🚧Project Management: Risk (Just the Quotes)

"But the greater the primary risk, the safer and more careful your secondary assumptions must be. A project is only as sound as its weakest assumption, or its largest uncertainty." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"Today, most project management practitioners focus on planning failure. If this aspect of the project can be compressed, or even eliminated, then the magnitude of the actual failure, should it occur, would be diminished. A good project management methodology helps to reduce planning failure. Today, we believe that planning failure, when it occurs, is due in large part to the project manager’s inability to perform effective risk management." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"Risks and benefits always go hand in hand. The reason that a project is full of risk is that it leads you into uncharted waters. It stretches your capability, which means that if you pull it off successfully, it's going to drive your competition batty. The ultimate coup is to stretch your own capability to a point beyond the competition's ability to respond. This is what gives you competitive advantage and helps you build a distinct brand in the market." (Tom DeMarco & Timothy Lister, "Waltzing with Bears: Managing Risk on Software Projects", 2003)

"The business of believing only what you have a right to believe is called risk management." (Tom DeMarco & Timothy Lister, "Waltzing with Bears: Managing Risk on Software Projects", 2003)

"In project management there are two levels of opportunities and risks. Because a project is the pursuit of an opportunity, the first category, the macro opportunity, is the project opportunity itself. The approach to achieving the project opportunity and the mitigation of associated project-level risks are structured into the strategy and tactics of the project cycle, the selected decision gates, the teaming arrangements, key personnel selected, and so on. The second level encompasses the tactical opportunities and risks within the project that become apparent at lower levels of decomposition and as project cycle phases are planned and executed. This can include emerging, unproven technology; incremental and evolutionary methods that promise high returns; and the temptation to circumvent proven practices in order to deliver better, faster, and cheaper." (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

"Opportunities and risks are endemic to the project environment. However well planned a project may be, there will always be residual project risk." (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

"When we pursue opportunity, we normally incur risk. The opportunity to experience the thrill of an exciting sport like hang gliding or scuba diving brings with it the attendant risks. Many people instinctively make the trade that the thrill is worth the risks. Others decline." (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

"For most projects there will be many sources of risk. Assumptions that seem quite reasonable at the start of a project may be proven otherwise if and when conditions in internal or external environments change during the project duration." (Roger Jones & Neil Murra, "Change, Strategy and Projects at Work", 2008)

"Routine tasks are, by their nature, familiar to us. The outcomes of performing routine tasks are therefore usually highly predictable. Project work by contrast includes elements of risk and uncertainty associated with the uniqueness and unfamiliarity of some of the work or the context in which it is carried out. Murphy’s Law expresses a ‘tongue-in-cheek’ but fallacious certainty of things going wrong, if it is possible for them to go wrong." (Roger Jones & Neil Murra, "Change, Strategy and Projects at Work", 2008)

"Whilst culture can help create a sense of belonging and shared destiny, it can also prove to be an obstacle to change especially where the existing culture is risk averse or if the change strategy is perceived by some to challenge prevailing group values. Where radical change is proposed, the achievement of cultural change may actually be a major objective of the proposed change." (Roger Jones & Neil Murra, "Change, Strategy and Projects at Work", 2008)

"A project is usually considered a failure if it is late, is over budget, or does not meet the customer’s expectations. Without the control that project management provides, a project is more likely to have problems with one of these areas. A problem with only one constraint (scope, schedule, cost, resources, quality, and risk) can jeopardize the entire project." (Sandra F Rowe, "Project Management for Small Projects" 3rd Ed., 2020)

🚧Project Management: Laws (Just the Quotes)

"Nothing will ever be attempted if all possible objections must first be overcome." (Samuel Johnson, 1759)

"Remember, if you fail to prepare you are preparing to fail." (H K Williams, 1919)

"If we fail to prepare we prepare to fail." (James H Hope, 1929)

"In anything at all, perfection is finally attained not when there is no longer anything to add, but when there is no longer anything to take away [...]" (Antoine de Saint Exupéry, "Wind, Sand and Stars", 1939) 

"Work expands so as to fill the time available for its completion." (C Northcote Parkinson, "Parkinson’s Law", 1957) 

"Adding manpower to a late software project makes it later." (Fred Brooks, "The Mythical Man-Month", 1975)

"By failing to plan, you will free very little, if any, time, and by failing to plan you will almost certainly fail […] Exactly because we lack time to plan, we should take time to plan." (Alan Lakein, "How to get control of your time and your life", 1974) 

"Program advocates like to keep bad news covered up until they have spent so much money they can advance the sunk-cost argument; that it's too late to cancel the program because we've spent too much already." (James P Stevenson, "The Pentagon Paradox: The development of the F-18 Hornet", 1993)

Graham's Law: "If they know nothing of what you are doing, they suspect you are doing nothing." (Robert J Graham et al, "The Complete Idiot's Guide to Project Management", 2007) 

O'Brochta's Law: "Project management is about applying common sense with uncommon discipline." (Michael O'Brochta, "Great Project Managers", 2008) 

"No project should be allowed to proceed without clear specification and acceptance  criteria, that are understood by all participants." (Tony Martyr, "Why Projects Fail", 2018)

Augustine's Law: "A bad idea executed to perfection is still a bad idea." (Norman R Augustine)

Cohn's Law: "The more time you spend in reporting on what you are doing, the less time you have to do anything. Stability is achieved when you spend all your time doing nothing but reporting on the nothing you are doing."

Dobbins’ Law: "When in doubt, use a bigger hammer."

Fitzgerald's Law: "There are two states to any large project: Too early to tell and too late to stop." (Ernest Fitzgerald)

Hoggarth's Law: "Attempts to get answers early in a project fail as there are many more wrong questions than right ones. Activity during the early stages should be dedicated to finding the correct questions. Once the correct questions have been identified correct answers will naturally fall out of subsequent work without grief or excitement and there will be understanding of what the project is meant to achieve."

Kinser's Law: "About the time you finish doing something, you know enough to start." (James C Kinser) 

Wolf ’s Law of Management: "The tasks to do immediately are the minor ones; otherwise, you’ll forget them. The major ones are often better to defer. They usually need more time for reflection. Besides, if you forget them, they’ll remind you."

Operations Management: Operations Research (Just the Quotes)

"No science has ever been born on a specific day. Each science emerges out of a convergence of an increased interest in some class of problems and the development of scientific methods, techniques, and tools which are adequate to solve these problems. Operations Research (O. R.) is no exception. Its roots are as old as science and the management function." (C West Churchman et al., "Introduction to Operations Research", 1957)

"An objective of O.R. as it emerged from this evolution of industrial organization, is to provide managers of the organizations with a scientific basis for solving problems involving the interaction of the components of the organization in the best interest of the organization as a whole. A decision which is best for the organization as a whole is called optimum decision." (C West Churchman et al, "Introduction to Operations Research", 1957) 

"The systems approach to problems does not mean that the most generally formulated problem must be solved in one research project. However desirable this may be, it is seldom possible to realize it in practice. In practice, parts of the total problem are usually solved in sequence. In many cases the total problem cannot be formulated in advance but the solution of one phase of it helps define the next phase. For example, a production control project may require determination of the most economic production quantities of different items. Once these are found it may turn out that these quantities cannot be produced on the available equipment in the available time. This, then, gives rise to a new problem whose solution will affect the solution obtained in the first phase." (C West Churchman et al, "Introduction to Operations Research", 1957) 

"The concern of OR with finding an optimum decision, policy, or design is one of its essential characteristics. It does not seek merely to define a better solution to a problem than the one in use; it seeks the best solution... [It] can be characterized as the application of scientific methods, techniques, and tools to problems involving the operations of systems so as to provide those in control of the operations with optimum solutions to the problems." (C West Churchman et al, "Introduction to Operations Research", 1957) 

"Operational research is the application of methods of the research scientist to various rather complex practical operations." (John F T Hassell, "The Scientific Approach", 1965) 

"Operations research (OR) is the securing of improvement in social systems by means of scientific method." (C West Churchman, "Operations research as a profession", 1970)

"Decision theory, as it has grown up in recent years, is a formalization of the problems involved in making optimal choices. In a certain sense - a very abstract sense, to be sure - it incorporates among others operations research, theoretical economics, and wide areas of statistics, among others." (Kenneth Arrow, "The Economics of Information", 1984) 

"The lag between knowing the facts and knowing the system which generates the facts can be considerable. […] Similarly there is a lag in passing from the stage in which sets of empirical observations constitute exciting discoveries, to the stage of insight into underlying mechanism, in every field of management today. In controlling the economy and diplomacy and society at large, in controlling business and industry and commerce, we have collected facts and perhaps identified systems. But we have barely begun to explain their underlying mechanism. This is what operational research is for." (Stanford Beer, "Decision and Control", 1994)

🎓Knowledge Management: Knowledge (Just the Quotes)

"There are two modes of acquiring knowledge, namely, by reasoning and experience. Reasoning draws a conclusion and makes us grant the conclusion, but does not make the conclusion certain, nor does it remove doubt so that the mind may rest on the intuition of truth unless the mind discovers it by the path of experience." (Roger Bacon, "Opus Majus", 1267)

"Knowledge being to be had only of visible and certain truth, error is not a fault of our knowledge, but a mistake of our judgment, giving assent to that which is not true." (John Locke, "An Essay Concerning Human Understanding", 1689)

"[…] the highest probability amounts not to certainty, without which there can be no true knowledge." (John Locke, "An Essay Concerning Human Understanding", 1689)

"It is your opinion, the ideas we perceive by our senses are not real things, but images, or copies of them. Our knowledge therefore is no farther real, than as our ideas are the true representations of those originals. But as these supposed originals are in themselves unknown, it is impossible to know how far our ideas resemble them; or whether they resemble them at all. We cannot therefore be sure we have any real knowledge." (George Berkeley, "Three Dialogues", 1713)

"Our knowledge springs from two fundamental sources of the mind; the first is the capacity of receiving representations (receptivity for impressions), the second is the power of knowing an object through these representations (spontaneity [in the production] of concepts)." (Immanuel Kant, "Critique of Pure Reason", 1781)

"Knowledge is only real and can only be set forth fully in the form of science, in the form of system." (G W Friedrich Hegel, "The Phenomenology of Mind", 1807)

"One may even say, strictly speaking, that almost all our knowledge is only probable; and in the small number of things that we are able to know with certainty, in the mathematical sciences themselves, the principal means of arriving at the truth - induction and analogy - are based on probabilities, so that the whole system of human knowledge is tied up with the theory set out in this essay." (Pierre-Simon Laplace, "Philosophical Essay on Probabilities", 1814) 

"We [...] are profiting not only by the knowledge, but also by the ignorance, not only by the discoveries, but also by the errors of our forefathers; for the march of science, like that of time, has been progressing in the darkness, no less than in the light." (Charles C Colton, "Lacon", 1820)

"Our knowledge of circumstances has increased, but our uncertainty, instead of having diminished, has only increased. The reason of this is, that we do not gain all our experience at once, but by degrees; so our determinations continue to be assailed incessantly by fresh experience; and the mind, if we may use the expression, must always be under arms." (Carl von Clausewitz, "On War", 1832)

"All knowledge is profitable; profitable in its ennobling effect on the character, in the pleasure it imparts in its acquisition, as well as in the power it gives over the operations of mind and of matter. All knowledge is useful; every part of this complex system of nature is connected with every other. Nothing is isolated. The discovery of to-day, which appears unconnected with any useful process, may, in the course of a few years, become the fruitful source of a thousand inventions." (Joseph Henry, "Report of the Secretary" [Sixth Annual Report of the Board of Regents of the Smithsonian Institution for 1851], 1852)

"Isolated facts and experiments have in themselves no value, however great their number may be. They only become valuable in a theoretical or practical point of view when they make us acquainted with the law of a series of uniformly recurring phenomena, or, it may be, only give a negative result showing an incompleteness in our knowledge of such a law, till then held to be perfect." (Hermann von Helmholtz, "The Aim and Progress of Physical Science", 1869)

"Simplification of modes of proof is not merely an indication of advance in our knowledge of a subject, but is also the surest guarantee of readiness for farther progress." (William T Kelvin, "Elements of Natural Philosophy", 1873)

"The whole value of science consists in the power which it confers upon us of applying to one object the knowledge acquired from like objects; and it is only so far, therefore, as we can discover and register resemblances that we can turn our observations to account." (William S Jevons, "The Principles of Science: A Treatise on Logic and Scientific Method", 1874)

"[…] when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the state of science." (William T Kelvin, "Electrical Units of Measurement", 1883)

"The smallest group of facts, if properly classified and logically dealt with, will form a stone which has its proper place in the great building of knowledge, wholly independent of the individual workman who has shaped it." (Karl Pearson, "The Grammar of Science", 1892)

"Without a theory all our knowledge of nature would be reduced to a mere inventory of the results of observation. Every scientific theory must be regarded as an effort of the human mind to grasp the truth, and as long as it is consistent with the facts, it forms a chain by which they are linked together and woven into harmony." (Thomas Preston, "The Theory of Heat", 1894)

"Knowledge is the distilled essence of our intuitions, corroborated by experience." (Elbert Hubbard, "A Thousand & One Epigrams, 1911)

"It is experience which has given us our first real knowledge of Nature and her laws. It is experience, in the shape of observation and experiment, which has given us the raw material out of which hypothesis and inference have slowly elaborated that richer conception of the material world which constitutes perhaps the chief, and certainly the most characteristic, glory of the modern mind." (Arthur J Balfour, "The Foundations of Belief", 1912)

"We have discovered that it is actually an aid in the search for knowledge to understand the nature of the knowledge we seek." (Arthur S Eddington, "The Philosophy of Physical Science", 1938)

"Science usually advances by a succession of small steps, through a fog in which even the most keen-sighted explorer can seldom see more than a few paces ahead. Occasionally the fog lifts, an eminence is gained, and a wider stretch of territory can be surveyed - sometimes with startling results. A whole science may then seem to undergo a kaleidoscopic ‘rearrangement’, fragments of knowledge being found to fit together in a hitherto unsuspected manner. Sometimes the shock of readjustment may spread to other sciences; sometimes it may divert the whole current of human thought." (James H Jeans, "Physics and Philosophy" 3rd Ed., 1943)

"Every bit of knowledge we gain and every conclusion we draw about the universe or about any part or feature of it depends finally upon some observation or measurement. Mankind has had again and again the humiliating experience of trusting to intuitive, apparently logical conclusions without observations, and has seen Nature sail by in her radiant chariot of gold in an entirely different direction." (Oliver J Lee, "Measuring Our Universe: From the Inner Atom to Outer Space", 1950)

"The essence of knowledge is generalization. That fire can be produced by rubbing wood in a certain way is a knowledge derived by generalization from individual experiences; the statement means that rubbing wood in this way will always produce fire. The art of discovery is therefore the art of correct generalization." (Hans Reichenbach, "The Rise of Scientific Philosophy", 1951)

"Knowledge rests on knowledge; what is new is meaningful because it departs slightly from what was known before; this is a world of frontiers, where even the liveliest of actors or observers will be absent most of the time from most of them." (J Robert Oppenheimer, "Science and the Common Understanding", 1954)

"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)

"Incomplete knowledge must be considered as perfectly normal in probability theory; we might even say that, if we knew all the circumstances of a phenomenon, there would be no place for probability, and we would know the outcome with certainty." (Félix E Borel, Probability and Certainty", 1963)

"Knowing reality means constructing systems of transformations that correspond, more or less adequately, to reality. They are more or less isomorphic to transformations of reality. The transformational structures of which knowledge consists are not copies of the transformations in reality; they are simply possible isomorphic models among which experience can enable us to choose. Knowledge, then, is a system of transformations that become progressively adequate." (Jean Piaget, "Genetic Epistemology", 1968)

"Scientific knowledge is not created solely by the piecemeal mining of discrete facts by uniformly accurate and reliable individual scientific investigations. The process of criticism and evaluation, of analysis and synthesis, are essential to the whole system. It is impossible for each one of us to be continually aware of all that is going on around us, so that we can immediately decide the significance of every new paper that is published. The job of making such judgments must therefore be delegated to the best and wisest among us, who speak, not with their own personal voices, but on behalf of the whole community of Science. […] It is impossible for the consensus - public knowledge - to be voiced at all, unless it is channeled through the minds of selected persons, and restated in their words for all to hear." (John M Ziman, "Public Knowledge: An Essay Concerning the Social Dimension of Science", 1968)

"Models constitute a framework or a skeleton and the flesh and blood will have to be added by a lot of common sense and knowledge of details."(Jan Tinbergen, "The Use of Models: Experience," 1969)

"Human knowledge is personal and responsible, an unending adventure at the edge of uncertainty." (Jacob Bronowski, "The Ascent of Man", 1973)

"Knowledge is not a series of self-consistent theories that converges toward an ideal view; it is rather an ever increasing ocean of mutually incompatible (and perhaps even incommensurable) alternatives, each single theory, each fairy tale, each myth that is part of the collection forcing the others into greater articulation and all of them contributing, via this process of competition, to the development of our consciousness." (Paul K Feyerabend, "Against Method: Outline of an Anarchistic Theory of Knowledge", 1975)

"Knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone 'memorizes' information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987)

"We admit knowledge whenever we observe an effective (or adequate) behavior in a given context, i.e., in a realm or domain which we define by a question (explicit or implicit)." (Humberto Maturana & Francisco J Varela, "The Tree of Knowledge", 1987)

"We live on an island surrounded by a sea of ignorance. As our island of knowledge grows, so does the shore of our ignorance." (John A Wheeler, Scientific American Vol. 267, 1992)

"Knowledge is theory. We should be thankful if action of management is based on theory. Knowledge has temporal spread. Information is not knowledge. The world is drowning in information but is slow in acquisition of knowledge. There is no substitute for knowledge." (William E Deming, "The New Economics for Industry, Government, Education", 1993) 

"Discourses are ways of referring to or constructing knowledge about a particular topic of practice: a cluster (or formation) of ideas, images and practices, which provide ways of talking about, forms of knowledge and conduct associated with, a particular topic, social activity or institutional site in society. These discursive formations, as they are known, define what is and is not appropriate in our formulation of, and our practices in relation to, a particular subject or site of social activity." (Stuart Hall, "Representation: Cultural Representations and Signifying Practices", 1997)

"An individual understands a concept, skill, theory, or domain of knowledge to the extent that he or she can apply it appropriately in a new situation." (Howard Gardner, "The Disciplined Mind", 1999)

"Knowledge is factual when evidence supports it and we have great confidence in its accuracy. What we call 'hard fact' is information supported by  strong, convincing evidence; this means evidence that, so far as we know, we cannot deny, however we examine or test it. Facts always can be questioned, but they hold up under questioning. How did people come by this information? How did they interpret it? Are other interpretations possible? The more satisfactory the answers to such questions, the 'harder' the facts." (Joel Best, Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists, 2001)

"Knowledge is in some ways the most important (though intangible) capital of a software engineering organization, and sharing of that knowledge is crucial for making an organization resilient and redundant in the face of change. A culture that promotes open and honest knowledge sharing distributes that knowledge efficiently across the organization and allows that organization to scale over time. In most cases, investments into easier knowledge sharing reap manyfold dividends over the life of a company." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)

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

🎓Knowledge Management: Information Overload (Just the Quotes)

"Everybody gets so much information all day long that they lose their common sense." (Gertrude Stein, "Reflection on the Atomic Bomb", 1946)

"Every person seems to have a limited capacity to assimilate information, and if it is presented to him too rapidly and without adequate repetition, this capacity will be exceeded and communication will break down." (R Duncan Luce, "Developments in Mathematical Psychology", 1960)

"Information overload occurs when the amount of input to a system exceeds its processing capacity. Decision makers have fairly limited cognitive processing capacity. Consequently, when information overload occurs, it is likely that a reduction in decision quality will occur." (Bertram Gross, "The Managing of Organizations", 1964)

"My experience indicates that most managers receive much more data (if not information) than they can possibly absorb even if they spend all of their time trying to do so. Hence they already suffer from an information overload." (Russell L Ackoff, "Management misinformation systems", 1967)

"One of the effects of living with electric information is that we live habitually in a state of information overload. There's always more than you can cope with." (Marshall McLuhan, "The Best of Ideas", 1967)

"Unless the information overload to which managers are subjected is reduced, any additional information made available by an MIS cannot be expected to be used effectively." (Russell L Ackoff, "Management misinformation systems", 1967)

"People today are in danger of drowning in information; but, because they have been taught that information is useful, they are more willing to drown than they need be. If they could handle information, they would not have to drown at all." (Idries Shah, "Reflections", 1968)

"Faced with information overload, we have no alternative but pattern-recognition."(Marshall McLuhan, "Counterblast", 1969)

"We live in and age of hyper-awareness, our senses extend around the globe, but it's the case of aesthetic overload: our technical zeal has outstripped our psychic capacity to cope with the influx of information." (Gene Youngblood, "Expanded Cinema", 1970)

"[...] in an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it." (Herbert Simon, "Designing Organizations for an Information-Rich World", 1971)

"Everyone spoke of an information overload, but what there was in fact was a non-information overload." (Richard S Wurman, "What-If, Could-Be", 1976)

"The greater the uncertainty, the greater the amount of decision making and information processing. It is hypothesized that organizations have limited capacities to process information and adopt different organizing modes to deal with task uncertainty. Therefore, variations in organizing modes are actually variations in the capacity of organizations to process information and make decisions about events which cannot be anticipated in advance." (John K Galbraith, "Organization Design", 1977)

"We are drowning in information but starved for knowledge." (John Naisbitt, "Megatrends: Ten New Directions Transforming Our Lives", 1982)

"In the Information Age, the first step to sanity is FILTERING. Filter the information: extract for knowledge. Filter first for substance. Filter second for significance. […] Filter third for reliability. […] Filter fourth for completeness." (Marc Stiegler, "David’s Sling", 1988)

"Intuition becomes increasingly valuable in the new information society precisely because there is so much data." (John Naisbit, "Re-Inventing the Corporation", 1988)

"What about confusing clutter? Information overload? Doesn't data have to be ‘boiled down’ and  ‘simplified’? These common questions miss the point, for the quantity of detail is an issue completely separate from the difficulty of reading. Clutter and confusion are failures of design, not attributes of information." (Edward R Tufte, "Envisioning Information", 1990)

"Traditional ways to deal with information - reading, listening, writing, talking - are painfully slow in comparison to 'viewing the big picture'. Those who survive information overload will be those who search for information with broadband thinking but apply it with a single-minded focus." (Kathryn Alesandrini, "Survive Information Overload: The 7 Best Ways to Manage Your Workload by Seeing the Big Picture", 1992)

"'Point of view' is that quintessentially human solution to information overload, an intuitive process of reducing things to an essential relevant and manageable minimum. [...] In a world of hyperabundant content, point of view will become the scarcest of resources." (Paul Saffo, "It's The Context, Stupid", 1994) 

"We live in a world where there is more and more information, and less and less meaning." (Jean Baudrillard, "Simulacra and simulation", 1994)

"Specialization, once a maneuver methodically to collect information, now is a manifestation of information overloads. The role of information has changed. Once justified as a means of comprehending the world, it now generates a conflicting and contradictory, fleeting and fragmentation field of disconnected and undigested data." (Stelarc, From Psycho-Body to Cyber-Systems: Images as Post-human Entities, 1998)

"We all would like to know more and, at the same time, to receive less information. In fact, the problem of a worker in today's knowledge industry is not the scarcity of information but its excess. The same holds for professionals: just think of a physician or an executive, constantly bombarded by information that is at best irrelevant. In order to learn anything we need time. And to make time we must use information filters allowing us to ignore most of the information aimed at us. We must ignore much to learn a little." (Mario Bunge, "Philosophy in Crisis: The Need for Reconstruction", 2001)

"One of the effects of living with electric information is that we live habitually in a state of information overload. There's always more than you can cope with." (Marshall McLuhan, "Understanding Me: Lectures and Interviews" , 2003)

"What’s next for technology and design? A lot less thinking about technology for technology’s sake, and a lot more thinking about design. Art humanizes technology and makes it understandable. Design is needed to make sense of information overload. It is why art and design will rise in importance during this century as we try to make sense of all the possibilities that digital technology now affords." (John Maeda, "Why Apple Leads the Way in Design", 2010) 

"The instinctual shortcut that we take when we have 'too much information' is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest." (Nate Silver, "The Signal and the Noise", 2012)

"Complexity has the propensity to overload systems, making the relevance of a particular piece of information not statistically significant. And when an array of mind-numbing factors is added into the equation, theory and models rarely conform to reality." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"In this time of 'information overload', people do not need more information. They want a story they can relate to." (Maarten Schafer, "Around the World in 80 Brands", 2014)

"Today, technology has lowered the barrier for others to share their opinion about what we should be focusing on. It is not just information overload; it is opinion overload." (Greg McKeown, "Essentialism: The Disciplined Pursuit of Less", 2014)

"There is so much information that our ability to focus on any piece of it is interrupted by other information, so that we bathe in information but hardly absorb or analyse it. Data are interrupted by other data before we've thought about the first round, and contemplating three streams of data at once may be a way to think about none of them." (Rebecca Solnit, "The Encyclopedia of Trouble and Spaciousness", 2014) 

"While having information is a crucial first step, more information isn't necessarily better. Take a look at your bookshelves and the list of seminars you have attended. If you have read more than one book about a subject or attended more than one seminar but still haven’t reached your goals, then your problem is not lack of information but rather lack of implementation." (Gudjon Bergmann)

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

🎓Knowledge Management: Data, Information, Knowledge, Wisdom (Just the Quotes)

 "Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information upon it." (Samuel Johnson, 1775)

"It is almost as difficult to make a man unlearn his errors as his knowledge. Mal-information is more hopeless than non-information; for error is always more busy than ignorance. Ignorance is a blank sheet, on which we may write; but error is a scribbled one, on which we must first erase. Ignorance is contented to stand still with her back to the truth; but error is more presumptuous, and proceeds in the same direction. Ignorance has no light, but error follows a false one. The consequence is, that error, when she retraces her footsteps, has further to go, before she can arrive at the truth, than ignorance." (Charles C Colton, “Lacon”, 1820)

"In every branch of knowledge the progress is proportional to the amount of facts on which to build, and therefore to the facility of obtaining data." (James C Maxwell, [Letter to Lewis Campbell] 1851) 

"[The information of a message can] be defined as the 'minimum number of binary decisions which enable the receiver to construct the message, on the basis of the data already available to him.' These data comprise both the convention regarding the symbols and the language used, and the knowledge available at the moment when the message started." (Dennis Gabor, "Optical transmission" in Information Theory : Papers Read at a Symposium on Information Theory, 1952)

"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 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)

"In perception itself, two distinct processes can be discerned. One is the gathering of the primary, sensory data or simple sensing of such things as light, moisture or pressure, and the other is the structuring of such data into information." (Edward Ihnatowicz, "The Relevance of Manipulation to the Process of Perception", 1977) 

"Data, seeming facts, apparent asso­ciations-these are not certain knowledge of something. They may be puzzles that can one day be explained; they may be trivia that need not be explained at all. (Kenneth Waltz, "Theory of International Politics", 1979)

"Knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone 'memorizes' information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"Information is data that has been given meaning by way of relational connection. This 'meaning' can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987) 

"Probabilities are summaries of knowledge that is left behind when information is transferred to a higher level of abstraction." (Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Network of Plausible, Inference", 1988)

"Information engineering has been defined with the reference to automated techniques as follows: An interlocking set of automated techniques in which enterprise models, data models and process models are built up in a comprehensive knowledge-base and are used to create and maintain data-processing systems." (James Martin, "Information Engineering, 1989)

"Knowledge is theory. We should be thankful if action of management is based on theory. Knowledge has temporal spread. Information is not knowledge. The world is drowning in information but is slow in acquisition of knowledge. There is no substitute for knowledge." (William E Deming, "The New Economics for Industry, Government, Education", 1993)

"Knowledge, truth, and information flow in networks and swarm systems. I have always been interested in the texture of scientific knowledge because it appears to be lumpy and uneven. Much of what we collectively know derives from a few small areas, yet between them lie vast deserts of ignorance. I can interpret that observation now as the effect of positive feedback and attractors. A little bit of knowledge illuminates much around it, and that new illumination feeds on itself, so one corner explodes. The reverse also holds true: ignorance breeds ignorance. Areas where nothing is known, everyone avoids, so nothing is discovered. The result is an uneven landscape of empty know-nothing interrupted by hills of self-organized knowledge." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995) 

"Now that knowledge is taking the place of capital as the driving force in organizations worldwide, it is all too easy to confuse data with knowledge and information technology with information." (Peter Drucker, "Managing in a Time of Great Change", 1995)

"Data is discrimination between physical states of things (black, white, etc.) that may convey or not convey information to an agent. Whether it does so or not depends on the agent's prior stock of knowledge." (Max Boisot, "Knowledge Assets", 1998)

"The unit of coding is the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon." (Richard Boyatzis, "Transforming qualitative information", 1998)

"While hard data may inform the intellect, it is largely soft data that generates wisdom." (Henry Mintzberg, "Strategy Safari: A Guided Tour Through The Wilds of Strategic Management", 1998)

"Information is just bits of data. Knowledge is putting them together. Wisdom is transcending them." (Ram Dass, "One-Liners: A Mini-Manual for a Spiritual Life (ed. Harmony", 2007)

"Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon."(Judea Pearl, "Causal inference in statistics: An overview", Statistics Surveys 3, 2009)

"We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out picture is clear and accurate. But is it?" (Leonard Mlodinow, "The Drunkard's Walk: How Randomness Rules Our Lives", 2009) 

"We reach wisdom when we achieve a deep understanding of acquired knowledge, when we not only 'get it', but when new information blends with prior experience so completely that it makes us better at knowing what to do in other situations, even if they are only loosely related to the information from which our original knowledge came. Just as not all the information we absorb leads to knowledge, not all of the knowledge we acquire leads to wisdom." (Alberto Cairo, "The Functional Art", 2011)

"Any knowledge incapable of being revised with advances in data and human thinking does not deserve the name of knowledge." (Jerry Coyne, "Faith Versus Fact", 2015)

"The term data, unlike the related terms facts and evidence, does not connote truth. Data is descriptive, but data can be erroneous. We tend to distinguish data from information. Data is a primitive or atomic state (as in ‘raw data’). It becomes information only when it is presented in context, in a way that informs. This progression from data to information is not the only direction in which the relationship flows, however; information can also be broken down into pieces, stripped of context, and stored as data. This is the case with most of the data that’s stored in computer systems. Data that’s collected and stored directly by machines, such as sensors, becomes information only when it’s reconnected to its context."  (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

"Real wisdom is not the knowledge of everything, but the knowledge of which things in life are necessary, which are less necessary, and which are completely unnecessary to know." (Lev N Tolstoy)

"The Information Age offers much to mankind, and I would like to think that we will rise to the challenges it presents. But it is vital to remember that information - in the sense of raw data - is not knowledge, that knowledge is not wisdom, and that wisdom is not foresight. But information is the first essential step to all of these." (Arthur C Clark)

🎓Knowledge Management: Domains (Just the Quotes)

"Great discoveries which give a new direction to currents of thoughts and research are not, as a rule, gained by the accumulation of vast quantities of figures and statistics. These are apt to stifle and asphyxiate and they usually follow rather than precede discovery. The great discoveries are due to the eruption of genius into a closely related field, and the transfer of the precious knowledge there found to his own domain." (Theobald Smith, Boston Medical and Surgical Journal Volume 172, 1915)

"Learning is any change in a system that produces a more or less permanent change in its capacity for adapting to its environment. Understanding systems, especially systems capable of understanding problems in new task domains, are learning systems." (Herbert A Simon, "The Sciences of the Artificial", 1968)

"A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in this domain. Living systems are cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system." (Humberto R Maturana, "Biology of Cognition", 1970)

"No theory ever agrees with all the facts in its domain, yet it is not always the theory that is to blame. Facts are constituted by older ideologies, and a clash between facts and theories may be proof of progress. It is also a first step in our attempt to find the principles implicit in familiar observational notions." (Paul K Feyerabend, "Against Method: Outline of an Anarchistic Theory of Knowledge", 1975)

"A cognitive map is a specific way of representing a person's assertions about some limited domain, such as a policy problem. It is designed to capture the structure of the person's causal assertions and to generate the consequences that follow front this structure. […]  a person might use his cognitive map to derive explanations of the past, make predictions for the future, and choose policies in the present." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"The thinking person goes over the same ground many times. He looks at it from varying points of view - his own, his arch-enemy’s, others’. He diagrams it, verbalizes it, formulates equations, constructs visual images of the whole problem, or of troublesome parts, or of what is clearly known. But he does not keep a detailed record of all this mental work, indeed could not. […] Deep understanding of a domain of knowledge requires knowing it in various ways. This multiplicity of perspectives grows slowly through hard work and sets the state for the re-cognition we experience as a new insight." (Howard E Gruber, "Darwin on Man", 1981)

"Metaphor [is] a pervasive mode of understanding by which we project patterns from one domain of experience in order to structure another domain of a different kind. So conceived metaphor is not merely a linguistic mode of expression; rather, it is one of the chief cognitive structures by which we are able to have coherent, ordered experiences that we can reason about and make sense of. Through metaphor, we make use of patterns that obtain in our physical experience to organise our more abstract understanding." (Mark Johnson, "The Body in the Mind", 1987)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987)

"[…] a mental model is a mapping from a domain into a mental representation which contains the main characteristics of the domain; a model can be ‘run’ to generate explanations and expectations with respect to potential states. Mental models have been proposed in particular as the kind of knowledge structures that people use to understand a specific domain […]" (Helmut Jungermann, Holger Schütz & Manfred Thuering, "Mental models in risk assessment: Informing people about drugs", Risk Analysis 8 (1), 1988)

"Algorithmic complexity theory and nonlinear dynamics together establish the fact that determinism reigns only over a quite finite domain; outside this small haven of order lies a largely uncharted, vast wasteland of chaos." (Joseph Ford, "Progress in Chaotic Dynamics: Essays in Honor of Joseph Ford's 60th Birthday", 1988)

"When partitioning a domain, we divide the information model so that the clusters remain intact. [...] Each section of the information model then becomes a separate subsystem. Note that when the information model is partitioned into subsystems, each object is assigned to exactly one subsystem."  (Stephen J Mellor, "Object-Oriented Systems Analysis: Modeling the World In Data", 1988) 

"While a small domain (consisting of fifty or fewer objects) can generally be analyzed as a unit, large domains must be partitioned to make the analysis a manageable task. To make such a partitioning, we take advantage of the fact that objects on an information model tend to fall into clusters: groups of objects that are interconnected with one another by many relationships. By contrast, relatively few relationships connect objects in different clusters." (Stephen J Mellor, "Object-Oriented Systems Analysis: Modeling the World In Data", 1988) 

"A law explains a set of observations; a theory explains a set of laws. […] a law applies to observed phenomena in one domain (e.g., planetary bodies and their movements), while a theory is intended to unify phenomena in many domains. […] Unlike laws, theories often postulate unobservable objects as part of their explanatory mechanism." (John L Casti, "Searching for Certainty: How Scientists Predict the Future", 1990)

"Generally speaking, problem knowledge for solving a given problem may consist of heuristic rules or formulas that comprise the explicit knowledge, and past-experience data that comprise the implicit, hidden knowledge. Knowledge represents links between the domain space and the solution space, the space of the independent variables and the space of the dependent variables." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Inference is the process of matching current facts from the domain space to the existing knowledge and inferring new facts. An inference process is a chain of matchings. The intermediate results obtained during the inference process are matched against the existing knowledge. The length of the chain is different. It depends on the knowledge base and on the inference method applied." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"An individual understands a concept, skill, theory, or domain of knowledge to the extent that he or she can apply it appropriately in a new situation." (Howard Gardner, "The Disciplined Mind", 1999)

"Knowledge maps are node-link representations in which ideas are located in nodes and connected to other related ideas through a series of labeled links. They differ from other similar representations such as mind maps, concept maps, and graphic organizers in the deliberate use of a common set of labeled links that connect ideas. Some links are domain specific (e.g., function is very useful for some topic domains...) whereas other links (e.g., part) are more broadly used. Links have arrowheads to indicate the direction of the relationship between ideas." (Angela M. O’Donnell et al, "Knowledge Maps as Scaffolds for Cognitive Processing", Educational Psychology Review Vol. 14 (1), 2002) 

"We build models to increase productivity, under the justified assumption that it's cheaper to manipulate the model than the real thing. Models then enable cheaper exploration and reasoning about some universe of discourse. One important application of models is to understand a real, abstract, or hypothetical problem domain that a computer system will reflect. This is done by abstraction, classification, and generalization of subject-matter entities into an appropriate set of classes and their behavior." (Stephen J Mellor, "Executable UML: A Foundation for Model-Driven Architecture", 2002)

"A domain model is not a particular diagram; it is the idea that the diagram is intended to convey. It is not just the knowledge in a domain expert’s head; it is a rigorously organized and selective abstraction of that knowledge." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Domain experts are usually not aware of how complex their mental processes are as, in the course of their work, they navigate all these rules, reconcile contradictions, and fill in gaps with common sense. Software can’t do this. It is through knowledge crunching in close collaboration with software experts that the rules are clarified, fleshed out, reconciled, or placed out of scope." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Effective domain modelers are knowledge crunchers. They take a torrent of information and probe for the relevant trickle. They try one organizing idea after another, searching for the simple view that makes sense of the mass. Many models are tried and rejected or transformed. Success comes in an emerging set of abstract concepts that makes sense of all the detail. This distillation is a rigorous expression of the particular knowledge that has been found most relevant." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Perception and memory are imprecise filters of information, and the way in which information is presented, that is, the frame, influences how it is received. Because too much information is difficult to deal with, people have developed shortcuts or heuristics in order to come up with reasonable decisions. Unfortunately, sometimes these heuristics lead to bias, especially when used outside their natural domains." (Lucy F Ackert & Richard Deaves, "Behavioral Finance: Psychology, Decision-Making, and Markets", 2010)

"This is always the case in analogical reasoning: Relations between two dissimilar domains never map completely to one another. In fact, it is often the salient similarities between the base and target domains that provoke thought and increase the usefulness of an analogy as a problem-solving tool." (Robbie T Nakatsu, "Diagrammatic Reasoning in AI", 2010)

"Conceptual models are best thought of as design-tools - a way for designers to straighten out and simplify the design and match it to the users’ task-domain, thereby making it clearer to users how they should think about the application. The designers’ responsibility is to devise a conceptual model that seems natural to users based on the users’ familiarity with the task domain. If designers do their job well, the conceptual model will be the basis for users’ mental models of the application." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)

"A model or conceptual model is a schematic or representation that describes how something works. We create and adapt models all the time without realizing it. Over time, as you gain more information about a problem domain, your model will improve to better match reality." (James Padolsey, "Clean Code in JavaScript", 2020)

"Knowledge graphs use an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle gives us an additional layer of organizing data (metadata) that adds connected context to support reasoning and knowledge discovery. […] Importantly, some processing can be done without knowledge of the domain, just by leveraging the features of the property graph model (the organizing principle)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

🚧Project Management: Success (Just the Quotes)

"Project management is needed only for situations which are out of the ordinary; but when the need exists, this may often be the only way by which the task may be handled successfully. These situations require a different attitude on the part of the top management, the undivided attention of a project manager and different methods for control and communications than those used in the normal routine business situation. […] Pure project management assigns complete responsibility for the task and resources needed for its accomplishment to one project manager. The organization of a large project, though it will be dissolved upon completion of the task, operates for its duration much like a regular division and is relatively independent of any other division or staff group." (Executive Sciences Institute, Operations Research/Management Science Vol 6, 1964)

"Basic to successful project management is recognizing when the project is needed - in other words, when to form a project, as opposed to when to use the regular functional organization to do the job." (David I Cleland & William R King, Systems Analysis and Project Management, 1968)

"Software projects fail for one of two general reasons: the project team lacks the knowledge to conduct a software project successfully, or the project team lacks the resolve to conduct a project effectively." (Steve C McConnell, "Software Project Survival Guide", 1997)

"Success in all types of organization depends increasingly on the development of customized software solutions, yet more than half of software projects now in the works will exceed both their schedules and their budgets by more than 50%." (Barry Boehm, "Software Cost Estimation with Cocomo II", 2000)

"Choosing a proper project strategy can mean the difference between success and failure." (James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"No project can succeed when the team members have no commitment to the plan, so the first rule of project planning is that the people who must do the work should help plan that part of the project. You will not only gain their commitment to the plan, but also most likely cover all of the important issues that you may individually have forgotten."(James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 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)

"Success or failure of a project depends upon the ability of key personnel to have sufficient data for decision-making. Project management is often considered to be both an art and a science. It is an art because of the strong need for interpersonal skills, and the project planning and control forms attempt to convert part of the 'art' into a science." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"Successful software development is a team effort - not just the development team, but the larger team consisting of customer, management and developers. [...] Every software project needs to deliver business value. To be successful, the team needs to build the right things, in the right order, and to be sure that what they build actually works." (Ron Jeffries, "Extreme Programming Installed", 2001)

"The only truly successful project is the one that delivers what it is supposed to, gets results, and meets stakeholder expectations." (James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"A project is composed of a series of steps where all must be achieved for success. Each individual step has some probability of failure. We often underestimate the large number of things that may happen in the future or all opportunities for failure that may cause a project to go wrong. Humans make mistakes, equipment fails, technologies don't work as planned, unrealistic expectations, biases including sunk cost-syndrome, inexperience, wrong incentives, contractor failure, untested technology, delays, wrong deliveries, changing requirements, random events, ignoring early warning signals are reasons for delays, cost overruns and mistakes. Often we focus too much on the specific project case and ignore what normally happens in similar situations (base rate frequency of outcomes- personal and others)." (Peter Bevelin, "Seeking Wisdom: From Darwin to Munger", 2003)

"Risks and benefits always go hand in hand. The reason that a project is full of risk is that it leads you into uncharted waters. It stretches your capability, which means that if you pull it off successfully, it's going to drive your competition batty. The ultimate coup is to stretch your own capability to a point beyond the competition's ability to respond. This is what gives you competitive advantage and helps you build a distinct brand in the market." (Tom DeMarco & Timothy Lister, "Waltzing with Bears: Managing Risk on Software Projects", 2003)

"Data migration is indeed a complex project. It is common for companies to underestimate the amount of time it takes to complete the data conversion successfully. Data quality usually suffers because it is the first thing to be dropped once the project is behind schedule. Make sure to allocate enough time to complete the task maintaining the highest standards of quality necessary. Migrate now, clean later typically leads to another source of mistrusted data, defeating the whole purpose of MDM." (Dalton Cervo & Mark Allen, "Master Data Management in Practice: Achieving true customer MDM", 2011)

"Stakeholder management to me is key, as success or failure is in the eye of the beholder. Time, cost and quality fall prey to the perceptions of the key stakeholders, who may have nothing to do with the running of the project." (Peter Parkes, "NLP for Project Managers", 2011)

"[...] consistently good project results are hard to come by, yet most organisations continue to think they’re doing a great job. It’s got to the stage where project failure has become so commonplace that we’ve started to see it as success, or we just aren’t seeing clearly at all." (Tony Martyr, "Why Projects Fail", 2018)

🎓Knowledge Management: Mental Model (Definitions)

"A mental model is a cognitive construct that describes a person's understanding of a particular content domain in the world." (John Sown, "Conceptual Structures: Information Processing in Mind and Machine", 1984)

"A mental model is a data structure, in a computational system, that represents a part of the real world or of a fictitious world." (Alan Granham, "Mental Models as Representations of Discourse and Text", 1987)

"[…] a mental model is a mapping from a domain into a mental representation which contains the main characteristics of the domain; a model can be ‘run’ to generate explanations and expectations with respect to potential states. Mental models have been proposed in particular as the kind of knowledge structures that people use to understand a specific domain […]" (Helmut Jungermann, Holger Schütz & Manfred Thuering, "Mental models in risk assessment: Informing people about drugs", Risk Analysis 8 (1), 1988)

 "A mental model is a knowledge structure that incorporates both declarative knowledge (e.g., device models) and procedural knowledge (e.g., procedures for determining distributions of voltages within a circuit), and a control structure that determines how the procedural and declarative knowledge are used in solving problems (e.g., mentally simulating the behavior of a circuit)." (Barbara Y White & John R Frederiksen, "Causal Model Progressions as a Foundation for Intelligent Learning Environments", Artificial Intelligence 42, 1990)

"’Mental models’ are deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action. [...] Mental models are deeply held internal images of how the world works, images that limit us to familiar ways of thinking and acting." (Peter Senge, "The Fifth Discipline”, 1990)

"[A mental model] is a relatively enduring and accessible, but limited, internal conceptual representation of an external system (historical, existing, or projected) [italics in original] whose structure is analogous to the perceived structure of that system." (James K Doyle & David N Ford, "Mental models concepts revisited: Some clarifications and a reply to Lane", System Dynamics Review 15 (4), 1999)

"In broad terms, a mental model is to be understood as a dynamic symbolic representation of external objects or events on the part of some natural or artificial cognitive system. Mental models are thought to have certain properties which make them stand out against other forms of symbolic representations." (Gert Rickheit & Lorenz Sichelschmidt, "Mental Models: Some Answers, Some Questions, Some Suggestions", 1999)

"A mental model is conceived […] as a knowledge structure possessing slots that can be filled not only with empirically gained information but also with ‘default assumptions’ resulting from prior experience. These default assumptions can be substituted by updated information so that inferences based on the model can be corrected without abandoning the model as a whole. Information is assimilated to the slots of a mental model in the form of ‘frames’ which are understood here as ‘chunks’ of knowledge with a well-defined meaning anchored in a given body of shared knowledge." (Jürgen Renn, “Before the Riemann Tensor: The Emergence of Einstein’s Double Strategy", 2005)

"A mental model is a mental representation that captures what is common to all the different ways in which the premises can be interpreted. It represents in 'small scale' how 'reality' could be - according to what is stated in the premises of a reasoning problem. Mental models, though, must not be confused with images." (Carsten Held et al, "Mental Models and the Mind", 2006)

"’Mental models’ are deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action." (Jossey-Bass Publishers, "The Jossey-Bass Reader on Educational Leadership”, 2nd Ed. 2007)

"A mental model is an internal representation with analogical relations to its referential object, so that local and temporal aspects of the object are preserved." (Gert Rickheit et al, "The concept of communicative competence" [in "Handbook of Communication Competence"], 2008)

"Internal representations constructed on the spot when required by demands of an external task or by a self-generated stimulus. It enables activation of relevant schemata, and allows new knowledge to be integrated. It specifies causal actions among concepts that take place within it, and it can be interacted with in the mind." (Daniel Churchill, "Mental Models" [in "Encyclopedia of Information Technology Curriculum Integration"] , 2008)

"Mental models are representations of reality built in people’s minds. These models are based on arrangements of assumptions, judgments, and values. A main weakness of mental models is that people’s assumptions and judgments change over time and are applied in inconsistent ways when building explanations of the world." (Luis F Luna-Reyes, "System Dynamics to Understand Public Information Technology", 2008)

"A mental model is the collection of concepts and relationships about the image of real world things we carry in our heads" (Hassan Qudrat-Ullah, "System Dynamics Based Technology for Decision Support", 2009)

"A mental recreation of the states of the world reproduced cognitively in order to offer itself as a basis for reasoning." (Eshaa M Alkhalifa, "Open Student Models", 2009)

[Shared Mental Model:] "A mental model that is shared among team members, and may include: 1) task-specific knowledge, 2) task-related knowledge, 3) knowledge of teammates and 4) attitudes/beliefs." (Rosemarie Reynolds et al, "Measuring Shared Mental Models in Unmanned Aircraft Systems", 2015) 

"A network of knowledge content, as well as the relationships among the content."(Rosemarie Reynolds et al, "Measuring Shared Mental Models in Unmanned Aircraft Systems", 2015)

"A mental model (aka mental representation/image/picture) is a mental structure that attempts to model (depict, imagine) how real or imaginary things look like, work or fit together." (The Web of Knowledge) [source]

Resources:
Quotes on "Mental Models" at the-web-of-knowledge.blogspot.com.

🎓Knowledge Management: Concept Map (Definitions)

"Concept maps are built of nodes connected by connectors, which have written descriptions called linking phrases instead of polarity of strength. Concept maps can be used to describe conceptual structures and relations in them and the concept maps suit also aggregation and preservation of knowledge" (Hannu Kivijärvi et al, "A Support System for the Strategic Scenario Process", 2008) 

"A hierarchal picture of a mental map of knowledge." (Gregory MacKinnon, "Concept Mapping as a Mediator of Constructivist Learning", 2009)

"A tool that assists learners in the understanding of the relationships of the main idea and its attributes, also used in brainstorming and planning." (Diane L Judd, "Constructing Technology Integrated Activities that Engage Elementary Students in Learning", 2009)

"Concept maps are graphical knowledge representations that are composed to two components: (1) Nodes: represent the concepts, and (2) Links: connect concepts using a relationship." (Faisal Ahmad et al, "New Roles of Digital Libraries", 2009)

"A concept map is a diagram that depicts concepts and their hierarchical relationships." (Wan Ng & Ria Hanewald, "Concept Maps as a Tool for Promoting Online Collaborative Learning in Virtual Teams with Pre-Service Teachers", 2010)

"A diagram that facilitates organization, presentation, processing and acquisition of knowledge by showing relationships among concepts as node-link networks. Ideas in a concept map are represented as nodes and connected to other ideas/nodes through link labels." (Olusola O Adesope & John C Nesbit, "A Systematic Review of Research on Collaborative Learning with Concept Maps", 2010)

"A visual construct composed of encircled concepts (nodes) that are meaningfully inter-connected by descriptive concept links either directly, by branch-points (hierarchies), or indirectly by cross-links (comparisons). The construction of a concept map can serve as a tool for enhancing communication, either between an author and a student for a reading task, or between two or more students engaged in problem solving. (Dawndra Meers-Scott, "Teaching Critical Thinking and Team Based Concept Mapping", 2010)

"Are graphical ways of working with ideas and presenting information. They reveal patterns and relationships and help students to clarify their thinking, and to process, organize and prioritize. The visual representation of information through word webs or diagrams enables learners to see how the ideas are connected and understand how to group or organize information effectively." (Robert Z Zheng & Laura B Dahl, "Using Concept Maps to Enhance Students' Prior Knowledge in Complex Learning", 2010)

"Concept maps are hierarchical trees, in which concepts are connected with labelled, graphical links, most general at the top." (Alexandra Okada, "Eliciting Thinking Skills with Inquiry Maps in CLE", 2010)

"One powerful knowledge presentation format, devised by Novak, to visualize conceptual knowledge as graphs in which the nodes represent the concepts, and the links between the nodes are the relationships between these concepts." (Diana Pérez-Marín et al, "Adaptive Computer Assisted Assessment", 2010)

"A form of visualization showing relationships among concepts as arrows between labeled boxes, usually in a downward branching hierarchy." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A graphical depiction of relationships ideas, principals, and activities leading to one major theme." (Carol A Brown, "Using Logic Models for Program Planning in K20 Education", 2013)

"A diagram that presents the relationships between concepts." (Gwo-Jen Hwang, "Mobile Technology-Enhanced Learning", 2015)

"A graphical two-dimensional display of knowledge. Concepts, usually presented within boxes or circles, are connected by directed arcs that encode, as linking phrases, the relationships between the pairs of concepts." (Anna Ursyn, "Visualization as Communication with Graphic Representation", 2015)

"A graphical tool for representing knowledge structure in a form of a graph whose nodes represent concepts, while arcs between nodes correspond to interrelations between them." (Yigal Rosen & Maryam Mosharraf, "Evidence-Centered Concept Map in Computer-Based Assessment of Critical Thinking", 2016) 

"Is a directed graph that shows the relationship between the concepts. It is used to organize and structure knowledge." (Anal Acharya & Devadatta Sinha, "A Web-Based Collaborative Learning System Using Concept Maps: Architecture and Evaluation", 2016)

"A graphic depiction of brainstorming, which starts with a central concept and then includes all related ideas." (Carolyn W Hitchens et al, "Studying Abroad to Inform Teaching in a Diverse Society", 2017)

"A graphic visualization of the connections between ideas in which concepts (drawn as nodes or boxes) are linked by explanatory phrases (on arrows) to form a network of propositions that depict the quality of the mapper’s understanding" (Ian M Kinchin, "Pedagogic Frailty and the Ecology of Teaching at University: A Case of Conceptual Exaptation", 2019)

"A diagram in which related concepts are linked to each other." (Steven Courchesne &Stacy M Cohen, "Using Technology to Promote Student Ownership of Retrieval Practice", 2020)

🎓Knowledge Management: Conceptual Model (Definitions)

"A conceptual model is a qualitative description of the system and includes the processes taking place in the system, the parameters chosen to describe the processes, and the spatial and temporal scales of the processes." (A Avogadro & R C Ragaini, "Technologies for Environmental Cleanup", 1993)

"A conceptual model is a model of the projected system that is independent of implementation details." (Michael Worboys, "GIS: A Computing Perspective", 1995)

"A conceptual model is what in the model theory is called a set of formulas making statements about the world." (Dickson Lukose [Eds.], "Conceptual Structures: Fulfilling Peirce's Dream" Vol 5, 1997)

"A conceptual model is a representation of the system expertise using this formalism. An internal model is derived from the conceptual model and from a specification of the system transactions and the performance constraints." (Zbigniew W. Ras & Andrzej Skowron [Eds.], Foundations of Intelligent Systems: 10th International Symposium Vol 10, 1997)

"A conceptual model is one which reflects reality by placing words which are concepts into the model in the same way that the model aeroplane builder puts wings, a fuselage, and a cockpit together." (Lynn Basford & ‎Oliver Slevin, "Theory and Practice of Nursing: An Integrated Approach to Caring Practice", 2003) 

"A conceptual model is simply a framework or schematic to understand the interaction of workforce education and development systems with other variables in a society." (Jay W Rojewski, "International Perspectives on Workforce Education and Development", 2004) 

"A conceptual model is a mental image of a system, its components, its interactions. It lays the foundation for more elaborate models, such as physical or numerical models. A conceptual model provides a framework in which to think about the workings of a system or about problem solving in general. An ensuing operational model can be no better than its underlying conceptualization." (Henry N Pollack, "Uncertain Science … Uncertain World", 2005)

"A particular kind of learning object design to be supplied to learners to support their mental modeling." (Daniel Churchill, "Mental Models" [in "Encyclopedia of Information Technology Curriculum Integration"], 2008)

"The concepts and constructs about real work things we have in our heads are called mental model." (Hassan Qudrat-Ullah, "System Dynamics Based Learning Environments" [in "Encyclopedia of Information Technology Curriculum Integration"], 2008)

"Representations of real or imaginary structure in the human mind enabling orientation as well as goal orientated actions and movements" (Ralf Wagner, "Customizing Multimedia with Multi-Trees" [in "Encyclopedia of Multimedia Technology and Networking" 2nd Ed.], 2009)

"A conceptual model is a qualitative description of 'some aspect of the behaviour of a natural system'. This description is usually verbal, but may also be accompanied by figures and graphs." (Howard S. Wheater et al., "Groundwater Modelling in Arid and Semi-Arid Areas, 2010) 

"[…] a conceptual model is a diagram connecting variables and constructs based on theory and logic that displays the hypotheses to be tested." (Mary Wolfinbarger Celsi et al, "Essentials of Business Research Methods", 2011) 

"A conceptual model of an interactive application is, in summary: the structure of the application - the objects and their operations, attributes, and relation-ships; an idealized view of the how the application works – the model designers hope users will internalize; the mechanism by which users accomplish the tasks the application is intended to support." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)

"Simply put, a conceptual model is a simplified representation of reality, devised for a certain purpose and seen from a certain point of view."(David W Emble & Bernhard Thalheim, "Handbook of Conceptual Modeling", 2012) 

"Briefly, a conceptual model is the configuration of conceptual elements and the navigation between them. As such, a conceptual model is the foundation of the user interface of any interactive system." (Avi Parush, "Conceptual Design for Interactive Systems", 2015)

"A conceptual model is a framework that is initially used in research to outline the possible courses of action or to present an idea or thought. When a conceptual model is developed in a logical manner, it will provide a rigor to the research process." (N Elangovan & R Rajendran, "Conceptual Model: A Framework for Institutionalizing the Vigor in Business Research", 2015) 

"A model or conceptual model is a schematic or representation that describes how something works. We create and adapt models all the time without realizing it. Over time, as you gain more information about a problem domain, your model will improve to better match reality." (James Padolsey, "Clean Code in JavaScript", 2020)

Resources:
Quotes on "Conceptual Models" at the-web-of-knowledge.blogspot.com.

🎓Knowledge Management: Cognitive Map (Definitions)

"A cognitive map is a specific way of representing a person's assertions about some limited domain, such as a policy problem. It is designed to capture the structure of the person's causal assertions and to generate the consequences that follow front this structure." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"A cognitive map is the representation of thinking about a problem that follows from the process of mapping." (Colin Eden, "Analyzing cognitive maps to help structure issues or problems", 2002)

"A mental representation of a portion of the physical environment and the relative locations of points within it." (Andrew M Colman, "A Dictionary of Psychology" 3rd Ed, 2008)

"A mental model (or map) of the external environment which may be constructed following exploratory behaviour." (Michael Allaby, "A Dictionary of Zoology" 3rd Ed., 2009)

"An FCM [Fuzzy Cognitive Map] is a directed graph with concepts like policies, events etc. as nodes and causalities as edges. It represents causal relationship between concepts." (Florentin Smarandache &  W B Vasantha Kandasamy, "Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps", 2014)

"A conceptual tool that provides a representation of particular natural or social environments in the form of a model." (Evangelos C Papakitsos et al, "The Challenges of Work-Based Learning via Systemic Modelling in the European Union", 2020)

"A representation of the conceptualization that the subject constructs of the system in which he evolves. The set of cognitive representations that emerge make it possible to understand his actions, the links between the factors structuring the cognitive patterns dictating his behaviors." (Henda E Karray & Souhaila Kammoun, "Strategic Orientation of the Managers of a Tunisian Family Group Before and After the Revolution", 2020)

"A cognitive map is a type of mental representation which serves an individual to acquire, code, store, recall, and decode information about the relative locations and attributes of phenomena in their everyday or metaphorical spatial environment." (Wikipedia) [source]

🎓Knowledge Management: Tacit Knowledge (Definitions)

"Know-how that is difficult to articulate and share; intuition or skills that cannot easily be put into words." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"The domain of subjective, cognitive, and experimental knowledge that is highly personal and difficult to formalize." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The knowledge that a person retains in their mind. It is relatively hard to transfer to others and to disseminate widely. Also known as implicit knowledge." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Knowledge that is based on experience and not easy to share, document, or explain." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Knowledge that resides in people's heads. Also referred to as know-how, rules of thumb, or heuristics." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"Tacit knowledge is sometimes referred to as knowledge inside people’s heads. It includes the skills and intuition that experienced people apply as a matter of course in their work. Tacit knowledge is contrasted with explicit knowledge, which is knowledge that is documented in a sharable form. One of the goals of knowledge management is to enable tacit knowledge to be shared by making it explicit knowledge." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"Undocumented information." (Project Management Institute, "Software Extension to the PMBOK® Guide 5th Ed", 2013)

"Personal knowledge that can be difficult to articulate and share such as beliefs, experience, and insights." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide)", 2017)

🎓Knowledge Management: Taxonomy (Definitions)

"A classification system." (Ruth C Clark & Chopeta Lyons, "Graphics for Learning", 2004)

"A hierarchical structure within which related items are organized, classified, or categorized, thus illustrating the relationships between them." (Richard Caladine, "Taxonomies for Technology", 2008)

"A taxonomy is a hierarchical structure displaying parent-child relationships (a classification). A taxonomy extends a vocabulary and is a special case of a the more general ontology." (Troels Andreasen & Henrik Bulskov, "Query Expansion by Taxonomy", 2008)

"An orderly classification that explicitly expresses the relationships, usually hierarchical (e.g., genus/species, whole/part, class/instance), between and among the things being classified." (J P Getty Trust, "Introduction to Metadata" 2nd Ed., 2008)

"This term traditionally refers to the study of the general principles of classification. It is widely used to describe computer-based systems that use hierarchies of topics to help users sift through information." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"A kind of classification method which organizes all kinds of things into predefined hierarchical structure." (Yong Yu et al, "Social Tagging: Properties and Applications", 2010)

"Any system of categories used to organize something, including documents, often less comprehensive than a thesaurus." (Steven Woods et al, "Knowledge Dissemination in Portals", 2011)

"Generally, a collection of controlled vocabulary terms organized into a structure of parent-child relationships. Each term is in at least one relationship with another term in the taxonomy. Each parent's relationship with all of its children are of only one type (whole-part, genus-species, or type-instance). The addition of associative relationships creates a thesaurus." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A definitional hierarchy of concepts. Traditional taxonomies are tree-structured (a concept is assumed to have exactly one superconcept and multiple subconcepts). The higher a concept is positioned in the definitional hierarchy, the more individuals it describes (the comprehension of the concept), but the less definitional properties are needed (the meaning of a concept)." (Marcus Spies & Said Tabet, "Emerging Standards and Protocols for Governance, Risk, and Compliance Management", 2012) 

"A hierarchical representation of metadata. The top level is the category, and each subsequent level provides a refinement (detail) of the top-level term." (Charles Cooper & Ann Rockley, "Managing Enterprise Content: A Unified Content Strategy" 2nd Ed., 2012)

"A hierarchical structure of information components, for example, a subject, business–unit, or functional taxonomy, any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Managing Electronic Records: Methods, Best Practices, and Technologies", 2013)

"A classification of text" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A hierarchical structure of information components (e.g., a subject, business unit, or functional taxonomy), any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"provides context within the ontology. Taxonomies are used to capture hierarchical relationships between elements of interest. " (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"Taxonomy is the science and practice of classification. Taxonomies are used when categorizing real-life as well as artificial phenomenon and the aim is to make systematic studies easier." (Ulf Larson et al, "Guidance for Selecting Data Collection Mechanisms for Intrusion Detection", 2015)

"A taxonomy is a hierarchy that is created by a set of interconnected class inclusion relationship." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

"A hierarchical structure of information components, for example, a subject, business unit, or functional taxonomy, any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Information Governance for Healthcare Professionals", 2018)

🎓Knowledge Management: Ontology (Definitions)

"A data model that represents the entities that are defined and evaluated by its own attributes, and organized according to a hierarchy and a semantic. Ontologies are used for representing knowledge on the whole of a specific domain or on of it." (Gervásio Iwens et al, "Programming Body Sensor Networks", 2008)

"An ontology specifies a conceptualization, that is, a structure of related concepts for a given domain." (Troels Andreasen & Henrik Bulskov, "Query Expansion by Taxonomy", 2008)

"A semantic structure useful to standardize and provide rigorous definitions of the terminology used in a domain and to describe the knowledge of the domain. It is composed of a controlled vocabulary, which describes the concepts of the considered domain, and a semantic network, which describes the relations among such concepts. Each concept is connected to other concepts of the domain through semantic relations that specify the knowledge of the domain. A general concept can be described by several terms that can be synonyms or characteristic of different domains in which the concept exists. For this reason the ontologies tend to have a hierarchical structure, with generic concepts/terms at the higher levels of the hierarchy and specific concepts/terms at the lover levels, connected by different types of relations." (Mario Ceresa, "Clinical and Biomolecular Ontologies for E-Health", Handbook of Research on Distributed Medical Informatics and E-Health, 2009)

"In the context of knowledge sharing, the chapter uses the term ontology to mean a specification of conceptual relations. An ontology is the concepts and relationships that can exist for an agent or a community of agents. The chapter refers to designing ontologies for the purpose of enabling knowledge sharing and re-use." (Ivan Launders, "Socio-Technical Systems and Knowledge Representation", 2009)

 "The systematic description of a given phenomenon, which often includes a controlled vocabulary and relationships, captures nuances in meaning and enables knowledge sharing and reuse. Typically, ontology defines data entities, data attributes, relations and possible functions and operations." (Mark Olive, "SHARE: A European Healthgrid Roadmap", 2009)

"Those things that exist are those things that have a formal representation within the context of a machine. Knowledge commits to an ontology if it adheres to the structure, vocabulary and semantics intrinsic to a particular ontology i.e. it conforms to the ontology definition. A formal ontology in computer science is a logical theory that represents a conceptualization of real world concepts." (Philip D. Smart, "Semantic Web Rule Languages for Geospatial Ontologies", 2009)

"A formal representation of a set of concepts within a domain and the relationships between those concepts. It is used to reason about the properties of that domain, and may be used to define the domain." (Yong Yu et al, "Social Tagging: Properties and Applications", 2010)

"Is set of well-defined concepts describing a specific domain." (Hak-Lae Kim et al, "Representing and Sharing Tagging Data Using the Social Semantic Cloud of Tags", 2010)

"An ontology is a 'formal, explicit specification of a shared conceptualisation'. It is composed of concepts and relations structured into hierarchies (i.e. they are linked together by using the Specialisation/Generalisation relationship). A heavyweight ontology is a lightweight ontology (i.e. an ontology simply based on a hierarchy of concepts and a hierarchy of relations) enriched with axioms used to fix the semantic interpretation of concepts and relations." (Francky Trichet et al, "OSIRIS: Ontology-Based System for Semantic Information Retrieval and Indexation Dedicated to Community and Open Web Spaces", 2011)

"The set of the things that can be dealt with in a particular domain, together with their relationships." (Steven Woods et al, "Knowledge Dissemination in Portals", 2011) 

"In semantic web and related technologies, an ontology (aka domain ontology) is a set of taxonomies together with typed relationships connecting concepts from the taxonomies and, possibly, sets of integrity rules and constraints defining classes and relationships." (Marcus Spies & Said Tabet, "Emerging Standards and Protocols for Governance, Risk, and Compliance Management", 2012)

"High-level knowledge and data representation structure. Ontologies provide a formal frame to represent the knowledge related with a complex domain, as a qualitative model of the system. Ontologies can be used to represent the structure of a domain by means of defining concepts and properties that relate them." (Lenka Lhotska et al, "Interoperability of Medical Devices and Information Systems", 2013)

"(a) In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between pairs of concepts. It can be used to model a domain and support reasoning about concepts. (b) In philosophy, ontology is the study of the nature of being, becoming, existence , or reality , as well as the basic categories of being and their relations. Traditionally listed as a part of the major branch of philosophy known as metaphysics, ontology deals with questions concerning what entities exist or can be said to exist, and how such entities can be grouped, related within a hierarchy, and subdivided according to similarities and differences." (Ronald J Lofaro, "Knowledge Engineering Methodology with Examples", 2015)

"It is a shared structure which classify and organizes all the entities of a given domain." (T R Gopalakrishnan Nair, "Intelligent Knowledge Systems", 2015)

"The study of how things relate. Used in big data to analyze seemingly unrelated data to discover insights." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"An ontology is a formal, explicit specification of a shared conceptualization." (Fu Zhang et al, "A Review of Answering Queries over Ontologies Based on Databases", 2016)