Showing posts with label quotes. Show all posts
Showing posts with label quotes. Show all posts

27 December 2025

🕸Systems Engineering: Limitations (Just the Quotes)

"From a more general philosophical perspective we can say that we wish to model complex systems because we want to understand them better.  The main requirement for our models accordingly shifts from having to be correct to being rich in information.  This does not mean that the relationship between the model and the system itself becomes less important, but the shift from control and prediction to understanding does have an effect on our approach to complexity: the evaluation of our models in terms of performance can be deferred. Once we have a better understanding of the dynamics of complexity, we can start looking for the similarities and differences between different complex systems and thereby develop a clearer understanding of the strengths and limitations of different models." (Paul Cilliers,"Complexity and Postmodernism: Understanding Complex Systems", 1998)

"The robustness of the misperceptions of feedback and the poor performance they cause are due to two basic and related deficiencies in our mental model. First, our cognitive maps of the causal structure of systems are vastly simplified compared to the complexity of the systems themselves. Second, we are unable to infer correctly the dynamics of all but the simplest causal maps. Both are direct consequences of bounded rationality, that is, the many limitations of attention, memory, recall, information processing capability, and time that constrain human decision making." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"The very essence of mass communication theory is a simple but all-embracing expression of technological determinism, since the essential features depend on what certain technologies have made possible, certain technologies have made possible, especially the following: communication at a distance, the multiplication and simultaneous distribution of diverse ‘messages’, the enormous capacity and speed of carriers, and the limitations on response. There is no escaping the implication that public communication as practised in modern societies is profoundly shaped by these general features." (Denis McQuail, "McQuail's Reader in Mass Communication Theory", 2002)

"A characteristic of such chaotic dynamics is an extreme sensitivity to initial conditions (exponential separation of neighboring trajectories), which puts severe limitations on any forecast of the future fate of a particular trajectory. This sensitivity is known as the ‘butterfly effect’: the state of the system at time t can be entirely different even if the initial conditions are only slightly changed, i.e., by a butterfly flapping its wings." (Hans J Korsch et al, "Chaos: A Program Collection for the PC", 2008)

"We are beginning to see the entire universe as a holographically interlinked network of energy and information, organically whole and self-referential at all scales of its existence. We, and all things in the universe, are non-locally connected with each other and with all other things in ways that are unfettered by the hitherto known limitations of space and time." (Ervin László, "Cosmos: A Co-creator's Guide to the Whole-World", 2010)

"Cyberneticists argue that positive feedback may be useful, but it is inherently unstable, capable of causing loss of control and runaway. A higher level of control must therefore be imposed upon any positive feedback mechanism: self-stabilising properties of a negative feedback loop constrain the explosive tendencies of positive feedback. This is the starting point of our journey to explore the role of cybernetics in the control of biological growth. That is the assumption that the evolution of self-limitation has been an absolute necessity for life forms with exponential growth." (Tony Stebbing, "A Cybernetic View of Biological Growth: The Maia Hypothesis", 2011)

"In an information economy, entrepreneurs master the science of information in order to overcome the laws of the purely physical sciences. They can succeed because of the surprising power of the laws of information, which are conducive to human creativity. The central concept of information theory is a measure of freedom of choice. The principle of matter, on the other hand, is not liberty but limitation - it has weight and occupies space." (George Gilder, "Knowledge and Power: The Information Theory of Capitalism and How it is Revolutionizing our World", 2013)

26 December 2025

♟️Strategic Management: Perfection (Just the Quotes)

"Information, that is imperfectly acquired, is generally as imperfectly retained." (William Playfair, "The Commercial and Political Atlas", 1786)

"The man who insists upon seeing with perfect clearness before he decides, never decides." (Henri-Frédéric Amiel, [journal entry] 1856)

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

"Planning is essentially the analysis and measurement of materials and processes in advance of the event and the perfection of records so that we may know exactly where we are at any given moment. In short it is attempting to steer each operation and department by chart and compass and chronometer - not by guess and by God." (Lyndall Urwick, "The Pattern of Management", 1956)

"In most management problems there are too many possibilities to expect experience, judgement, or intuition to provide good guesses, even with perfect information." (Russell L Ackoff, "Management Science", 1967)

"In the objectives system, the corporation's aims, or plans, are broken down into a hierarchy of lesser aims or plans; and the grand total of all those objectives adds up to those of the corporation. Then all the executives have to do is meet their planned and agreed objectives and - hey presto - the corporation does the same. Perfection in management, at last, has arrived, except that it hasn't and won't." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"You can teach the rudiments of cooking, as of management, but you cannot make a great cook or a great manager. In both activities, you ignore fundamentals at grave risk  - but sometimes succeed. In both, science can be extremely useful but is no substitute for the art itself. In both, inspired amateurs can outdo professionals. In both, perfection is rarely achieved, and failure is more common than the customers realize. In both, practitioners don't need recipes that detail timing down to the last second, ingredients to the last fraction of an ounce, and procedures down to the Just flick of the wrist; they need reliable maxims, instructive anecdotes, and no dogmatism." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"There is no absolute knowledge. And those who claim it open the door to tragedy. All information is imperfect. We have to treat it with humility." (Jacob Bronowski, "The Ascent of Man", 1973)

"So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand. [...] It is in those outliers and imperfections that the wildness lurks." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)

"A belief model is clung to not because it is 'correct'  - there is no way to know this - but rather because it has worked in the past and must cumulate a record of failure before it is worth discarding. In general, there may be a constant slow turnover of hypotheses acted upon. One could speak of this as a system of temporarily fulfilled expectations - beliefs or models or hypotheses that are temporarily fulfilled" (though not perfectly), which give way to different beliefs or hypotheses when they cease to be fulfilled." (W Brian Arthur, "Complexity and the Economy", 2015)

"Sometimes, the best way to broaden your search is to look inside your own organization. Great solutions often come along at the wrong time, and the sprint can be a perfect opportunity to rejuvenate them. Also look for ideas that are in progress but unfinished - and even old ideas that have been abandoned." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"Professional organizations had believed that by hiring well qualified, technically capable people, quality control would take care of itself. [...] Managers rationalized that quality control lapses could not be helped and were simply another cost of doing business. Now wasn't the only perfectionist in the university business." (Garth Peterson)


📉Graphical Representation: Perfection (Just the Quotes)

"Information that is imperfectly acquired, is generally as imperfectly retained; and a man who has carefully investigated a printed table, finds, when done, that he has only a very faint and partial idea of what he has read; and that like a figure imprinted on sand, is soon totally erased and defaced." (William Playfair, "The Commercial and Political Atlas", 1786)

"[…] it must be noticed that these diagrams do not naturally harmonize with the propositions of ordinary life or ordinary logic. […] The great bulk of the propositions which we commonly meet with are founded, and rightly founded, on an imperfect knowledge of the actual mutual relations of the implied classes to one another. […] one very marked characteristic about these circular diagrams is that they forbid the natural expression of such uncertainty, and are therefore only directly applicable to a very small number of such propositions as we commonly meet with." (John Venn, "On the Diagrammatic and Mechanical Representation of Propositions and Reasonings", 1880)

"Planning is essentially the analysis and measurement of materials and processes in advance of the event and the perfection of records so that we may know exactly where we are at any given moment. In short it is attempting to steer each operation and department by chart and compass and chronometer - not by guess and by God." (Lyndall Urwick, "The Pattern of Management", 1956)

"Skewness is a measure of symmetry. For example, it's zero for the bell-shaped normal curve, which is perfectly symmetric about its mean. Kurtosis is a measure of the peakedness, or fat-tailedness, of a distribution. Thus, it measures the likelihood of extreme values." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Clearly principles and guidelines for good presentation graphics have a role to play in exploratory graphics, but personal taste and individual working style also play important roles. The same data may be presented in many alternative ways, and taste and customs differ as to what is regarded as a good presentation graphic. Nevertheless, there are principles that should be respected and guidelines that are generally worth following. No one should expect a perfect consensus where graphics are concerned." (Antony Unwin, Good Graphics?"[in "Handbook of Data Visualization"], 2008)

"Once these different measures of performance are consolidated into a single number, that statistic can be used to make comparisons […] The advantage of any index is that it consolidates lots of complex information into a single number. We can then rank things that otherwise defy simple comparison […] Any index is highly sensitive to the descriptive statistics that are cobbled together to build it, and to the weight given to each of those components. As a result, indices range from useful but imperfect tools to complete charades." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"The correlation coefficient has two fabulously attractive characteristics. First, for math reasons that have been relegated to the appendix, it is a single number ranging from –1 to 1. A correlation of 1, often described as perfect correlation, means that every change in one variable is associated with an equivalent change in the other variable in the same direction. A correlation of –1, or perfect negative correlation, means that every change in one variable is associated with an equivalent change in the other variable in the opposite direction. The closer the correlation is to 1 or –1, the stronger the association. […] The second attractive feature of the correlation coefficient is that it has no units attached to it. […] The correlation coefficient does a seemingly miraculous thing: It collapses a complex mess of data measured in different units" (like our scatter plots of height and weight) into a single, elegant descriptive statistic." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"The idiom of scatterplots encodes two quantitative value variables using both the vertical and horizontal spatial position channels, and the mark type is necessarily a point. Scatterplots are effective for the abstract tasks of providing overviews and characterizing distributions, and specifically for finding outliers and extreme values. Scatterplots are also highly effective for the abstract task of judging the correlation between two attributes. With this visual encoding, that task corresponds the easy perceptual judgement of noticing whether the points form a line along the diagonal. The stronger the correlation, the closer the points fall along a perfect diagonal line; positive correlation is an upward slope, and negative is downward." (Tamara Munzner, "Visualization Analysis and Design", 2014)

"A perfectly relevant visualization that breaks a few presentation rules is far more valuable - it’s better - than a perfectly executed, beautiful chart that contains the wrong data, communicates the wrong message, or fails to engage its audience." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)


🕸Systems Engineering: Perfection (Just the Quotes)

"The concept of an independent system is a pure creation of the imagination. For no material system is or can ever be perfectly isolated from the rest of the world. Nevertheless it completes the mathematician’s ‘blank form of a universe’ without which his investigations are impossible. It enables him to introduce into his geometrical space, not only masses and configurations, but also physical structure and chemical composition." (Lawrence J Henderson, "The Order of Nature: An Essay", 1917)

"Knowledge is not something which exists and grows in the abstract. It is a function of human organisms and of social organization. Knowledge, that is to say, is always what somebody knows: the most perfect transcript of knowledge in writing is not knowledge if nobody knows it. Knowledge however grows by the receipt of meaningful information - that is, by the intake of messages by a knower which are capable of reorganising his knowledge." (Kenneth E Boulding, "General Systems Theory: The Skeleton of Science", Management Science Vol. 2 (3), 1956)

"The hardest problems we have to face do not come from philosophical questions about whether brains are machines or not. There is not the slightest reason to doubt that brains are anything other than machines with enormous numbers of parts that work in perfect accord with physical laws. As far as anyone can tell, our minds are merely complex processes. The serious problems come from our having had so little experience with machines of such complexity that we are not yet prepared to think effectively about them." (Marvin Minsky, 1986)

"Nature behaves in ways that look mathematical, but nature is not the same as mathematics. Every mathematical model makes simplifying assumptions; its conclusions are only as valid as those assumptions. The assumption of perfect symmetry is excellent as a technique for deducing the conditions under which symmetry-breaking is going to occur, the general form of the result, and the range of possible behaviour. To deduce exactly which effect is selected from this range in a practical situation, we have to know which imperfections are present" (Ian Stewart & Martin Golubitsky, "Fearful Symmetry: Is God a Geometer?", 1992)

"Skewness is a measure of symmetry. For example, it's zero for the bell-shaped normal curve, which is perfectly symmetric about its mean. Kurtosis is a measure of the peakedness, or fat-tailedness, of a distribution. Thus, it measures the likelihood of extreme values." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Swarm systems generate novelty for three reasons: (1) They are 'sensitive to initial conditions' - a scientific shorthand for saying that the size of the effect is not proportional to the size of the cause - so they can make a surprising mountain out of a molehill. (2) They hide countless novel possibilities in the exponential combinations of many interlinked individuals. (3) They don’t reckon individuals, so therefore individual variation and imperfection can be allowed. In swarm systems with heritability, individual variation and imperfection will lead to perpetual novelty, or what we call evolution." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Bounded rationality simultaneously constrains the complexity of our cognitive maps and our ability to use them to anticipate the system dynamics. Mental models in which the world is seen as a sequence of events and in which feedback, nonlinearity, time delays, and multiple consequences are lacking lead to poor performance when these elements of dynamic complexity are present. Dysfunction in complex systems can arise from the misperception of the feedback structure of the environment. But rich mental models that capture these sources of complexity cannot be used reliably to understand the dynamics. Dysfunction in complex systems can arise from faulty mental simulation-the misperception of feedback dynamics. These two different bounds on rationality must both be overcome for effective learning to occur. Perfect mental models without a simulation capability yield little insight; a calculus for reliable inferences about dynamics yields systematically erroneous results when applied to simplistic models." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"Yet, with the discovery of the butterfly effect in chaos theory, it is now understood that there is some emergent order over time even in weather occurrence, so that weather prediction is not next to being impossible as was once thought, although the science of meteorology is far from the state of perfection." (Peter Baofu, "The Future of Complexity: Conceiving a Better Way to Understand Order and Chaos", 2007)

"The word ‘symmetry’ conjures to mind objects which are well balanced, with perfect proportions. Such objects capture a sense of beauty and form. The human mind is constantly drawn to anything that embodies some aspect of symmetry. Our brain seems programmed to notice and search for order and structure. Artwork, architecture and music from ancient times to the present day play on the idea of things which mirror each other in interesting ways. Symmetry is about connections between different parts of the same object. It sets up a natural internal dialogue in the shape." (Marcus du Sautoy, "Symmetry: A Journey into the Patterns of Nature", 2008)

"[...] a high degree of unpredictability is associated with erratic trajectories. This not only because they look random but mostly because infinitesimally small uncertainties on the initial state of the system grow very quickly - actually exponentially fast. In real world, this error amplification translates into our inability to predict the system behavior from the unavoidable imperfect knowledge of its initial state." (Massimo Cencini et al, "Chaos: From Simple Models to Complex Systems", 2010)

"Because the perfect system cannot be designed, there will always be weak spots that human ingenuity and resourcefulness can exploit." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

See also: Failure, Good, Bad, Ugly


25 December 2025

♟️Strategic Management: The Good (Just the Quotes)

"Present opportunities are neglected, and attainable good is slighted, by minds busied in extensive ranges and intent upon future advantages." (Samuel Johnson, "The Idler", 1801)

"For any manager to utilize graphic methods for visualizing the vital facts of his business, in the first place it must be impressed upon his that the method will produce the results for him and then he must know how to get up a chart correctly, and last, but far from least, he must know what the essential facts of his business are. Charts, in themselves, mean little and like many another force for the accomplishment of good, if misdirected, may result unprofitably." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"The fine art of executive decision consists in not deciding questions that are not now pertinent, in not deciding prematurely, in not making decision that cannot be made effective, and in not making decisions that others should make. Not to decide questions that are not pertinent at the time is uncommon good sense, though to raise them may be uncommon perspicacity. Not to decide questions prematurely is to refuse commitment of attitude or the development of prejudice. Not to make decisions that cannot be made effective is to refrain from destroying authority. Not to make decisions that others should make is to preserve morale, to develop competence, to fix responsibility, and to preserve authority.

From this it may be seen that decisions fall into two major classes, positive decisions - to do something, to direct action, to cease action, to prevent action; and negative decisions, which are decisions not to decide. Both are inescapable; but the negative decisions are often largely unconscious, relatively nonlogical, instinctive, 'good sense'. It is because of the rejections that the selection is good."" (Chester I Barnard, "The Functions of the Executive", 1938)"

"Good management are rarely overcompensated to an extent that makes any significant difference with respect to the stockholder's position. Poor management are always overcompensated, because they are worth less than nothing to the owners." (Benjamin Graham, "The Intelligent Investor", 1949)

"If charts do not reflect actual organization and if the organization is intended to be as charted, it is the job of effective management to see that actual organization conforms with that desired. Organization charts cannot supplant good organizing, nor can a chart take the place of spelling out authority relationships clearly and completely, of outlining duties of managers and their subordinates, and of defining responsibilities." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"While good charting will attempt, as far as possible, to make levels on the chart conform to levels of importance in the business enterprise, it cannot always do so. This problem can be handled by clearly spelling out authority relationships." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"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)

"The successful manager must be a good diagnostician and must value a spirit of inquiry." (Edgar H Schein, "Organizational Psychology", 1965)

"Good mission statements focus on a limited number of goals, stress the company's major policies and values, and define the company's major competitive scopes." (Philip Kotler, "Marketing Management", 1967)

"In most management problems there are too many possibilities to expect experience, judgement, or intuition to provide good guesses, even with perfect information." (Russell L Ackoff, "Management Science", 1967)

"Good results without good planning come from good luck, not good management." (David Jaquith, "The Time Trap", 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)

"Any approach to strategy quickly encounters a conflict between corporate objectives and corporate capabilities. Attempting the impossible is not good strategy; it is just a waste of resources." (Bruce Henderson, Henderson on Corporate Strategy, 1979)

"Executive stress is difficult to overstate when there is a conflict among policy restrictions, near-term performance, long-term good of the company, and personal survival." (Bruce Henderson, "Henderson on Corporate Strategy", 1979)

"[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)

"A leader is one who, out of madness or goodness, volunteers to take upon himself the woe of the people. There are few men so foolish, hence the erratic quality of leadership in the world." (John Updike, "They Thought They Were Better", TIME magazine, 1980)

"Because the importance of training is so commonly underestimated, the manager who wants to make a dramatic improvement in organizational effectiveness without challenging the status quo will find a training program a good way to start." (Theodore Caplow, "Managing an Organization", 1983)

"It seems to me that we too often focus on the inside aspects of the job of management, failing to give proper attention to the requirement for a good manager to maintain those relationships between his organization and the environment in which it must operate which permits it to move ahead and get the job done." (Breene Kerr, Giants in Management, 1985) 

"Managers who are skilled communicators may also be good at covering up real problems." (Chris Argyris, Harvard Business Review, 1986)

"Operating managers should in no way ignore short-term performance imperatives [when implementing productivity improvement programs.] The pressures arise from many sources and must be dealt with. Moreover, unless managers know that the day-to-day job is under control and improvements are being made, they will not have the time, the perspective, the self-confidence, or the good working relationships that are essential for creative, realistic strategic thinking and decision making." (Robert H Schaefer, Harvard Business Review, 1986)

"Some management groups are not good at problem solving and decision making precisely because the participants have weak egos and are uncomfortable with competition." (Chris Argyris, Harvard Business Review, 1986)

"The chain of command is an inefficient communication system. Although my staff and I had our goals, tasks, and priorities well defined, large parts of the organization didn't know what was going on. Frequent, thorough, open communication to every employee is essential to get the word out and keep walls from building within the company. And while face-to-face communication is more effective than impersonal messages, it's a good idea to vary the medium and the message so that no one (including top management) relies too much on ''traditional channels of communication." (William H Peace, Harvard Business Review, 1986)

"The source of good management is found in the imagination of leaders, persons who form new visions and manifest them with a high degree of craft. The blending of vision and craft communicates the purpose. In the arts, people who do that well are masters. In business, they are leaders." (Henry M. Boettinger, Harvard Business Review on Human Relations, 1986)

"Employees are most apt to deal with their problems when they believe that they will be helped in good faith." (Paul V Lyons, "Management", 1987)

"Good people can fix a lot of flaws in poor planning, but it's never the other way around." (Roland Shmitt, "Government Executive", 1987)

"Some people are excited about learning a new piece of software. Other people get very depressed. Good managers anticipate both situations they involve the persons to be affected in the process of selecting a particular program, and they provide time and resources for training. Training is the key in both cases." (Jonathan P Siegel, "Communications", 1988)

"Pressure can also make managers act out of character. Degrees of panic will cause a normally good manager to lose self-confidence and focus. Under stress, even a good plan can be abandoned." (Wheeler L Baker, "Crisis Management: A Model for Managers", 1993)

"Managers must clearly distinguish operational effectiveness from strategy. Both are essential, but the two agendas are different. The operational agenda involves continual improvement everywhere there are no trade-offs. Failure to do this creates vulnerability even for companies with a good strategy. The operational agenda is the proper place for constant change, flexibility, and relentless efforts to achieve best practice. In contrast, the strategic agenda is the right place for defining a unique position, making clear trade-offs, and tightening fit. It involves the continual search for ways to reinforce and extend the company’s position. The strategic agenda demands discipline and continuity; its enemies are distraction and compromise." (Michael E Porter, "What is Strategy?", Harvard Business Review, 1996)

"There's a fundamental distinction between strategy and operational effectiveness. Strategy is about making choices, trade-offs; it's about deliberately choosing to be different. Operational effectiveness is about things that you really shouldn't have to make choices on; it's about what's good for everybody and about what every business should be doing. " (Michael E Porter, "What is Strategy?", Harvard Business Review, 1996)

"An effective leader leaves a legacy; they leave their footprints on the road for others to follow. A good leader develops themselves and they develop others. They bring people together rather than divide them." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Good leaders are ethical, responsible and effective. Ethical because leadership connects you to others through shared values. Responsible because leadership means self-development and not simply giving orders, however charismatically, to get others to do what you want. Effective because shared values and goals give the strongest motivation for getting tasks done. There are no guarantees, but this sort of leadership will bring you closer to people and give you the greatest chance of success." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Managing [...] used to be about planning and control. Top management decided what was to be done, middle management worked out how to do it and everyone else did as they were told. This model assumed, of course, that top management knew what needed to be done, that the orders had time to percolate their way down and that, like a good army, the lower ranks would obey." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Good leaders make people feel that they're at the very heart of things, not at the periphery. Everyone feels that he or she makes a difference to the success of the organization. When that happens, people feel centered and that gives their work meaning." (Warren Bennis, "Managing People Is Like Herding Cats", 1999)

"Making good judgments when one has complete data, facts, and knowledge is not leadership - it's bookkeeping." (Dee Hock, "Birth of the Chaordic Age", 1999)

"Data have to be filtered in some manner to make them intelligible. This filtration may be based upon a person's experience plus his presuppositions and assumptions, or it may be more formalized and less subjective, but there will always be some method of analysis. If experience is the basis for interpreting the data, then the interpretation is only as good as the manager's past experience. If the current situation is outside the manager’s experience, then his interpretation of the data may well be incorrect. Likewise, flawed assumptions or flawed presuppositions can also result in flawed interpretations. However, in the absence of formal and standardized data, most managers use the scat-of-the-pants approach. and in the end, about all they can say that some days appear to be better than others." (Donald J Wheeler," Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"I've learned that mistakes can often be as good a teacher as success." (Jack Welch, "Jack: Straight from the Gut", 2001)

"Project failures are not always the result of poor methodology; the problem may be poor implementation. Unrealistic objectives or poorly defined executive expectations are two common causes of poor implementation. Good methodologies do not guarantee success, but they do imply that the project will be managed correctly." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"The key to good decision making is not knowledge. It is understanding. We are swimming in the former. We are desperately lacking in the latter." (Malcolm Gladwell, "Blink: The Power of Thinking Without Thinking", 2005)

"Wisdom and good governance require more than the consistent application of abstract principles." (Anthony Daniels, "Romancing Opiates: Pharmacological Lies and the Addiction Bureaucracy", 2006)

"Enterprise architecture is the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of the company's operating model. The operating model is the desired state of business process integration and business process standardization for delivering goods and services to customers." (Peter Weill, "Innovating with Information Systems Presentation", 2007)

"Whereas strategy is abstract and based on long-term goals, tactics are concrete and based on finding the best move right now. Tactics are conditional and opportunistic, all about threat and defense. No matter what pursuit you’re engaged in - chess, business, the military, managing a sports team - it takes both good tactics and wise strategy to be successful." (Garry Kasparov, "How Life Imitates Chess", 2007)

"A bad strategy will fail no matter how good your information is and lame execution will stymie a good strategy. If you do enough things poorly, you will go out of business." (Bill Gates, "Business @ the Speed of Thought: Succeeding in the Digital Economy", 2009)

"A good strategy is one that takes into account not only the requirements of the position, but also the opponent's strategy and tactics. Strategy lies between science and art. It supports the ability to evaluate positions, recognize patterns and imagine adequate plans." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"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)

"Almost by definition, one is rarely privileged to 'control' a disaster. Yet the activity somewhat loosely referred to by this term is a substantial portion of Management, perhaps the most important part. […] It is the business of a good Manager to ensure, by taking timely action in the real world, that scenarios of disaster remain securely in the realm of Fantasy." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Clearly, total feedback is Not a Good Thing. Too much feedback can overwhelm the response channels, leading to paralysis and inaction. Even in a system designed to accept massive feedback" (such as the human brain), if the system is required to accommodate to all incoming data, equilibrium will never be reached. The point of decision will be delayed indefinitely, and no action will be taken." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Despite the roar of voices wanting to equate strategy with ambition, leadership, 'vision', planning, or the economic logic of competition, strategy is none of these. The core of strategy work is always the same: discovering the critical factors in a situation and designing a way of coordinating and focusing actions to deal with those factors." (Richard Rumelt, "Good Strategy Bad Strategy", 2011)

"Having conflicting goals, dedicating resources to unconnected targets, and accommodating incompatible interests are the luxuries of the rich and powerful, but they make for bad strategy. Despite this, most organizations will not create focused strategies. Instead, they will generate laundry lists of desirable outcomes and, at the same time, ignore the need for genuine competence in coordinating and focusing their resources. Good strategy requires leaders who are willing and able to say no to a wide variety of actions and interests. Strategy is at least as much about what an organization does not do as it is about what it does." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"It is hard to avoid the conclusion that while strategy is undoubtedly a good thing to have, it is a hard thing to get right. […] So what turns something that is not quite strategy into strategy is a sense of actual or imminent instability, a changing context that induces a sense of conflict. Strategy therefore starts with an existing state of affairs and only gains meaning by an awareness of how, for better or worse, it could be different." (Lawrence Freedman, “Strategy: A history”, 2013)

"You can only look so far, and so you better just keep looking frequently. That’s the most important element of strategy: You understand the direction you’re going, but you also know what you’re going to do in the next six months. Most companies will do a pretty good job many times about the direction, but then they never break it down to shorter metrics. Intel did a super job on that. When you ask why [we] succeeded, this is one of the reasons." (Les Vadasz, 2013)

"Good decision-making is like playing chess and you must avoid making hasty decisions without thinking of how that particular decision will impact on different aspects of your work and organization. The worst kind of decision-making is to decide to delay a difficult decision until later or to pass it to someone else to have to make. You will never excel and be valued by your colleagues if you get into these habits of procrastination and passing responsibility to others." (Nigel Cumberland, "Secrets of Success at Work: 50 techniques to excel", 2014)

"Good governance is less about structure and rules than being focused, effective and accountable." (Pearl Zhu,  "Digitizing Boardroom: The Multifaceted Aspects of Digital Ready Boards", 2016)

"Good mission statements have five major characteristics. (1) They focus on a limited number of goals. (2) They stress the company’s major policies and values. (3) They define the major competitive spheres within which the company will operate. (4) They take a long-term view." (5) They are as short, memorable, and meaningful as possible." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016)

"No methodology can guarantee success. But a good methodology can provide a feedback loop for continual improvement and learning." (Ash Maurya, "Scaling Lean: Mastering the Key Metrics for Startup Growth", 2016)

"Decision trees are considered a good predictive model to start with, and have many advantages. Interpretability, variable selection, variable interaction, and the flexibility to choose the level of complexity for a decision tree all come into play." (Ralph Winters, "Practical Predictive Analytics", 2017)

"Random forests are essentially an ensemble of trees. They use many short trees, fitted to multiple samples of the data, and the predictions are averaged for each observation. This helps to get around a problem that trees, and many other machine learning techniques, are not guaranteed to find optimal models, in the way that linear regression is. They do a very challenging job of fitting non-linear predictions over many variables, even sometimes when there are more variables than there are observations. To do that, they have to employ 'greedy algorithms', which find a reasonably good model but not necessarily the very best model possible." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"If you do not conduct sufficient analysis and if you do not have firm technical knowledge, you cannot carry out improvement or standardization, nor can you perform good control or prepare control charts useful for effective control." (Kaoru Ishikawa)

"The traditional approach to leadership values decision-making conviction and consistency; good leaders 'stick to their guns'. By contrast, the emerging approach recognizes that in fast-changing environments, decisions often need to be reversed or adapted, and that changing course in response to new information is a strength, not a weakness. If this tension is not managed wisely, leaders run the risk of seeming too rigid, on the one hand, or too wishy-washy on the other." (Jennifer Jordan et al, "Every Leader Needs to Navigate These 7 Tensions", Harvard Business Review, 2020)


🕸Systems Engineering: The Good (Just the Quotes)

"Plasticity, then, in the wide sense of the word, means the possession of a structure weak enough to yield to an influence, but strong enough not to yield all at once. Each relatively stable phase of equilibrium in such a structure is marked by what we may call a new set of habits." (William James, "The Laws of Habit", 1887)

"The engineer must be able not only to design, but to execute. A draftsman may be able to design, but unless he is able to execute his designs to successful operation he cannot be classed as an engineer. The production engineer must be able to execute his work as he has planned it. This requires two qualifications in addition to technical engineering ability: He must know men, and he must have creative ability in applying good statistical, accounting, and 'system' methods to any particular production work he may undertake." (Hugo Diemer, "Industrial Engineering", 1905)

"A system is said to be coherent if every fact in the system is related every other fact in the system by relations that are not merely conjunctive. A deductive system affords a good example of a coherent system." (Lizzie S Stebbing, "A modern introduction to logic", 1930)

"Stability is commonly thought of as desirable, for its presence enables the system to combine of flexibility and activity in performance with something of permanence. Behaviour that is goal-seeking is an example of behaviour that is stable around a state of equilibrium. Nevertheless, stability is not always good, for a system may persist in returning to some state that, for other reasons, is considered undesirable." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"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)

"The idea of knowledge as an improbable structure is still a good place to start. Knowledge, however, has a dimension which goes beyond that of mere information or improbability. This is a dimension of significance which is very hard to reduce to quantitative form. Two knowledge structures might be equally improbable but one might be much more significant than the other." (Kenneth E Boulding, "Beyond Economics: Essays on Society", 1968)

"Perhaps the most important single characteristic of modern organizational cybernetics is this: That in addition to concern with the deleterious impacts of rigidly-imposed notions of what constitutes the application of good 'principles of organization and management' the organization is viewed as a subsystem of a larger system(s), and as comprised itself of functionally interdependent subsystems." (Richard F Ericson, "Organizational cybernetics and human values", 1969)  

"Indeed, except for the very simplest physical systems, virtually everything and everybody in the world is caught up in a vast, nonlinear web of incentives and constraints and connections. The slightest change in one place causes tremors everywhere else. We can't help but disturb the universe, as T.S. Eliot almost said. The whole is almost always equal to a good deal more than the sum of its parts. And the mathematical expression of that property - to the extent that such systems can be described by mathematics at all - is a nonlinear equation: one whose graph is curvy." (M Mitchell Waldrop, "Complexity: The Emerging Science at the Edge of Order and Chaos", 1992)

"Reliable information processing requires the existence of a good code or language, i.e., a set of rules that generate information at a given hierarchical level, and then compress it for use at a higher cognitive level. To accomplish this, a language should strike an optimum balance between variety (stochasticity) and the ability to detect and correct errors" (memory).(John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"System dynamics models are not derived statistically from time-series data. Instead, they are statements about system structure and the policies that guide decisions. Models contain the assumptions being made about a system. A model is only as good as the expertise which lies behind its formulation. A good computer model is distinguished from a poor one by the degree to which it captures the essence of a system that it represents. Many other kinds of mathematical models are limited because they will not accept the multiple-feedback-loop and nonlinear nature of real systems." (Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"Fuzzy systems are excellent tools for representing heuristic, commonsense rules. Fuzzy inference methods apply these rules to data and infer a solution. Neural networks are very efficient at learning heuristics from data. They are 'good problem solvers' when past data are available. Both fuzzy systems and neural networks are universal approximators in a sense, that is, for a given continuous objective function there will be a fuzzy system and a neural network which approximate it to any degree of accuracy." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Our simplistic cause-effect analyses, especially when coupled with the desire for quick fixes, usually lead to far more problems than they solve - impatience and knee-jerk reactions included. If we stop for a moment and take a good look our world and its seven levels of complex and interdependent systems, we begin to understand that multiple causes with multiple effects are the true reality, as are circles of causality-effects." (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)

"The internet model has many lessons for the new economy but perhaps the most important is its embrace of dumb swarm power. The aim of swarm power is superior performance in a turbulent environment. When things happen fast and furious, they tend to route around central control. By interlinking many simple parts into a loose confederation, control devolves from the center to the lowest or outermost points, which collectively keep things on course. A successful system, though, requires more than simply relinquishing control completely to the networked mob." (Kevin Kelly, "New Rules for the New Economy: 10 radical strategies for a connected world", 1998)

"An equilibrium is not always an optimum; it might not even be good. This may be the most important discovery of game theory." (Ivar Ekeland, "Le meilleur des mondes possibles" ["The Best of All Possible Worlds"], 2000)

"Periods of rapid change and high exponential growth do not, typically, last long. A new equilibrium with a new dominant technology and/or competitor is likely to be established before long. Periods of punctuation are therefore exciting and exhibit unusual uncertainty. The payoff from establishing a dominant position in this short time is therefore extraordinarily high. Dominance is more likely to come from skill in marketing and positioning than from superior technology itself." (Richar Koch, "The Power Laws", 2000)

"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) 

"A smaller model with fewer covariates has two advantages: it might give better predictions than a big model and it is more parsimonious (simpler). Generally, as you add more variables to a regression, the bias of the predictions decreases and the variance increases. Too few covariates yields high bias; this called underfitting. Too many covariates yields high variance; this called overfitting. Good predictions result from achieving a good balance between bias and variance. […] fiding a good model involves trading of fit and complexity." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"All models are mental projections of our understanding of processes and feedbacks of systems in the real world. The general approach is that models are as good as the system upon which they are based. Models should be designed to answer specific questions and only incorporate the necessary details that are required to provide an answer." (Hördur V Haraldsson & Harald U Sverdrup, "Finding Simplicity in Complexity in Biogeochemical Modelling", 2004)

"The laws of thermodynamics tell us something quite different. Economic activity is merely borrowing low-entropy energy inputs from the environment and transforming them into temporary products and services of value. In the transformation process, often more energy is expended and lost to the environment than is embedded in the particular good or service being produced." (Jeremy Rifkin, "The Third Industrial Revolution", 2011)

🏗️Software Engineering: The Good (Just the Quotes)

"The engineer must be able not only to design, but to execute. A draftsman may be able to design, but unless he is able to execute his designs to successful operation he cannot be classed as an engineer. The production engineer must be able to execute his work as he has planned it. This requires two qualifications in addition to technical engineering ability: He must know men, and he must have creative ability in applying good statistical, accounting, and 'system' methods to any particular production work he may undertake." (Hugo Diemer, "Industrial Engineering", 1905)

"It is a good rule of thumb that a program should read from top to bottom in the order that it will be executed; if this is not true, watch out for the bugs that often accompany poor structure. Make your programs read from top to bottom." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"The best documentation for a computer program is a clean structure. It also helps if the code is well formatted, with good mnemonic identifiers and labels" (if any are needed), and a smattering of enlightening comments. Flowcharts and program descriptions are of secondary importance; the only reliable documentation of a computer program is the code itself. The reason is simple -whenever there are multiple representations of a program, the chance for discrepancy exists. If the code is in error, artistic flowcharts and detailed comments are to no avail. Only by reading the code can the programmer know for sure what the program does." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"A good information system both exposes interface errors and stimulates their correction" (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"A good top-down design avoids bugs in several ways. First, the clarity of structure and representation makes the precise statement of requirements and functions of the modules easier. Second, the partitioning and independence of modules avoids system bugs. Third, the suppression of detail makes flaws in the structure more apparent. Fourth, the design can be tested at each of its refinement steps, so testing can start earlier and focus on the proper level of detail at each step." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"I will contend that conceptual integrity is the most important consideration in system design. It is better to have a system omit certain anomalous features and improvements, but to reflect one set of design ideas, than to have one that contains many good but independent and uncoordinated ideas." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"In the good old days physicists repeated each other's experiments, just to be sure. Today they stick to FORTRAN, so that they can share each other's programs, bugs included." (Edsger W Dijkstra, "How do we tell truths that might hurt?", 1975)

"A good scientist is a person with original ideas. A good engineer is a person who makes a design that works with as few original ideas as possible. There are no prima donnas in engineering." (Freeman Dyson, "Disturbing the Universe", 1979)

"Today's scientific investigations are so complicated that even experts in related fields may not understand them well. But there is a logic in the planning of experiments and in the analysis of their results that all intelligent people can grasp, and this logic is a great help in determining when to believe what we hear and read and when to be skeptical. This logic has a great deal to do with statistics, which is why statisticians have a unique interest in the scientific method, and why some knowledge of statistics can so often be brought to bear in distinguishing good arguments from bad ones." (Robert Hooke, "How to Tell the Liars from the Statisticians", 1983)

"Object-oriented programming increases the value of these metrics by managing this complexity. The most effective tool available for dealing with complexity is abstraction. Many types of abstraction can be used, but encapsulation is the main form of abstraction by which complexity is managed in object-oriented programming. Programming in an object-oriented language, however, does not ensure that the complexity of an application will be well encapsulated. Applying good programming techniques can improve encapsulation, but the full benefit of object-oriented programming can be realized only if encapsulation is a recognized goal of the design process." (Rebecca Wirfs-Brock," Object-Oriented Design: A responsibility-driven approach", 1989)

"Programming in an object-oriented language, however, does not ensure that the complexity of an application will be well encapsulated. Applying good programming techniques can improve encapsulation, but the full benefit of object-oriented programming can be realized only if encapsulation is a recognized goal of the design process." (Rebecca Wirfs-Brock, "Object-oriented Design: A responsibility-driven approach", 1989)

"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)

"Users are not designers, so it is not reasonable to expect them to come up with design ideas from scratch. However, they are very good at reacting to concrete designs they do not like or that will not work in practice. To get full benefits from user involvement, it is necessary to present these suggested system designs in a form the users can understand." (Jakob Nielsen, "Usability Engineering", 1993)

"Users often do not know what is good for them. [...] Users have a very hard time predicting how they will interact with potential future systems with which they have no experience. [...] Furthermore, users will often have divergent opinions when asked about details of user interface design." (Jakob Nielsen, "Usability Engineering", 1993)

"When one considers how hard it is to write a computer program even approaching the intellectual scope of a good mathematical paper, and how much greater time and effort have to be put into it to make it 'almost' formally correct, it is preposterous to claim that mathematics as we practice it is anywhere near formally correct." (William P Thurston, "On proof and progress in mathematics", Bulletin of the AMS 30" (2), 1994)

"Good design protects you from the need for too many highly accurate components in the system. But such design principles are still, to this date, ill-understood and need to be researched extensively. Not that good designers do not understand this intuitively, merely it is not easily incorporated into the design methods you were taught in school. Good minds are still needed in spite of all the computing tools we have developed." (Richard Hamming, "The Art of Doing Science and Engineering: Learning to Learn", 1997)

"The woes of software engineering are not due to lack of tools or proper management, but largely due to lack of sufficient technical competence. A good designer must rely on experience; on precise, logical thinking; and on pedantic exactness. No magic will do. In light of all this, it is particularly sad that, in many informatics curricula, programming in the large is badly neglected. Design has become a non-topic." (Niklaus Wirth, [interview in Software Development] 1997)

"Any fool can write code that a computer can understand. Good programmers write code that humans can understand." (Martin Fowler, "Refactoring: Improving the Design of Existing Code", 1999)

"Good programmers know what to write. Great ones know what to rewrite." (Eric S Raymond, "The Cathedral and the Bazaar", 1999)

"The next best thing to having good ideas is recognizing good ideas from your users. Sometimes the latter is better." (Eric S Raymond, "The Cathedral & the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary", 1999)

"Project failures are not always the result of poor methodology; the problem may be poor implementation. Unrealistic objectives or poorly defined executive expectations are two common causes of poor implementation. Good methodologies do not guarantee success, but they do imply that the project will be managed correctly." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"The physical design process is a key phase in the overall design process. It is too often ignored until the last minute in the vain hope that performance will be satisfactory. Without a good physical design, performance is rarely satisfactory and throwing hardware at the problem is rarely completely effective. There is no substitute for a good physical design, and the time and effort spent in the physical design process will be rewarded with an efficient and well-tuned database, not to mention happy users!" (Ken England, "Microsoft SQL Server 2000 Performance Optimization and Tuning Handbook", 2001)

"Good design is also an act of communication between the designer and the user, except that all the communication has to come about by the appearance of the device itself. The device must explain itself." (Donald Norman, "The Design of Everyday Things", 2002)

"All OO languages show some tendency to suck programmers into the trap of excessive layering. Object frameworks and object browsers are not a substitute for good design or documentation, but they often get treated as one. Too many layers destroy transparency: It becomes too difficult to see down through them and mentally model what the code is actually doing. The Rules of Simplicity, Clarity, and Transparency get violated wholesale, and the result is code full of obscure bugs and continuing maintenance problems." (Eric S Raymond, "The Art of Unix Programming", 2003)

"Many things can put a project off course: bureaucracy, unclear objectives, and lack of resources, to name a few. But it is the approach to design that largely determines how complex software can become. When complexity gets out of hand, developers can no longer understand the software well enough to change or extend it easily and safely. On the other hand, a good design can create opportunities to exploit those complex features." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"The effectiveness of an overall design is very sensitive to the quality and consistency of fine-grained design and implementation decisions. With a MODEL-DRIVEN DESIGN, a portion of the code is an expression of the model; changing that code changes the model. Programmers are modelers, whether anyone likes it or not. So it is better to set up the project so that the programmers do good modeling work." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Coupling describes how tightly a class or routine is related to other classes or routines. The goal is to create classes and routines with small, direct, visible, and flexible relations to other classes and routines, which is known as loose coupling." The concept of coupling applies equally to classes and routines [...] Good coupling between modules is loose enough that one module can easily be used by other modules."" (Steve C McConnell, "Code Complete: A Practical Handbook of Software Construction" 2nd Ed., 2004)

"Good code is its own best documentation." (Steve McConnell, "Code Complete", 2004)

"On a related topic, let me say that I'm not much of a fan of object-oriented design. I've seen some beautiful stuff done with OO, and I've even done some OO stuff myself, but it's just one way to approach a problem. For some problems, it's an ideal way; for others, it's not such a good fit. [...] OO is great for problems where an interface applies naturally to a wide range of types, not so good for managing polymorphism" (the machinations to get collections into OO languages are astounding to watch and can be hellish to work with), and remarkably ill-suited for network computing. That's why I reserve the right to match the language to the problem, and even - often - to coordinate software written in several languages towards solving a single problem. It's that last point - different languages for different subproblems - that sometimes seems lost to the OO crowd." (Rob Pike, [interview] 2004) 

"The flip side of the coin was that even good programmers and language designers tended to do terrible extensions when they were in the heat of programming, because design is something that is best done slowly and carefully." (Alan Kay, [ACM Queue A Conversation with Alan Kay Vol. 2" (9)] 2004-2005)

"Abstractions matter to users too. Novice users want programs whose abstractions are simple and easy to understand; experts want abstractions that are robust and general enough to be combined in new ways. When good abstractions are missing from the design, or erode as the system evolves, the resulting program grows barnacles of complexity. The user is then forced to master a mass of spurious details, to develop workarounds, and to accept frequent, inexplicable failures." (Daniel Jackson, "Software Abstractions", 2006)

"In fact, I'm a huge proponent of designing your code around the data, rather than the other way around, and I think it's one of the reasons git has been fairly successful. [... I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships." (Linus Torvalds, [email] 2006)

"Much data in databases has a long history. It might have come from old 'legacy' systems or have been changed several times in the past. The usage of data fields and value codes changes over time. The same value in the same field will mean totally different thing in different records. Knowledge or these facts allows experts to use the data properly. Without this knowledge, the data may bc used literally and with sad consequences. The same is about data quality. Data users in the trenches usually know good data from bad and can still use it efficiently. They know where to look and what to check. Without these experts, incorrect data quality assumptions are often made and poor data quality becomes exposed." (Arkady Maydanchik, "Data Quality Assessment", 2007)

"One of the essential parts of a formal training in programming is a long and demanding study of the large collection of algorithms that have already been discovered and analyzed, together with the Data Structures" (carefully tailored, seemingly unnatural ways of organizing data for effective access) that go with them. As with any other engineering profession, it is impossible to do a good job without a thorough knowledge of what has been tried before. If a programmer starts the job fully armed with what is already known, they will have some chance of finding something new. Inventiveness is important: not all problems have been seen before. A programmer who does not already know the standard algorithms and data structures is doomed to nothing more than rediscovering the basics." (Robert Plant & Stephen Murrell, "An Executive's Guide to Information Technology: Principles, Business Models, and Terminology", 2007)

"We are also limited by the fact that verbalization works best when mental model manipulation is an inherent element of the task of interest. Troubleshooting, computer programming, and mathematics are good examples of tasks where mental model manipulation is central and explicit. In contrast, the vast majority of tasks do not involve explicit manipulation of task representations. Thus, our access of mental models - and the access of people doing these tasks - is limited." (William B Rouse, "People and Organizations: Explorations of Human-Centered Design", 2007)

"Well-commented code is one hallmark of good programming practice; it shows that you care about what you're doing, and it is considerate to those who will come after you to fix your bugs. But comments also serve as a kind of back channel for programmer-to-programmer communication and even occasionally as a competitive arena or an outlet for silliness." (Scott Rosenberg, "Dreaming in Code", 2007)

"Good software designs accommodate change without huge investments and rework. When we use code that is out of our control, special care must be taken to protect our investment and make sure future change is not too costly." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Most programming languages contain good parts and bad parts. I discovered that I could be better programmer by using only the good parts and avoiding the bad parts." (Douglas Crockford, "JavaScript: The Good Parts", 2008) 

"Standards make it easier to reuse ideas and components, recruit people with relevant experience, encapsulate good ideas, and wire components together. However, the process of creating standards can sometimes take too long for industry to wait, and some standards lose touch with the real needs of the adopters they are intended to serve." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"A good system design is based on a sound conceptual model" (architecture). A system design that has no conceptual structure and little logic to its organization is ultimately going to be unsuccessful. Good architecture will address all the requirements of the system at the right level of abstraction." (Vasudeva Varma, "Software Architecture: A Case Based Approach", 2009)

"Anti-patterns are the dark side of patterns - common mistakes we fall into repeatedly. Sometimes they seem to be good solutions that experience has demonstrated don���t work in practice. On other occasions, we know that they're not a good idea, but we fall into them anyway." (Paul Butcher, "Debug It! Find, Repair, and Prevent Bugs in Your Code", 2009)

"One final point - whether fixing bugs or implementing new functionality, it's good practice to always examine exactly what it is that you're about to check in before every check-in. It won't take long, and every once in a while, you'll catch a change that you really didn't intend to make from slipping through."" (Paul Butcher, "Debug It! Find, Repair, and Prevent Bugs in Your Code", 2009)

"For some reason software developers don't think of debugging time as coding time. They think of debugging time as a call of nature, something that just has to be done. But debugging time is just as expensive to the business as coding time is, and therefore anything we can do to avoid or diminish it is good." (Robert C Martin, "The Clean Coder: A code of conduct for professional programmers", 2011)

"A complete data analysis will involve the following steps: (i) Finding a good model to fit the signal based on the data. (ii) Finding a good model to fit the noise, based on the residuals from the model." (iii) Adjusting variances, test statistics, confidence intervals, and predictions, based on the model for the noise." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

"A good heuristic decision is made by 1) knowing what to look for, 2) knowing when enough information is enough" (the 'threshold of decision' ), and 3) knowing what decision to make." (Patrick Van Horne, "Left of Bang", 2014)

"Good architecture is all about splitting stuff reliably into self-contained parcels that allow work on them to continue relatively independently in parallel" (often these days in different locations)." (Richard Hopkins & Stephen Harcombe, "Agile Architecting: Enabling the Delivery of Complex Agile Systems Development Projects", 2014)

"Good architecture provides good interfaces that separate the shear layers of its implementation: a necessity for evolution and maintenance. Class-oriented programming puts both data evolution and method evolution in the same shear layer: the class. Data tend to remain fairly stable over time, while methods change regularly to support new services and system operations. The tension in these rates of change stresses the design." (James O Coplien & Trygve Reenskaug, "The DCI Paradigm: Taking Object Orientation into the Architecture World", 2014)

"In more ways than one, architecture is all about avoiding bottlenecks. In architecture, the term bottleneck typically refers to a design problem that is preventing processing from occurring at full speed. [...] A good architecture will avoid bottlenecks in both." (Richard Hopkins & Stephen Harcombe, "Agile Architecting: Enabling the Delivery of Complex Agile Systems Development Projects", 2014)

"There is a tendency to believe that good architecture leads to systems that perform better and are more secure, but such claims relate less to any given architectural principle than to the timing of big-picture deliberations in the design cycle and to the proper engagement of suitable stakeholders." (James O Coplien & Trygve Reenskaug, "The DCI Paradigm: Taking Object Orientation into the Architecture World", 2014)

"When designers intentionally trick users into inviting friends or blasting a message to their social networks, they may see some initial growth, but it comes at the expense of users' goodwill and trust. When people discover they've been duped, they vent their frustration and stop using the product." (Nir Eyal, "Hooked: How to Build Habit-Forming Products", 2014)

"When you write a computer program you've got to not just list things out and sort of take an algorithm and translate it into a set of instructions. But when there's a bug - and all programs have bugs - you've got to debug it. You've got to go in, change it, and then re-execute [...] and you iterate. And that iteration is really a very, very good approximation of learning." (Nicholas Negroponte, "A 30-year history of the future", [Ted Talk] 2014)

"Areas of low complexity or that are unlikely to be invested in can be built without the need for perfect code quality; working software is good enough. Sometimes feedback and first-to-market are core to the success of a product; in this instance, it can make business sense to get working software up as soon as possible, whatever the architecture." (Scott Millett, "Patterns Principles and Practices of Domain Driven Design", 2015)

"But 'average cost to fix one defect' is a stupid metric [...] It makes bad projects look good, and good projects look bad. How? By failing to divide the costs of fixing into two categories: fixed costs of detecting and fixing defects - costs which are the same no matter how buggy or how good the product is - and variable costs, those which you pay for each defect." (Laurent Bossavit, "The Leprechauns of Software Engineering", 2015)

"Good user stories are expected to be independent, negotiable, valuable, estimable, small, and testable" (mnemonic INVEST). Good tasks are expected to be specific, measurable, achievable, relevant, and time-boxed" (mnemonic SMART). The key difference is that tasks need not be independent or valuable by themselves." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"This is what the Agile Manifesto means when it says responding to change over following a plan. To maximize adaptability, it is essential to have good, fast feedback loops. This is why there is so much emphasis on iterative development." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"No methodology can guarantee success. But a good methodology can provide a feedback loop for continual improvement and learning." (Ash Maurya, "Scaling Lean: Mastering the Key Metrics for Startup Growth", 2016)

"Sometimes you can't fit everything in. Remember that the sprint is great for testing risky solutions that might have a huge payoff. So you'l have to reverse the way you would normally prioritize. If a small fix is so good and low-risk that you're already planning to build it next week, then seeing it in a prototype won't teach you much. Skip those easy wins in favor of big, bold bets." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"Feature extraction is also the most creative part of data science and the one most closely tied to domain expertise. Typically, a really good feature will correspond to some real-world phenomenon. Data scientists should work closely with domain experts and understand what these phenomena mean and how to distill them into numbers." (Field Cady, "The Data Science Handbook", 2017)

"Making good engineering decisions is all about weighing all of the available inputs and making informed decisions about the trade-offs. Sometimes, those decisions are based on instinct or accepted best practice, but only after we have exhausted approaches that try to measure or estimate the true underlying costs." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)


24 December 2025

🕸Systems Engineering: The Bad (Just the Quotes)

"The concept of teleological mechanisms however it be expressed in many terms, may be viewed as an attempt to escape from these older mechanistic formulations that now appear inadequate, and to provide new and more fruitful conceptions and more effective methodologies for studying self-regulating processes, self-orienting systems and organisms, and self-directing personalities. Thus, the terms feedback, servomechanisms, circular systems, and circular processes may be viewed as different but equivalent expressions of much the same basic conception." (Lawrence K Frank, 1948)

"[...] the concept of 'feedback', so simple and natural in certain elementary cases, becomes artificial and of little use when the interconnexions between the parts become more complex. When there are only two parts joined so that each affects the other, the properties of the feedback give important and useful information about the properties of the whole. But when the parts rise to even as few as four, if every one affects the other three, then twenty circuits can be traced through them; and knowing the properties of all the twenty circuits does not give complete information about the system. Such complex systems cannot be treated as an interlaced set of more or less independent feedback circuits, but only as a whole. For understanding the general principles of dynamic systems, therefore, the concept of feedback is inadequate in itself. What is important is that complex systems, richly cross-connected internally, have complex behaviours, and that these behaviours can be goal-seeking in complex patterns." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"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)

"The purpose and real value of systems engineering is [...] to keep going around the loop; find inadequacies and make improvements." (Robert E Machol, "Mathematicians are useful", 1971)

"Systems with unknown behavioral properties require the implementation of iterations which are intrinsic to the design process but which are normally hidden from view. Certainly when a solution to a well-understood problem is synthesized, weak designs are mentally rejected by a competent designer in a matter of moments. On larger or more complicated efforts, alternative designs must be explicitly and iteratively implemented. The designers perhaps out of vanity, often are at pains to hide the many versions which were abandoned and if absolute failure occurs, of course one hears nothing. Thus the topic of design iteration is rarely discussed. Perhaps we should not be surprised to see this phenomenon with software, for it is a rare author indeed who publicizes the amount of editing or the number of drafts he took to produce a manuscript." (Fernando J Corbató, "A Managerial View of the Multics System Development", 1977)

"How can a cognitive system process environmental input and stored knowledge so as to benefit from experience? More specific versions of this question include the following: How can a system organize its experience so that it has some basis for action even in unfamiliar situations? How can a system determine that rules in its knowledge base are inadequate? How can it generate plausible new rules to replace the inadequate ones? How can it refine rules that are useful but non-optimal? How can it use metaphor and analogy to transfer information and procedures from one domain to another?" (John H Holland et al, "Induction: Processes Of Inference, Learning, And Discovery", 1986)

"[…] the complexity of a given system is always determined relative to another system with which the given system interacts. Only in extremely special cases, where one of these reciprocal interactions is so much weaker than the other that it can be ignored, can we justify the traditional attitude regarding complexity as an intrinsic property of the system itself." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Complex adaptive systems have the property that if you run them - by just letting the mathematical variable of 'time' go forward - they'll naturally progress from chaotic, disorganized, undifferentiated, independent states to organized, highly differentiated, and highly interdependent states. Organized structures emerge spontaneously. [...]A weak system gives rise only to simpler forms of self-organization; a strong one gives rise to more complex forms, like life." (J Doyne Farmer, The Third Culture: Beyond the Scientific Revolution", 1995)

"No plea about inadequacy of our understanding of the decision-making processes can excuse us from estimating decision making criteria. To omit a decision point is to deny its presence - a mistake of far greater magnitude than any errors in our best estimate of the process." (Jay W Forrester, "Perspectives on the modelling process", 2000)

"Remember a networked learning machine’s most basic rule: strengthen the connections to those who succeed, weaken them to those who fail." (Howard Bloom, "Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century", 2000)

"A fundamental reason for the difficulties with modern engineering projects is their inherent complexity. The systems that these projects are working with or building have many interdependent parts, so that changes in one part often have effects on other parts of the system. These indirect effects are frequently unanticipated, as are collective behaviors that arise from the mutual interactions of multiple components. Both indirect and collective effects readily cause intolerable failures of the system. Moreover, when the task of the system is intrinsically complex, anticipating the many possible demands that can be placed upon the system, and designing a system that can respond in all of the necessary ways, is not feasible. This problem appears in the form of inadequate specifications, but the fundamental issue is whether it is even possible to generate adequate specifications for a complex system." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"It is no longer sufficient for engineers merely to design boxes such as computers with the expectation that they would become components of larger, more complex systems. That is wasteful because frequently the box component is a bad fit in the system and has to be redesigned or worse, can lead to system failure. We must learn how to design large-scale, complex systems from the top down so that the specification for each component is derivable from the requirements for the overall system. We must also take a much larger view of systems. We must design the man-machine interfaces and even the system-society interfaces. Systems engineers must be trained for the design of large-scale, complex, man-machine-social systems." (A Wayne Wymore, "Systems Movement: Autobiographical Retrospectives", 2004)

"Synergy is the combined action that occurs when people work together to create new alternatives and solutions. In addition, the greatest opportunity for synergy occurs when people have different viewpoints, because the differences present new opportunities. The essence of synergy is to value and respect differences and take advantage of them to build on strengths and compensate for weaknesses." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Because the perfect system cannot be designed, there will always be weak spots that human ingenuity and resourcefulness can exploit." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

See also: Failure, Good, Bad, Ugly, Perfection

♟️Strategic Management: The Bad (Just the Quotes)

"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)

"The definition of a problem and the action taken to solve it largely depend on the view which the individuals or groups that discovered the problem have of the system to which it refers. A problem may thus find itself defined as a badly interpreted output, or as a faulty output of a faulty output device, or as a faulty output due to a malfunction in an otherwise faultless system, or as a correct but undesired output from a faultless and thus undesirable system. All definitions but the last suggest corrective action; only the last definition suggests change, and so presents an unsolvable problem to anyone opposed to change." (Herbert Brün, "Technology and the Composer", 1971)

"The dogma of delegation is simple - the Sixth Truth of Management again: either the delegatee is capable of running the operation successfully by himself or he isn't. This handy formula relieves the top executive of any responsibility except that of finding, supervising, and" (at the appropriate time) moving the men who are doing all the work. He Can then truly manage by exception: he does not get worked up over operations that are going well, but concentrates on the plague spots, where everything, including the management, is going badly." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"Managers sometimes justify the stick by pointing to better results, with the assumption that the threats caused the improvements. Alas, this is unlikely. One event coming before another does not automatically mean that the first is the cause of the second; the rooster does not make the sun rise every morning, although it may think it does. Bad results are much more likely to improve than get worse due to the simple law of statistics known as regression: results average out over time. Poor performance will eventually improve even when left to itself." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Managers are incurably susceptible to panacea peddlers. They are rooted in the belief that there are simple, if not simple-minded, solutions to even the most complex of problems. And they do not learn from bad experiences. Managers fail to diagnose the failures of the fads they adopt; they do not understand them. […] Those at the top feel obliged to pretend to omniscience, and therefore refuse to learn anything new even if the cost of doing so is success." (Russell L Ackoff, "A Lifetime Of Systems Thinking", Systems Thinker, 1999)

"It’s tempting to view the multitude of monster projects gone bad as anomalies, excrescences of corporate and government bureaucracies run amok. But you will find similar tales of woe emerging from software projects big and small, public and private, old and new. Though details differ, the pattern is depressingly repetitive: Moving targets. Fluctuating goals. Unrealistic schedules. Missed deadlines. Ballooning costs. Despair. Chaos." (Scott Rosenberg, "Dreaming in Code", 2007)

"A bad strategy will fail no matter how good your information is and lame execution will stymie a good strategy. If you do enough things poorly, you will go out of business." (Bill Gates, "Business @ the Speed of Thought: Succeeding in the Digital Economy", 2009)

"A leader’s most important job is creating and constantly adjusting this strategic bridge between goals and objectives." (Richard Rumelt, "Good Strategy Bad Strategy", 2011)

"A strategy coordinates action to address a specific challenge. It is not defined by the pay grade of the person authorizing the action." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"Having conflicting goals, dedicating resources to unconnected targets, and accommodating incompatible interests are the luxuries of the rich and powerful, but they make for bad strategy. Despite this, most organizations will not create focused strategies. Instead, they will generate laundry lists of desirable outcomes and, at the same time, ignore the need for genuine competence in coordinating and focusing their resources. Good strategy requires leaders who are willing and able to say no to a wide variety of actions and interests. Strategy is at least as much about what an organization does not do as it is about what it does." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"Our minds, especially our intuitions, are not equipped to deal with a probabilistic world. Risk and prediction are widely misunderstood, […] All decision making in a probabilistic world involves estimating the likelihood of an event and how much we will value it" (affective forecasting). Humans are bad at both - ​​​​​ particularly at the former. […] In business, understanding the psychology of risk is more important than understanding the mathematics of risk." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"The bad news is that companies tend to focus on three out of the four elements of the balanced scorecard and emphasis is skewed away from the customer component, which is the least understood and believed by many to be the least quantifiable." (Alan Pennington, "The Customer Experience Book", 2016)


🏗️Software Engineering: The Bad (Just the Quotes)

"At the present time, choosing a programming language is equivalent to choosing a data structure, and if that data structure does not fit the data you want to manipulate then it is too bad. It would, in a sense, be more logical first to choose a data structure appropriate to the problem and then look around for, or construct with a kit of tools provided, a language suitable for manipulating that data structure." (Maurice V Wilkes, "Computers Then and Now", 1968)

"Don't comment bad code - rewrite it." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Test input for validity and plausibility. [...] Make sure input cannot violate the limits of the program. [...] Identify bad input; recover if possible. [...] Test programs at their boundary values." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Failure to allow enough time for system test, in particular, is peculiarly disastrous. Since the delay comes at the end of the schedule, no one is aware of schedule trouble until almost the delivery date. Bad news, late and without warning, is unsettling to customers and to managers." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"Flow charts show the decision structure of a program, which is only one aspect of its structure. They show decision structure rather elegantly when the flow chart is on one page, but the overview breaks down badly when one has multiple pages, sewed together with numbered exits and connectors." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"There is no programming language, no matter how structured, that will prevent programmers from making bad programs." (Larry Flon, "On research in structured programming", SIGPLAN 10(10), 1975)

"Today's scientific investigations are so complicated that even experts in related fields may not understand them well. But there is a logic in the planning of experiments and in the analysis of their results that all intelligent people can grasp, and this logic is a great help in determining when to believe what we hear and read and when to be skeptical. This logic has a great deal to do with statistics, which is why statisticians have a unique interest in the scientific method, and why some knowledge of statistics can so often be brought to bear in distinguishing good arguments from bad ones." (Robert Hooke, "How to Tell the Liars from the Statisticians", 1983)

"The woes of software engineering are not due to lack of tools or proper management, but largely due to lack of sufficient technical competence. A good designer must rely on experience; on precise, logical thinking; and on pedantic exactness. No magic will do. In light of all this, it is particularly sad that, in many informatics curricula, programming in the large is badly neglected. Design has become a non-topic." (Niklaus Wirth, [interview in Software Development] 1997)

"Don't produce voluminous design documents at the beginning. Don't even produce them in the middle: produce them at the end. Extreme Programming teaches you how to keep the design flexible, for highest flexibility and fastest implementation. The design documents you produce at the beginning will go out of date very quickly" (they always do, even on non-Extreme projects), and you 'Il either waste time updating the docs or let them get out of date. Either is bad." (Ron Jeffries, "Extreme Programming Installed", 2001)

"In fact, I'm a huge proponent of designing your code around the data, rather than the other way around, and I think it's one of the reasons git has been fairly successful. [...] I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships." (Linus Torvalds, [email] 2006)

"Much data in databases has a long history. It might have come from old 'legacy' systems or have been changed several times in the past. The usage of data fields and value codes changes over time. The same value in the same field will mean totally different thing in different records. Knowledge or these facts allows experts to use the data properly. Without this knowledge, the data may bc used literally and with sad consequences. The same is about data quality. Data users in the trenches usually know good data from bad and can still use it efficiently. They know where to look and what to check. Without these experts, incorrect data quality assumptions are often made and poor data quality becomes exposed." (Arkady Maydanchik, "Data Quality Assessment", 2007)

"It is not enough for code to work. Code that works is often badly broken. Programmers who satisfy themselves with merely working code are behaving unprofessionally. They may fear that they don't have time to improve the structure and design of their code, but I disagree. Nothing has a more profound and long-term degrading effect upon a development project than bad code." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Most programming languages contain good parts and bad parts. I discovered that I could be better programmer by using only the good parts and avoiding the bad parts." (Douglas Crockford, "JavaScript: The Good Parts", 2008) 

"Nothing has a more profound and long-term degrading effect upon a development project than bad code. Bad schedules can be redone, bad requirements can be redefined. Bad team dynamics can be repaired. But bad code rots and ferments, becoming an inexorable weight that drags the team down." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"If a pattern represents a best practice, then an antipattern represents lessons learned from a bad design. [...] Antipatterns are valuable because they help us to recognise why a particular design alternative might seem at first like an attractive solution, but later on lead to complicacies and finally turn out to be a poor solution." (Rajib Mall, "Fundamentals of Software Engineering" 4th Ed., 2014)

"But 'average cost to fix one defect' is a stupid metric [...] It makes bad projects look good, and good projects look bad. How? By failing to divide the costs of fixing into two categories: fixed costs of detecting and fixing defects - costs which are the same no matter how buggy or how good the product is - and variable costs, those which you pay for each defect." (Laurent Bossavit, "The Leprechauns of Software Engineering", 2015)

"Creating mockups to communicate is not intrinsically a bad idea. But, as we are subject to confirmation bias, there.s always a risk that we will stop at our first design attempt and become reluctant to ask if there are better ways to achieve the same goals. Making these first ideas very detailed; putting them into a document; and especially blessing that document with the label 'requirements' are all moves which make further revision less likely, and put us more at risk from confirmation bias." (Laurent Bossavit, "The Leprechauns of Software Engineering", 2015)

"We, newbies and young programmers, don't like chaos because it makes us dependent on experts. We have to beg for information and feel bad." (Yegor Bugayenko, "Code Ahead", 2018)

"In addition to developing the proper culture, invest in your testing infrastructure by developing linters, documentation, or other assistance that makes it more difficult to write bad tests." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)


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