiae is hardly unique to our time. In every age the challenge has been to move from information to knowledge. And the value of experts lies in their capacity to extract meaning from the reams of facts. Rather than being swamped by raw data, the connoisseur, craftsman, engineer, clinician, or scientist is selectively and self-consciously blind. Knowing what to ignore, recognizing what is extraneous, is the key to deriving pattern, form, and insight.

Today we chase after information, without gaining knowledge. We take note of everything, without gaining insight. We communicate constantly, without participating in a community. We save masses of data, without keeping track of memories. We accumulate friends and followers, without encountering others. This is how information develops a lifeform: inexistant and impermanent.

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

the world produced more chips in 2021 than ever before—over 1.1 trillion semiconductor devices, according to research firm IC Insights. This was a 13 percent increase compared to 2020. The semiconductor shortage is mostly a story of demand growth rather than supply issues. It’s driven by new PCs, 5G phones, AI-enabled data centers—and, ultimately, our insatiable demand for computing power.

Alphabet, is worth nearly $800 billion, only about $100 billion less than Apple. How do you get rich by giving things away? Google does it through one of the most ingenious technical schemes in the history of commerce. Page’s and Brin’s crucial insight was that the existing advertising system, epitomized by Madison Avenue, was linked to the old information economy, led by television, which Google would overthrow.

This is a promising new source of insight that can supplement survey data but can’t replace it for the foreseeable future. That’s because the tools have a ways to go before they can accurately gauge sentiment about specific customer interactions as precisely and consistently as a survey. You should consider this option when your measurement program matures, but start out with the tried-and-true approach of fielding surveys.

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.

Relativistic twins? When one looks at the paths that Newton and Einstein followed while pursuing their theories of gravity, one is struck by the many similarities: the unexplained data on orbits, the sudden insight about falling objects, the need for a new mathematics, the calculational difficulties, the retroactive agreements, the controversy, the problem-plagued expeditions, and the final triumph and acclaim.. Both men had worked in the same eccentric and lonely way, divorced from other scientists, armed with a great feeling of self-reliance while struggling with new concepts and difficult mathematics, and both produced earth-shaking results. One can't help but wonder if these two greatest of scientists, born 237 years apart, were "relativistically related", conceived as twins in some ethereal plane in a far-off galaxy and sent to earth to solve a matter of some gravity.

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

Another simile is that of the man who was born and raised in a prison and who has never set foot outside. All he knows is prison life. He would have no conception of the freedom that is beyond his world. And he would not understand that prison is suffering. If anybody suggested that his world was dukkha, he would disagree, for prison is the limit of his experience. But one day he might find the escape tunnel dug long ago that leads beyond the prison walls to the unimaginable and expansive world of real freedom. Only when he has entered that tunnel and escaped from his prison does he realize how much suffering prison actually was, and the end of that suffering, escaping from jail is happiness. In this simile the prison is the body, the high prison walls are the five senses, and the relentless demanding prison guard is one's own will, the doer. The tunnel dug long ago, through which one escapes, is called jhana [meditation] (as at AN IX, 42). Only when one has experienced jhana does one realize that the five-sense world, even at its best, is really a five-walled prison, some parts of it is a little more comfortable but still a jail with everyone on death row! Only after deep jhana does one realize that "will" was the torturer, masquerading as freedom, but preventing one ever resting happily at peace. Only outside of prison can one gain the data that produces the deep insight that discovers the truth about dukkha. In summary, without experience of jhana, one's knowledge of the world is too limited to fully understand dukkha, as required by the first noble truth, and proceed to enlightenmen.

model’s blind spots reflect the judgments and priorities of its creators. While the choices in Google Maps and avionics software appear cut and dried, others are far more problematic. The value-added model in Washington, D.C., schools, to return to that example, evaluates teachers largely on the basis of students’ test scores, while ignoring how much the teachers engage the students, work on specific skills, deal with classroom management, or help students with personal and family problems. It’s overly simple, sacrificing accuracy and insight for efficiency.

Data that did not fit the commonly accepted assumptions of a discipline would either be discounted or explained away for as long as possible. The more contradictions accumulated, the more convoluted the rationalizations became. 'In science, as in the playing card experiment, novelty emerges only with difficulty,' Kuhn wrote. But then, finally, someone came along who was willing to call a red spade a red spade. Crisis led to insight, and the old framework gave way to a new one. This is how great scientific discoveries or, to use the term Kuhn made so popular, 'paradigm shifts' took place.

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

Wild animals enjoying one another and taking pleasure in their world is so immediate and so real, yet this reality is utterly absent from textbooks and academic papers about animals and ecology. There is a truth revealed here, absurd in its simplicity. This insight is not that science is wrong or bad. On the contrary: science, done well, deepens our intimacy with the world. But there is a danger in an exclusively scientific way of thinking. The forest is turned into a diagram; animals become mere mechanisms; nature's workings become clever graphs. Today's conviviality of squirrels seems a refutation of such narrowness. Nature is not a machine. These animals feel. They are alive; they are our cousins, with the shared experience kinship implies. And they appear to enjoy the sun, a phenomenon that occurs nowhere in the curriculum of modern biology. Sadly, modern science is too often unable or unwilling to visualize or feel what others experience. Certainly science's "objective" gambit can be helpful in understanding parts of nature and in freeing us from some cultural preconceptions. Our modern scientific taste for dispassion when analyzing animal behaviour formed in reaction to the Victorian naturalists and their predecessors who saw all nature as an allegory confirming their cultural values. But a gambit is just an opening move, not a coherent vision of the whole game. Science's objectivity sheds some assumptions but takes on others that, dressed up in academic rigor, can produce hubris and callousness about the world. The danger comes when we confuse the limited scope of our scientific methods with the true scope of the world. It may be useful or expedient to describe nature as a flow diagram or an animal as a machine, but such utility should not be confused with a confirmation that our limited assumptions reflect the shape of the world. Not coincidentally, the hubris of narrowly applied science serves the needs of the industrial economy. Machines are bought, sold, and discarded; joyful cousins are not. Two days ago, on Christmas Eve, the U.S. Forest Service opened to commercial logging three hundred thousand acres of old growth in the Tongass National Forest, more than a billion square-meter mandalas. Arrows moved on a flowchart, graphs of quantified timber shifted. Modern forest science integrated seamlessly with global commodity markets—language and values needed no translation. Scientific models and metaphors of machines are helpful but limited. They cannot tell us all that we need to know. What lies beyond the theories we impose on nature? This year I have tried to put down scientific tools and to listen: to come to nature without a hypothesis, without a scheme for data extraction, without a lesson plan to convey answers to students, without machines or probes. I have glimpsed how rich science is but simultaneously how limited in scope and in spirit. It is unfortunate that the practice of listening generally has no place in the formal training of scientists. In this absence science needlessly fails. We are poorer for this, and possibly more hurtful. What Christmas Eve gifts might a listening culture give its forests? What was the insight that brushed past me as the squirrels basked? It was not to turn away from science. My experience of animals is richer for knowing their stories, and science is a powerful way to deepen this understanding. Rather, I realized that all stories are partly wrapped in fiction—the fiction of simplifying assumptions, of cultural myopia and of storytellers' pride. I learned to revel in the stories but not to mistake them for the bright, ineffable nature of the world.

The House of the Seven Gables (Nathaniel Hawthorne) - La tua evidenziazione a pagina 37 | posizione 567-571 | Aggiunto in data venerdì 17 marzo 2023 11:52:39 Nevertheless, if we look through all the heroic fortunes of mankind, we shall find this same entanglement of something mean and trivial with whatever is noblest in joy or sorrow. Life is made up of marble and mud. And, without all the deeper trust in a comprehensive sympathy above us, we might hence be led to suspect the insult of a sneer, as well as an immitigable frown, on the iron countenance of fate. What is called poetic insight is the gift of discerning, in this sphere of strangely mingled elements, the beauty and the majesty which are compelled to assume a garb so sordid.

What does it mean when customers don't take a deal? Does it mean that they didn't want the product as much as they did want the one they bought? Is a negative signal as strong as a positive one? Perhaps they like Champagne but already have a lot in stock. Maybe they just didn't see your e-mail newsletter that month. There are a lot of reasons why someone doesn't take an action, but there are few reasons why someone does. In other words, you should care about purchases, not non-purchases. The fancy way to say this is that there's an “asymmetry” in the data. The 1s are worth more than the 0s. If a customer matches another customer on three 1s, that's more important than matching some other customer on three 0s. What stinks though is that while the 1s are so important, there are very few of them in the data—hence, the term “sparse.

Agricultural commodity traders, on the other hand, buy from thousands of individual farmers. That makes the traders’ job harder, but it also provides an opportunity: dealing with so many farmers gives the largest traders valuable information. Long before the concept of ‘big data’ became popular, the agricultural traders were putting it to work, aggregating information from thousands of farmers to get a real-time insight into the state of the markets. Each month, when the US Department of Agriculture published its update on the world’s key crops, the agricultural houses’ traders were able to bet on what it would say with near-certainty that they were right. Within most trading houses, there was a group of traders whose sole job was to speculate profitably with the company’s money – they were known as the proprietary, or ‘prop’, traders.

In 1831, the Royal Navy sent the ship HMS Beagle to map the coasts of South America, the Falklands Islands and the Galapagos Islands. The navy needed this knowledge in order to be better prepared in the event of war. The ship’s captain, who was an amateur scientist, decided to add a geologist to the expedition to study geological formations they might encounter on the way. After several professional geologists refused his invitation, the captain offered the job to a twenty-two-year-old Cambridge graduate, Charles Darwin. Darwin had studied to become an Anglican parson but was far more interested in geology and natural sciences than in the Bible. He jumped at the opportunity, and the rest is history. The captain spent his time on the voyage drawing military maps while Darwin collected the empirical data and formulated the insights that would eventually become the theory of evolution.

In 1831, the Royal Navy sent the ship HMS Beagle to map the coasts of South America, the Falklands Islands and the Galapagos Islands. The navy needed this knowledge in order to tighten Britain’s imperial grip over South America. The ship’s captain, who was an amateur scientist, decided to add a geologist to the expedition to study geological formations they might encounter on the way. After several professional geologists refused his invitation, the captain offered the job to a twenty-two-year-old Cambridge graduate, Charles Darwin. Darwin had studied to become an Anglican parson but was far more interested in geology and natural sciences than in the Bible. He jumped at the opportunity, and the rest is history. The captain spent his time on the voyage drawing military maps while Darwin collected the empirical data and formulated the insights that would eventually become the theory of evolution.

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

Search engine query data is not the product of a designed statistical experiment and finding a way to meaningfully analyse such data and extract useful knowledge is a new and challenging field that would benefit from collaboration. For the 2012–13 flu season, Google made significant changes to its algorithms and started to use a relatively new mathematical technique called Elasticnet, which provides a rigorous means of selecting and reducing the number of predictors required. In 2011, Google launched a similar program for tracking Dengue fever, but they are no longer publishing predictions and, in 2015, Google Flu Trends was withdrawn. They are, however, now sharing their data with academic researchers... Google Flu Trends, one of the earlier attempts at using big data for epidemic prediction, provided useful insights to researchers who came after them... The Delphi Research Group at Carnegie Mellon University won the CDC’s challenge to ‘Predict the Flu’ in both 2014–15 and 2015–16 for the most accurate forecasters. The group successfully used data from Google, Twitter, and Wikipedia for monitoring flu outbreaks.

Saint John Paul II wrote, “when its concepts and conclusions can be integrated into the wider human culture and its concerns for ultimate meaning and value.”7 Religion, too, develops best when its doctrines are not abstract and fixed in an ancient past but integrated into the wider stream of life. Albert Einstein once said that “science without religion is lame and religion without science is blind.”8 So too, John Paul II wrote: “Science can purify religion from error and superstition; religion can purify science from idolatry and false absolutes. Each can draw the other into a wider world, a world in which both can flourish.”9 Teilhard de Chardin saw that dialogue alone between the disciplines is insufficient; what we need is a new synthesis of science and religion, drawing insights from each discipline into a new unity. In a remarkable letter to the director of the Vatican Observatory, John Paul II wrote: The church does not propose that science should become religion or religion science. On the contrary, unity always presupposes the diversity and integrity of its elements. Each of these members should become not less itself but more itself in a dynamic interchange, for a unity in which one of the elements is reduced to the other is destructive, false in its promises of harmony, and ruinous of the integrity of its components. We are asked to become one. We are not asked to become each other. . . . Unity involves the drive of the human mind towards understanding and the desire of the human spirit for love. When human beings seek to understand the multiplicities that surround them, when they seek to make sense of experience, they do so by bringing many factors into a common vision. Understanding is achieved when many data are unified by a common structure. The one illuminates the many: it makes sense of the whole. . . . We move towards unity as we move towards meaning in our lives. Unity is also the consequence of love. If love is genuine, it moves not towards the assimilation of the other but towards union with the other. Human community begins in desire when that union has not been achieved, and it is completed in joy when those who have been apart are now united.10 The words of the late pope highlight the core of catholicity: consciousness of belonging to a whole and unity as a consequence of love.

Will those insights be tested,or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies use data, I'm often reminded of phrenology, a pseudoscience that was briefly the rage in the nineteenth century. Phrenologists would run their fingers over the patient's skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits that existed in twenty-seven regions of the brain. Usually the conclusion of the phrenologist jibed with the observations he made. If the patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation - which, in turn, bolstered faith in the science of phrenology. Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and black-balled foreign medical students and St. George's can lock people out, even when the "science" inside them is little more than a bundle of untested assumptions.

Will those insights be tested, or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies use data, I'm often reminded of phrenology, a pseudoscience that was briefly the rage in the nineteenth century. Phrenologists would run their fingers over the patient's skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits that existed in twenty-seven regions of the brain. Usually the conclusion of the phrenologist jibed with the observations he made. If the patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation - which, in turn, bolstered faith in the science of phrenology. Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and black-balled foreign medical students and St. George's can lock people out, even when the "science" inside them is little more than a bundle of untested assumptions.

As information processing machines, our ability to process data about the external world begins at the level of sensory perception. Although most of us are rarely aware of it, our sensory receptors are designed to detect information at the energy level. Because everything around us - the air we breathe, even the materials we use to build with, are composed of spinning and vibrating atomic particles, you and I are literally swimming in a turbulent sea of electromagnetic fields. We are part of it. We are enveloped within in, and through our sensory apparatus we experience what is. Each of our sensory systems is made up of a complex cascade of neurons that process the incoming neural code from the level of the receptor to specific areas within the brain. Each group of neurons along the cascade alters or enhances the code, and passes it on to the next set of cells in the system, which further defines and refines the message. By the time the code reaches the outermost portion of our brain, the higher levels of the cerebral cortex, we become conscious of the stimulation. However, if any of the cells along the pathway fail in their ability to function normally, then the final perception is skewed away from normal reality.

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

Despite the advancements of systematic experimental pipelines, literature-curated protein-interaction data continue to be the primary data for investigation of focused biological mechanisms. Notwithstanding the variable quality of curated interactions available in public databases, the impact of inspection bias on the ability of literature maps to provide insightful information remains equivocal. The problems posed by inspection bias extend beyond mapping of protein interactions to the development of pharmacological agents and other aspects of modern biomedicine. Essentially the same 10% of the proteome is being investigated today as was being investigated before the announcement of completion of the reference genome sequence. One way forward, at least with regard to interactome mapping, is to continue the transition toward systematic and relatively unbiased experimental interactome mapping. With continued advancement of systematic protein-interaction mapping efforts, the expectation is that interactome 'deserts', the zones of the interactome space where biomedical knowledge researchers simply do not look for interactions owing to the lack of prior knowledge, might eventually become more populated. Efforts at mapping protein interactions will continue to be instrumental for furthering biomedical research.

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

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.

In the beginning, there was the internet: the physical infrastructure of wires and servers that lets computers, and the people in front of them, talk to each other. The U.S. government’s Arpanet sent its first message in 1969, but the web as we know it today didn’t emerge until 1991, when HTML and URLs made it possible for users to navigate between static pages. Consider this the read-only web, or Web1. In the early 2000s, things started to change. For one, the internet was becoming more interactive; it was an era of user-generated content, or the read/write web. Social media was a key feature of Web2 (or Web 2.0, as you may know it), and Facebook, Twitter, and Tumblr came to define the experience of being online. YouTube, Wikipedia, and Google, along with the ability to comment on content, expanded our ability to watch, learn, search, and communicate. The Web2 era has also been one of centralization. Network effects and economies of scale have led to clear winners, and those companies (many of which I mentioned above) have produced mind-boggling wealth for themselves and their shareholders by scraping users’ data and selling targeted ads against it. This has allowed services to be offered for “free,” though users initially didn’t understand the implications of that bargain. Web2 also created new ways for regular people to make money, such as through the sharing economy and the sometimes-lucrative job of being an influencer.

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

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 mid-twentieth century, the subfield of cosmology—not to be confused with cosmetology—didn’t have much data. And where data are sparse, competing ideas abound that are clever and wishful. The existence of the CMB was predicted by the Russian-born American physicist George Gamow and colleagues during the 1940s. The foundation of these ideas came from the 1927 work of the Belgian physicist and priest Georges Lemaître, who is generally recognized as the “father” of big bang cosmology. But it was American physicists Ralph Alpher and Robert Herman who, in 1948, first estimated what the temperature of the cosmic background ought to be. They based their calculations on three pillars: 1) Einstein’s 1916 general theory of relativity; 2) Edwin Hubble’s 1929 discovery that the universe is expanding; and 3) atomic physics developed in laboratories before and during the Manhattan Project that built the atomic bombs of World War II. Herman and Alpher calculated and proposed a temperature of 5 degrees Kelvin for the universe. Well, that’s just plain wrong. The precisely measured temperature of these microwaves is 2.725 degrees, sometimes written as simply 2.7 degrees, and if you’re numerically lazy, nobody will fault you for rounding the temperature of the universe to 3 degrees. Let’s pause for a moment. Herman and Alpher used atomic physics freshly gleaned in a lab, and applied it to hypothesized conditions in the early universe. From this, they extrapolated billions of years forward, calculating what temperature the universe should be today. That their prediction even remotely approximated the right answer is a stunning triumph of human insight.

When, in treating a case of neurosis, we try to supplement the inadequate attitude (or adaptedness) of the conscious mind by adding to it contents of the unconscious, our aim is to create a wider personality whose centre of gravity does not necessarily coincide with the ego, but which, on the contrary, as the patient’s insights increase, may even thwart his ego-tendencies. Like a magnet, the new centre attracts to itself that which is proper to it, the “signs of the Father,” i.e., everything that pertains to the original and unalterable character of the individual ground-plan. All this is older than the ego and acts towards it as the “blessed, nonexistent God” of the Basilidians acted towards the archon of the Ogdoad, the demiurge, and—paradoxically enough—as the son of the demiurge acted towards his father. The son proves superior in that he has knowledge of the message from above and can therefore tell his father that he is not the highest God. This apparent contradiction resolves itself when we consider the underlying psychological experience. On the one hand, in the products of the unconscious the self appears as it were a priori, that is, in well-known circle and quaternity symbols which may already have occurred in the earliest dreams of childhood, long before there was any possibility of consciousness or understanding. On the other hand, only patient and painstaking work on the contents of the unconscious, and the resultant synthesis of conscious and unconscious data, can lead to a “totality,” which once more uses circle and quaternity symbols for purposes of self-description.15 In this phase, too, the original dreams of childhood are remembered and understood. The alchemists, who in their own way knew more about the nature of the individuation process than we moderns do, expressed this paradox through the symbol of the uroboros, the snake that bites its own tail.

But states have difficulty evaluating cybersecurity threats. If a state does detect an intrusion in one of its vital networks and if that intrusion looks to be from another state, what should the state suffering the intrusion conclude? On the one hand, it might be a defensive-minded intrusion, only checking out the intruded-upon state’s capabilities and providing reassuring intelligence to the intruding state. This might seem unsettling but not necessarily threatening, presuming the state suffering the intrusion was not developing capabilities for attack or seeking conflict. On the other hand, the intrusion might be more nefarious. It could be a sign of some coming harm, such as a cyber attack or an expanding espionage operation. The state suffering the intrusion will have to decide which of these two possibilities is correct, interpreting limited and almost certainly insufficient amounts of data to divine the intentions of another state. Thus Chapter Four’s argument is vitally important: intrusions into a state’s strategically important networks pose serious risks and are therefore inherently threatening. Intrusions launched by one state into the networks of another can cause a great deal of harm at inopportune times, even if the intrusion at the moment of discovery appears to be reasonably benign. The intrusion can also perform reconnaissance that enables a powerful and well-targeted cyber attack. Even operations launched with fully defensive intent can serve as beachheads for future attack operations, so long as a command and control mechanism is set up. Depending on its target, the intrusion can collect information that provides great insight into the communications and strategies of policy-makers. Network intrusions can also pose serious counterintelligence risks, revealing what secrets a state has learned about other states and provoking a damaging sense of paranoia. Given these very real threats, states are likely to view any serious intrusion with some degree of fear. They therefore have significant incentive to respond strongly, further animating the cybersecurity dilemma.

This is not a hypothetical example. In the middle of the nineteenth century Karl Marx reached brilliant economic insights. Based on these insights he predicted an increasingly violent conflict between the proletariat and the capitalists, ending with the inevitable victory of the former and the collapse of the capitalist system. Marx was certain that the revolution would start in countries that spearheaded the Industrial Revolution – such as Britain, France and the USA – and spread to the rest of the world. Marx forgot that capitalists know how to read. At first only a handful of disciples took Marx seriously and read his writings. But as these socialist firebrands gained adherents and power, the capitalists became alarmed. They too perused Das Kapital, adopting many of the tools and insights of Marxist analysis. In the twentieth century everybody from street urchins to presidents embraced a Marxist approach to economics and history. Even diehard capitalists who vehemently resisted the Marxist prognosis still made use of the Marxist diagnosis. When the CIA analysed the situation in Vietnam or Chile in the 1960s, it divided society into classes. When Nixon or Thatcher looked at the globe, they asked themselves who controls the vital means of production. From 1989 to 1991 George Bush oversaw the demise of the Evil Empire of communism, only to be defeated in the 1992 elections by Bill Clinton. Clinton’s winning campaign strategy was summarised in the motto: ‘It’s the economy, stupid.’ Marx could not have said it better. As people adopted the Marxist diagnosis, they changed their behaviour accordingly. Capitalists in countries such as Britain and France strove to better the lot of the workers, strengthen their national consciousness and integrate them into the political system. Consequently when workers began voting in elections and Labour gained power in one country after another, the capitalists could still sleep soundly in their beds. As a result, Marx’s predictions came to naught. Communist revolutions never engulfed the leading industrial powers such as Britain, France and the USA, and the dictatorship of the proletariat was consigned to the dustbin of history. This is the paradox of historical knowledge. Knowledge that does not change behaviour is useless. But knowledge that changes behaviour quickly loses its relevance. The more data we have and the better we understand history, the faster history alters its course, and the faster our knowledge becomes outdated.

Experience constrains and checks our fancies, prejudices, and speculations. When empiricist and phenomenalist philosophers became more concerned with the character of “sensations,” “impressions,” “sense data,” etc., the brute constraining force of experience tended to get obscured and neglected. But the insight that originally led philosophers to valorize experience – its brute compulsiveness – is what Peirce underscores with Secondness. Acknowledgment of this bruteness – the way in which experience “says NO!” – is required to make sense of the self-corrective character of inquiry and experimentation. Experiments must always finally be checked by experience. Peirce would have been repelled and horrified by Rorty’s claim that the only constraints upon us are “conversational constraints.” To speak in this manner is to ignore the facticity, the surprise, shock, and brute constraint of our experiential encounters.

Customer research is a tradeoff: deep insights on real needs from a tiny set of people, versus broad, reliable purchasing data from a wide range and large number of people. We need both. Designers understand what people really need. Marketing understands what people actually buy. These are not the same things, which is why both approaches are required: marketing and design researchers should work together in complementary teams.

What is Tableau Tableau Software is a highly scalable client-server architecture. It is an application that resides on your computer and used for individuals and publishing data sources as well as workbooks to tableau server. Which is allows for instantaneous insight by transforming data into interactive visualizations? Read from OnlineITGuru at Tableau Online Course

Relying on data—indeed, expecting every conversation to be rooted in data—upends the traditional role of managers. It transforms them from being providers of intuition to facilitators in a search for truth, with the most useful facts being brought to bear on each decision.

As Hal Varian told me, “Relying on data helps out everyone. Senior executives shouldn’t be wasting time debating whether the best background color for an ad is yellow or blue. Just run an experiment. This leaves management free to worry about the stuff that is hard to quantify, which is usually a much better use of their time.

Instead, decisions should be made at the lowest possible level of an organization. The only questions that should rise up the org chart are ones where, Serrat continues, “given the same data and information,” more senior leaders would make a different decision than the rank and file.

So we’re left with two paths to assembling phenomenal talent. You can find a way to hire the very best, or you can hire average performers and try to turn them into the best. Put bluntly, which of the following situations would you rather be in? We hire 90th percentile performers, who start doing great work right away. We hire average performers, and through our training programs hope eventually to turn them into 90th percentile performers. Doesn’t seem like a hard choice when it’s put that way, especially once you realize there’s probably enough money in your budget to get these exceptional people—it’s just being spent in the wrong places. Companies continue to invest substantially more in training than in hiring, according to the Corporate Executive Board.74 Per employee Training spend: $606.36 Hiring spend: $456.44 % of total HR expense Training spend: 18.3% Hiring spend: 13.6% % of revenue Training spend: 0.18% Hiring spend: 0.15% Companies spent more on training current employees than on hiring new employees. Data from 2012.

As a result, the most important recommendation for organizations of all shapes and sizes moving forward is to anticipate worst case scenarios at a minimum. Even in cases where organizations cannot or will not make some of the operational changes recommended below, the exercise of focusing on nonsoftware areas of a given business can help identify under-realized or -appreciated assets within an organization. Particularly ones for whom the sale of software has been low effort, brainstorming about other potential revenue opportunities is unlikely to be time wasted. One vendor in the business intelligence and analytics space has privately acknowledged doing just this; based on current research and projecting current trends forward, it is in the process of building out a 10-year plan over which it assumes that the upfront licensing model will gradually approach zero revenue. In its place, the vendor plans to build out subscription and data-based revenue streams. Even if the plan ultimately proves to be unnecessary, the exercise has been enormously useful internally for the insight gained into its business.

The best way for marketing to “know the customer” now is to truly function as a knowledge center, iterating through efforts to connect with customers, optimizing for insight, seeking to create more meaningful relationships with customers by getting clearer and clearer about what different people value for different reasons.

The primary insight offered in a pie chart comes from slices that are smaller or larger than you would expect. Readers must imagine what they would expect the pie chart to look like and then find the differences.

A company can also use the technology to gain insights to help make informed business decisions, setting in place even greater improvements. Thinking

Our understanding of the sociology of knowledge leads to the conclusion that the sociologies of language and religion cannot be considered peripheral specialties of little interest to sociological theory as such, but have essential contributions to make to it. This insight is not new. Durkheim and his school had it, but it was lost for a variety of theoretically irrelevant reasons. We hope we have made it clear that the sociology of knowledge presupposes a sociology of language, and that a sociology of knowledge without a sociology of religion is impossible (and vice versa). Furthermore, we believe that we have shown how the theoretical positions of Weber and Durkheim can be combined in a comprehensive theory of social action that does not lose the inner logic of either. Finally, we would contend that the linkage we have been led to make here between the sociology of knowledge and the theoretical core of the thought of Mead and his school suggests an interesting possibility for what might be called a sociological psychology, that is, a psychology that derives its fundamental perspectives from a sociological understanding of the human condition. The observations made here point to a program that seems to carry theoretical promise. More generally, we would contend that the analysis of the role of knowledge in the dialectic of individual and society, of personal identity and social structure, provides a crucial complementary perspective for all areas of sociology. This is certainly not to deny that purely structural analyses of social phenomena are fully adequate for wide areas of sociological inquiry, ranging from the study of small groups to that of large institutional complexes, such as the economy or politics. Nothing is further from our intentions than the suggestion that a sociology-of-knowledge “angle” ought somehow to be injected into all such analyses. In many cases this would be unnecessary for the cognitive goal at which these studies aim. We are suggesting, however, that the integration of the findings of such analyses into the body of sociological theory requires more than the casual obeisance that might be paid to the “human factor” behind the uncovered structural data. Such integration requires a systematic accounting of the dialectical relation between the structural realities and the human enterprise of constructing reality—in history. We

in market research: as businesses are faced with a “river” of continuously generated data, the goal of research is not to expensively manufacture data, but to find the right tools to “fish” in that river in order to draw forth the insights and intelligence needed.12

Altogether there is little that can be said about "knowledge" in pre-Islamic Arabia. There existed, it seems, an original elementary concept of knowledge as the piecemeal acquisition of material data. It was, in the course of time, replaced by, or rather, amalgamated with a concept of knowledge as something possessing different degrees of realization. Eventually, there came the additional insight that knowledge constituted a higher and truer form of reality. Such was "knowledge" in Arabia when Muhammad (SAWS) came and forged the concept into the basic tool and objective of divine revelation, thus setting the stage for that reverence for knowledge which was to become the main theme of Islamic civilization.