Business Intelligence: A Software Engineer's Perspective I (Houston, we have a Problem!)

Business Intelligence Series

One of the critics addressed to the BI/Data Analytics, Data Engineering and even Data Science fields is their resistance to applying Software Engineering (SE) methods in practice. SE can be regarded as the application of sound methods, methodologies, techniques, principles, and practices to obtain high quality economic software in a reproducible manner. At minimum, should be applied SE techniques and practices proven to work, for example the use of best practices, reference technologies, standardized processes for requirements gathering and management, etc. This doesn't mean that one should apply the full extent of SE but consider a minimum that makes sense to adopt.

Unfortunately, the creation of data artifacts (queries, reports, data models, data pipelines, data visualizations, etc.) as process seem to be done after the principle of least action, though least action means here the minimum interaction to push pieces on a board rather than getting the things done. At high level, the process is as follows: get the requirements, build something, present results, get more requirements, do changes, present the results, and the process is repeated ad infinitum.

Given that data artifact's creation finds itself at the intersection of two or more knowledge areas in which knowledge is exchanged in several iterations between the parties involved until a common ground is achieved, this process is totally inefficient from multiple perspectives. First of all, it takes considerably more time than planned to reach a solution, resources being wasted in the process, multiple forms of waste being involved. Secondly, the exchange and retention of knowledge resulting from the process is minimal, mainly on a need by basis. This might look as an efficient approach on the short term, but is inefficient overall.

BI reflects the general issues from SE - most of the issues can be traced back to requirements - if the requirements are incorrect and there's no magic involved in between, then one can't expect for the solution to be correct. The bigger the difference between the initial and final requirements elicited in the process, the more resources are wasted. The more time passes between the start of the development phase and the time a solution is presented to the customer, the longer it takes to build the final solution. Same impact have the time it takes to establish a common ground and other critical factors for success involved in the process.

One can address these issues through better requirements elicitation, rapid prototyping, the use of agile methodologies and similar approaches, though the general feeling is that even if they bring improvements, they don't address the root causes - lack of data literacy skills, lack of knowledge about the business, lack of maturity in planning and executing tasks, the inexistence of well-designed processes and procedures, respectively the lack of an engineering mindset.

These inefficiencies have low impact when building a report occasionally, though they accumulate and tend to create systemic issues in what concerns the overall BI effort. They are addressed locally by experts and in general through a strategic approach like the elaboration of a BI strategy, though organizations seldom pay attention to them. Some organizations consider that they are automatically addressed as part of the data culture, though data culture focuses in general on data literacy and not on the whole set of assumptions mentioned above.

An experienced data professional sees more likely the inefficiencies, tries to address them locally in his interactions with the various stakeholders, he/she can build a business case for addressing them, though it depends on organizations to recognize that they have a problem, respective address the inefficiencies in a strategic and systemic manner!

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Data Science: Data Scientists (Just the quotes)

"[...] be wary of analysts that try to quantify the unquantifiable." (Ralph Keeney & Raiffa Howard, "Decisions with Multiple Objectives: Preferences and Value Trade-offs", 1976)

"Most people like to believe something is or is not true. Great scientists tolerate ambiguity very well. They believe the theory enough to go ahead; they doubt it enough to notice the errors and faults so they can step forward and create the new replacement theory. If you believe too much you'll never notice the flaws; if you doubt too much you won't get started. It requires a lovely balance." (Richard W Hamming, "You and Your Research", 1986) 

"Many new data scientists tend to rush past it to get their data into a minimally acceptable state, only to discover that the data has major quality issues after they apply their (potentially computationally intensive) algorithm and get a nonsense answer as output. (Sandy Ryza, "Advanced Analytics with Spark: Patterns for Learning from Data at Scale", 2009)

"Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: 'there’s a lot of data, what can you make from it?'" (Mike Loukides, "What Is Data Science?", 2011)

"As data scientists, we prefer to interact with the raw data. We know how to import it, transform it, mash it up with other data sources, and visualize it. Most of your customers can’t do that. One of the biggest challenges of developing a data product is figuring out how to give data back to the user. Giving back too much data in a way that’s overwhelming and paralyzing is 'data vomit'. It’s natural to build the product that you would want, but it’s very easy to overestimate the abilities of your users. The product you want may not be the product they want." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"In an emergency, a data product that just produces more data is of little use. Data scientists now have the predictive tools to build products that increase the common good, but they need to be aware that building the models is not enough if they do not also produce optimized, implementable outcomes." (Jeremy Howard et al, "Designing Great Data Products", 2012)

"Smart data scientists don’t just solve big, hard problems; they also have an instinct for making big problems small." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills - skills that are also necessary for understanding biases in the data, and for debugging logging output from code. Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms - some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications." (Rachel Schutt, "Doing Data Science: Straight Talk from the Frontline", 2013)

"Unfortunately, creating an objective function that matches the true goal of the data mining is usually impossible, so data scientists often choose based on faith and experience." (Foster Provost, "Data Science for Business", 2013)

"[...] a data scientist role goes beyond the collection and reporting on data; it must involve looking at a business The role of a data scientist goes beyond the collection and reporting on data. application or process from multiple vantage points and determining what the main questions and follow-ups are, as well as recommending the most appropriate ways to employ the data at hand." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017)

"In terms of characteristics, a data scientist has an inquisitive mind and is prepared to explore and ask questions, examine assumptions and analyse processes, test hypotheses and try out solutions and, based on evidence, communicate informed conclusions, recommendations and caveats to stakeholders and decision makers." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017)

"Repeated observations of the same phenomenon do not always produce the same results, due to random noise or error. Sampling errors result when our observations capture unrepresentative circumstances, like measuring rush hour traffic on weekends as well as during the work week. Measurement errors reflect the limits of precision inherent in any sensing device. The notion of signal to noise ratio captures the degree to which a series of observations reflects a quantity of interest as opposed to data variance. As data scientists, we care about changes in the signal instead of the noise, and such variance often makes this problem surprisingly difficult." (Steven S Skiena, "The Data Science Design Manual", 2017)

"Data scientists should have some domain expertise. Most data science projects begin with a real-world, domain-specific problem and the need to design a data-driven solution to this problem. As a result, it is important for a data scientist to have enough domain expertise that they understand the problem, why it is important, an dhow a data science solution to the problem might fit into an organization’s processes. This domain expertise guides the data scientist as she works toward identifying an optimized solution." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"A data scientist should be able to wrangle, mung, manipulate, and consolidate datasets before performing calculations on that data that help us to understand it. Analysis is a broad term, but it's clear that the end result is knowledge of your dataset that you didn't have before you started, no matter how basic or complex. [...] A data scientist usually has to be able to apply statistical, mathematical, and machine learning models to data in order to explain it or perform some sort of prediction." (Andrew P McMahon, "Machine Learning Engineering with Python", 2021)

"Data scientists are advanced in their technical skills. They like to do coding, statistics, and so forth. In its purest form, data science is where an individual uses the scientific method on data." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"The ideal data scientist is a multi-disciplinary person, persistent in pursuing the solution." (Anil Maheshwari, "Data Analytics Made Accessible", 2021)

"Overall [...] everyone also has a need to analyze data. The ability to analyze data is vital in its understanding of product launch success. Everyone needs the ability to find trends and patterns in the data and information. Everyone has a need to ‘discover or reveal (something) through detailed examination’, as our definition says. Not everyone needs to be a data scientist, but everyone needs to drive questions and analysis. Everyone needs to dig into the information to be successful with diagnostic analytics. This is one of the biggest keys of data literacy: analyzing data." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"A data scientist is someone who can obtain, scrub, explore, model and interpret data, blending hacking, statistics and machine learning. Data scientists not only are adept at working with data, but appreciate data itself as a first-class product." (Hillary Mason)

"A data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician." (Josh Blumenstock) [attributed]

"All businesses could use a garden where Data Scientists plant seeds of possibility and water them with collaboration." (Damian Mingle)

"Data scientist (noun): Person who is better at statistics than any software engineer and better at software engineering than any statistician." (Josh Wills)

"Data Scientists should recall innovation often times is not providing fancy algorithms, but rather value to the customer." (Damian Mingle)

"Data Scientists should refuse to be defined by someone else's vision of what's possible." (Damian Mingle)

Data Science: Method (Just the Quotes)

"There are two aspects of statistics that are continually mixed, the method and the science. Statistics are used as a method, whenever we measure something, for example, the size of a district, the number of inhabitants of a country, the quantity or price of certain commodities, etc. […] There is, moreover, a science of statistics. It consists of knowing how to gather numbers, combine them and calculate them, in the best way to lead to certain results. But this is, strictly speaking, a branch of mathematics." (Alphonse P de Candolle, "Considerations on Crime Statistics", 1833)

"The process of discovery is very simple. An unwearied and systematic application of known laws to nature, causes the unknown to reveal themselves. Almost any mode of observation will be successful at last, for what is most wanted is method." (Henry D Thoreau, "A Week on the Concord and Merrimack Rivers", 1862)

"As systematic unity is what first raises ordinary knowledge to the rank of science, that is, makes a system out of a mere aggregate of knowledge, architectonic is the doctrine of the scientific in our knowledge, and therefore necessarily forms part of the doctrine of method." (Immanuel Kant, "Critique of Pure Reason", 1871)

"Nothing is more certain in scientific method than that approximate coincidence alone can be expected. In the measurement of continuous quantity perfect correspondence must be accidental, and should give rise to suspicion rather than to satisfaction." (William S Jevons, "The Principles of Science: A Treatise on Logic and Scientific Method", 1874)

"The object of statistical science is to discover methods of condensing information concerning large groups of allied facts into brief and compendious expressions suitable for discussion. The possibility of doing this is based on the constancy and continuity with which objects of the same species are found to vary." (Sir Francis Galton, "Inquiries into Human Faculty and Its Development, Statistical Methods", 1883)

"Physical research by experimental methods is both a broadening and a narrowing field. There are many gaps yet to be filled, data to be accumulated, measurements to be made with great precision, but the limits within which we must work are becoming, at the same time, more and more defined." (Elihu Thomson, "Annual Report of the Board of Regents of the Smithsonian Institution", 1899)

"A statistical estimate may be good or bad, accurate or the reverse; but in almost all cases it is likely to be more accurate than a casual observer’s impression, and the nature of things can only be disproved by statistical methods." (Sir Arthur L Bowley, "Elements of Statistics", 1901)

"A method is a dangerous thing unless its underlying philosophy is understood, and none more dangerous than the statistical. […] Over-attention to technique may actually blind one to the dangers that lurk about on every side- like the gambler who ruins himself with his system carefully elaborated to beat the game. In the long run it is only clear thinking, experienced methods, that win the strongholds of science." (Edwin B Wilson, "The Statistical Significance of Experimental Data", Science, Volume 58 (1493), 1923)

"[…] the methods of statistics are so variable and uncertain, so apt to be influenced by circumstances, that it is never possible to be sure that one is operating with figures of equal weight." (Havelock Ellis, "The Dance of Life", 1923)

"Statistics may be regarded as (i) the study of populations, (ii) as the study of variation, and (iii) as the study of methods of the reduction of data." (Sir Ronald A Fisher, "Statistical Methods for Research Worker", 1925)

"Science is but a method. Whatever its material, an observation accurately made and free of compromise to bias and desire, and undeterred by consequence, is science." (Hans Zinsser, "Untheological Reflections", The Atlantic Monthly, 1929)

"The most important application of the theory of probability is to what we may call 'chance-like' or 'random' events, or occurrences. These seem to be characterized by a peculiar kind of incalculability which makes one disposed to believe - after many unsuccessful attempts - that all known rational methods of prediction must fail in their case. We have, as it were, the feeling that not a scientist but only a prophet could predict them. And yet, it is just this incalculability that makes us conclude that the calculus of probability can be applied to these events." (Karl R Popper, "The Logic of Scientific Discovery", 1934)

"The fundamental difference between engineering with and without statistics boils down to the difference between the use of a scientific method based upon the concept of laws of nature that do not allow for chance or uncertainty and a scientific method based upon the concepts of laws of probability as an attribute of nature." (Walter A Shewhart, 1940)

"[Statistics] is both a science and an art. It is a science in that its methods are basically systematic and have general application; and an art in that their successful application depends to a considerable degree on the skill and special experience of the statistician, and on his knowledge of the field of application, e.g. economics." (Leonard H C Tippett, "Statistics", 1943)

"Statistics is the branch of scientific method which deals with the data obtained by counting or measuring the properties of populations of natural phenomena. In this definition 'natural phenomena' includes all the happenings of the external world, whether human or not " (Sir Maurice G Kendall, "Advanced Theory of Statistics", Vol. 1, 1943)

"We can scarcely imagine a problem absolutely new, unlike and unrelated to any formerly solved problem; but if such a problem could exist, it would be insoluble. In fact, when solving a problem, we should always profit from previously solved problems, using their result or their method, or the experience acquired in solving them." (George Polya, 1945)

"The enthusiastic use of statistics to prove one side of a case is not open to criticism providing the work is honestly and accurately done, and providing the conclusions are not broader than indicated by the data. This type of work must not be confused with the unfair and dishonest use of both accurate and inaccurate data, which too commonly occurs in business. Dishonest statistical work usually takes the form of: (1) deliberate misinterpretation of data; (2) intentional making of overestimates or underestimates; and (3) biasing results by using partial data, making biased surveys, or using wrong statistical methods." (John R Riggleman & Ira N Frisbee, "Business Statistics", 1951)

"Statistics is the fundamental and most important part of inductive logic. It is both an art and a science, and it deals with the collection, the tabulation, the analysis and interpretation of quantitative and qualitative measurements. It is concerned with the classifying and determining of actual attributes as well as the making of estimates and the testing of various hypotheses by which probable, or expected, values are obtained. It is one of the means of carrying on scientific research in order to ascertain the laws of behavior of things - be they animate or inanimate. Statistics is the technique of the Scientific Method." (Bruce D Greenschields & Frank M Weida, "Statistics with Applications to Highway Traffic Analyses", 1952)

"The methods of science may be described as the discovery of laws, the explanation of laws by theories, and the testing of theories by new observations. A good analogy is that of the jigsaw puzzle, for which the laws are the individual pieces, the theories local patterns suggested by a few pieces, and the tests the completion of these patterns with pieces previously unconsidered." (Edwin P Hubble, "The Nature of Science and Other Lectures", 1954)

"We have to remember that what we observe is not nature herself, but nature exposed to our method of questioning." (Werner K Heisenberg, "Physics and Philosophy: The revolution in modern science", 1958)

"We are committed to the scientific method, and measurement is the foundation of that method; hence we are prone to assume that whatever is measurable must be significant and that whatever cannot be measured may as well be disregarded." (Joseph W Krutch, "Human Nature and the Human Condition", 1959)

"Scientific method is the way to truth, but it affords, even in principle, no unique definition of truth. Any so-called pragmatic definition of truth is doomed to failure equally." (Willard v O Quine, "Word and Object", 1960)

"Observation, reason, and experiment make up what we call the scientific method." (Richard Feynman, "Mainly mechanics, radiation, and heat", 1963)

"Engineering is the art of skillful approximation; the practice of gamesmanship in the highest form. In the end it is a method broad enough to tame the unknown, a means of combing disciplined judgment with intuition, courage with responsibility, and scientific competence within the practical aspects of time, of cost, and of talent." (Ronald B Smith, "Professional Responsibility of Engineering", Mechanical Engineering Vol. 86 (1), 1964)

"Statistics is a body of methods and theory applied to numerical evidence in making decisions in the face of uncertainty." (Lawrence Lapin, "Statistics for Modern Business Decisions", 1973)

"Statistical methods of analysis are intended to aid the interpretation of data that are subject to appreciable haphazard variability." (David V. Hinkley & David Cox, "Theoretical Statistics", 1974)

"Scientists use mathematics to build mental universes. They write down mathematical descriptions - models - that capture essential fragments of how they think the world behaves. Then they analyse their consequences. This is called 'theory'. They test their theories against observations: this is called 'experiment'. Depending on the result, they may modify the mathematical model and repeat the cycle until theory and experiment agree. Not that it's really that simple; but that's the general gist of it, the essence of the scientific method." (Ian Stewart & Martin Golubitsky, "Fearful Symmetry: Is God a Geometer?", 1992)

"But our ways of learning about the world are strongly influenced by the social preconceptions and biased modes of thinking that each scientist must apply to any problem. The stereotype of a fully rational and objective ‘scientific method’, with individual scientists as logical (and interchangeable) robots, is self-serving mythology." (Stephen J Gould, "This View of Life: In the Mind of the Beholder", Natural History Vol. 103, No. 2, 1994)

"The methods of science include controlled experiments, classification, pattern recognition, analysis, and deduction. In the humanities we apply analogy, metaphor, criticism, and (e)valuation. In design we devise alternatives, form patterns, synthesize, use conjecture, and model solutions." (Béla H Bánáthy, "Designing Social Systems in a Changing World", 1996) 

"Data are generally collected as a basis for action. However, unless potential signals are separated from probable noise, the actions taken may be totally inconsistent with the data. Thus, the proper use of data requires that you have simple and effective methods of analysis which will properly separate potential signals from probable noise." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"No matter what the data, and no matter how the values are arranged and presented, you must always use some method of analysis to come up with an interpretation of the data.

While every data set contains noise, some data sets may contain signals. Therefore, before you can detect a signal within any given data set, you must first filter out the noise." (Donald J Wheeler," Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Scientists pursue ideas in an ill-defined but effective way that is often called the scientific method. There is no strict rule of procedure that will lead you from a good idea to a Nobel prize or even to a publishable discovery. Some scientists are meticulously careful; others are highly creative. The best scientists are probably both careful and creative. Although there are various scientific methods in use, a typical approach consists of a series of steps." (Peter Atkins et al, "Chemical Principles: The Quest for Insight" 6th ed., 2013)

"Science, at its core, is simply a method of practical logic that tests hypotheses against experience. Scientism, by contrast, is the worldview and value system that insists that the questions the scientific method can answer are the most important questions human beings can ask, and that the picture of the world yielded by science is a better approximation to reality than any other." (John M Greer, "After Progress: Reason and Religion at the End of the Industrial Age", 2015)

Data Science: Approximation (Just the Quotes)

"Man’s mind cannot grasp the causes of events in their completeness, but the desire to find those causes is implanted in man’s soul. And without considering the multiplicity and complexity of the conditions any one of which taken separately may seem to be the cause, he snatches at the first approximation to a cause that seems to him intelligible and says: ‘This is the cause!’" (Leo Tolstoy, "War and Peace", 1867)

"[It] may be laid down as a general rule that, if the result of a long series of precise observations approximates a simple relation so closely that the remaining difference is undetectable by observation and may be attributed to the errors to which they are liable, then this relation is probably that of nature." (Pierre-Simon Laplace, "Mémoire sur les Inégalites Séculaires des Planètes et des Satellites", 1787)

"Although this may seem a paradox, all exact science is dominated by the idea of approximation. When a man tells you that he knows the exact truth about anything, you are safe in inferring that he is an inexact man." (Bertrand Russell, "The Scientific Outlook", 1931)

"We live in a system of approximations. Every end is prospective of some other end, which is also temporary; a round and final success nowhere. We are encamped in nature, not domesticated." (Ralph W Emerson, "Essays", 1865)

"It is well to notice in this connection [the mutual relations between the results of counting and measuring] that a natural law, in the statement of which measurable magnitudes occur, can only be understood to hold in nature with a certain degree of approximation; indeed natural laws as a rule are not proof against sufficient refinement of the measuring tools." (Luitzen E J Brouwer, "Intuitionism and Formalism", Bulletin of the American Mathematical Society, Vol. 20, 1913)

"[…] as the sciences have developed further, the notion has gained ground that most, perhaps all, of our laws are only approximations." (William James, "Pragmatism: A New Name for Some Old Ways of Thinking", 1914)

"Science does not aim at establishing immutable truths and eternal dogmas; its aim is to approach the truth by successive approximations, without claiming that at any stage final and complete accuracy has been achieved." (Bertrand Russell, "The ABC of Relativity", 1925)

"[…] reality is a system, completely ordered and fully intelligible, with which thought in its advance is more and more identifying itself. We may look at the growth of knowledge […] as an attempt by our mind to return to union with things as they are in their ordered wholeness. […] and if we take this view, our notion of truth is marked out for us. Truth is the approximation of thought to reality […] Its measure is the distance thought has travelled […] toward that intelligible system […] The degree of truth of a particular proposition is to be judged in the first instance by its coherence with experience as a whole, ultimately by its coherence with that further whole, all comprehensive and fully articulated, in which thought can come to rest." (Brand Blanshard, "The Nature of Thought" Vol. II, 1939) 

"The most important maxim for data analysis to heed, and one which many statisticians seem to have shunned is this: ‘Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.’ Data analysis must progress by approximate answers, at best, since its knowledge of what the problem really is will at best be approximate." (John W Tukey, "The Future of Data Analysis", Annals of Mathematical Statistics, Vol. 33, No. 1, 1962)

"Because engineering is science in action - the practice of decision making at the earliest moment - it has been defined as the art of skillful approximation. No situation in engineering is simple enough to be solved precisely, and none worth evaluating is solved exactly. Never are there sufficient facts, sufficient time, or sufficient money for an exact solution, for if by chance there were, the answer would be of academic and not economic interest to society. These are the circumstances that make engineering so vital and so creative." (Ronald B Smith, "Engineering Is…", Mechanical Engineering Vol. 86 (5), 1964)

"Each piece, or part, of the whole of nature is always merely an approximation to the complete truth, or the complete truth so far as we know it. In fact, everything we know is only some kind of approximation, because we know that we do not know all the laws as yet. Therefore, things must be learned only to be unlearned again or, more likely, to be corrected." (Richard Feynman, "The Feynman Lectures on Physics" Vol. 1, 1964)

"Engineering is the art of skillful approximation; the practice of gamesmanship in the highest form. In the end it is a method broad enough to tame the unknown, a means of combing disciplined judgment with intuition, courage with responsibility, and scientific competence within the practical aspects of time, of cost, and of talent." (Ronald B Smith, "Professional Responsibility of Engineering", Mechanical Engineering Vol. 86 (1), 1964)

"Measurement, we have seen, always has an element of error in it. The most exact description or prediction that a scientist can make is still only approximate." (Abraham Kaplan, "The Conduct of Inquiry: Methodology for Behavioral Science", 1964)

"One grievous error in interpreting approximations is to allow only good approximations." (Preston C Hammer, "Mind Pollution", Cybernetics, Vol. 14, 1971)

"The fact that [the model] is an approximation does not necessarily detract from its usefulness because models are approximations. All models are wrong, but some are useful." (George Box, 1987)

"Science is more than a mere attempt to describe nature as accurately as possible. Frequently the real message is well hidden, and a law that gives a poor approximation to nature has more significance than one which works fairly well but is poisoned at the root." (Robert H March, "Physics for Poets", 1996)

"Most physical systems, particularly those complex ones, are extremely difficult to model by an accurate and precise mathematical formula or equation due to the complexity of the system structure, nonlinearity, uncertainty, randomness, etc. Therefore, approximate modeling is often necessary and practical in real-world applications. Intuitively, approximate modeling is always possible. However, the key questions are what kind of approximation is good, where the sense of 'goodness' has to be first defined, of course, and how to formulate such a good approximation in modeling a system such that it is mathematically rigorous and can produce satisfactory results in both theory and applications." (Guanrong Chen & Trung Tat Pham, "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems", 2001)

"Mathematical modeling is as much ‘art’ as ‘science’: it requires the practitioner to (i) identify a so-called ‘real world’ problem (whatever the context may be); (ii) formulate it in mathematical terms (the ‘word problem’ so beloved of undergraduates); (iii) solve the problem thus formulated (if possible; perhaps approximate solutions will suffice, especially if the complete problem is intractable); and (iv) interpret the solution in the context of the original problem." (John A Adam, "Mathematics in Nature", 2003)

"All models are approximations. Essentially, all models are wrong, but some are useful. However, the approximate nature of the model must always be borne in mind." (George E P Box & Norman R Draper, "Response Surfaces, Mixtures, and Ridge Analyses", 2007)

"Science, at its core, is simply a method of practical logic that tests hypotheses against experience. Scientism, by contrast, is the worldview and value system that insists that the questions the scientific method can answer are the most important questions human beings can ask, and that the picture of the world yielded by science is a better approximation to reality than any other." (John M Greer, "After Progress: Reason and Religion at the End of the Industrial Age", 2015)

"Science is about finding ever better approximations rather than pretending you have already found ultimate truth." (Friedrich Nietzsche)

More quotes on "Approximation" at the-web-of-knowledge.blogspot.com. 

Data Science: Creativity (Just the Quotes)

"[…] science conceived as resting on mere sense-perception, with no other source of observation, is bankrupt, so far as concerns its claim to self-sufficiency. Science can find no individual enjoyment in nature: Science can find no aim in nature: Science can find no creativity in nature; it finds mere rules of succession. These negations are true of Natural Science. They are inherent in it methodology." (Alfred N Whitehead, "Modes of Thought", 1938)

"The design process involves a series of operations. In map design, it is convenient to break this sequence into three stages. In the first stage, you draw heavily on imagination and creativity. You think of various graphic possibilities, consider alternative ways." (Arthur H Robinson, "Elements of Cartography", 1953)

"At each level of complexity, entirely new properties appear. [And] at each stage, entirely new laws, concepts, and generalizations are necessary, requiring inspiration and creativity to just as great a degree as in the previous one." (Herb Anderson, 1972)

"Facts do not ‘speak for themselves’; they are read in the light of theory. Creative thought, in science as much as in the arts, is the motor of changing opinion. Science is a quintessentially human activity, not a mechanized, robot-like accumulation of objective information, leading by laws of logic to inescapable interpretation." (Stephen J Gould, "Ever Since Darwin", 1977)

"Science is not a heartless pursuit of objective information. It is a creative human activity, its geniuses acting more as artists than information processors. Changes in theory are not simply the derivative results of the new discoveries but the work of creative imagination influenced by contemporary social and political forces." (Stephen J Gould, "Ever Since Darwin: Reflections in Natural History", 1977)

"Science, since people must do it, is a socially embedded activity. It progresses by hunch, vision, and intuition. Much of its change through time does not record a closer approach to absolute truth, but the alteration of cultural contexts that influence it so strongly. Facts are not pure and unsullied bits of information; culture also influences what we see and how we see it. Theories, moreover, are not inexorable inductions from facts. The most creative theories are often imaginative visions imposed upon facts; the source of imagination is also strongly cultural." (Stephen J Gould, "The Mismeasure of Man", 1980) 

"Some methods, such as those governing the design of experiments or the statistical treatment of data, can be written down and studied. But many methods are learned only through personal experience and interactions with other scientists. Some are even harder to describe or teach. Many of the intangible influences on scientific discovery - curiosity, intuition, creativity - largely defy rational analysis, yet they are often the tools that scientists bring to their work." (Committee on the Conduct of Science, "On Being a Scientist", 1989)

"All of engineering involves some creativity to cover the parts not known, and almost all of science includes some practical engineering to translate the abstractions into practice." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"Good engineering is not a matter of creativity or centering or grounding or inspiration or lateral thinking, as useful as those might be, but of decoding the clever, even witty, messages the solution space carves on the corpses of the ideas in which you believed with all your heart, and then building the road to the next message." (Fred Hapgood, "Up the infinite Corridor: MIT and the Technical Imagination", 1993) 

"[…] creativity is the ability to see the obvious over the long term, and not to be restrained by short-term conventional wisdom." (Arthur J Birch, "To See the Obvious", 1995)

"Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things." (Steve Jobs, 1996)

"The pursuit of science is more than the pursuit of understanding. It is driven by the creative urge, the urge to construct a vision, a map, a picture of the world that gives the world a little more beauty and coherence than it had before." (John A Wheeler, "Geons, Black Holes, and Quantum Foam: A Life in Physics", 1998)

"Simple observation generally gets us nowhere. It is the creative imagination that increases our understanding by finding connections between apparently unrelated phenomena, and forming logical, consistent theories to explain them. And if a theory turns out to be wrong, as many do, all is not lost. The struggle to create an imaginative, correct picture of reality frequently tells us where to go next, even when science has temporarily followed the wrong path." (Richard Morris, "The Universe, the Eleventh Dimension, and Everything: What We Know and How We Know It", 1999)

"Science, and physics in particular, has developed out of the Newtonian paradigm of mechanics. In this world view, every phenomenon we observe can be reduced to a collection of atoms or particles, whose movement is governed by the deterministic laws of nature. Everything that exists now has already existed in some different arrangement in the past, and will continue to exist so in the future. In such a philosophy, there seems to be no place for novelty or creativity." (Francis Heylighen, "The science of self-organization and adaptivity", 2001) 

"Evolution moves towards greater complexity, greater elegance, greater knowledge, greater intelligence, greater beauty, greater creativity, and greater levels of subtle attributes such as love. […] Of course, even the accelerating growth of evolution never achieves an infinite level, but as it explodes exponentially it certainly moves rapidly in that direction." (Ray Kurzweil, "The Singularity is Near", 2005)

"Systemic problems trace back in the end to worldviews. But worldviews themselves are in flux and flow. Our most creative opportunity of all may be to reshape those worldviews themselves. New ideas can change everything." (Anthony Weston, "How to Re-Imagine the World", 2007)

More quotes on "Creativity" at the-web-of-knowledge.blogspot.com. 

Data Science: The Science in Data Science (Just the Quotes)

"The aim of every science is foresight. For the laws of established observation of phenomena are generally employed to foresee their succession. All men, however little advanced make true predictions, which are always based on the same principle, the knowledge of the future from the past." (Auguste Compte, "Plan des travaux scientifiques nécessaires pour réorganiser la société", 1822)

"Science is nothing but the finding of analogy, identity, in the most remote parts." (Ralph W Emerson, 1837)

"Therefore science always goes abreast with the just elevation of the man, keeping step with religion and metaphysics; or, the state of science is an index of our self-knowledge." (Ralph W Emerson, "The Poet", 1844)

"It may sound quite strange, but for me, as for other scientists on whom these kinds of imaginative images have a greater effect than other poems do, no science is at its very heart more closely related to poetry, perhaps, than is chemistry." (Just Liebig, 1854)

"Science is the systematic classification of experience." (George H Lewes, "The Physical Basis of Mind", 1877)

"Science is the observation of things possible, whether present or past; prescience is the knowledge of things which may come to pass, though but slowly." (Leonardo da Vinci, "The Notebooks of Leonardo da Vinci", 1883)

"While science is pursuing a steady onward movement, it is convenient from time to time to cast a glance back on the route already traversed, and especially to consider the new conceptions which aim at discovering the general meaning of the stock of facts accumulated from day to day in our laboratories." (Dmitry Mendeleyev, "The Periodic Law of the Chemical Elements", Journal of the Chemical Society Vol. 55, 1889)

"The aim of science is always to reduce complexity to simplicity." (William James, "The Principles of Psychology", 1890)

"Science is not the monopoly of the naturalist or the scholar, nor is it anything mysterious or esoteric. Science is the search for truth, and truth is the adequacy of a description of facts." (Paul Carus, "Philosophy as a Science", 1909)

"Science is reduction. Mathematics is its ideal, its form par excellence, for it is in mathematics that assimilation, identification, is most perfectly realized. The universe, scientifically explained, would be a certain formula, one and eternal, regarded as the equivalent of the entire diversity and movement of things." (Émile Boutroux, "Natural law in Science and Philosophy", 1914)

"Abstract as it is, science is but an outgrowth of life. That is what the teacher must continually keep in mind. […] Let him explain […] science is not a dead system - the excretion of a monstrous pedantism - but really one of the most vigorous and exuberant phases of human life." (George A L Sarton, "The Teaching of the History of Science", The Scientific Monthly, 1918)

"The aim of science is to seek the simplest explanations of complex facts. We are apt to fall into the error of thinking that the facts are simple because simplicity is the goal of our quest. The guiding motto in the life of every natural philosopher should be, ‘Seek simplicity and distrust it’." (Alfred N Whitehead, "The Concept of Nature", 1919)

"Science is simply setting out on a fishing expedition to see whether it cannot find some procedure which it can call measurement of space and some procedure which it can call the measurement of time, and something which it can call a system of forces, and something which it can call masses." (Alfred N Whitehead, "The Concept of Nature", 1920)

"Science is a magnificent force, but it is not a teacher of morals. It can perfect machinery, but it adds no moral restraints to protect society from the misuse of the machine. It can also build gigantic intellectual ships, but it constructs no moral rudders for the control of storm tossed human vessel. It not only fails to supply the spiritual element needed but some of its unproven hypotheses rob the ship of its compass and thus endangers its cargo." (William J Bryan, "Undelivered Trial Summation Scopes Trial", 1925)

"Science is but a method. Whatever its material, an observation accurately made and free of compromise to bias and desire, and undeterred by consequence, is science." (Hans Zinsser, "Untheological Reflections", The Atlantic Monthly, 1929)

"Although this may seem a paradox, all exact science is dominated by the idea of approximation. When a man tells you that he knows the exact truth about anything, you are safe in inferring that he is an inexact man." (Bertrand Russell, "The Scientific Outlook", 1931)

"The common view of science is that it is a sort of machine for increasing the race’s store of dependable facts. It is that only in part; in even larger part it is a machine for upsetting undependable facts." (Will Durant, 1931)

"One has to recognize that science is not metaphysics, and certainly not mysticism; it can never bring us the illumination and the satisfaction experienced by one enraptured in ecstasy. Science is sobriety and clarity of conception, not intoxicated vision."(Ludwig Von Mises, "Epistemological Problems of Economics", 1933)

"Modern positivists are apt to see more clearly that science is not a system of concepts but rather a system of statements." (Karl R Popper, "The Logic of Scientific Discovery", 1934)

"Science is a system of statements based on direct experience, and controlled by experimental verification. Verification in science is not, however, of single statements but of the entire system or a sub-system of such statements." (Rudolf Carnap, "The Unity of Science", 1934)

"Science is the attempt to discover, by means of observation, and reasoning based upon it, first, particular facts about the world, and then laws connecting facts with one another and (in fortunate cases) making it possible to predict future occurrences." (Bertrand Russell, "Religion and Science, Grounds of Conflict", 1935)

"[…] that all science is merely a game can be easily discarded as a piece of wisdom too easily come by. But it is legitimate to enquire whether science is not liable to indulge in play within the closed precincts of its own method. Thus, for instance, the scientist’s continuous penchant for systems tends in the direction of play." (Johan Huizinga, "Homo Ludens", 1938)

"Science makes no pretension to eternal truth or absolute truth; some of its rivals do. That science is in some respects inhuman may be the secret of its success in alleviating human misery and mitigating human stupidity." (Eric T Bell, "Mathematics: Queen and Servant of Science", 1938)

"Science is the attempt to make the chaotic diversity of our sense experience correspond to a logically uniform system of thought." (Albert Einstein, "Considerations Concerning the Fundaments of Theoretical Physics", Science Vol. 91 (2369), 1940)

"Science is the organised attempt of mankind to discover how things work as causal systems. The scientific attitude of mind is an interest in such questions. It can be contrasted with other attitudes, which have different interests; for instance the magical, which attempts to make things work not as material systems but as immaterial forces which can be controlled by spells; or the religious, which is interested in the world as revealing the nature of God." (Conrad H Waddington, "The Scientific Attitude", 1941)

"Science, in the broadest sense, is the entire body of the most accurately tested, critically established, systematized knowledge available about that part of the universe which has come under human observation. For the most part this knowledge concerns the forces impinging upon human beings in the serious business of living and thus affecting man’s adjustment to and of the physical and the social world. […] Pure science is more interested in understanding, and applied science is more interested in control […]" (Austin L Porterfield, "Creative Factors in Scientific Research", 1941)

"Science is an interconnected series of concepts and schemes that have developed as a result of experimentation and observation and are fruitful of further experimentation and observation."(James B Conant, "Science and Common Sense", 1951)

"[…] theoretical science is essentially disciplined exploitation of metaphor." (Anatol Rapoport, "Operational Philosophy", 1953)

"Prediction is all very well; but we must make sense of what we predict. The mainspring of science is the conviction that by honest, imaginative enquiry we can build up a system of ideas about Nature which has some legitimate claim to ‘reality’." (Stephen Toulmin, "The Philosophy of Science: An Introduction", 1953)

"An engineering science aims to organize the design principles used in engineering practice into a discipline and thus to exhibit the similarities between different areas of engineering practice and to emphasize the power of fundamental concepts. In short, an engineering science is predominated by theoretical analysis and very often uses the tool of advanced mathematics." (Qian Xuesen, "Engineering cybernetics", 1954))

"The true aim of science is to discover a simple theory which is necessary and sufficient to cover the facts, when they have been purified of traditional prejudices." (Lancelot L Whyte, "Accent on Form", 1954)

"Science is the creation of concepts and their exploration in the facts. It has no other test of the concept than its empirical truth to fact." (Jacob Bronowski, "Science and Human Values", 1956)

"The progress of science is the discovery at each step of a new order which gives unity to what had seemed unlike." (Jacob Bronowski, "Science and Human Values", 1956)

"[…] any serious examination of the basic concepts of any science is far more difficult than the elaboration of their ultimate consequences." (George F J Temple, "Turning Points in Physics", 1959)

"Science is usually understood to depict a universe of strict order and lawfulness, of rigorous economy - one whose currency is energy, convertible against a service charge into a growing common pool called entropy." (Paul A Weiss,"Organic Form: Scientific and Aesthetic Aspects", 1960)

"[…] the progress of science is a little like making a jig-saw puzzle. One makes collections of pieces which certainly fit together, though at first it is not clear where each group should come in the picture as a whole, and if at first one makes a mistake in placing it, this can be corrected later without dismantling the whole group." (Sir George Thomson, "The Inspiration of Science", 1961)

"Science is the reduction of the bewildering diversity of unique events to manageable uniformity within one of a number of symbol systems, and technology is the art of using these symbol systems so as to control and organize unique events. Scientific observation is always a viewing of things through the refracting medium of a symbol system, and technological praxis is always handling of things in ways that some symbol system has dictated. Education in science and technology is essentially education on the symbol level." (Aldous L Huxley, "Essay", Daedalus, 1962)

"The important distinction between science and those other systematizations [i.e., art, philosophy, and theology] is that science is self-testing and self-correcting. Here the essential point of science is respect for objective fact. What is correctly observed must be believed [...] the competent scientist does quite the opposite of the popular stereotype of setting out to prove a theory; he seeks to disprove it." (George G Simpson, "Notes on the Nature of Science", 1962)

"What, then, is science according to common opinion? Science is what scientists do. Science is knowledge, a body of information about the external world. Science is the ability to predict. Science is power, it is engineering. Science explains, or gives causes and reasons." (John Bremer "What Is Science?" [in "Notes on the Nature of Science"], 1962)

"Science is a matter of disinterested observation, patient ratiocination within some system of logically correlated concepts. In real-life conflicts between reason and passion the issue is uncertain. Passion and prejudice are always able to mobilize their forces more rapidly and press the attack with greater fury; but in the long run (and often, of course, too late) enlightened self-interest may rouse itself, launch a counterattack and win the day for reason." (Aldous L Huxley, "Literature and Science", 1963)

"Science is a way to teach how something gets to be known, what is not known, to what extent things are known (for nothing is known absolutely), how to handle doubt and uncertainty, what the rules of evidence are, how to think about things so that judgments can be made, how to distinguish truth from fraud, and from show." (Richard P Feynman, "The Problem of Teaching Physics in Latin America", Engineering and Science, 1963)

"The aim of science is to apprehend this purely intelligible world as a thing in itself, an object which is what it is independently of all thinking, and thus antithetical to the sensible world. [...] The world of thought is the universal, the timeless and spaceless, the absolutely necessary, whereas the world of sense is the contingent, the changing and moving appearance which somehow indicates or symbolizes it." (Robin G Collingwood, "Essays in the Philosophy of Art", 1964)

"The central task of a natural science is to make the wonderful commonplace: to show that complexity, correctly viewed, is only a mask for simplicity; to find pattern hidden in apparent chaos." (Herbert A Simon, "The Sciences of the Artificial", 1969)

"The central task of a natural science is to make the wonderful commonplace: to show that complexity, correctly viewed, is only a mask for simplicity; to find pattern hidden in apparent chaos." (Herbert A Simon, "The Sciences of the Artificial", 1969)

"Science is a product of man, of his mind; and science creates the real world in its own image." (Frank E Egler, "The Way of Science", 1970)

"To do science is to search for repeated patterns, not simply to accumulate facts [...]" (Robert H. MacArthur, "Geographical Ecology", 1972)

"Science is systematic organisation of knowledge about the universe on the basis of explanatory hypotheses which are genuinely testable. Science advances by developing gradually more comprehensive theories; that is, by formulating theories of greater generality which can account for observational statements and hypotheses which appear as prima facie unrelated." (Francisco J Ayala, "Studies in the Philosophy of Biology: Reduction and Related Problems", 1974)

"A mature science, with respect to the matter of errors in variables, is not one that measures its variables without error, for this is impossible. It is, rather, a science which properly manages its errors, controlling their magnitudes and correctly calculating their implications for substantive conclusions." (Otis D Duncan, "Introduction to Structural Equation Models", 1975)

"The very nature of science is such that scientists need the metaphor as a bridge between old and new theories." (Earl R MacCormac, "Metaphor and Myth in Science and Religion", 1976)

"Facts do not ‘speak for themselves’; they are read in the light of theory. Creative thought, in science as much as in the arts, is the motor of changing opinion. Science is a quintessentially human activity, not a mechanized, robot-like accumulation of objective information, leading by laws of logic to inescapable interpretation." (Stephen J Gould, "Ever Since Darwin", 1977)

"Science is not a heartless pursuit of objective information. It is a creative human activity, its geniuses acting more as artists than information processors. Changes in theory are not simply the derivative results of the new discoveries but the work of creative imagination influenced by contemporary social and political forces." (Stephen J Gould, "Ever Since Darwin: Reflections in Natural History", 1977)

"Engineering or Technology is the making of things that did not previously exist, whereas science is the discovering of things that have long existed." (David Billington, "The Tower and the Bridge: The New Art of Structural Engineering", 1983)

"Science is a process. It is a way of thinking, a manner of approaching and of possibly resolving problems, a route by which one can produce order and sense out of disorganized and chaotic observations. Through it we achieve useful conclusions and results that are compelling and upon which there is a tendency to agree." (Isaac Asimov, "‘X’ Stands for Unknown", 1984)

"If doing mathematics or science is looked upon as a game, then one might say that in mathematics you compete against yourself or other mathematicians; in physics your adversary is nature and the stakes are higher." (Mark Kac, "Enigmas Of Chance", 1985)

"Science is defined as a set of observations and theories about observations." (F Albert Matsen, "The Role of Theory in Chemistry", Journal of Chemical Education Vol. 62 (5), 1985)

"We expect to learn new tricks because one of our science based abilities is being able to predict. That after all is what science is about. Learning enough about how a thing works so you'll know what comes next. Because as we all know everything obeys the universal laws, all you need is to understand the laws." (James Burke, "The Day the Universe Changed", 1985)

"Science is human experience systematically extended (by intent, methodology and instrumentation) for the purpose of learning more about the natural world and for the critical empirical testing and possible falsification of all ideas about the natural world. Scientific hypotheses may incorporate only elements of the natural empirical world, and thus may contain no element of the supernatural." (Robert E Kofahl, Correctly Redefining Distorted Science: A Most Essential Task", Creation Research Society Quarterly Vol. 23, 1986)

"Science is not a given set of answers but a system for obtaining answers. The method by which the search is conducted is more important than the nature of the solution. Questions need not be answered at all, or answers may be provided and then changed. It does not matter how often or how profoundly our view of the universe alters, as long as these changes take place in a way appropriate to science. For the practice of science, like the game of baseball, is covered by definite rules." (Robert Shapiro, "Origins: A Skeptic’s Guide to the Creation of Life on Earth", 1986)

"Science doesn't purvey absolute truth. Science is a mechanism. It's a way of trying to improve your knowledge of nature. It's a system for testing your thoughts against the universe and seeing whether they match. And this works, not just for the ordinary aspects of science, but for all of life. I should think people would want to know that what they know is truly what the universe is like, or at least as close as they can get to it." (Isaac Asimov, [Interview by Bill Moyers] 1988)

"Science doesn’t purvey absolute truth. Science is a mechanism, a way of trying to improve your knowledge of nature. It’s a system for testing your thoughts against the universe, and seeing whether they match." (Isaac Asimov, [interview with Bill Moyers in The Humanist] 1989)

"The view of science is that all processes ultimately run down, but entropy is maximized only in some far, far away future. The idea of entropy makes an assumption that the laws of the space-time continuum are infinitely and linearly extendable into the future. In the spiral time scheme of the timewave this assumption is not made. Rather, final time means passing out of one set of laws that are conditioning existence and into another radically different set of laws. The universe is seen as a series of compartmentalized eras or epochs whose laws are quite different from one another, with transitions from one epoch to another occurring with unexpected suddenness." (Terence McKenna, "True Hallucinations", 1989)

"Science is (or should be) a precise art. Precise, because data may be taken or theories formulated with a certain amount of accuracy; an art, because putting the information into the most useful form for investigation or for presentation requires a certain amount of creativity and insight." (Patricia H Reiff, "The Use and Misuse of Statistics in Space Physics", Journal of Geomagnetism and Geoelectricity 42, 1990)

"In science if you know what you are doing you should not be doing it. In engineering if you do not know what you are doing you should not be doing it. Of course, you seldom, if ever, see either pure state." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"On this view, we recognize science to be the search for algorithmic compressions. We list sequences of observed data. We try to formulate algorithms that compactly represent the information content of those sequences. Then we test the correctness of our hypothetical abbreviations by using them to predict the next terms in the string. These predictions can then be compared with the future direction of the data sequence. Without the development of algorithmic compressions of data all science would be replaced by mindless stamp collecting - the indiscriminate accumulation of every available fact. Science is predicated upon the belief that the Universe is algorithmically compressible and the modern search for a Theory of Everything is the ultimate expression of that belief, a belief that there is an abbreviated representation of the logic behind the Universe's properties that can be written down in finite form by human beings." (John D Barrow, New Theories of Everything", 1991)

"The goal of science is to make sense of the diversity of Nature." (John D Barrow, "Theories of Everything: The Quest for Ultimate Explanation", 1991)

"Science is not about control. It is about cultivating a perpetual condition of wonder in the face of something that forever grows one step richer and subtler than our latest theory about it. It is about  reverence, not mastery." (Richard Power, "Gold Bug Variations", 1993)

"Statistics as a science is to quantify uncertainty, not unknown." (Chamont Wang, "Sense and Nonsense of Statistical Inference: Controversy, Misuse, and Subtlety", 1993)

"Clearly, science is not simply a matter of observing facts. Every scientific theory also expresses a worldview. Philosophical preconceptions determine where facts are sought, how experiments are designed, and which conclusions are drawn from them." (Nancy R Pearcey & Charles B. Thaxton, "The Soul of Science: Christian Faith and Natural Philosophy", 1994)

"Science is distinguished not for asserting that nature is rational, but for constantly testing claims to that or any other affect by observation and experiment." (Timothy Ferris, "The Whole Shebang: A State-of-the Universe’s Report", 1996)

"Science is more than a mere attempt to describe nature as accurately as possible. Frequently the real message is well hidden, and a law that gives a poor approximation to nature has more significance than one which works fairly well but is poisoned at the root." (Robert H March, "Physics for Poets", 1996)

"The art of science is knowing which observations to ignore and which are the key to the puzzle." (Edward W Kolb, "Blind Watchers of the Sky", 1996)

"Mathematics is the study of analogies between analogies. All science is. Scientists want to show that things that don’t look alike are really the same. That is one of their innermost Freudian motivations. In fact, that is what we mean by understanding." (Gian-Carlo Rota, "Indiscrete Thoughts", 1997)

"Religion is the antithesis of science; science is competent to illuminate all the deep questions of existence, and does so in a manner that makes full use of, and respects the human intellect. I see neither need nor sign of any future reconciliation." (Peter W Atkins, "Religion - The Antithesis to Science", 1997)

"[…] the pursuit of science is more than the pursuit of understanding. It is driven by the creative urge, the urge to construct a vision, a map, a picture of the world that gives the world a little more beauty and coherence than it had before." (John A Wheeler, "Geons, Black Holes, and Quantum Foam: A Life in Physics", 1998)

"The rate of the development of science is not the rate at which you make observations alone but, much more important, the rate at which you create new things to test." (Richard Feynman, "The Meaning of It All", 1998)

"The passion and beauty and joy of science is that we humans have invented a process to understand the universe in a way that is true for everyone. We are finding universal truths." (Bill Nye, 2000)

"The poetry of science is in some sense embodied in its great equations, and these equations can also be peeled. But their layers represent their attributes and consequences, not their meanings." (Graham Farmelo, 2002)

"Science is the art of the appropriate approximation. While the flat earth model is usually spoken of with derision it is still widely used. Flat maps, either in atlases or road maps, use the flat earth model as an approximation to the more complicated shape." (Byron K. Jennings, "On the Nature of Science", Physics in Canada Vol. 63 (1), 2007)

"It is ironic but true: the one reality science cannot reduce is the only reality we will ever know. This is why we need art. By expressing our actual experience, the artist reminds us that our science is incomplete, that no map of matter will ever explain the immateriality of our consciousness." (Jonah Lehrer, "Proust Was a Neuroscientist", 2011)

"Science isn’t about being right. It is about convincing others of the correctness of an idea through a methodology all will accept using data everyone can trust. New ideas take time to be accepted because they compete with others that have already passed the test." (Tom Koch, "Commentary: Nobody loves a critic: Edmund A Parkes and John Snow’s cholera", International Journal of Epidemiology Vol. 42 (6), 2013)

"Science, at its core, is simply a method of practical logic that tests hypotheses against experience. Scientism, by contrast, is the worldview and value system that insists that the questions the scientific method can answer are the most important questions human beings can ask, and that the picture of the world yielded by science is a better approximation to reality than any other." (John M Greer, "After Progress: Reason and Religion at the End of the Industrial Age", 2015)

More quotes on "Science" at quotablemath.blogspot.com.

Strategic Management: Enterprise Engineering (Just the Quotes)

"Enterprise Engineering is based on the belief that an enterprise, as any other complex system can be designed or improved in an orderly fashion thus giving a better overall result than ad hoc organisation and design." (Peter Bernus et al, "Possibilities and limitations of reusing enterprise models", 1994) 

"Enterprise engineering is an integrated set of disciplines for building an enterprise, its processes, and systems." (James Martin, "The Great Transition, 1995)

"Enterprise Engineering is not a single methodology, but a sophisticated synthesis of the most important and successful of today's change methods. 'Enterprise Engineering' first explains in detail all the critical disciplines (including continuous improvement, radical reinvention of business processes, enterprise redesign, and strategic visioning). It then illustrates how to custom-design the right combination of these change methods for your organization's specific needs." (James Martin, "The Great Transition, 1995)

"Various perspectives exist in an enterprise, such as efficiency, quality, and cost. Any system for enterprise engineering must be capable of representing and managing these different perspectives in a well-defined way." (Michael Grüninger & Mark S Fox, "Benchmarking - Theory and Practice", 1995)

"Enterprise Engineering is defined as that body of knowledge, principles, and practices having to do with the analysis, design, implementation and operation of an enterprise. In a continually changing and unpredictable competitive environment, the Enterprise Engineer addresses a fundamental question: 'how to design and improve all elements associated with the total enterprise through the use of engineering and analysis methods and tools to more effectively achieve its goals and objectives' [...]" (Donald H Liles, "The Enterprise Engineering Discipline", 1996)

"There are several world view assumptions present in enterprise engineering. The first assumption is that the enterprise can be viewed as a complex system. This is necessary because systems in organizations are systems of organized complexity. Complexity is the result of the multiplicity and intricacy of man’s interaction with other components of the system. Secondly, the enterprise is to be viewed as a system of processes. These processes are engineered both individually and holistically. The final assumption is the use of engineering rigor in transforming the enterprise. The enterprise engineering paradigm views the enterprise as a complex system of processes that can be engineered to accomplish specific organizational objectives. In the Enterprise Engineering paradigm, the enterprise is viewed as a complex system of processes that can be engineered to accomplish specific organizational objectives." (Donald H Liles, "Enterprise modeling within an enterprise engineering framework", 1996)

"Enterprise Engineering is the collection of those tools and methods which one can use to design and continually maintain an enterprise." (Peter Bernus et al , (eds.), "Handbook on Enterprise Architecture", 2003)

"Enterprise architecture (EA) promotes the belief that an enterprise, as a complex system, can be designed or improved in an orderly fashion achieving better overall results than ad-hoc organisation and design. EA is a co-operative effort of designers, analysts and managers and uses enterprise models in the process [...] enterprise models carry meaning. This resulted in requirements for the enterprise engineering process, which - if not met - can limit the viability of the process. The analysis of the same factors resulted in requirements for improved Enterprise Modelling Tools." (Peter Bernus, "Enterprise models for enterprise architecture and ISO9000: 2000", 2003) 

"EE [Enterprise Engineering] is where the business side meets IT. It is a methodology for active collaboration between business and IT during project development. When business people and IT people know how to work together, the result is faster development and higher quality." (Alan Chmura & J Mark Heumann, "Logical Data Modeling: What it is and How to do it", 2005)

"Enterprise engineering is an emerging discipline that studies enterprises from an engineering perspective. The first paradigm of this discipline is that enterprises are purposefully designed and implemented systems. Consequently, they can be re-designed and re-implemented if there is a need for change. The second paradigm of enterprise engineering is that enterprises are social systems. This means that the system elements are social individuals, and that the essence of an enterprise's operation lies in the entering into and complying with commitments between these social individuals." (Erik Proper, "Advances in Enterprise Engineering II", 2009)

"Enterprise engineering is rooted in both the organizational sciences and the information system sciences. In our current understanding, three concepts are paramount to the theoretical and practical pursuit of enterprise engineering: enterprise ontology, enterprise architecture, and enterprise governance." (Erik Proper, "Advances in Enterprise Engineering II", 2009)

Strategic Management: Managing Change (Just the Quotes)

"Inconsistencies of opinion, arising from changes of circumstances, are often justifiable." (Daniel Webster, [speech] 1846)

"Progress, far from consisting in change, depends on retentiveness. [...] Those who cannot remember the past are condemned to fulfil it." (George Santayana, "The Life of Reason", 1905-1906)

"To improve is to change; to be perfect is to change often." (Winston Churchill, [Speech, House of Commons] 1925)

"When an active individual of sound common sense perceives the sordid state of the world, desire to change it becomes the guiding principle by which he organizes given facts and shapes them into a theory. The methods and categories as well as the transformation of the theory can be understood only in connection with his taking of sides. This, in turn, discloses both his sound common sense and the character of the world. Right thinking depends as much on right willing as right willing on right thinking." (Max Horkheimer, "The Latest Attack on Metaphysics", 1937)

"Many of the obstacles for change which have been attributed to human nature are in fact due to the inertia of institutions and to the voluntary desire of powerful classes to maintain the existing status." (John Dewey, 1938)

"Doing engineering is practicing the art of the organized forcing of technological change." (George Spencer-Brown, Electronics, Vol. 32 (47),  1959)

"People fear change because it undermines their security." (Thomas R Bennett III, Planning For Change, 1961)

"Every part of the system is so related to every other part that a change in a particular part causes a changes in all other parts and in the total system." (Arthur D Hall, "A methodology for systems engineering", 1962)

"To say a system is 'self-organizing' leaves open two quite different meanings. There is a first meaning that is simple and unobjectionable. This refers to the system that starts with its parts separate (so that the behavior of each is independent of the others' states) and whose parts then act so that they change towards forming connections of some type. Such a system is 'self-organizing' in the sense that it changes from 'parts separated' to 'parts joined'. […] In general such systems can be more simply characterized as 'self-connecting', for the change from independence between the parts to conditionality can always be seen as some form of 'connection', even if it is as purely functional […]  'Organizing' […] may also mean 'changing from a bad organization to a good one' […] The system would be 'self-organizing' if a change were automatically made to the feedback, changing it from positive to negative; then the whole would have changed from a bad organization to a good." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"So much has been written about employees' resistance to change that we are sometimes tempted to forget that they can also react favorably." (Nathaniel Stewart, "Leadership in the Office", 1963)

"We have overwhelming evidence that available information plus analysis does not lead to knowledge. The management science team can properly analyse a situation and present recommendations to the manager, but no change occurs. The situation is so familiar to those of us who try to practice management science that I hardly need to describe the cases." (C West Churchman, "Managerial acceptance of scientific recommendations", California Management Review Vol 7, 1964)

"[...] long-range plans are most valuable when they are revised and adjusted and set anew at shorter periods. The five-year plan is reconstructed each year in turn for the following five years. The soundest basis for this change is accurate measurement of the results of the first year's experience with the plan against the target of the plan." (George S Odiorne, "Management by Objectives", 1965)

"Any understanding of social and cultural change is impossible without a knowledge of the way media works as environments." (Marshall McLuhan, "The Medium is the Massage: An inventory of effects", 1967)

"Technological invention and innovation are the business of engineering. They are embodied in engineering change." (Daniel V DeSimone & Hardy Cross, "Education for Innovation", 1968)

"The systems approach to problems focuses on systems taken as a whole, not on their parts taken separately. Such an approach is concerned with total - system performance even when a change in only one or a few of its parts is contemplated because there are some properties of systems that can only be treated adequately from a holistic point of view. These properties derive from the relationship between parts of systems: how the parts interact and fit together." (Russell L Ackoff, "Towards a System of Systems Concepts", 1971) 

"Every goal and every change from the status quo has a price tag on it." (Lyle E Schaller, "The Change Agent", 1972)

"To be productive the individual has to have control, to a substantial extent, over the speed, rhythm, and attention spans with which he is working […] While work is, therefore, best laid out as uniform, working is best organized with a considerable degree of diversity. Working requires latitude to change speed, rhythm, and attention span fairly often. It requires fairly frequent changes in operating routines as well. What is good industrial engineering for work is exceedingly poor human engineering for the worker." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"Perhaps the fault [for the poor implementation record for models] lies in the origins of managerial model-making - the translation of methods and principles of the physical sciences into wartime operations research. [...] If hypothesis, data, and analysis lead to proof and new knowledge in science, shouldn’t similar processes lead to change in organizations? The answer is obvious-NO! Organizational changes (or decisions or policies) do not instantly pow from evidence, deductive logic, and mathematical optimization." (Edward B Roberts, "Interface", 1977)

"It is change, continuing change, inevitable change, that is the dominant factor in society today. No sensible decision can be made any longer without taking into account not only the world as it is, but the world as it will be. [...] This, in turn, means that our statesmen, our businessmen, our everyman must take on a science fictional way of thinking." (Isaac Asimov, "My Own View", Encyclopedia of Science Fiction, 1978)

"All organizations do change when put under sufficient pressure. This pressure must be either external to the organization or the result of very strong leadership." (Bruce Henderson, Henderson on Corporate Strategy, 1979)

"It is rare for any organization to generate sufficient pressure internally to produce significant change in direction. Indeed, internal pressure is likely to be regarded as a form of dissatisfaction with the organization's leadership." (Bruce Henderson, Henderson on Corporate Strategy, 1979)

"The acceptance of project management has not been easy, however. Many executives are not willing to accept change and are inflexible when it comes to adapting to a different environment." (Harold Kerzner, "Project Management", 1979)

"A competent manager can usually explain necessary planning changes in terms of specific facts which have contributed to the change. The existing fear, or attitude of failure, which results from missed completion dates should be replaced by a more constructive fear of failing to keep a plan updated." (Philip F Gehring Jr. & Udo W Pooch, "Advances in Computer Programming Management", 1980)

"[Organizational] change is intervention, and intervention even with good intentions can lead to negative results in both the short and long run. For example, a change in structure in going from application of one theory to another might cause the unwanted resignation of a key executive, or the loss of an important customer. [...] the factor of change, acts as an overriding check against continual organizational alterations. It means that regardless of how well meant a change is, or how much logic dictates this change, its possible negative effects must be carefully weighed against the hoped-for benefits." (William A Cohen, "Principles of Technical Management", 1980)

"[...] strategic change is likely to call for different management techniques than continuous running of well-established business-units.... If effectively done, strategic management can have even greater payoffs in rough seas than in clear sailing." (Boris Yavitz & William H Newman, "Strategy in Action", 1982)

"Every system of whatever size must maintain its own structure and must deal with a dynamic environment, i.e., the system must strike a proper balance between stability and change. The cybernetic mechanisms for stability (i.e., homeostasis, negative feedback, autopoiesis, equifinality) and change (i.e., positive feedback, algedonodes, self-organization) are found in all viable systems." (Barry Clemson, "Cybernetics: A New Management Tool", 1984)

"Change occurs only when there is a confluence of changing values and economic necessity." (John Naisbett & Patricia Aburdene, "Re-inventing the Corporation", 1985)

"With the changes in technological complexity, especially in information technology, the leadership task has changed. Leadership in a networked organization is a fundamentally different thing from leadership in a traditional hierarchy." (Edgar Schein, "Organizational Culture and Leadership", 1985)

"An ability to tolerate ambiguity helps to avoid overdetermining one's goals. [...] As they proceed, peak performers can adjust goals. [...] What they are doing is balancing between change and stasis, between innovation and consolidation." (Charles Garfield, "Peak Performers", 1986)

"Most organizations, left to their own devices, are going to atrophy, to get so institutional, so bureaucratic, that they get to the point where their original reason for existence has been lost, and they stagnate. So you have to have change, and by that I mean dramatic change." (William G McGowan, Inc. Magazine, August 1986)

"[...] strategic planning and crisis management are complimentary. They coexist comfortably because both deal with the management of change. Crisis management concentrates on those brief moments of instability that must be dealt with first in order to get on with the larger and less time-sensitive job of reaching strategic objectives." (Gerald C Meyers, "When It Hits the Fan", 1986)

"The only [management] practice that's now constant is the practice of constantly accommodating to change." (William G. McGowan, Inc. Magazine, 1986)

"Training frequently fails to pay off in behavioral changes on the job: Trainees go back to work and do it the way they've always done it instead of the way you taught them to do it." (Ruth C Clark, "Manager, Training and Information Services", Training, 1986)

"You can change behavior in an entire organization, provided you treat training as a process rather than an event." (Edward W Jones, "Training", 1986)

"Constant change by everyone requires a dramatic increase in the capacity to accept disruption." (Tom Peters, "Thriving on Chaos", 1987)

"People are asking more cogent questions, and they're observing behavior that begins to be amenable to the ideas of chaotic dynamics." (James Ramsey, The New York Times, 1987)

"Problems can be reduced by allowing employees to help plan changes rather than directing them to execute a plan made by others." (Eugene Raudsepp, MTS Digest, 1987)

"There are only two ways to get people to support corporate change. You should give employees the information they need to understand the reasons for change, and put enough influence behind the information to [gain their] support." (Carla O'Dell, 1987)

"[...] a strategic inflection point is a time in the life of business when its fundamentals are about to change. That change can mean an opportunity to rise to new heights. But it may just as likely signal the beginning of the end." (Andrew S Grove, "Only the Paranoid Survive: How to Exploit the Crisis Points that Challenge Every Company and Career", 1988)

"[...] technology always fosters radical social change." (Neil Postman, "Conscientious Objections", 1988)

"Model is used as a theory. It becomes theory when the purpose of building a model is to understand the mechanisms involved in the developmental process. Hence as theory, model does not carve up or change the world, but it explains how change takes place and in what way or manner. This leads to build change in the structures." (Laxmi K Patnaik, "Model Building in Political Science", The Indian Journal of Political Science Vol. 50 (2), 1989)

"Systems thinking is a discipline for seeing the 'structures' that underlie complex situations, and for discerning high from low leverage change. That is, by seeing wholes we learn how to foster health. To do so, systems thinking offers a language that begins by restructuring how we think." (Peter Senge, "The Fifth Discipline", 1990)

"Systems thinking is a discipline for seeing wholes. It is a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static 'snapshots'. It is a set of general principles- distilled over the course of the twentieth century, spanning fields as diverse as the physical and social sciences, engineering, and management. [...] During the last thirty years, these tools have been applied to understand a wide range of corporate, urban, regional, economic, political, ecological, and even psychological systems. And systems thinking is a sensibility for the subtle interconnectedness that gives living systems their unique character." (Peter Senge, "The Fifth Discipline", 1990)

"The importance of top management commitment to organizational change is so well accepted that it is almost cliché to repeat the fact. We would therefore expect managerial values to be just as important in this area as in others that require strategic direction and leadership" (Thomas A Kochan,"The Mutual Gains Enterprise", 1994) 

"Enterprise Engineering is not a single methodology, but a sophisticated synthesis of the most important and successful of today's change methods. 'Enterprise Engineering' first explains in detail all the critical disciplines (including continuous improvement, radical reinvention of business processes, enterprise redesign, and strategic visioning). It then illustrates how to custom-design the right combination of these change methods for your organization's specific needs." (James Martin, "The Great Transition, 1995)

"Even though these complex systems differ in detail, the question of coherence under change is the central enigma for each." (John H Holland," Hidden Order: How Adaptation Builds Complexity", 1995)

"Commonly, the threats to strategy are seen to emanate from outside a company because of changes in technology or the behavior of competitors. Although external changes can be the problem, the greater threat to strategy often comes from within. A sound strategy is undermined by a misguided view of competition, by organizational failures, and, especially, by the desire to grow." (Michael E Porter, "What is Strategy?", Harvard Business Review, 1996)

"Architecture is that set of design artifacts, or descriptive representations, that are relevant for describing an object, such that it can be produced to requirements (quality) as well as maintained over the period of its useful life (change)." (John A Zachman, "Enterprise architecture: The issue of the century", Database Programming and Design Vol. 10 (3), 1997)

"Issues of quality, timeliness and change are the conditions that are forcing us to face up to the issues of enterprise architecture. The precedent of all the older disciplines known today establishes the concept of architecture as central to the ability to produce quality and timely results and to manage change in complex products. Architecture is the cornerstone for containing enterprise frustration and leveraging technology innovations to fulfill the expectations of a viable and dynamic Information Age enterprise." (John Zachman, "Enterprise Architecture: The Issue of The Century", 1997)

"The basis of leadership is the capacity of the leader to change the mindset, the framework of the other person." (Warren Bennis, "Managing People is Like Herding Cats", 1997)

"Projects sometimes fail long before they deliver anything. At some point they may be determined to be too expensive to continue. Or perhaps they took too long to develop and the business need evaporated. Or perhaps the requirements change so often that the developers can never finish one thing without having to stop and start all over on something new. Certainly these are planning failures." (Kent Beck & Martin Fowler, "Planning Extreme Programming", 2000)

"Strategic planning and strategic change management are really 'strategic thinking'. It’s about clarity and simplicity, meaning and purpose, and focus and direction." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"The business changes. The technology changes. The team changes. The team members change. The problem isn't change, per se, because change is going to happen; the problem, rather, is the inability to cope with change when it comes." (Kent Beck, Extreme Programming Explained, 2000)

"Systems thinking means the ability to see the synergy of the whole rather than just the separate elements of a system and to learn to reinforce or change whole system patterns. Many people have been trained to solve problems by breaking a complex system, such as an organization, into discrete parts and working to make each part perform as well as possible. However, the success of each piece does not add up to the success of the whole. to the success of the whole. In fact, sometimes changing one part to make it better actually makes the whole system function less effectively." (Richard L Daft, "The Leadership Experience", 2002)

"An Enterprise Architecture is a dynamic and powerful tool that helps organisations understand their own structure and the way they work. It provides a ‘map’ of the enterprise and a ‘route planner’ for business and technology change. A well-constructed Enterprise Architecture provides a foundation for the ‘Agile’ business." (Bob Jarvis, "Enterprise Architecture: Understanding the Bigger Picture - A Best Practice Guide for Decision Makers in IT", 2003)

"An enterprise architecture is a blueprint for organizational change defined in models [using words, graphics, and other depictions] that describe (in both business and technology terms) how the entity operates today and how it intends to operate in the future; it also includes a plan for transitioning to this future state." (US Government Accountability Office, "Enterprise Architecture: Leadership Remains Key to Establishing and Leveraging Architectures for Organizational Transformation", GAO-06-831, 2006)

"Change pressures arise from different sectors of a system. At times it is mandated from the top of a hierarchy, other times it forms from participants at a grass-roots level. Some changes are absorbed by the organization without significant impact on, or alterations of, existing methods. In other cases, change takes root. It causes the formation of new methods (how things are done and what is possible) within the organization." (George Siemens, "Knowing Knowledge", 2006)

"Enterprise architecture is the process of translating business vision and strategy into effective enterprise change by creating, communicating and improving the key requirements, principles and models that describe the enterprise's future state and enable its evolution. The scope of the enterprise architecture includes the people, processes, information and technology of the enterprise, and their relationships to one another and to the external environment. Enterprise architects compose holistic solutions that address the business challenges of the enterprise and support the governance needed to implement them." (Anne Lapkin et al, "Gartner Clarifies the Definition of the Term 'Enterprise Architecture", 2008)

"Strategy is the serious work of figuring out how to translate vision and mission into action. Strategy is a general plan of action that describes resource allocation and other activities for dealing with the environment and helping the organization reach its goals. Like vision, strategy changes, but successful companies develop strategies that focus on core competence, develop synergy, and create value for customers. Strategy is implemented through the systems and structures that are the basic architecture for how things get done in the organization." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"The butterfly effect demonstrates that complex dynamical systems are highly responsive and interconnected webs of feedback loops. It reminds us that we live in a highly interconnected world. Thus our actions within an organization can lead to a range of unpredicted responses and unexpected outcomes. This seriously calls into doubt the wisdom of believing that a major organizational change intervention will necessarily achieve its pre-planned and highly desired outcomes. Small changes in the social, technological, political, ecological or economic conditions can have major implications over time for organizations, communities, societies and even nations." (Elizabeth McMillan, "Complexity, Management and the Dynamics of Change: Challenges for practice", 2008)

"The other element of systems thinking is learning to influence the system with reinforcing feedback as an engine for growth or decline. [...] Without this kind of understanding, managers will hit blockages in the form of seeming limits to growth and resistance to change because the large complex system will appear impossible to manage. Systems thinking is a significant solution." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Enterprise engineering is an emerging discipline that studies enterprises from an engineering perspective. The first paradigm of this discipline is that enterprises are purposefully designed and implemented systems. Consequently, they can be re-designed and re-implemented if there is a need for change. The second paradigm of enterprise engineering is that enterprises are social systems. This means that the system elements are social individuals, and that the essence of an enterprise's operation lies in the entering into and complying with commitments between these social individuals." (Erik Proper, "Advances in Enterprise Engineering II", 2009)

"And even if we make good plans based on the best information available at the time and people do exactly what we plan, the effects of our actions may not be the ones we wanted because the environment is nonlinear and hence is fundamentally unpredictable. As time passes the situation will change, chance events will occur, other agents such as customers or competitors will take actions of their own, and we will find that what we do is only one factor among several which create a new situation." (Stephen Bungay, "The Art of Action: How Leaders Close the Gaps between Plans, Actions, and Results", 2010)

"With each theory or model, our concepts of reality and of the fundamental constituents of the universe have changed." (Stephen Hawking & Leonard Mlodinow, "The Grand Design", 2010)

"Without precise predictability, control is impotent and almost meaningless. In other words, the lesser the predictability, the harder the entity or system is to control, and vice versa. If our universe actually operated on linear causality, with no surprises, uncertainty, or abrupt changes, all future events would be absolutely predictable in a sort of waveless orderliness." (Lawrence K Samuels, "Defense of Chaos", 2013)

"Cybernetics studies the concepts of control and communication in living organisms, machines and organizations including self-organization. It focuses on how a (digital, mechanical or biological) system processes information, responds to it and changes or being changed for better functioning (including control and communication)." (Dmitry A Novikov, "Cybernetics 2.0", 2016)

"Information or data is only valuable if it can be used to provide insights which then actually drive change. Sadly the most effort and expertise and applause is given to those who design and deliver incredibly complex statistical reviews of data over time - the beauty is in the complexity and the presentation not in the usability." (Alan Pennington, "The Customer Experience Book", 2016)

"It is not about deep data analysis to predict behaviour, it is about actively designing experiences and then applying data to enable the delivery. Cumulatively making lots of little changes using very specific pieces of data will aggregate to a bigger impact." (Alan Pennington, "The Customer Experience Book", 2016)

"Remember that for change to happen it has to be relevant at a local and individual level" (Alan Pennington, "The Customer Experience Book", 2016)

"Given enough time and enough users, even the most innocuous change will break something; your analysis of the value of that change must incorporate the difficulty in investigating, identifying, and resolving those breakages." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)

"Because management deals mostly with the status quo and leadership deals mostly with change, in the next century we are going to have to try to become much more skilled at creating leaders." (John P Kotter)

"Enterprise architecture (EA) is a discipline for proactively and holistically leading enterprise responses to disruptive forces by identifying and analyzing the execution of change toward desired business vision and outcomes. EA delivers value by presenting business and IT leaders with signature-ready recommendations for adjusting policies and projects to achieve target business outcomes that capitalize on relevant business disruptions. EA is used to steer decision making toward the evolution of the future state architecture." (Gartner)

"The normal 'cascade' strategy for implementing change is usually ineffective, because memories remain embedded in the way the organization works after the change. This applies particularly if the change relates to the culture rather than to work practices or systems." (Dick Beckhard)

"There is a remarkable agreement upon the definition of learning as being reflected in a change of behavior as the result of experience." (Ernest A Haggard)