♟️Strategic Management: Strategy [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources. 
Last updated: 18-Mar-2024

Strategy

• {definition} "the determination of the long-term goals and objectives of an enterprise, and the adoption of courses of action and the allocation of resources necessary for carrying out these goals" [4]
• {goal} bring all tools and insights together to create an integrative narrative about what the  organization should do moving forward [1]
• a good strategy emerges out of the values, opportunities and capabilities of the organization [1]
• {characteristic} robust
• {characteristic} flexible
• {characteristic} needs to embrace the uncertainty and complexity of the world
• {characteristic} fact-based and informed by research and analytics
• {characteristic} testable
• {concept} strategy analysis 
• {definition} the assessment of an organization's current competitive position and the identification of future valuable competitive positions and how the firm plans to achieve them [1]
• done from a general perspective
• in terms of different functional elements within the organization [1]
• in terms of being integrated across different concepts and tools and frameworks [1]
• a good strategic analysis integrates various tools and frameworks that are in our strategist toolkit [1]
• approachable in terms of 
• dynamics
• complexity
• competition
• {step} identify the mission and values of the organization
• critical for understanding what the firm values and how it may influence where opportunities they look for and what actions they might be willing to take
• {step} analyze the competitive environment
• looking at what opportunities the environment provides, how are competitors likely to react
• {step} analyze competitive positions
• think about  own capabilities are and how they might relate to the opportunities that are available
• {step} analyze and recommend strategic actions 
• actions for future improvement
• {question} how do we create more value?
• {question} how can we improve our current competitive position?
• {question} how can we in essence, create more value in our competitive environment
• alternatives
• scaling the business
• entering new markets
• innovating
• acquiring a competitor/another player within a market segment of interest
• recommendations
• {question} what do we recommend doing going forward?
• {question} what are the underlying assumptions of these recommendations?
• {question} do they meet our tests that we might have for providing value?
• move from analysis to action
• actions come from asking a series of questions about what opportunities, what actions can we take moving forward
• {step} strategy formulation
• {step} strategy implementation
• {tool} competitor analysis
• {question} what market is the firm in, and who are the players in these markets? 
• {tool} environmental analysis
• {benefit} provides a picture on the broader competitive environment
• {question} what are the major trends impacting this industry?
• {question} are there changes in the sociopolitical environment that are going to have important implications for this industry?
• {question} is this an attractive market or the barrier to competition?
• {tool} five forces analysis
• {benefit} provides an overview of the market structure/industry structure
• {benefit} helps understand the nature of the competitive game that we are playing as we then devise future strategies [1]
• provides a dynamic perspective in our understanding of a competitive market
• {question} how's the competitive structure in a market likely to evolve?
• {tool} competitive lifestyle analysis
• {tool} SWOT (strengths, weaknesses, opportunities, threats) analysis
• {tool} stakeholder analysis
• {benefit} valuable in trying to understand those mission and values and then the others expectations of a firm
• {tool} capabilities analysis
• {question} what are the firm's unique resources and capabilities?
• {question} how sustainable as any advantage that these assets provide?
• {tool} portfolio planning matrix
• {benefit} helps us now understand how they might leverage these assets across markets, so as to improve their position in any given market here
• {question} how should we position ourselves in the market relative to our rivals?
• {tool} capability analysis
• {benefit} understand what the firm does well and see what opportunities they might ultimately want to attack and go after in terms of these valuable competitive positions
• via Strategy Maps and Portfolio Planning matrices
• {tool} hypothesis testing
• {question} how competitors are likely to react to these actions?
• {question} does it make sense in the future worlds we envision?
• [game theory] pay off matrices can be useful to understand what actions taken by various competitors within an industry
• {tool} scenario planning
• {benefit} helps us envision future scenarios and then work back to understand what are the actions we might need to take in those various scenarios if they play out.
• {question} does it provide strategic flexibility?
• {tool} real options analysis 
• highlights the desire to have strategic flexibility or at least the value of strategic flexibility provides
• {tool} acquisition analysis
• {benefit} helps understand the value of certain action versus others
• {benefit} useful as an understanding of opportunity costs for other strategic investments one might make
• focused on mergers and acquisitions
• {tool} If-Then thinking
• sequential in nature
• different from causal logic
• commonly used in network diagrams, flow charts, Gannt charts, and computer programming
• {tool} Balanced Scorecard
• {definition} a framework to look at the strategy used for value creation from four different perspectives [5]
• {perspective} financial 
• {scope} the strategy for growth, profitability, and risk viewed from the perspective of the shareholder [5]
• {question} what are the financial objectives for growth and productivity? [5]
• {question} what are the major sources of growth? [5]
• {question} If we succeed, how will we look to our shareholders? [5]
• {perspective} customer
• {scope} the strategy for creating value and differentiation from the perspective of the customer [5]
• {question} who are the target customers that will generate revenue growth and a more profitable mix of products and services? [5]
• {question} what are their objectives, and how do we measure success with them? [5]
• {perspective} internal business processes
• {scope} the strategic priorities for various business processes, which create customer and shareholder satisfaction [5] 
• {perspective} learning and growth 
• {scope} defines the skills, technologies, and corporate culture needed to support the strategy. 
• enable a company to align its human resources and IT with its strategy
• {benefit} enables the strategic hypotheses to be described as a set of cause-and-effect relationships that are explicit and testable [5]
• require identifying the activities that are the drivers (or lead indicators) of the desired outcomes (lag indicators)  [5]
• everyone in the organization must clearly understand the underlying hypotheses, to align resources with the hypotheses, to test the hypotheses continually, and to adapt as required in real time [5]
• {tool} strategy map
• {definition} a visual representation of a company’s critical objectives and the crucial relationships that drive organizational performance [2]
• shows the cause-and effect links by which specific improvements create desired outcomes [2]
• {benefit} shows how an organization will convert its initiatives and resources–including intangible assets such as corporate culture and employee knowledge into tangible outcomes [2]
• {component} mission
• {question} why we exist?
• {component} core values
• {question} what we believe in?
• ⇐ mission and the core values  remain fairly stable over time [5]
• {component} vision
• {question} what we want to be?
• paints a picture of the future that clarifies the direction of the organization [5]
• helps-individuals to understand why and how they should support the organization [5]

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References:
[1] University of Virginia (2022) Strategic Planning and Execution (MOOC, Coursera)
[2] Robert S Kaplan & David P Norton (2000) Having Trouble with Your Strategy? Then Map It (link)
[3] Harold Kerzner (2001) Strategic planning for project management using a project management maturity model
[4] Alfred D Chandler Jr. (1962) "Strategy and Structure"
[5] Robert S Kaplan & David P Norton (2000) The Strategy-focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment

🧮ERP: Planning (Part II: It’s all about Scope I - Functional Requirements)

Introduction

ERP (Enterprise Resource Planning) Implementations tend to be expensive projects, often the actual costs overrunning the expectations by an important factor. The causes for this are multiple, the most important ones ranging from the completeness and complexity of the requirements and the impact they have on the organization to the availability of internal and external skilled resources, project methodology, project implementation, organization’s maturity in running projects, etc. 

The most important decision in an ERP implementation is deciding what one needs, respectively what will be considered for the implementation, aspects reflected in a set of functional and nonfunctional requirements. 

Functional Requirements 

The functional requirements (FRs) reflect the expected behavior of the system in respect to the inputs and outputs – what the system must do. Typically, they encompass end-users’ requirements in the area of processes, interfaces and data processing, though are not limited to them. 

The FRs are important because they reflect the future behavior of the system as perceived by the business, serving further as basis for identifying project’s scope, the gaps between end-users’ requirements and system’s functionality, respectively for estimating project’s duration and areas of focus. Further they are used as basis for validating system’s behavior and getting the sign-off for the system. Therefore, the FRs need to have the adequate level of detail, be complete, clear, comprehensible and implementable, otherwise any gaps in requirements can impact the project in adverse ways. To achieve this state of art they need to go through several iterations in which the requirements are reevaluated, enhanced, checked for duplication, relevance or any other important aspect. In the process it makes sense to categorize the requirements and provide further metadata needed for their appraisal (e.g. process, procedure, owner, status, priority). 

Once brought close to a final form, the FRs are checked against the functionality available in the targeted system, or systems when more systems are considered for evaluation. Ideally all the requirements can be implemented with the proper parametrization of the systems, though it’s seldom the case as each business has certain specifics. The gaps need to be understood, their impact evaluated and decided whether the gaps need to be implemented. In general, it’s recommended to remain close to the standard functionality, as each further gap requires further changes to the system, gaps that in time can generate further quality-related and maintenance costs. 

It can become a tedious effort, as in the process an impact and cost-benefit analysis need to be performed for each gap. Therefore, gaps’ estimation needs to occur earlier or intermixed with their justification. Once the list of the FRs is finalized and frozen, they will be used for estimating the final costs of the project, identifying the work packages, respectively planning the further work.  Once the FRs frozen, any new requirements or changes to requirements (including taking out a requirement) need to go through the Change Management process and all the consequences deriving from it – additional effort, costs, delays, etc. This can trigger again an impact and cost-benefit analysis. 

The FRs are documented in a specification document (aka functional requirement specification), which is supposed to track all the FRs through their lifetime. When evaluating the FRs against system’s functionality it’s recommended to provide general information on how they will be implemented, respectively which system function(s) will be used for that purpose. Besides the fact that it provides transparence, the information can be used as basic ground for further discussions. 

Seldom all the FRs will be defined upfront or complete. Moreover, some requirements will become obsolete during project’s execution, or gaps will be downgraded as standard and vice-versa. Therefore, it’s important to recollect the unexpected.

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🔭Data Science: Creativity (Just the Quotes)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

🔭Data Science: Control (Just the Quotes)

"An inference, if it is to have scientific value, must constitute a prediction concerning future data. If the inference is to be made purely with the help of the distribution theory of statistics, the experiments that constitute evidence for the inference must arise from a state of statistical control; until that state is reached, there is no universe, normal or otherwise, and the statistician’s calculations by themselves are an illusion if not a delusion. The fact is that when distribution theory is not applicable for lack of control, any inference, statistical or otherwise, is little better than a conjecture. The state of statistical control is therefore the goal of all experimentation. (William E Deming, "Statistical Method from the Viewpoint of Quality Control", 1939)

"Sampling is the science and art of controlling and measuring the reliability of useful statistical information through the theory of probability." (William E Deming, "Some Theory of Sampling", 1950)

"The well-known virtue of the experimental method is that it brings situational variables under tight control. It thus permits rigorous tests of hypotheses and confidential statements about causation. The correlational method, for its part, can study what man has not learned to control. Nature has been experimenting since the beginning of time, with a boldness and complexity far beyond the resources of science. The correlator’s mission is to observe and organize the data of nature’s experiments." (Lee J Cronbach, "The Two Disciplines of Scientific Psychology", The American Psychologist Vol. 12, 1957)

"In complex systems cause and effect are often not closely related in either time or space. The structure of a complex system is not a simple feedback loop where one system state dominates the behavior. The complex system has a multiplicity of interacting feedback loops. Its internal rates of flow are controlled by nonlinear relationships. The complex system is of high order, meaning that there are many system states (or levels). It usually contains positive-feedback loops describing growth processes as well as negative, goal-seeking loops. In the complex system the cause of a difficulty may lie far back in time from the symptoms, or in a completely different and remote part of the system. In fact, causes are usually found, not in prior events, but in the structure and policies of the system." (Jay W Forrester, "Urban dynamics", 1969)

"To adapt to a changing environment, the system needs a variety of stable states that is large enough to react to all perturbations but not so large as to make its evolution uncontrollably chaotic. The most adequate states are selected according to their fitness, either directly by the environment, or by subsystems that have adapted to the environment at an earlier stage. Formally, the basic mechanism underlying self-organization is the (often noise-driven) variation which explores different regions in the system’s state space until it enters an attractor. This precludes further variation outside the attractor, and thus restricts the freedom of the system’s components to behave independently. This is equivalent to the increase of coherence, or decrease of statistical entropy, that defines self-organization." (Francis Heylighen, "The Science Of Self-Organization And Adaptivity", 1970)

"Science consists simply of the formulation and testing of hypotheses based on observational evidence; experiments are important where applicable, but their function is merely to simplify observation by imposing controlled conditions." (Henry L Batten, "Evolution of the Earth", 1971)

"Thus, the construction of a mathematical model consisting of certain basic equations of a process is not yet sufficient for effecting optimal control. The mathematical model must also provide for the effects of random factors, the ability to react to unforeseen variations and ensure good control despite errors and inaccuracies." (Yakov Khurgin, "Did You Say Mathematics?", 1974)

"Uncontrolled variation is the enemy of quality." (W Edwards Deming, 1980)

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

"A mathematical model uses mathematical symbols to describe and explain the represented system. Normally used to predict and control, these models provide a high degree of abstraction but also of precision in their application." (Lars Skyttner, "General Systems Theory: Ideas and Applications", 2001)

"A model is an imitation of reality and a mathematical model is a particular form of representation. We should never forget this and get so distracted by the model that we forget the real application which is driving the modelling. In the process of model building we are translating our real world problem into an equivalent mathematical problem which we solve and then attempt to interpret. We do this to gain insight into the original real world situation or to use the model for control, optimization or possibly safety studies." (Ian T Cameron & Katalin Hangos, "Process Modelling and Model Analysis", 2001)

"Dashboards and visualization are cognitive tools that improve your 'span of control' over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"The methodology of feedback design is borrowed from cybernetics (control theory). It is based upon methods of controlled system model’s building, methods of system states and parameters estimation (identification), and methods of feedback synthesis. The models of controlled system used in cybernetics differ from conventional models of physics and mechanics in that they have explicitly specified inputs and outputs. Unlike conventional physics results, often formulated as conservation laws, the results of cybernetical physics are formulated in the form of transformation laws, establishing the possibilities and limits of changing properties of a physical system by means of control." (Alexander L Fradkov, "Cybernetical Physics: From Control of Chaos to Quantum Control", 2007)

"Put simply, statistics is a range of procedures for gathering, organizing, analyzing and presenting quantitative data. […] Essentially […], statistics is a scientific approach to analyzing numerical data in order to enable us to maximize our interpretation, understanding and use. This means that statistics helps us turn data into information; that is, data that have been interpreted, understood and are useful to the recipient. Put formally, for your project, statistics is the systematic collection and analysis of numerical data, in order to investigate or discover relationships among phenomena so as to explain, predict and control their occurrence." (Reva B Brown & Mark Saunders, "Dealing with Statistics: What You Need to Know", 2008)

"One technique employing correlational analysis is multiple regression analysis (MRA), in which a number of independent variables are correlated simultaneously (or sometimes sequentially, but we won’t talk about that variant of MRA) with some dependent variable. The predictor variable of interest is examined along with other independent variables that are referred to as control variables. The goal is to show that variable A influences variable B 'net of' the effects of all the other variables. That is to say, the relationship holds even when the effects of the control variables on the dependent variable are taken into account." (Richard E Nisbett, "Mindware: Tools for Smart Thinking", 2015)

"The correlational technique known as multiple regression is used frequently in medical and social science research. This technique essentially correlates many independent (or predictor) variables simultaneously with a given dependent variable (outcome or output). It asks, 'Net of the effects of all the other variables, what is the effect of variable A on the dependent variable?' Despite its popularity, the technique is inherently weak and often yields misleading results. The problem is due to self-selection. If we don’t assign cases to a particular treatment, the cases may differ in any number of ways that could be causing them to differ along some dimension related to the dependent variable. We can know that the answer given by a multiple regression analysis is wrong because randomized control experiments, frequently referred to as the gold standard of research techniques, may give answers that are quite different from those obtained by multiple regression analysis." (Richard E Nisbett, "Mindware: Tools for Smart Thinking", 2015)

"The theory behind multiple regression analysis is that if you control for everything that is related to the independent variable and the dependent variable by pulling their correlations out of the mix, you can get at the true causal relation between the predictor variable and the outcome variable. That’s the theory. In practice, many things prevent this ideal case from being the norm." (Richard E Nisbett, "Mindware: Tools for Smart Thinking", 2015)

"Too little attention is given to the need for statistical control, or to put it more pertinently, since statistical control (randomness) is so rarely found, too little attention is given to the interpretation of data that arise from conditions not in statistical control." (William E Deming)

🔭Data Science: Analysis (Just the Quotes)

"Analysis is a method where one assumes that which is sought, and from this, through a series of implications, arrives at something which is agreed upon on the basis of synthesis; because in analysis, one assumes that which is sought to be known, proved, or constructed, and examines what this is a consequence of and from what this latter follows, so that by backtracking we end up with something that is already known or is part of the starting points of the theory; we call such a method analysis; it is, in a sense, a solution in reversed direction. In synthesis we work in the opposite direction: we assume the last result of the analysis to be true. Then we put the causes from analysis in their natural order, as consequences, and by putting these together we obtain the proof or the construction of that which is sought. We call this synthesis." (Pappus of Alexandria, cca. 4th century BC)

"Analysis is the obtaining of the thing sought by assuming it and so reasoning up to an admitted truth; synthesis is the obtaining of the thing sought by reasoning up to the inference and proof of it." (Eudoxus, cca. 4th century BC)

"The analysis of concepts is for the understanding nothing more than what the magnifying glass is for sight." (Moses Mendelssohn, 1763)

"As the analysis of a substantial composite terminates only in a part which is not a whole, that is, in a simple part, so synthesis terminates only in a whole which is not a part, that is, the world." (Immanuel Kant, "Inaugural Dissertation", 1770)

"But ignorance of the different causes involved in the production of events, as well as their complexity, taken together with the imperfection of analysis, prevents our reaching the same certainty about the vast majority of phenomena. Thus there are things that are uncertain for us, things more or less probable, and we seek to compensate for the impossibility of knowing them by determining their different degrees of likelihood. So it was that we owe to the weakness of the human mind one of the most delicate and ingenious of mathematical theories, the science of chance or probability." (Pierre-Simon Laplace, "Recherches, 1º, sur l'Intégration des Équations Différentielles aux Différences Finies, et sur leur Usage dans la Théorie des Hasards", 1773)

"It has never yet been supposed, that all the facts of nature, and all the means of acquiring precision in the computation and analysis of those facts, and all the connections of objects with each other, and all the possible combinations of ideas, can be exhausted by the human mind." (Nicolas de Condorcet, "Outlines Of An Historical View Of The Progress Of The Human Mind", 1795)

"It is interesting thus to follow the intellectual truths of analysis in the phenomena of nature. This correspondence, of which the system of the world will offer us numerous examples, makes one of the greatest charms attached to mathematical speculations." (Pierre-Simon Laplace, "Exposition du système du monde", 1799)

"With the synthesis of every new concept in the aggregation of coordinate characteristics the extensive or complex distinctness is increased; with the further analysis of concepts in the series of subordinate characteristics the intensive or deep distinctness is increased. The latter kind of distinctness, as it necessarily serves the thoroughness and conclusiveness of cognition, is therefore mainly the business of philosophy and is carried farthest especially in metaphysical investigations." (Immanuel Kant, "Logic", 1800)

"It is easily seen from a consideration of the nature of demonstration and analysis that there can and must be truths which cannot be reduced by any analysis to identities or to the principle of contradiction but which involve an infinite series of reasons which only God can see through." (Gottfried W Leibniz, "Nouvelles lettres et opuscules inédits", 1857)

"Analysis and synthesis, though commonly treated as two different methods, are, if properly understood, only the two necessary parts of the same method. Each is the relative and correlative of the other. Analysis, without a subsequent synthesis, is incomplete; it is a mean cut of from its end. Synthesis, without a previous analysis, is baseless; for synthesis receives from analysis the elements which it recomposes." (Sir William Hamilton, "Lectures on Metaphysics and Logic: 6th Lecture on Metaphysics", 1858)

"Hence, even in the domain of natural science the aid of the experimental method becomes indispensable whenever the problem set is the analysis of transient and impermanent phenomena, and not merely the observation of persistent and relatively constant objects." (Wilhelm Wundt, "Principles of Physiological Psychology", 1874)

"In fact, the opposition of instinct and reason is mainly illusory. Instinct, intuition, or insight is what first leads to the beliefs which subsequent reason confirms or confutes; but the confirmation, where it is possible, consists, in the last analysis, of agreement with other beliefs no less instinctive. Reason is a harmonising, controlling force rather than a creative one. Even in the most purely logical realms, it is insight that first arrives at what is new." (Bertrand Russell, "Our Knowledge of the External World", 1914)

"In obedience to the feeling of reality, we shall insist that, in the analysis of propositions, nothing 'unreal' is to be admitted. But, after all, if there is nothing unreal, how, it may be asked, could we admit anything unreal? The reply is that, in dealing with propositions, we are dealing in the first instance with symbols, and if we attribute significance to groups of symbols which have no significance, we shall fall into the error of admitting unrealities, in the only sense in which this is possible, namely, as objects described." (Bertrand Russell, "Introduction to Mathematical Philosophy" , 1919)

"It requires a very unusual mind to undertake the analysis of the obvious." (Alfred N Whitehead, "Science in the Modern World", 1925)

"The failure of the social sciences to think through and to integrate their several responsibilities for the common problem of relating the analysis of parts to the analysis of the whole constitutes one of the major lags crippling their utility as human tools of knowledge." (Robert S Lynd, "Knowledge of What?", 1939)

"Analogies are useful for analysis in unexplored fields. By means of analogies an unfamiliar system may be compared with one that is better known. The relations and actions are more easily visualized, the mathematics more readily applied, and the analytical solutions more readily obtained in the familiar system." (Harry F Olson, "Dynamical Analogies", 1943)

"Only by the analysis and interpretation of observations as they are made, and the examination of the larger implications of the results, is one in a satisfactory position to pose new experimental and theoretical questions of the greatest significance." (John A Wheeler, "Elementary Particle Physics", American Scientist, 1947)

"The study of the conditions for change begins appropriately with an analysis of the conditions for no change, that is, for the state of equilibrium." (Kurt Lewin, "Quasi-Stationary Social Equilibria and the Problem of Permanent Change", 1947)

"A synthetic approach where piecemeal analysis is not possible due to the intricate interrelationships of parts that cannot be treated out of context of the whole;" (Walter F Buckley, "Sociology and modern systems theory", 1967)

"In general, complexity and precision bear an inverse relation to one another in the sense that, as the complexity of a problem increases, the possibility of analysing it in precise terms diminishes. Thus 'fuzzy thinking' may not be deplorable, after all, if it makes possible the solution of problems which are much too complex for precise analysis." (Lotfi A Zadeh, "Fuzzy languages and their relation to human intelligence", 1972)

"Discovery is a double relation of analysis and synthesis together. As an analysis, it probes for what is there; but then, as a synthesis, it puts the parts together in a form by which the creative mind transcends the bare limits, the bare skeleton, that nature provides." (Jacob Bronowski, "The Ascent of Man", 1973)

"The complexities of cause and effect defy analysis." (Douglas Adams, "Dirk Gently's Holistic Detective Agency", 1987)

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

"Either one or the other [analysis or synthesis] may be direct or indirect. The direct procedure is when the point of departure is known-direct synthesis in the elements of geometry. By combining at random simple truths with each other, more complicated ones are deduced from them. This is the method of discovery, the special method of inventions, contrary to popular opinion." (André-Marie Ampère)

🔬Data Science: Meta-Analysis (Definitions)

"A set of statistical procedures designed to accumulate experimental and correlational results across independent studies that address related sets of research questions." (Ying-Chieh Liu et al, "Meta-Analysis Research on Virtual Team Performance", 2008)

"A statistical technique in which the outcomes from multiple experimental comparisons are synthesized by evaluating effect sizes. Because the recommendations are based on multiple experiments, practitioners can have greater confidence in the results from an effective meta-analysis." (Ruth C Clark, "Building Expertise: Cognitive Methods for Training and Performance Improvement", 2008)

"Study characteristics can be thought of as the independent variable." (Ernest W Brewer, "Using Meta-Analysis as a Research Tool in Making Educational and Organizational Decisions", 2009)

"The exhaustive search process which comprises numerous and versatile algorithmic procedures to exploit the gene expression results by combining or further processing them with sophisticated statistical learning and data mining techniques coupled with annotated information concerning functional properties of these genes residing in large databases." (Aristotelis Chatziioannou & Panagiotis Moulos, "DNA Microarrays: Analysis and Interpretation", 2009)

"The statistical analysis of a group of relevantly similar experimental studies, in order to summarize their results considered as a whole." (Saul Fisher, "Cost-Effectiveness", 2009)

"A quantitative research review that applies statistical techniques to examine, standardize and combine the results of different empirical studies that investigate a set of related research hypotheses." (Olusola O Adesope & John C Nesbit, "A Systematic Review of Research on Collaborative Learning with Concept Maps", 2010)

"Analysis of a number of comparable studies with the aim to combine those studies in a statistically valid way to test hypotheses (about the effect of an intervention)." (Cor van Dijkum  & Laura Vegter, "A Client Perspective on E-Health: Illustrated with an Example from The Netherlands", 2010)

"A computation of average effect sizes among many experiments. Data based on a meta-analysis give us greater confidence in the results because they reflect many research studies." (Ruth C Clark & Richard E Mayer, "e-Learning and the Science of Instruction", 2011)

"Analysis of previously analyzed data relating to the same or similar biological phenomena or treatment studied across the same or similar technology platforms." (Padmalatha S Reddy et al, "Knowledge-Driven, Data-Assisted Integrative Pathway Analytics", 2011)

"A set of techniques for the quantitative analysis of results from two or more studies on the same or similar issues." (Geoff Cumming, "Understanding The New Statistics", 2013)

"A method of combining effect sizes from individual studies into a single composite effect size." (Jonathan van‘t Riet et al, "The Effects of Active Videogames on BMI among Young People: A Meta-Analysis", 2016)

"A procedure that allows the statistical averaging of results from independent studies of the same phenomena. Meta-analysis essentially combines studies on the same topic into a single large study, providing an index of how strongly the independent variable affected the dependent variable on an average in the set of studies." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A research design that combines and synthesize different types of data from multiple sources." (Mzoli Mncanca & Chinedu Okeke, "Early Exposure to Domestic Violence and Implications for Early Childhood Education Services", 2019)

"A quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research." (Helena H Borba et al, "Challenges in Evidence-Based Practice Education: From Teaching Concepts Towards Decision-Making Learning", 2021)

🔬Data Science: Cluster Analysis (Definitions)

"Generally, cluster analysis, or clustering, comprises a wide array of mathematical methods and algorithms for grouping similar items in a sample to create classifications and hierarchies through statistical manipulation of given measures of samples from the population being clustered. (Hannu Kivijärvi et al, "A Support System for the Strategic Scenario Process", 2008) 

"Defining groups based on the 'degree' to which an item belongs in a category. The degree may be determined by indicating a percentage amount." (Mary J Lenard & Pervaiz Alam, "Application of Fuzzy Logic to Fraud Detection", 2009)

"A technique that identifies homogenous subgroups or clusters of subjects or study objects." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A statistical technique for finding natural groupings in data; it can also be used to assign new cases to groupings or categories." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"Techniques for organizing data into groups of similar cases." (Meta S Brown, "Data Mining For Dummies", 2014)

"A statistical technique whereby data or objects are classified into groups (clusters) that are similar to one another but different from data or objects in other clusters." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2018)

"Clustering or cluster analysis is a set of techniques of multivariate data analysis aimed at selecting and grouping homogeneous elements in a data set. Clustering techniques are based on measures relating to the similarity between the elements. In many approaches this similarity, or better, dissimilarity, is designed in terms of distance in a multidimensional space. Clustering algorithms group items on the basis of their mutual distance, and then the belonging to a set or not depends on how the element under consideration is distant from the collection itself." (Crescenzio Gallo, "Building Gene Networks by Analyzing Gene Expression Profiles", 2018)

"A type of an unsupervised learning that aims to partition a set of objects in such a way that objects in the same group (called a cluster) are more similar, whereas characteristics of objects assigned into different clusters are quite distinct." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)

"Cluster analysis is the process of identifying objects that are similar to each other and cluster them in order to understand the differences as well as the similarities within the data." (Analytics Insight)

🔬Data Science: Heuristic (Definitions)

"Problem solving or analysis by experimental and especially trial-and-error methods." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"The mode of analysis in which the next step is determined by the results of the current step. Used for decision support processing." (Margaret Y Chu, "Blissful Data ", 2004)

"A type of analysis in which the next step is determined by the results of the current step of analysis." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling 2nd Ed.", 2005)

"The mode of analysis in which the next step is determined by the results of the current step of analysis. Used for decision-support processing." (William H Inmon, "Building the Data Warehouse", 2005)

"An algorithmic technique designed to solve a problem that ignores whether the solution can be proven to be correct." (Omar F El-Gayar et al, "Current Issues and Future Trends of Clinical Decision Support Systems", 2008)

"General advice that is usually efficient but sometimes cannot be used; also it is a validate function that adds a number to the state of the problem." (Attila Benko & Cecília S Lányi, "History of Artificial Intelligence", 2009) 

"These methods, found through discovery and observation, are known to produce incorrect or inexact results at times but likely to produce correct or sufficiently exact results when applied in commonly occurring conditions." (Vineet R Khare & Frank Z Wang, "Bio-Inspired Grid Resource Management", Handbook of Research on Grid Technologies and Utility Computing, 2009)

"Refers to a search and discovery approach, in which we proceed gradually, without trying to find out immediately whether the partial result, which is only adopted on a provisional basis, is true or false. This method is founded on a gradual approach to a given question, using provisional hypotheses and successive evaluations." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence: Anticipation Tool for Managers", 2011)

"'Rules of thumb' and approximation methods for obtaining a goal, a high quality solution, or improved performance. It sacrifices completeness to increase efficiency, as some potential solutions would not be practicable or acceptable due to their 'rareness' or 'complexity'. This method may not always find the best solution, but it will find an acceptable solution within a reasonable timeframe for problems that will require almost infinite or longer than acceptable times to compute." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An experience-based technique for solving problems that emphasizes personal knowledge and quick decision making." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

[heuristic process:] "An iterative process, where the next step of analysis depends on the results attained in the current level of analysis" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"An algorithm that gives a good solution for a problem but that doesn’t guarantee to give you the best solution possible." (Rod Stephens, "Beginning Software Engineering", 2015)

"Rules of thumb derived by experience, intuition, and simple logic." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Problem-solving technique that yields a sub-optimal solution judged to be sufficient." (Karl Beecher, "Computational Thinking - A beginner's guide to problem-solving and programming", 2017)

"An algorithm to solve a problem simply and quickly with an approximate solution, as compared to a complex algorithm that provides a precise solution, but may take a prohibitively long time." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

⛏️Data Management: Data Collection (Definitions)

"The gathering of information through focus groups, interviews, surveys, and research as required to develop a strategic plan." (Teri Lund & Susan Barksdale, "10 Steps to Successful Strategic Planning", 2006)

"The process of gathering raw or primary specific data from a single source or from multiple sources." (Adrian Stoica et al, "Field Evaluation of Collaborative Mobile Applications", 2008) 

"A combination of human activities and computer processes that get data from sources into files. It gets the file data using empirical methods such as questionnaire, interview, observation, or experiment." (Jens Mende, "Data Flow Diagram Use to Plan Empirical Research Projects", 2009)

"A systematic process of gathering and measuring information about the phenomena of interest." (Kaisa Malinen et al, "Mobile Diary Methods in Studying Daily Family Life", 2015)

"The process of capturing events in a computer system. The result of a data collection operation is a log record. The term logging is often used as a synonym for data collection." (Ulf Larson et al, "Guidance for Selecting Data Collection Mechanisms for Intrusion Detection", 2015)

"This refers to the various approaches used to collect information." (Ken Sylvester, "Negotiating in the Leadership Zone", 2015)

"Set of techniques that allow gathering and measuring information on certain variables of interest." (Sara Eloy et al, "Digital Technologies in Architecture and Engineering: Exploring an Engaged Interaction within Curricula", 2016)

"with respect to research, data collection is the recording of data for the purposes of a study. Data collection for a study may or may not be the original recording of the data." (Meredith Zozus, "The Data Book: Collection and Management of Research Data", 2017)

"The process of retrieving data from different sources and storing them in a unique location for further use." (Deborah Agostino et al, "Social Media Data Into Performance Measurement Systems: Methodologies, Opportunities, and Risks", 2018)

"It is the process of gathering data from a variety of relevant sources in an established systematic fashion for analysis purposes." (Yassine Maleh et al, 'Strategic IT Governance and Performance Frameworks in Large Organizations", 2019)

"A process of storing and managing data." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"The process and techniques for collecting the information for a research project." (Tiffany J Cresswell-Yeager & Raymond J Bandlow, "Transformation of the Dissertation: From an End-of-Program Destination to a Program-Embedded Process", 2020)

"The method of collecting and evaluating data on selected variables, which helps in analyzing and answering relevant questions is known as data collection." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"Datasets are created by collecting data in different ways: from manual or automatic measurements (e.g. weather data), surveys (census data), records of decisions (budget data) or ongoing transactions (spending data), aggregation of many records (crime data), mathematical modelling (population projections), etc." (Open Data Handbook)

⛏️Data Management: Data Quality Management [DQM] (Definitions)

[Total Data Quality Management:] "An approach that manages data proactively as the outcome of a process, a valuable asset rather than the traditional view of data as an incidental by-product." (Karolyn Kerr, "Improving Data Quality in Health Care", 2009)

"The application of total quality management concepts and practices to improve data and information quality, including setting data quality policies and guidelines, data quality measurement (including data quality auditing and certification), data quality analysis, data cleansing and correction, data quality process improvement, and data quality education." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Data Quality Management (DQM) is about employing processes, methods, and technologies to ensure the quality of the data meets specific business requirements." (Mark Allen & Dalton Cervo, "Strategy, Scope, and Approach" [in "Multi-Domain Master Data Management"], 2015)

"DQM is the management of company data in a manner aware of quality. It is a sub-function of data management and analyzes, improves and assures the quality of data in the company. DQM includes all activities, procedures and systems to achieve the data quality required by the business strategy. Among other things, DQM transfers approaches for the management of quality for physical goods to immaterial goods like data." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"Data quality management (DQM) is a set of practices aimed at improving and maintaining the quality of data across a company’s business units." (altexsoft) [source]

"Data quality management is a set of practices that aim at maintaining a high quality of information. DQM goes all the way from the acquisition of data and the implementation of advanced data processes, to an effective distribution of data. It also requires a managerial oversight of the information you have." (Data Pine) [source]

"Data quality management is a setup process, which is aimed at achieving and maintaining high data quality. Its main stages involve the definition of data quality thresholds and rules, data quality assessment, data quality issues resolution, data monitoring and control." (ScienceSoft) [source]

"Data quality management is the act of ensuring suitable data quality." (Xplenty) [source]

"Data quality management provides a context-specific process for improving the fitness of data that’s used for analysis and decision making. The goal is to create insights into the health of that data using various processes and technologies on increasingly bigger and more complex data sets." (SAS) [source]

"Data quality management (DQM) refers to a business principle that requires a combination of the right people, processes and technologies all with the common goal of improving the measures of data quality that matter most to an enterprise organization." (BMC) [source]

"Put most simply, data quality management is the process of reviewing and updating your customer data to minimize inaccuracies and eliminate redundancies, such as duplicate customer records and duplicate mailings to the same address." (EDQ) [source]

♟️Strategic Management: Cause & Effect (Quotes)

"All effects follow not with like certainty from their supposed causes." (David Hume, "An Enquiry Concerning Human Understanding", 1748)

"The first obligation of Simplicity is that of using the simplest means to secure the fullest effect. But although the mind instinctively rejects all needless complexity, we shall greatly err if we fail to recognise the fact, that what the mind recoils from is not the complexity, but the needlessness." (George H Lewes, "The Principles of Success in Literature", 1865)

"In complex systems cause and effect are often not closely related in either time or space. The structure of a complex system is not a simple feedback loop where one system state dominates the behavior. The complex system has a multiplicity of interacting feedback loops. Its internal rates of flow are controlled by nonlinear relationships. The complex system is of high order, meaning that there are many system states (or levels). It usually contains positive-feedback loops describing growth processes as well as negative, goal-seeking loops. In the complex system the cause of a difficulty may lie far back in time from the symptoms, or in a completely different and remote part of the system. In fact, causes are usually found, not in prior events, but in the structure and policies of the system." (Jay W Forrester, "Urban dynamics", 1969)

"[…] fitting lines to relationships between variables is often a useful and powerful method of summarizing a set of data. Regression analysis fits naturally with the development of causal explanations, simply because the research worker must, at a minimum, know what he or she is seeking to explain." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The language of association and prediction is probably most often used because the evidence seems insufficient to justify a direct causal statement. A better practice is to state the causal hypothesis and then to present the evidence along with an assessment with respect to the causal hypothesis - instead of letting the quality of the data determine the language of the explanation." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"A system is a set of two or more elements that satisfies the following three conditions. (1) The behavior of each element has an effect on the behavior of the whole. (2) The behavior of the elements and their effects on the whole are interdependent. the way each element behaves and the way it affects the whole depends on how at least one other element behaves. (3) However subgroups of the elements are formed, each has an effect on the behavior of the whole and none has an independent effect on it." (Russell L Ackoff, "Creating the Corporate Future", 1981) 

"The complexities of cause and effect defy analysis." (Douglas Adams, "Dirk Gently's Holistic Detective Agency", 1987)

"Chaos demonstrates that deterministic causes can have random effects […] There's a similar surprise regarding symmetry: symmetric causes can have asymmetric effects. […] This paradox, that symmetry can get lost between cause and effect, is called symmetry-breaking. […] From the smallest scales to the largest, many of nature's patterns are a result of broken symmetry; […]" (Ian Stewart & Martin Golubitsky, "Fearful Symmetry: Is God a Geometer?", 1992)

"The multiplier effect is a major feature of networks and flows. It arises regardless of the particular nature of the resource, be it goods, money, or messages." (John H Holland, "Hidden Order - How Adaptation Builds Complexity", 1995) 

"Delay time, the time between causes and their impacts, can highly influence systems. Yet the concept of delayed effect is often missed in our impatient society, and when it is recognized, it’s almost always underestimated. Such oversight and devaluation can lead to poor decision making as well as poor problem solving, for decisions often have consequences that don’t show up until years later. Fortunately, mind mapping, fishbone diagrams, and creativity/brainstorming tools can be quite useful here." (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)

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

♟️Strategic Management: Analysis (Just the Quotes)

"Business executives cannot afford to ignore the merits of graphical representation which have for so long been accepted by the engineer and man of science. They must look behind the graphical method and study the conditions leading to the picture along with the picture itself. No business is too small to profit by an examination which shall analyze and scrutinize nor too large to ignore its possibilities. Each business must adjust the graphical methods to its own peculiarities and each diagram must be adjusted to the individual for whom it is prepared or the individual must be educated up to the use and importance of these methods of analysis." (William C Marshall, "Graphical methods for schools, colleges, statisticians, engineers and executives", 1921)

"The pattern of personal characteristics of the leader must bear some relevant relationship to the characteristics, activities, and goals of the followers. [...] It becomes clear that an adequate analysis of leadership involves not only a study of leadership but also of situations." (R M Stodgill, "Journal of Psychology", 1948)

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

"Another approach to management theory, undertaken by a growing and scholarly group, might be referred to as the decision theory school. This group concentrates on rational approach to decision-the selection from among possible alternatives of a course of action or of an idea. The approach of this school may be to deal with the decision itself, or to the persons or organizational group making the decision, or to an analysis of the decision process. Some limit themselves fairly much to the economic rationale of the decision, while others regard anything which happens in an enterprise the subject of their analysis, and still others expand decision theory to cover the psychological and sociological aspect and environment of decisions and decision-makers." (Harold Koontz, "The Management Theory Jungle," 1961)

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

"Analysis is not a scientific procedure for reaching decisions which avoid intuitive elements, but rather a mechanism for sharpening the intuition of the decision maker." (James R Schlesinger, "Memorandum to Senate Committee on Government Operations", 1968)

"The concept of organizational goals, like the concepts of power, authority, or leadership, has been unusually resistant to precise, unambiguous definition. Yet a definition of goals is necessary and unavoidable in organizational analysis. Organizations are established to do something; they perform work directed toward some end." (Charles Perrow, "Organizational Analysis: A Sociological View", 1970)

"Strategic planning is not the 'application of scientific methods to business decision' […] . It is the application of thought, analysis, imagination, and judgment. It is responsibility, rather than technique. […] Strategy planning is not forecasting. […] Strategic planning is necessary precisely because we cannot forecast. […] Strategic planning does nor deal with future decisions. It deals with the futurity of present decisions. […] Strategic planning is not an attempt to eliminate risk. It is not even an attempt to minimize risk." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

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

"Managers are not confronted with problems that are independent of each other, but with dynamic situations that consist of complex systems of changing problems that interact with each other. I call such situations messes. Problems are extracted from messes by analysis. Managers do not solve problems, they manage messes." (Russell L Ackoff, "The future of operational research is past", 1979)

"Analysis is the critical starting point of strategic thinking. Faced with problems, trends, events, or situations that appear to constitute a harmonious whole or come packaged as a whole by common sense of the day, the strategic thinker dissects them into their constituent parts. Then, having discovered the significance of these constituents, he reassembles them in a way calculated to maximize his advantage." (Kenichi Ohmae, "The Mind Of The Strategist", 1982) 

"In business as on the battlefield, the object of strategy is to bring about the conditions most favorable to one's own side, judging precisely the right moment to attack or withdraw and always assessing the limits of compromise correctly. Besides the habit of analysis, what marks the mind of the strategist is an intellectual elasticity or flexibility that enables him to come up with realistic responses to changing situations, not simply to discriminate with great precision among different shades of gray." (Kenichi Ohmae, "The Mind Of The Strategist", 1982)

"No matter how difficult or unprecedented the problem, a breakthrough to the best possible solution can come only from a combination of rational analysis, based on the real nature of things, and imaginative reintegration of all the different items into a new pattern, using nonlinear brainpower. This is always the most effective approach to devising strategies for dealing successfully with challenges and opportunities, in the market arena as on the battlefield." (Kenichi Ohmae, "The Mind Of The Strategist", 1982)

"View thinking as a strategy. Thinking is the best way to resolve difficulties. Maintain faith in your ability to think your way out of problems. Recognize the difference between worrying and thinking. The former is repeated, needless problem analysis while the latter is solution generation." (Timothy W Firnstahl, Harvard Business Review, 1986)

"[…] the most successful strategies are visions, not plans. Strategic planning isn’t strategic thinking. One is analysis, and the other is synthesis." (Henry Mintzberg, "The Fall and Rise of Strategic Planning", Harvard Business Review, 1994)

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

"Without meaningful data there can be no meaningful analysis. The interpretation of any data set must be based upon the context of those data. Unfortunately, much of the data reported to executives today are aggregated and summed over so many different operating units and processes that they cannot be said to have any context except a historical one - they were all collected during the same time period. While this may be rational with monetary figures, it can be devastating to other types of data." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"[...] strategy is about determining the problems and opportunities in front of you, defining them properly, and shaping a course of action that will give your business the greatest advantage. Balancing problem solving with creating and exploiting new opportunities through imagination and analysis is the cornerstone of a great strategy." (Eben Hewitt, "Technology Strategy Patterns: Architecture as strategy" 2nd Ed., 2019)

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

♜Strategic Management: Risk Analysis (Definitions)

"The evaluation, classification, and prioritization of risks." (Sandy Shrum et al, "CMMI®: Guidelines for Process Integration and Product Improvement", 2003)

"The process of identifying, characterizing, and prioritizing risks." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"The process of assessing identified risks to estimate their impact and probability of occurrence (likelihood)." (Tilo Linz et al, "Software Testing Practice: Test Management", 2007)

"The process of measuring and analyzing the risks associated with financial and investment decisions. Risk refers to the variability of expected returns (earnings or cash flows)." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"The process of assessing identified risks to estimate their impact and probability of occurrence (likelihood)." (Requirements Engineering Qualifications Board, "Standard glossary of terms used in Requirements Engineering", 2011)

"A formal definition of risks based on asset identification, threat enumeration, and consequence evaluation." (Mark Rhodes-Ousley, "Information Security: The Complete Reference, Second Edition, 2nd Ed.", 2013)

"Systematic use of available information to determine how often specified events may occur and the magnitude of their likely consequences." (Chartered Institute of Building, "Code of Practice for Project Management for Construction and Development" 5th Ed., 2014)

"The process to comprehend the nature of risk and to determine the level of risk." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"A process undertaken to comprehend the nature of risk and to determine the level of risk." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"The process to comprehend the nature of risk and to determine the level of risk" (ISO Guide 73:2009). 

"The process of assessing identified project or product risks to determine their level of risk, typically by estimating their impact and probability of occurrence (likelihood)" (ISTQB)

♜Strategic Management: Value Proposition (Definitions)

"The benefit received for the investment made." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)

"A three- to five-sentence statement that conveys to customers the value and benefits that a business brings to them. The value proposition should convey why the customer should purchase that business’s products and services over the competition’s." (Gina Abudi & Brandon Toropov, "The Complete Idiot's Guide to Best Practices for Small Business", 2011)

"The analysis of the benefits of using the specific model (tangible and intangible), including the customers' value proposition." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The promise of value to be delivered by an organization. Typically addresses which customer needs the organization will meet and how it will price its offerings." (Andrew Pham et al, "From Business Strategy to Information Technology Roadmap", 2016)

"A statement about how customers will benefit from a product or service." (Duncan Angwin & Stephen Cummings, "The Strategy Pathfinder" 3rd Ed., 2017)

"A statement of the value that your product brings to your customer. The main reason that a customer should buy from you." (Pamela Schure & Brian Lawley, "Product Management For Dummies", 2017)

"A short statement (pre-project) that describes the tangible results or value a decision maker can expect from implementing a recommended course of action and its resulting benefit to the organization. It is expressed in a quantified fashion in the Business Case, where Value = Benefits – Cost (where Cost includes Risk). (See Business Case.) Vision Statement: It provides a view of the future desired state or condition of an organization. (A vision should stretch the organization to become the best that it can be.) The Vision Statement provides an effective tool to help develop objectives." (H James Harrington & William S Ruggles, "Project Management for Performance Improvement Teams", 2018)

"A statement that identifies clear, measurable, and demonstrable benefits consumers get when buying a particular product or service. It should convince consumers that this product or service is better than others on the market." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"The promise of value to be delivered by an organization. Typically addresses which customer needs the organization will meet and how it will price its offerings." (Tiffany Pham et al, "From Business Strategy to Information Technology Roadmap", 2018)

♜Strategic Management: Cost-Benefit Analysis [CBA] (Definitions)

"The process of comparing the cost of achieving a goal against the benefit to be gained by its achievement." (Dale Furtwengler, "Ten Minute Guide to Performance Appraisals", 2000)

"An analysis to determine whether the favorable results of an alternative are sufficient to justify the cost of taking that alternative. This analysis is widely used in connection with capital expenditure projects." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"An evaluation that determines the value of an approach relative to its costs and benefits; used in risk management to evaluate mitigation strategies." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"Comparison of the estimated value of business benefits over time to the estimated cost of expenditures required to realize these benefits." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Investigation to determine whether the benefits exceed the costs for a proposed course of action. Often used to evaluate whether to add features or complexity to a cost accounting system or to choose a course of action in a business decision." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed., 2011)

"Study that helps in decisions on IT investments by determining if the benefits (possibly including intangible ones) exceed the costs." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"A technique that weighs expected costs against expected financial and nonfinancial benefits (value) to determine the best (according to relevant criteria) course of action." (Project Management Institute, "The Standard for Portfolio Management 3rd Ed.", 2012)

"A financial analysis tool used to determine the benefits provided by a project against its costs." (For Dummies, "PMP Certification All-in-One For Dummies" 2nd Ed., 2013)

"An analysis of costs and benefits related to an expenditure. A CBA identifies and analyzes the costs and benefits to simplify the decision-making process." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"An estimate of the equivalent monetary value of proposed benefits and the estimated costs associated with a control in order to establish whether the control is feasible." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"A method of determining the expenses and impacts for a given investment. Example: a cost-benefit analysis will be used to determine whether we engage in a specific investment." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"A financial analysis tool used to determine the benefits provided by a project against its costs." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide ", 2017)

"A tool used in decision support special studies that can assist in the allocation of capital. Cost–Benefit Analysis is a systematic, quantitative method for assessing the life cycle costs and benefits of competing alternatives. It identifies both tangible and intangible costs and benefits." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"An assessment that is performed to ensure that the cost of a safeguard does not outweigh the benefit of the safeguard. Spending more to protect an asset than the asset is actually worth does not make good business sense. All possible safeguards must be evaluated to ensure that the most security-effective and cost-effective choice is made." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide, 8th Ed", 2018)

♜Strategic Management: Risk Analysis (Definitions)

 "The evaluation, classification, and prioritization of risks." (Sandy Shrum et al, "CMMI®: Guidelines for Process Integration and Product Improvement", 2003)

"The process of identifying, characterizing, and prioritizing risks." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"The process of assessing identified risks to estimate their impact and probability of occurrence (likelihood)." (Tilo Linz et al, "Software Testing Practice: Test Management", 2007)

"The process of measuring and analyzing the risks associated with financial and investment decisions. Risk refers to the variability of expected returns (earnings or cash flows)." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"A formal definition of risks based on asset identification, threat enumeration, and consequence evaluation." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"Systematic use of available information to determine how often specified events may occur and the magnitude of their likely consequences." (Chartered Institute of Building, "Code of Practice for Project Management for Construction and Development" 5th Ed., 2014)

"The process to comprehend the nature of risk and to determine the level of risk [3]" (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"This is the part where we combine the impact and the likelihood (or probability) to calculate the level of risk and to plot it onto a risk matrix, which allows us to compare risks for their severity and to decide which are in greatest need of treatment." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"Determining the nature and likelihood of the risks to key data" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"A process undertaken to comprehend the nature of risk and to determine the level of risk." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"The process of assessing identified risks to estimate their impact and probability of occurrence (likelihood)." (IQBBA)

"The process to comprehend the nature of risk and to determine the level of risk" (ISO Guide 73:2009)

♜Strategic Management: SWOT Analysis (Definitions)

"A scan of the business environment to identify the organization's strengths and weaknesses and the opportunities and threats it faces." (Teri Lund & Susan Barksdale, "10 Steps to Successful Strategic Planning", 2006)

"A general method used as an element of strategic planning. SWOT is an acronym for strengths, weaknesses, opportunities, and threats. Within the context of Product Management, SWOT is used to synthesize the many elements of the business environment for a product or product line (as opposed to a corporate or divisional entity). The generalized quadrant structure of the SWOT model is used." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"A method of analyzing a situation or business to determine whether it’s viable." (Sue Johnson & Gwen Moran, "The Complete Idiot's Guide To Business Plans", 2010)

"A method that enables companies to view strengths, weaknesses, opportunities, and threats together." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"A planning method used to evaluate the strengths, weaknesses, opportunities, and threats involved in a particular strategic direction for your business." (Gina Abudi & Brandon Toropov, "The Complete Idiot's Guide to Best Practices for Small Business", 2011)

 "A type of analysis that provides companies with both internal and external factors that could affect the long-term success of the company." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An analysis used to determine strength and weak sides of the performance of an organization and to identify opportunities and dangers in the form of weaknesses and both internal and external threats. The four attributes of SWOT are: Strengths, Weaknesses, Opportunities, Threats." (International Qualifications Board for Business Analysis, "Standard glossary of terms used in Software Engineering", 2011)

"Involves the evaluation of strengths and weaknesses, which are internal factors, and opportunities and threats, which are external factors." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"Method of studying and identifying an organization's strengths, weaknesses, opportunities, and threats." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed., 2011)

"This information gathering technique examines the project from the perspective of each project's strengths, weaknesses, opportunities, and threats to increase the breadth of the risks considered by risk management." (Cynthia Stackpole, "PMP Certification All-in-One For Dummies", 2011)

"A problem-solving or decision analysis technique in which strengths, weaknesses, opportunities, and threats to the project or organization are examined." (Bonnie Biafore & Teresa Stover, "Your Project Management Coach: Best Practices for Managing Projects in the Real World", 2012)

"A SWOT analysis is an approach to developing strategy that begins by identifying an organization’s strengths, weaknesses, opportunities, and threats (hence SWOT). From these categories, an organization can identify ways to build on its strengths, improve its weaknesses, take advantage of opportunities, and minimize the potential impact of threats." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"An analysis process highlighting strengths, weaknesses, opportunities, and threats to an entity." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"The analysis of strengths, weaknesses, opportunities, and threats of an organization, project, or option." (Project Management Institute, "Navigating Complexity: A Practice Guide", 2014)

"An analysis of the company’s strengths and weaknesses compared to the opportunities and threats in the market place." (Pamela Schure & Brian Lawley, "Product Management For Dummies", 2017)

"Analysis of strengths, weaknesses, opportunities, and threats of an organization, project, or option." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide)", 2017)

"The main purpose of this analysis is to determine the extent to which an organisation 'fits' with the demands of its context." (Duncan Angwin & Stephen Cummings, "The Strategy Pathfinder 3rd Ed.", 2017)

"The SWOT framework classifies the factors relevant for a firm’s strategic decision making into four categories: strengths, weaknesses, opportunities, and threats." (Robert M Grant, "Contemporary Strategy Analysis" 10th Ed., 2018)

"Technique that reviews and analyses the internal strength and weakness of an organization, and the external opportunities and threats it faces" (ITIL)

♜Strategic Management: Business Impact Analysis (Definitions)

"The process of delineating the functions most critical to the survival of a business." (Yvette Ghormley, "Business Continuity and Disaster Recovery Plans", 2009)

"A management-level analysis which identifies the impacts of losing company resources. The BIA measures the effect of resource loss and escalating losses over time, in order to provide senior management with reliable data on which to base decisions concerning risk mitigation and continuity planning." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)

"A method or exercise to determine the impact of losing the support or availability of a resource." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"Aims to (a) identify critical business processes, stakeholders, assets, resources and internal/external dependencies and (b) assesses and evaluates potential damages or losses at business level that may be caused by a threat to IT landscape." (Ulrich Winkler & Wasif Gilani, "Business Continuity Management of Business Driven IT Landscapes", 2012)

"A process used to analyze the business and identify critical functions and services. The BIA also helps the organization determine the cost impact of losing these functions and services. Organizations use the results as part of an overall business continuity plan." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"The identification of services and products that are critical to the organization." (Manish Agrawal, "Information Security and IT Risk Management", 2014)

"The process of analysing activities and the effect that a business disruption might have upon them." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"An exercise that determines the impact of losing the support of any resource to an organization, establishes the escalation of that loss over time, identifies the minimum resources needed to recover, and prioritizes the recovery of processes and supporting systems." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"A functional analysis in which a team collects data, documents business functions, develops a hierarchy of business functions, and applies a classification scheme to indicate each individual function’s criticality level." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"The analysis of an information system’s requirements, functions, and interdependencies used to characterize system contingency requirements and priorities in the event of a significant disruption." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"A business continuity management activity which is mainly intended for defining the core business functions, the recovery priorities regarding these functions and the corresponding time required for the resumption of each function." (Athanasios Podaras et al, "Regression-Based Recovery Time Predictions in Business Continuity Management: A Public College Case Study", 2021)

"Activity that identifies the VMF and their dependencies" (ITIL)

"An analysis of an information system’s requirements, functions, and interdependencies used to characterize system contingency requirements and priorities in the event of a significant disruption." (CNSSI 4009-2015)

♜Strategic Management: Gap Analysis (Definitions)

"In the managerial planning process, this is the analysis taken following an exercise to determine what improvements in the process are required." (Robert McCrie, "Security Operations Management 2nd Ed.", 2006)

"An assessment of a system in comparison with another system or a set of requirements, listing those items that are not common between them." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A technique to evaluate the current portfolio mix of components and determine changes needed so components may be added, changed, or terminated to rebalance the portfolio." (Project Management Institute, "The Standard for Portfolio Management" 3rd Ed., 2012)

"Describes the difference between current results and consequences and desired results and consequences." (Joan C Dessinger, "Fundamentals of Performance Improvement 3rd Ed", 2012)

"A formal analysis of the differences between what the policy or regulation requires and what’s actually being done in the organization. Used to generate a list of action items required to become compliant with the policy or regulation." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"A comparison between the actual outcome and the desired outcome." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

📊Business Intelligence: Metric (Definitions)

"(1) The degree to which a product, process, or project possesses some attribute of interest. (2) A measured quantity (such as size, effort, duration, or quality). (3) The distance between two points in a vector space." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"A summarizable numerical value used to monitor business activity; it is also known as a fact." (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006)

"A metric is a measurement. When a plan is put into place, a way to measure the outcome is needed. When a market share forecast is created and the outcomes are measured at a future date, the planned metric is compared with the actual metric to determine the degree to which the metric was met. From this data, strategies can be revised and tactical options can be reconsidered." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"A numerical value describing a procedure, process, product attribute, or goal. A distinction is made between basic metrics (that can be measured directly) and derived metrics which result from mathematical operations using basic metrics." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

"a measurement of some parameter, usually used in the assessment of a technology, approach, or design." (Bruce P Douglass, "Real-Time Agility: The Harmony/ESW Method for Real-Time and Embedded Systems Development", 2009)

"A metric is a standard unit of measure, such as meter or mile for length, or gram or ton for weight, or, more generally, part of a system of parameters, or systems of measurement, or a set of ways of quantitatively and periodically measuring, assessing, controlling, or selecting a person, process, event, or institution, along with the procedures to carry out measurements and the procedures for the interpretation of the assessment in the light of previous or comparable assessments." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)

"Groupings of data, or numbers, that reflect specific measures or subjects." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide To Risk Management", 2010)

"a calculated value based on measurements used to monitor and control a process or business activity. Most metrics are ratios comparing one measurement to another." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A specific, measurable standard against which actual performance is compared." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011) 

"Generally, a unit of measure selected used to monitor and control a process." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"In a data warehouse, numeric facts that measure a business characteristic of interest to the end user." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Measurement of a particular characteristic of a task (for example, duration, effort, quality, cost, value delivered, or customer satisfaction)." (Charles Cooper & Ann Rockley, "Managing Enterprise Content: A Unified Content Strategy" 2nd Ed., 2012)

"1. A value from measuring a certain program or component attribute. Finding metrics is a task for static analysis. 2. A measurement scale and the method used for measurement." (Tilo Linz et al, "Software Testing Foundations" 4th Ed., 2014)

"A method of measuring something. It provides quantifiable data used to gauge the effectiveness of a process; metrics are commonly used to measure the effectiveness of a help desk." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"A value that you use to study some aspect of a project. A metric can be an attribute (such as the number of bugs) or a calculated value (such as the number of bugs per line of code)." (Rod Stephens, "Beginning Software Engineering", 2015)

"A measurement used to support the monitoring of a key performance indicator (KPI). A metric can have targets and can be used as a service level." (by Brian Johnson & Leon-Paul de Rouw, "Collaborative Business Design", 2017)

"Facts and figures representing the effectiveness of business processes that organizations track and monitor to assess the state of the company." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"A metric is the measurement of a particular characteristic of a company’s performance or efficiency. Metrics are the variables whose measured values are tied to the performance of the organization. They are also known as the performance metrics because they are performance indicators." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"A measurable quantity that indicates progress toward some goal." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"Any number (often one calculated using two or more input numbers) used to evaluate some part of an organization's performance." (Marci S. Thomas & Kim Strom-Gottfried, "Best of Boards" 2nd Ed., 2018)

"Metrics are agreed-upon measures used to evaluate how well the organization is progressing toward the Portfolio, Large Solution, Program, and Team’s business and technical objectives." (Dean Leffingwell, "SAFe 4.5 Reference Guide: Scaled Agile Framework for Lean Enterprises" 2nd Ed., 2018)

"In a machine learning context, a metric is a measure of how good or bad a particular model is at its task. In a software context, a metric is a measure defined for an application, program, or function." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"A business calculation defined by an expression built with functions, facts, attributes, or other metrics." (Microstrategy)

"A measurement scale and the method used for measurement" (ISO 14598)

"Quantifiable measures used to track, monitor, and gauge the results and success of various business processes. Metrics are meant to communicate a company’s progression toward certain long and short term objectives. This often requires the input of key stakeholders in the business as to which metrics matter to them." (Insight Software)

"Tools designed to facilitate decision making and improve performance and accountability through collection, analysis, and reporting of relevant performance-related data." (NIST SP 800-55)