🤝Governance: Compliance (Definitions)

"(1) Conforming or acquiescing to requirements from a third party. (2) A subset of data retention policies and procedures that must adhere to more rigid and rigorous conditions." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"The successful fulfillment of regulations, usually set by a financial institution (for borrowing purposes) or industry standards." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"The process of conforming, completing, performing, or adapting actions to meet the rules, demands, or wishes of another party. Commonly used when discussing conformance to external government or industry regulations." (Craig S Mullins, "Database Administration: The Complete Guide to DBA Practices and Procedures 2nd Ed", 2012)

"The ability to operate in the way defined by a regulation. Many organizations are introduced to governance concepts as they begin the process of complying with business regulations, such as Sarbanes|Oxley or Basel II. These regulations are enforced by audits that determine whether business decisions were made by the appropriate staff according to appropriate policies. To pass these audits, organizations must document their decision rights, policies, and records, specifically that each of the decisions was in fact made by the appropriate person according to policy." (Paul C Dinsmore et al, "Enterprise Project Governance", 2012)

"The process of conforming, completing, performing, or adapting actions to meet the rules, demands, or wishes of another party. Commonly used when discussing conformance to external government or industry regulations." (Craig S Mullins, "Database Administration", 2012)

"A general concept of conforming to a rule, standard, law, or requirement such that the assessment of compliance results in a binomial result stated as 'compliant' or 'noncompliant'." (For Dummies, "PMP Certification All-in-One For Dummies, 2nd Ed.", 2013)

"Business rules enforced by legislation or some other governing body" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"Compliance refers to a strategy and a set of activities and artifacts that allow teams to apply Lean-Agile development methods to build systems that have the highest possible quality, while simultaneously assuring they meet any regulatory, industry, or other relevant standards." (Dean Leffingwell, "SAFe 4.5 Reference Guide: Scaled Agile Framework for Lean Enterprises 2nd Ed", 2018)

"Ensuring that a standard or set of guidelines is followed, or that proper, consistent accounting or other practices are being employed." (ITIL)

"The capability of the software product to adhere to standards, conventions or regulations in laws and similar prescriptions." [ISO 9126]

🤝Governance: Standard (Definitions)

"A rule, policy, principle, or measure either established by an organization or established by a recognized standards body and adopted by that organization. Adherence is expected and mandatory until revoked or revised. Exceptions are allowed provided appropriate process is followed." (Tilak Mitra et al, "SOA Governance", 2008)

"A document that provides, for common and repeated use, rules, guidelines, or characteristics for activities or their results, aimed at the achievement of the optimum degree of order in a given context." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"A standard is something considered by an authority or by general consent as a basis of comparison; an approved model. Or it is a rule or principle that is used as a basis for judgment. Standards embody expectations in a formal manner. To standardize something means to cause it to conform to a standard; or to choose or establish a standard for something. (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement", 2012)

"Data quality standards are assertions about the expected condition of the data that relate directly to quality dimensions: how complete the data is, how well it conforms to defined rules for validity, integrity, and consistency, as well as how it adheres to defined expectations for presentation." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement", 2012)

"The principles or criteria for consistent, ultimate, superior performance outcomes or for how individuals and organizations conduct themselves (ethics)." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"A core set of common, repeatable best practices and protocols that have been agreed on by a business or industry group. Typically, vendors, industry user groups, and end users collaborate to develop standards based on the broad expertise of a large number of stakeholders. Organizations can leverage these standards as a common foundation and innovate on top of them." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A document that provides, for common and repeated use, rules, guidelines, or characteristics for activities or their results, aimed at the achievement of the optimum degree of order in a given context." (For Dummies, "PMP Certification All-in-One For Dummies" 2nd Ed., 2013)

"A document that supports a policy. It consists of mandated rules, which support the higher-level policy goals." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"A document established by an authority, custom, or general consent as a model or example." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

"[technical standard:] A specification or requirement or technical characteristic that becomes a norm for a product or process thereby ensuring compatibility." (Robert M Grant, "Contemporary Strategy Analysis 10th Ed", 2018)

"A published specification for, e.g., the structure of a particular file format, recommended nomenclature to use in a particular domain, a common set of metadata fields, etc. Conforming to relevant standards greatly increases the value of published data by improving machine readability and easing data integration." (Open Data Handbook)

" Documented agreements containing technical specifications or other precise criteria to be used consistently as rules, guidelines, or definitions of characteristics, to ensure that materials, products, processes and services are fit for their purpose." (SDMX) 

"Formal, possibly mandatory, set of requirements developed and used to prescribe consistent approaches to the way of working or to provide guidelines (e.g., ISO/IEC standards, IEEE standards, and organizational standards)." [CMMI]

"Mandatory requirements employed and enforced to prescribe a disciplined uniform approach to software development, that is, mandatory conventions and practices are in fact standards." (IEEE Std 983-1986) 

"The metric, specification, gauge, statement, category, segment, grouping, behavior, event or physical product sample against which the outputs of a process are compared and declared acceptable or unacceptable." (ASQ)

🤝Governance: Authority (Definitions)

[formal authority:] "Explicit power granted to meet an explicit set of service expectations, such as those in job descriptions or legislative mandates." (Alexander Grashow et al, "The Practice of Adaptive Leadership", 2009)

"Formal or informal power within a system, entrusted by one party to another in exchange for a service. The basic services, or social functions, provided by authorities are: (1) direction; (2) protection; and (3) order." (Alexander Grashow et al, "The Practice of Adaptive Leadership", 2009)

[informal authority:] "Power granted implicitly to meet a set of service expectations, such as representing cultural norms like civility or being given moral authority to champion the aspirations of a movement." (Alexander Grashow et al, "The Practice of Adaptive Leadership", 2009)

[Decision-making authority:] "Refers to the decisions that agents are authorized to make on behalf of principals. (585)" (Leslie G Eldenburg & Susan K Wolcott, "Cost Management 2nd Ed", 2011)

"The right to apply project resources, expend funds, make decisions, or give approvals." (Cynthia Stackpole, "PMP Certification All-in-One For Dummies", 2011)

"The explicit or implicit delegation of power or responsibility for a particular activity." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"The power vested in a person by virtue of her role to expend resources: financial, material, technical, and human." (Fred MacKenzie, "7 Paths to Managerial Leadership", 2016)

"The ability of a role incumbent to apply resources to a task without reference to another person." (Catherine Burke et al, "Systems Leadership" 2nd Ed., 2018)

"‘The right, given by constitution, law, role description or mutual agreement for one person to require another person to act in a prescribed way (specified in the document or agreement). The likelihood of exercising authority effectively will usually depend upon good Social Process Skills’. The acceptance of the exercise of authority within a work organisation is a function of the contract of employment. Is it essential that there is a clear understanding of the difference between authority and power and that authority is not a one-way process. In a correctly functioning organisation, for example, a manager has the authority to assign tasks to a direct report and the direct report has the authority to require a task performance review by the manager." (Catherine Burke et al, "Systems Leadership" 2nd Ed., 2018)

"power to direct and exact performance from others. It includes the right to prescribe the means and methods by which work will be done. However, the authority to direct is only as good as one individual’s willingness to accept direction from another. Moreover, with authority comes responsibility and accountability." (All Business, "Dictionary of Accounting Terms")

"(1) power over others by sanctioned personnel within an organization. Managers have the authority to hire and fire personnel in an organization. With authority comes responsibility for one’s actions. (2) a government corporation or agency that administers a public enterprise." (All Business, "Dictionary of Business Terms")

🤝Governance: Guideline (Definitions)

"An indication or outline of policy or conduct. Adherence to guidelines is recommended but is not mandatory." (Tilak Mitra et al, "SOA Governance", 2008)

"A kind of business rule that is suggested, but not enforced." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"An official recommendation or advice that indicates policies, standards, or procedures for how something should be accomplished." (For Dummies, "PMP Certification All-in-One For Dummies, 2nd Ed.", 2013)

"A document that support standards and policies, but is not mandatory." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"Non-enforced suggestions for increasing functioning and performance." (Mike Harwood, "Internet Security: How to Defend Against Attackers on the Web" 2nd Ed., 2015)

"Recommended actions and operational guides for users, IT staff, operations staff, and others when a specific standard does not apply." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed, 2018)

"A description of a particular way of accomplishing something that is less prescriptive than a procedure." (ISTQB)

"A description that clarifies what should be done and how, to achieve the objectives set out in policies"

(ISO/IEC 13335-1:2004)

🤝Governance: Policy (Definitions)

"A general, usually strategically focused statement, rule, or regulation that describes how a particular activity, operation, or group of operations will be carried out within a company." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"A deliberate plan of action to guide decisions and achieve rationale outcomes." (Tilak Mitra et al, "SOA Governance", 2008)

"Clear and measurable statements of preferred direction and behaviour to condition the decisions made within an organization." (ISO/IEC 38500:2008, 2008)

"The encoding of rules particular to a business domain, its data content, and the application systems designed to operate in this domain on this set of data." (Alex Berson & Lawrence Dubov, "Master Data Management and Data Governance", 2010)

"A rule or principle that guides or constrains the behavior of someone given decision rights. Policies provide guidelines, sometimes set limits, and sometimes enables behavior. Policies guide decision rights, which are generally conditional." (Paul C Dinsmore et al, "Enterprise Project Governance", 2012)

"A structured pattern of actions adopted by an organization such that the organization’s policy can be explained as a set of basic principles that govern the organization’s conduct." (For Dummies, "PMP Certification All-in-One For Dummies, 2nd Ed.", 2013)

"A high-level overall plan, containing a set of principles that embrace the general goals of the organization and are used as a basis for decisions. A policy can include some specifics of processes allowed and not allowed." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"The intentions of an organisation as formally expressed by its top management [1]" (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"A document that regulates conduct through a general statement of beliefs, goals, and objectives." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"A structured pattern of actions adopted by an organization such that the organization's policy can be explained as a set of basic principles that govern the organization's conduct." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide)" 6th Ed., 2017)

"A high-level overall plan, containing a set of principles that embrace the general goals of the organization and are used as a basis for decisions. Can include some specifics of processes allowed and not allowed." (Robert F Smallwood, "Information Governance for Healthcare Professionals", 2018)

"A statement of objectives, rules, practices or regulations governing the activities of people within a certain context." (NISTIR 4734)

"Statements, rules, or assertions that specify the correct or expected behavior of an entity." (NIST SP 1800-15B)

🤝Governance: Accountability (Definitions)

"The obligation to answer for a responsibility conferred. It is a relationship based on the obligation to demonstrate and take responsibility for performance in light of agreed expectations, whether or not those actions were within your direct control." (Paul C Dinsmore et al, "Enterprise Project Governance", 2012)

"The ability to trace activities on information resources to unique individuals who accept responsibility for their activities on the network." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"The obligation to answer for a responsibility that has been conferred. It presumes the existence of at least two parties: one who allocates responsibility and one who accepts it with the undertaking to report upon the manner in which it has been discharged." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"A component of a work relationship between two people wherein one accepts the requirement to provide an account to the other of the following three questions relating to work. What did you do? How did you do it? Why did you do it that way? The most common application of the concept of accountability is that which applies as a function of a contract of employment within an organisation and though in our experience this requirement to accept accountability is rarely articulated clearly in the contract; it should be. An effective accountability discussion includes a discussion of the three questions above including how and why the person used particular processes to turn inputs into required outputs. Accountability is not a collective noun for tasks, as in ‘your accountabilities are …’. Too often this is used in employment, contracts and in role descriptions, which confuses work and accountability. A role may describe work but we are still to discover if the person is actually held to account for that work. Accountability as a concept applying within coherent social groups is brought to the fore for society in general by the process of the courts wherein people in the witness box are required to answer, in public, questions as to what, how and why something was, or was not, done and judgement is passed as an outcome of this process." (Catherine Burke et al, "Systems Leadership", 2nd Ed., 2018)

"A security principle indicating that individuals must be identifiable and must be held responsible for their actions." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"Assuming a transparent and appropriate level of responsibility for data assets that are under one’s care, which includes honoring obligations associated with good practice." (Kevin J Sweeney, "Re-Imagining Data Governance", 2018)

"The property of a system or system resource which ensures that the actions of a system entity may be traced uniquely to that entity, which can then be held responsible for its actions." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"Responsibility of data processing actors to put in place appropriate and effective measures to ensure compliance with the GDPR and be able to demonstrate so." (Yordanka Ivanova, "Data Controller, Processor, or Joint Controller: Towards Reaching GDPR Compliance in a Data- and Technology-Driven World", 2020)

"Principle that an individual is entrusted to safeguard and control equipment, keying material, and information and is answerable to proper authority for the loss or misuse of that equipment or information." (CNSSI-4009)

"The security goal that generates the requirement for actions of an entity to be traced uniquely to that entity. This supports nonrepudiation, deterrence, fault isolation, intrusion detection and prevention, and after-action recovery and legal action." (SP 800-27)

🔬Data Science: Evolutionary Algorithm (Definitions)

"An Evolutionary Algorithm (EA) is a general class of fitting or maximization techniques. They all maintain a pool of structures or models that can be mutated and evolve. At every stage in the algorithm, each model is graded and the better models are allowed to reproduce or mutate for the next round. Some techniques allow the successful models to crossbreed. They are all motivated by the biologic process of evolution. Some techniques are asexual (so, there is no crossbreeding between techniques) while others are bisexual, allowing successful models to swap ''genetic' information. The asexual models allow a wide variety of different models to compete, while sexual methods require that the models share a common 'genetic' code." (William J Raynor Jr., "The International Dictionary of Artificial Intelligence", 1999)

"Meta-heuristic optimization approach inspired by natural evolution, which begins with potential solution models, then iteratively applies algorithms to find the fittest models from the set to serve as inputs to the next iteration, ultimately leading to a sub-optimal solution which is close to the optimal one." (Gilles Lebrun et al, "EA Multi-Model Selection for SVM", 2009)

"Evolutionary algorithms are search methods that can be used for solving optimization problems. They mimic working principles from natural evolution by employing a population–based approach, labeling each individual of the population with a fitness and including elements of random, albeit the random is directed through a selection process." (Ivan Zelinka & Hendrik Richter, "Evolutionary Algorithms for Chaos Researchers", Studies in Computational Intelligence Vol. 267, 2010)

"Population-based optimization algorithms in which each member of the population represents a candidate solution. In an iterative process the population members evolve and are then evaluated by a fitness function. Genetic Algorithms and Particle Swarm Optimization are examples of evolutionary algorithms." (Efstathios Kirkos, "Composite Classifiers for Bankruptcy Prediction", 2014)

"A collective term for all variants of (probabilistic) optimization and approximation algorithms that are inspired by Darwinian evolution. Optimal states are approximated by successive improvements based on the variation-selection paradigm. Thereby, the variation operators produce genetic diversity and the selection directs the evolutionary search." (Harish Garg, "A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data", 2015)

🤝Governance: Criteria (Definitions)

"Standards by which alternatives are judged. Attributes that describe certain (information) characteristics." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"Conditions that enable a decision to be made, especially at a decision point within the areas of work related to New Product Planning and New Product Introduction." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"Standards, rules, or tests on which a judgment or decision can be based, or by which a product, service, result, or process can be evaluated." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"Standards or expectation specifying what should exist (what success looks like)." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

[definite criteria] "A special purpose framework using a definite set of criteria having substantial support that is applied to all material items appearing in financial statements, such as the price-level basis of accounting." (Tom Klammer, "Statement of Cash Flows: Preparation, Presentation, and Use", 2018)

[common criteria:] "A set of internationally accepted semantic tools and constructs for describing the security needs of customers and the security attributes of products." (NIST SP 800-32)

[common criteria:] "Governing document that provides a comprehensive, rigorous method for specifying security function and assurance requirements for products and systems." (CNSSI 4009-2015)

[evaluation criteria:] "The standards by which accomplishments of technical and operational effectiveness or suitability characteristics may be assessed. Evaluation criteria are a benchmark, standard, or factor against which conformance, performance, and suitability of a technical capability, activity, product, or plan is measured." (NIST SP 800-137A)

🤝Governance: Delegation (Just the Quotes)

"Failure to delegate causes managers to be crushed and fail under the weight of accumulated duties that they do not know and have not learned to delegate." (James D Mooney, "Onward Industry!", 1931)

"Delegation means the conferring of a specified authority by a higher authority. In its essence it involves a dual responsibility. The one to whom responsibility is delegated becomes responsible to the superior for doing the job. but the superior remains responsible for getting the Job done. This principle of delegation is the center of all processes in formal organization. Delegation is inherent in the very nature of the relation between superior and subordinate. The moment the objective calls for the organized effort of more than one person, there is always leadership with its delegation of duties." (James D Mooney, "The Principles of Organization", 1947)

"The only way for a large organization to function is to decentralize, to delegate real authority and responsibility to the man on the job. But be certain you have the right man on the job." (Robert E Wood, 1951)

"You can delegate authority, but you can never delegate responsibility by delegating a task to someone else. If you picked the right man, fine, but if you picked the wrong man, the responsibility is yours - not his." (Richard E Krafve, The Boston Sunday Globe, 1960)

"Centralized controls are designed to ensure that the chief executive can find out how well the delegated authority and responsibility are being exercised." (Ernest Dale, "Management: Theory and practice", 1965)

"Guidelines for bureaucrats: (1) When in charge, ponder. (2) When in trouble, delegate. (3) When in doubt, mumble." (James Boren, New York Times, 1970)

"We find that the manager, particularly at senior levels, is overburdened with work. With the increasing complexity of modern organizations and their problems, he is destined to become more so. He is driven to brevity, fragmentation, and superficiality in his tasks, yet he cannot easily delegate them because of the nature of his information. And he can do little to increase his available time or significantly enhance his power to manage. Furthermore, he is driven to focus on that which is current and tangible in his work, even though the complex problems facing many organizations call for reflection and a far-sighted perspective." (Henry Mintzberg, "The structuring of organizations", 1979)

"Do not delegate an assignment and then attempt to manage it yourself - you will make an enemy of the overruled subordinate." (Wess Roberts, "Leadership Secrets of Attila the Hun", 1985)

"Surround yourself with the best people you can find, delegate authority, and don't interfere." (Ronald Reagan, Fortune, 1986)

"People and organizations don't grow much without delegation and completed staff work because they are confined to the capacities of the boss and reflect both personal strengths and weaknesses." (Stephen Covey, "Principle Centered Leadership", 1992)

"Responsibility is a unique concept [...] You may share it with others, but your portion is not diminished. You may delegate it, but it is still with you. [...] If responsibility is rightfully yours, no evasion, or ignorance or passing the blame can shift the burden to someone else. Unless you can point your finger at the man who is responsible when something goes wrong, then you have never had anyone really responsible." (Hyman G Rickover, "The Rickover Effect", 1992)

"We accomplish all that we do through delegation - either to time or to other people." (Stephen Covey, "Daily Reflections for Highly Effective People", 1994)

"The inability to delegate is one of the biggest problems I see with managers at all levels." (Eli Broad, "The Art of Being Unreasonable: Lessons in Unconventional Thinking", 2012)

"Delegation of authority is one of the most important functions of a leader, and he should delegate authority to the maximum degree possible with regard to the capabilities of his people. Once he has established policy, goals, and priorities, the leader accomplishes his objectives by pushing authority right down to the bottom. Doing so trains people to use their initiative; not doing so stifles creativity and lowers morale." (Thornas H Moorer)

🤝Governance: Authority (Just the Quotes)

"When the general is weak and without authority; when his orders are not clear and distinct; when there are no fixed duties assigned to officers and men, and the ranks are formed in a slovenly haphazard manner, the result is utter disorganization." (Sun Tzu, "The Art of War", cca. 5th century)

"Authority is never without hate." (Euripides, "Ion", cca. 422 BC)

"In questions of science, the authority of a thousand is not worth the humble reasoning of a single individual" (Galileo Galilei, 1632)

"Authority without wisdom is like a heavy axe without an edge, fitter to bruise than polish." (Anne Bradstreet, "Meditations Divine and Moral", 1664)

"Lawful and settled authority is very seldom resisted when it is well employed." (Samuel Johnson, "The Rambler", 1750)

"The most absolute authority is that which penetrates into a man's innermost being and concerns itself no less with his will than with his actions." (Jean-Jacques Rousseau, "On the origin of inequality", 1755)

"The wise executive never looks upon organizational lines as being settled once and for all. He knows that a vital organization must keep growing and changing with the result that its structure must remain malleable. Get the best organization structure you can devise, but do not be afraid to change it for good reason: This seems to be the sound rule. On the other hand, beware of needless change, which will only result in upsetting and frustrating your employees until they become uncertain as to what their lines of authority actually are." (Marshall E Dimock, "The Executive in Action", 1915)

"No amount of learning from books or of listening to the words of authority can be substituted for the spade-work of investigation." (Richard Gregory, "Discovery; or, The Spirit and Service of Science", 1916)

"In organization it means the graduation of duties, not according to differentiated functions, for this involves another and distinct principle of organization, but simply according to degrees of authority and corresponding responsibility." (James D Mooney, "Onward Industry!", 1931)

"It is sufficient here to observe that the supreme coordinating authority must be anterior to leadership in logical order, for it is this coordinating force which makes the organization. Leadership, on the other hand, always presupposes the organization. There can be no leader without something to lead." (James D Mooney, "Onward Industry!", 1931)

"Leadership is the form that authority assumes when it enters into process. As such it constitutes the determining principle of the entire scalar process, existing not only at the source, but projecting itself through its own action throughout the entire chain, until, through functional definition, it effectuates the formal coordination of the entire structure." (James D Mooney, "Onward Industry!", 1931)

"The staff function in organization means the service of advice or counsel, as distinguished from the function of authority or command. This service has three phases, which appear in a clearly integrated relationship. These phases are the informative, the advisory, and the supervisory." (James D Mooney, "Onward Industry!", 1931)

"Human beings are compounded of cognition and emotion and do not function well when treated as though they were merely cogs in motion.... The task of the administrator must be accomplished less by coercion and discipline, and more and more by persuasion.... Management of the future must look more to leadership and less to authority as the primary means of coordination." (Luther H Gulick, "Papers on the Science of Administration", 1937)

"A person can and will accept a communication as authoritative only when four conditions simultaneously obtain: (a) he can and does understand the communication; (b) at the time of his decision he believes that it is not inconsistent with the purpose of the organization; (c) at the time of his decision, he believes it to be compatible with his personal interest as a whole; and (d) he is able mentally and physically to comply with it." (Chester I Barnard, "The Functions of the Executive", 1938)

"The fine art of executive decision consists in not deciding questions that are not now pertinent, in not deciding prematurely, in not making decision that cannot be made effective, and in not making decisions that others should make. Not to decide questions that are not pertinent at the time is uncommon good sense, though to raise them may be uncommon perspicacity. Not to decide questions prematurely is to refuse commitment of attitude or the development of prejudice. Not to make decisions that cannot be made effective is to refrain from destroying authority. Not to make decisions that others should make is to preserve morale, to develop competence, to fix responsibility, and to preserve authority.
From this it may be seen that decisions fall into two major classes, positive decisions - to do something, to direct action, to cease action, to prevent action; and negative decisions, which are decisions not to decide. Both are inescapable; but the negative decisions are often largely unconscious, relatively nonlogical, "instinctive," "good sense." It is because of the rejections that the selection is good." (Chester I Barnard, "The Functions of the Executive", 1938)

"To hold a group or individual accountable for activities of any kind without assigning to him or them the necessary authority to discharge that responsibility is manifestly both unsatisfactory and inequitable. It is of great Importance to smooth working that at all levels authority and responsibility should be coterminous and coequal." (Lyndall Urwick, "Dynamic Administration", 1942)

"All behavior involves conscious or unconscious selection of particular actions out of all those which are physically possible to the actor and to those persons over whom he exercises influence and authority." (Herbert A Simon, "Administrative Behavior: A Study of Decision-making Processes in Administrative Organization", 1947)

"Coordination, therefore, is the orderly arrangement of group efforts, to provide unity of action in the pursuit of a common purpose. As coordination is the all inclusive principle of organization it must have its own principle and foundation in authority, or the supreme coordination power. Always, in every form of organization, this supreme authority must rest somewhere, else there would be no directive for any coordinated effort." (James D Mooney, "The Principles of Organization", 1947)

"Delegation means the conferring of a specified authority by a higher authority. In its essence it involves a dual responsibility. The one to whom responsibility is delegated becomes responsible to the superior for doing the job. but the superior remains responsible for getting the Job done. This principle of delegation is the center of all processes in formal organization. Delegation is inherent in the very nature of the relation between superior and subordinate. The moment the objective calls for the organized effort of more than one person, there is always leadership with its delegation of duties." (James D Mooney, "The Principles of Organization", 1947)

"Power on the one side, fear on the other, are always the buttresses on which irrational authority is built." (Erich Fromm, "Man for Himself: An Inquiry Into the Psychology of Ethics", 1947)

"Authority is not a quality one person 'has', in the sense that he has property or physical qualities. Authority refers to an interpersonal relation in which one person looks upon another as somebody superior to him." (Erich Fromm, "The Fear of Freedom", 1950)

"The only way for a large organization to function is to decentralize, to delegate real authority and responsibility to the man on the job. But be certain you have the right man on the job." (Robert E Wood, 1951)

"[...] authority - the right by which superiors are able to require conformity of subordinates to decisions - is the basis for responsibility and the force that binds organization together. The process of organizing encompasses grouping of activities for purposes of management and specification of authority relationships between superiors and subordinates and horizontally between managers. Consequently, authority and responsibility relationships come into being in all associative undertakings where the superior-subordinate link exists. It is these relationships that create the basic character of the managerial job." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"Although organization charts are useful, necessary, and often revealing tools, they are subject to many important limitations. In the first place, a chart shows only formal authority relationships and omits the many significant informal and informational relationships that exist in a living organization. Moreover, it does not picture how much authority exists at any point in the organization." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"[...] authority for given tasks is limited to that for which an individual may properly held responsible." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"Authority delegations from a superior to a subordinate may be made in large or small degree. The tendency to delegate much authority through the echelons of an organization structure is referred tojas decentralization of authority. On the other hand, authority is said to be centralized wherever a manager tends not to delegate authority to his subordinates." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"Authority is, of course, completely centralized when a manager delegates none, and it is possible to think of the reverse situation - an infinite delegation of authority in which no manager retains any authority other than the implicit power to recover delegated authority. But this kind of delegation is obviously impracticable, since, at some point in the organization structure, delegations must stop." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

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

"It is highly important for managers to be honest and clear in describing what authority they are keeping and what role they are asking their subordinates to assume." (Robert Tannenbaum & Warren H Schmidt, Harvard Business Review, 1958)

"Formal theories of organization have been taught in management courses for many years, and there is an extensive literature on the subject. The textbook principles of organization — hierarchical structure, authority, unity of command, task specialization, division of staff and line, span of control, equality of responsibility and authority, etc. - comprise a logically persuasive set of assumptions which have had a profound influence upon managerial behavior." (Douglas McGregor, 'The Human Side of Enterprise", 1960)

"If there is a single assumption which pervades conventional organizational theory, it is that authority is the central, indispensable means of managerial control." (Douglas McGregor, "The Human Side of Enterprise", 1960)

"The ingenuity of the average worker is sufficient to outwit any system of controls devised by management." (Douglas McGregor, "The Human Side of Enterprise", 1960)

"You can delegate authority, but you can never delegate responsibility by delegating a task to someone else. If you picked the right man, fine, but if you picked the wrong man, the responsibility is yours - not his." (Richard E Krafve, The Boston Sunday Globe, 1960)

"Centralized controls are designed to ensure that the chief executive can find out how well the delegated authority and responsibility are being exercised." (Ernest Dale, "Management: Theory and practice", 1965)

"In large-scale organizations, the factual approach must be constantly nurtured by high-level executives. The more layers of authority through which facts must pass before they reach the decision maker, the greater the danger that they will be suppressed, modified, or softened, so as not to displease the 'brass"' For this reason, high-level executives must keep reaching for facts or soon they won't know what is going on. Unless they make visible efforts to seek and act on facts, major problems will not be brought to their attention, the quality of their decisions will decline, and the business will gradually get out of touch with its environment." (Marvin Bower, "The Will to Manage", 1966)

"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)

"[Management] has authority only as long as it performs." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"'Management' means, in the last analysis, the substitution of thought for brawn and muscle, of knowledge for folkways and superstition, and of cooperation for force. It means the substitution of responsibility for obedience to rank, and of authority of performance for authority of rank. (Peter F Drucker, "People and Performance", 1977)

"The key to successful leadership today is influence, not authority." (Kenneth H Blanchard, "Managing By Influence", 1986)

"Strange as it sounds, great leaders gain authority by giving it away." (James B Stockdale, "Military Ethics" 1987)

"Perhaps nothing in our society is more needed for those in positions of authority than accountability." (Larry Burkett, "Business By The Book: Complete Guide of Biblical Principles for the Workplace", 1990)

"When everything is connected to everything in a distributed network, everything happens at once. When everything happens at once, wide and fast moving problems simply route around any central authority. Therefore overall governance must arise from the most humble interdependent acts done locally in parallel, and not from a central command. " (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Authority alone is like pushing from behind. What automatic reaction do you have when pushed from behind? Resistance - unless you are travelling in that direction anyway and you experience the push as helpful. When you do not know what lies ahead and you are not sure whether you want to move forward, resistance is completely understandable. [...] Authority alone pushes. Leadership pulls, because it draws people towards a vision of the future that attracts them." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Authority works best where you have an accepted hierarchy [...]. Then people move together because of the strong implicit accepted values that everyone shares. If you are trying to lead people who do not share similar goals and values, then authority is not enough." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"The ultimate authority must always rest with the individual's own reason and critical analysis." (Tenzin Gyatso, "Path To Tranquility", 1998)

"The premise here is that the hierarchy lines on the chart are also the only communication conduit. Information can flow only along the lines. [...] The hierarchy lines are paths of authority. When communication happens only over the hierarchy lines, that's a priori evidence that the managers are trying to hold on to all control. This is not only inefficient but an insult to the people underneath." (Tom DeMarco, "Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency", 2001)

"A system is a framework that orders and sequences activity within the organisation to achieve a purpose within a band of variance that is acceptable to the owner of the system.  Systems are the organisational equivalent of behaviour in human interaction. Systems are the means by which organisations put policies into action.  It is the owner of a system who has the authority to change it, hence his or her clear acceptance of the degree of variation generated by the existing system." (Catherine Burke et al, "Systems Leadership" 2nd Ed., 2018)

"Responsibility means an inevitable punishment for mistakes; authority means full power to make them." (Yegor Bugayenko, "Code Ahead", 2018)

"Control is not leadership; management is not leadership; leadership is leadership. If you seek to lead, invest at least 50% of your time in leading yourself–your own purpose, ethics, principles, motivation, conduct. Invest at least 20% leading those with authority over you and 15% leading your peers." (Dee Hock)

"Delegation of authority is one of the most important functions of a leader, and he should delegate authority to the maximum degree possible with regard to the capabilities of his people. Once he has established policy, goals, and priorities, the leader accomplishes his objectives by pushing authority right down to the bottom. Doing so trains people to use their initiative; not doing so stifles creativity and lowers morale." (Thornas H Moorer)

"Leadership means that a group, large or small, is willing to entrust authority to a person who has shown judgement, wisdom, personal appeal, and proven competence." (Walt Disney)

"The teams and staffs through which the modern commander absorbs information and exercises his authority must be a beautifully interlocked, smooth-working mechanism. Ideally, the whole should be practically a single mind." (Dwight D Eisenhower)

"While basic laws underlie command authority, the real foundation of successful leadership is the moral authority derived from professional competence and integrity. Competence and integrity are not separable." (William C Westmoreland)

🤝Governance: Accountability (Just the Quotes)

"To hold a group or individual accountable for activities of any kind without assigning to him or them the necessary authority to discharge that responsibility is manifestly both unsatisfactory and inequitable. It is of great Importance to smooth working that at all levels authority and responsibility should be coterminous and coequal." (Lyndall Urwick, "Dynamic Administration", 1942)

"Complete accountability is established and enforced throughout; and if there there is any error committed, it will be discovered on a comparison with the books and can be traced to its source." (Alfred D Chandler Jr, "The Visible Hand", 1977)

"If responsibility - and particularly accountability - is most obviously upwards, moral responsibility also reaches downwards. The commander has a responsibility to those whom he commands. To forget this is to vitiate personal integrity and the ethical validity of the system." (Roger L Shinn, "Military Ethics", 1987)

"Perhaps nothing in our society is more needed for those in positions of authority than accountability." (Larry Burkett, "Business By The Book: Complete Guide of Biblical Principles for the Workplace", 1990)

"Corporate governance is concerned with holding the balance between economic and social goals and between individual and communal goals. The governance framework is there to encourage the efficient use of resources and equally to require accountability for the stewardship of those resources. The aim is to align as nearly as possible the interests of individuals, corporations and society." (Dominic Cadbury, "UK, Commission Report: Corporate Governance", 1992)

"Accountability is essential to personal growth, as well as team growth. How can you improve if you're never wrong? If you don't admit a mistake and take responsibility for it, you're bound to make the same one again." (Pat Summitt, "Reach for the Summit", 1999)

"Responsibility equals accountability equals ownership. And a sense of ownership is the most powerful weapon a team or organization can have." (Pat Summitt, "Reach for the Summit", 1999)

"There's not a chance we'll reach our full potential until we stop blaming each other and start practicing personal accountability." (John G Miller, "QBQ!: The Question Behind the Question", 2001)

"Democracy is not about trust; it is about distrust. It is about accountability, exposure, open debate, critical challenge, and popular input and feedback from the citizenry." (Michael Parenti, "Superpatriotism", 2004)

"No individual can achieve worthy goals without accepting accountability for his or her own actions." (Dan Miller, "No More Dreaded Mondays", 2008)

"In putting together your standards, remember that it is essential to involve your entire team. Standards are not rules issued by the boss; they are a collective identity. Remember, standards are the things that you do all the time and the things for which you hold one another accountable." (Mike Krzyzewski, "The Gold Standard: Building a World-Class Team", 2009)

"Nobody can do everything well, so learn how to delegate responsibility to other winners and then hold them accountable for their decisions." (George Foreman, "Knockout Entrepreneur: My Ten-Count Strategy for Winning at Business", 2010)

"Failing to hold someone accountable is ultimately an act of selfishness." (Patrick Lencioni, "The Advantage, Enhanced Edition: Why Organizational Health Trumps Everything Else In Business", 2012)

"We cannot have a just society that applies the principle of accountability to the powerless and the principle of forgiveness to the powerful. This is the America in which we currently reside." (Chris Hayes, "Twilight of the Elites: America After Meritocracy", 2012)

"Artificial intelligence is a concept that obscures accountability. Our problem is not machines acting like humans - it's humans acting like machines." (John Twelve Hawks, "Spark", 2014)

"In order to cultivate a culture of accountability, first it is essential to assign it clearly. People ought to clearly know what they are accountable for before they can be held to it. This goes beyond assigning key responsibility areas (KRAs). To be accountable for an outcome, we need authority for making decisions, not just responsibility for execution. It is tempting to refrain from the tricky exercise of explicitly assigning accountability. Executives often hope that their reports will figure it out. Unfortunately, this is easier said than done." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Some hierarchy is essential for the effective functioning of an organization. Eliminating hierarchy has the frequent side effect of slowing down decision making and diffusing accountability." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Accountability makes no sense when it undermines the larger goals of education." (Diane Ravitch, "The Death and Life of the Great American School System", 2016)

"[...] high-accountability teams are characterized by having members that are willing and able to resolve issues within the team. They take responsibility for their own actions and hold each other accountable. They take ownership of resolving disputes and feel empowered to do so without intervention from others. They learn quickly by identifying issues and solutions together, adopting better patterns over time. They are able to work without delay because they don’t need anyone else to resolve problems. Their managers are able to work more strategically without being bogged down by day-to-day conflict resolution." (Morgan Evans, "Engineering Manager's Handbook", 2023)

"In a workplace setting, accountability is the willingness to take responsibility for one’s actions and their outcomes. Accountable team members take ownership of their work, admit their mistakes, and are willing to hold each other accountable as peers." (Morgan Evans, "Engineering Manager's Handbook", 2023)

"Low-accountability teams can be recognized based on their tendency to shift blame, avoid addressing issues within the team, and escalate most problems to their manager. In low-accountability teams, it is difficult to determine the root of problems, failures are met with apathy, and managers have to spend much of their time settling disputes and addressing performance. Members of low-accountability teams believe it is not their role to resolve disputes and instead shift that responsibility up to the manager, waiting for further direction. These teams fall into conflict and avoidance deadlocks, unable to move quickly because they cannot resolve issues within the team."

🤝Governance: Enterprise Risk Management (Definitions)

"A model for IT governance that is risk-based integrating internal control, the Sarbanes-Oxley Act mandates, and strategic planning." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)

"Process of continuously identifying, assessing, mitigating, and monitoring relevant business risks in a comprehensive and integrated way." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed, 2011)

"The process of planning, organizing, leading, and controlling the activities of an organization in order to minimize the effects of risk on its capital and earnings. ERM includes not only risks associated with accidental losses, but also financial, strategic, operational, and other risks." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The application of risk management approaches across an organization in a structured and disciplined manner." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"The governing process for managing risks and opportunities." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"Enterprise risk management is a framework for risk management, including organization and governance, internal controls, key processes, systems and information and risk culture. ERM begins by identifying events or circumstances relevant to the organization's objectives (risks and opportunities), assessing them in terms of likelihood and magnitude of impact, determining a response strategy and monitoring progress." (Thomas C Wilson, "Value and Capital Management", 2015)

🔭Data Science: Big Data (Just the Quotes)

"If we gather more and more data and establish more and more associations, however, we will not finally find that we know something. We will simply end up having more and more data and larger sets of correlations." (Kenneth N Waltz, "Theory of International Politics Source: Theory of International Politics", 1979)

“There are those who try to generalize, synthesize, and build models, and there are those who believe nothing and constantly call for more data. The tension between these two groups is a healthy one; science develops mainly because of the model builders, yet they need the second group to keep them honest.” (Andrew Miall, “Principles of Sedimentary Basin Analysis”, 1984)

"Big data can change the way social science is performed, but will not replace statistical common sense." (Thomas Landsall-Welfare, "Nowcasting the mood of the nation", Significance 9(4), 2012)

"Big Data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it." (Edd Wilder-James, "What is big data?", 2012) [source]

"The secret to getting the most from Big Data isn’t found in huge server farms or massive parallel computing or in-memory algorithms. Instead, it’s in the almighty pencil." (Matt Ariker, "The One Tool You Need To Make Big Data Work: The Pencil", 2012)

"Big data is the most disruptive force this industry has seen since the introduction of the relational database." (Jeffrey Needham, "Disruptive Possibilities: How Big Data Changes Everything", 2013)

"No subjective metric can escape strategic gaming [...] The possibility of mischief is bottomless. Fighting ratings is fruitless, as they satisfy a very human need. If one scheme is beaten down, another will take its place and wear its flaws. Big Data just deepens the danger. The more complex the rating formulas, the more numerous the opportunities there are to dress up the numbers. The larger the data sets, the harder it is to audit them." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

"There is convincing evidence that data-driven decision-making and big data technologies substantially improve business performance. Data science supports data-driven decision-making - and sometimes conducts such decision-making automatically - and depends upon technologies for 'big data' storage and engineering, but its principles are separate." (Foster Provost & Tom Fawcett, "Data Science for Business", 2013)

"Our needs going forward will be best served by how we make use of not just this data but all data. We live in an era of Big Data. The world has seen an explosion of information in the past decades, so much so that people and institutions now struggle to keep pace. In fact, one of the reasons for the attachment to the simplicity of our indicators may be an inverse reaction to the sheer and bewildering volume of information most of us are bombarded by on a daily basis. […] The lesson for a world of Big Data is that in an environment with excessive information, people may gravitate toward answers that simplify reality rather than embrace the sheer complexity of it." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"The other buzzword that epitomizes a bias toward substitution is 'big data'. Today’s companies have an insatiable appetite for data, mistakenly believing that more data always creates more value. But big data is usually dumb data. Computers can find patterns that elude humans, but they don’t know how to compare patterns from different sources or how to interpret complex behaviors. Actionable insights can only come from a human analyst (or the kind of generalized artificial intelligence that exists only in science fiction)." (Peter Thiel & Blake Masters, "Zero to One: Notes on Startups, or How to Build the Future", 2014)

"We have let ourselves become enchanted by big data only because we exoticize technology. We’re impressed with small feats accomplished by computers alone, but we ignore big achievements from complementarity because the human contribution makes them less uncanny. Watson, Deep Blue, and ever-better machine learning algorithms are cool. But the most valuable companies in the future won’t ask what problems can be solved with computers alone. Instead, they’ll ask: how can computers help humans solve hard problems?" (Peter Thiel & Blake Masters, "Zero to One: Notes on Startups, or How to Build the Future", 2014)

"As business leaders we need to understand that lack of data is not the issue. Most businesses have more than enough data to use constructively; we just don't know how to use it. The reality is that most businesses are already data rich, but insight poor." (Bernard Marr, Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, 2015)

"Big data is based on the feedback economy where the Internet of Things places sensors on more and more equipment. More and more data is being generated as medical records are digitized, more stores have loyalty cards to track consumer purchases, and people are wearing health-tracking devices. Generally, big data is more about looking at behavior, rather than monitoring transactions, which is the domain of traditional relational databases. As the cost of storage is dropping, companies track more and more data to look for patterns and build predictive models." (Neil Dunlop, "Big Data", 2015)

"Big Data often seems like a meaningless buzz phrase to older database professionals who have been experiencing exponential growth in database volumes since time immemorial. There has never been a moment in the history of database management systems when the increasing volume of data has not been remarkable." (Guy Harrison, "Next Generation Databases: NoSQL, NewSQL, and Big Data", 2015)

"Dimensionality reduction is essential for coping with big data - like the data coming in through your senses every second. A picture may be worth a thousand words, but it’s also a million times more costly to process and remember. [...] A common complaint about big data is that the more data you have, the easier it is to find spurious patterns in it. This may be true if the data is just a huge set of disconnected entities, but if they’re interrelated, the picture changes." (Pedro Domingos, "The Master Algorithm", 2015)

"Science’s predictions are more trustworthy, but they are limited to what we can systematically observe and tractably model. Big data and machine learning greatly expand that scope. Some everyday things can be predicted by the unaided mind, from catching a ball to carrying on a conversation. Some things, try as we might, are just unpredictable. For the vast middle ground between the two, there’s machine learning." (Pedro Domingos, "The Master Algorithm", 2015)

"The human side of analytics is the biggest challenge to implementing big data." (Paul Gibbons, "The Science of Successful Organizational Change", 2015)

"To make progress, every field of science needs to have data commensurate with the complexity of the phenomena it studies. [...] With big data and machine learning, you can understand much more complex phenomena than before. In most fields, scientists have traditionally used only very limited kinds of models, like linear regression, where the curve you fit to the data is always a straight line. Unfortunately, most phenomena in the world are nonlinear. [...] Machine learning opens up a vast new world of nonlinear models." (Pedro Domingos, "The Master Algorithm", 2015)

"Underfitting is when a model doesn’t take into account enough information to accurately model real life. For example, if we observed only two points on an exponential curve, we would probably assert that there is a linear relationship there. But there may not be a pattern, because there are only two points to reference. [...] It seems that the best way to mitigate underfitting a model is to give it more information, but this actually can be a problem as well. More data can mean more noise and more problems. Using too much data and too complex of a model will yield something that works for that particular data set and nothing else." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"We are moving slowly into an era where Big Data is the starting point, not the end." (Pearl Zhu, "Digital Master: Debunk the Myths of Enterprise Digital Maturity", 2015)

"A popular misconception holds that the era of Big Data means the end of a need for sampling. In fact, the proliferation of data of varying quality and relevance reinforces the need for sampling as a tool to work efficiently with a variety of data, and minimize bias. Even in a Big Data project, predictive models are typically developed and piloted with samples." (Peter C Bruce & Andrew G Bruce, "Statistics for Data Scientists: 50 Essential Concepts", 2016)

"Big data is, in a nutshell, large amounts of data that can be gathered up and analyzed to determine whether any patterns emerge and to make better decisions." (Daniel Covington, Analytics: Data Science, Data Analysis and Predictive Analytics for Business, 2016)

"Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead. Sometimes that will mean putting fairness ahead of profit." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"While Big Data, when managed wisely, can provide important insights, many of them will be disruptive. After all, it aims to find patterns that are invisible to human eyes. The challenge for data scientists is to understand the ecosystems they are wading into and to present not just the problems but also their possible solutions." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"Big Data allows us to meaningfully zoom in on small segments of a dataset to gain new insights on who we are." (Seth Stephens-Davidowitz, "Everybody Lies: What the Internet Can Tell Us About Who We Really Are", 2017)

"Effects without an understanding of the causes behind them, on the other hand, are just bunches of data points floating in the ether, offering nothing useful by themselves. Big Data is information, equivalent to the patterns of light that fall onto the eye. Big Data is like the history of stimuli that our eyes have responded to. And as we discussed earlier, stimuli are themselves meaningless because they could mean anything. The same is true for Big Data, unless something transformative is brought to all those data sets… understanding." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"The term [Big Data] simply refers to sets of data so immense that they require new methods of mathematical analysis, and numerous servers. Big Data - and, more accurately, the capacity to collect it - has changed the way companies conduct business and governments look at problems, since the belief wildly trumpeted in the media is that this vast repository of information will yield deep insights that were previously out of reach." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"There are other problems with Big Data. In any large data set, there are bound to be inconsistencies, misclassifications, missing data - in other words, errors, blunders, and possibly lies. These problems with individual items occur in any data set, but they are often hidden in a large mass of numbers even when these numbers are generated out of computer interactions." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"Just as they did thirty years ago, machine learning programs (including those with deep neural networks) operate almost entirely in an associational mode. They are driven by a stream of observations to which they attempt to fit a function, in much the same way that a statistician tries to fit a line to a collection of points. Deep neural networks have added many more layers to the complexity of the fitted function, but raw data still drives the fitting process. They continue to improve in accuracy as more data are fitted, but they do not benefit from the 'super-evolutionary speedup'."  (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"One of the biggest myths is the belief that data science is an autonomous process that we can let loose on our data to find the answers to our problems. In reality, data science requires skilled human oversight throughout the different stages of the process. [...] The second big myth of data science is that every data science project needs big data and needs to use deep learning. In general, having more data helps, but having the right data is the more important requirement. [...] A third data science myth is that modern data science software is easy to use, and so data science is easy to do. [...] The last myth about data science [...] is the belief that data science pays for itself quickly. The truth of this belief depends on the context of the organization. Adopting data science can require significant investment in terms of developing data infrastructure and hiring staff with data science expertise. Furthermore, data science will not give positive results on every project." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"Apart from the technical challenge of working with the data itself, visualization in big data is different because showing the individual observations is just not an option. But visualization is essential here: for analysis to work well, we have to be assured that patterns and errors in the data have been spotted and understood. That is only possible by visualization with big data, because nobody can look over the data in a table or spreadsheet." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"With the growing availability of massive data sets and user-friendly analysis software, it might be thought that there is less need for training in statistical methods. This would be naïve in the extreme. Far from freeing us from the need for statistical skills, bigger data and the rise in the number and complexity of scientific studies makes it even more difficult to draw appropriate conclusions. More data means that we need to be even more aware of what the evidence is actually worth." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"Big data is revolutionizing the world around us, and it is easy to feel alienated by tales of computers handing down decisions made in ways we don’t understand. I think we’re right to be concerned. Modern data analytics can produce some miraculous results, but big data is often less trustworthy than small data. Small data can typically be scrutinized; big data tends to be locked away in the vaults of Silicon Valley. The simple statistical tools used to analyze small datasets are usually easy to check; pattern-recognizing algorithms can all too easily be mysterious and commercially sensitive black boxes." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Making big data work is harder than it seems. Statisticians have spent the past two hundred years figuring out what traps lie in wait when we try to understand the world through data. The data are bigger, faster, and cheaper these days, but we must not pretend that the traps have all been made safe. They have not." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Many people have strong intuitions about whether they would rather have a vital decision about them made by algorithms or humans. Some people are touchingly impressed by the capabilities of the algorithms; others have far too much faith in human judgment. The truth is that sometimes the algorithms will do better than the humans, and sometimes they won’t. If we want to avoid the problems and unlock the promise of big data, we’re going to need to assess the performance of the algorithms on a case-by-case basis. All too often, this is much harder than it should be. […] So the problem is not the algorithms, or the big datasets. The problem is a lack of scrutiny, transparency, and debate." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"The problem is the hype, the notion that something magical will emerge if only we can accumulate data on a large enough scale. We just need to be reminded: Big data is not better; it’s just bigger. And it certainly doesn’t speak for itself." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"[...] the focus on Big Data AI seems to be an excuse to put forth a number of vague and hand-waving theories, where the actual details and the ultimate success of neuroscience is handed over to quasi- mythological claims about the powers of large datasets and inductive computation. Where humans fail to illuminate a complicated domain with testable theory, machine learning and big data supposedly can step in and render traditional concerns about finding robust theories. This seems to be the logic of Data Brain efforts today. (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"We live on islands surrounded by seas of data. Some call it 'big data'. In these seas live various species of observable phenomena. Ideas, hypotheses, explanations, and graphics also roam in the seas of data and can clarify the waters or allow unsupported species to die. These creatures thrive on visual explanation and scientific proof. Over time new varieties of graphical species arise, prompted by new problems and inner visions of the fishers in the seas of data." (Michael Friendly & Howard Wainer, "A History of Data Visualization and Graphic Communication", 2021)

"Visualizations can remove the background noise from enormous sets of data so that only the most important points stand out to the intended audience. This is particularly important in the era of big data. The more data there is, the more chance for noise and outliers to interfere with the core concepts of the data set." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Visualisation is fundamentally limited by the number of pixels you can pump to a screen. If you have big data, you have way more data than pixels, so you have to summarise your data. Statistics gives you lots of really good tools for this." (Hadley Wickham)

🔭Data Science: Information (Just the Quotes)

"Probability, however, is not something absolute, [it is] drawn from certain information which, although it does not suffice to resolve the problem, nevertheless ensures that we judge correctly which of the two opposites is the easiest given the conditions known to us." (Gottfried W Leibniz, "Forethoughts for an encyclopaedia or universal science", cca. 1679)

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

"What is called science today consists of a haphazard heap of information, united by nothing, often utterly unnecessary, and not only failing to present one unquestionable truth, but as often as not containing the grossest errors, today put forward as truths, and tomorrow overthrown." (Leo Tolstoy, "What Is Art?", 1897)

"There can be no unique probability attached to any event or behaviour: we can only speak of ‘probability in the light of certain given information’, and the probability alters according to the extent of the information." (Sir Arthur S Eddington, "The Nature of the Physical World" , 1928)

"As words are not the things we speak about, and structure is the only link between them, structure becomes the only content of knowledge. If we gamble on verbal structures that have no observable empirical structures, such gambling can never give us any structural information about the world. Therefore such verbal structures are structurally obsolete, and if we believe in them, they induce delusions or other semantic disturbances." (Alfred Korzybski, "Science and Sanity", 1933)

"Much of the waste in business is due to lack of information. And when the information is available, waste often occurs because of lack of application or because of misapplication." (John R Riggleman & Ira N Frisbee, "Business Statistics", 1938)

"Upon this gifted age, in its dark hour, rains from the sky a meteoric shower of facts […] they lie, unquestioned, uncombined. Wisdom enough to leach us of our ill is daily spun; but there exists no loom to weave it into a fabric." (Edna St. Vincent Millay, "Huntsman, What Quarry?", 1939)

"Just as entropy is a measure of disorganization, the information carried by a set of messages is a measure of organization. In fact, it is possible to interpret the information carried by a message as essentially the negative of its entropy, and the negative logarithm of its probability. That is, the more probable the message, the less information it gives. Clichés, for example, are less illuminating than great poems." (Norbert Wiener, "The Human Use of Human Beings", 1950)

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

"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)

"This is the key of modern science and it was the beginning of the true understanding of Nature - this idea to look at the thing, to record the details, and to hope that in the information thus obtained might lie a clue to one or another theoretical interpretation." (Richard P Feynman, "The Character of Physical Law", 1965)

"[...] 'information' is not a substance or concrete entity but rather a relationship between sets or ensembles of structured variety." (Walter F Buckley, "Sociology and modern systems theory", 1967)

"There are as many types of questions as components in the information." (Jacques Bertin, Semiology of graphics [Semiologie Graphique], 1967)

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

"When action grows unprofitable, gather information; when information grows unprofitable, sleep. (Ursula K Le Guin, "The Left Hand of Darkness", 1969)

"What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it." (Herbert Simon, "Computers, Communications and the Public Interest", 1971)

"What we mean by information - the elementary unit of information - is a difference which makes a difference, and it is able to make a difference because the neural pathways along which it travels and is continually transformed are themselves provided with energy. The pathways are ready to be triggered. We may even say that the question is already implicit in them." (Gregory Bateson, "Steps to an Ecology of Mind", 1972)

"Science gets most of its information by the process of reductionism, exploring the details, then the details of the details, until all the smallest bits of the structure, or the smallest parts of the mechanism, are laid out for counting and scrutiny. Only when this is done can the investigation be extended to encompass the whole organism or the entire system. So we say. Sometimes it seems that we take a loss, working this way." (Lewis Thomas, "The Medusa and the Snail: More Notes of a Biology Watcher", 1974)

"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)

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

"To a considerable degree science consists in originating the maximum amount of information with the minimum expenditure of energy. Beauty is the cleanness of line in such formulations along with symmetry, surprise, and congruence with other prevailing beliefs." (Edward O Wilson, "Biophilia", 1984)

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

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

"Probability plays a central role in many fields, from quantum mechanics to information theory, and even older fields use probability now that the presence of 'noise' is officially admitted. The newer aspects of many fields start with the admission of uncertainty." (Richard W Hamming, "Methods of Mathematics Applied to Calculus, Probability, and Statistics", 1985)

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

"Information exists. It does not need to be perceived to exist. It does not need to be understood to exist. It requires no intelligence to interpret it. It does not have to have meaning to exist. It exists." (Tom Stonier, "Information and the Internal Structure of the Universe: An Exploration into Information Physics", 1990)

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

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

"The science of statistics may be described as exploring, analyzing and summarizing data; designing or choosing appropriate ways of collecting data and extracting information from them; and communicating that information. Statistics also involves constructing and testing models for describing chance phenomena. These models can be used as a basis for making inferences and drawing conclusions and, finally, perhaps for making decisions." (Fergus Daly et al, "Elements of Statistics", 1995)

"[Schemata are] knowledge structures that represent objects or events and provide default assumptions about their characteristics, relationships, and entailments under conditions of incomplete information." (Paul J DiMaggio, "Culture and Cognition", Annual Review of Sociology No. 23, 1997)

"When it comes to information, it turns out that one can have too much of a good thing. At a certain level of input, the law of diminishing returns takes effect; the glut of information no longer adds to our quality of life, but instead begins to cultivate stress, confusion, and even ignorance." (David Shenk, "Data Smog", 1997)

"Each element in the system is ignorant of the behavior of the system as a whole, it responds only to information that is available to it locally. This point is vitally important. If each element ‘knew’ what was happening to the system as a whole, all of the complexity would have to be present in that element." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems" , 1998)

"Complexity is that property of a model which makes it difficult to formulate its overall behaviour in a given language, even when given reasonably complete information about its atomic components and their inter-relations." (Bruce Edmonds, "Syntactic Measures of Complexity", 1999)

"A model isolates one or a few causal connections, mechanisms, or processes, to the exclusion of other contributing or interfering factors - while in the actual world, those other factors make their effects felt in what actually happens. Models may seem true in the abstract, and are false in the concrete. The key issue is about whether there is a bridge between the two, the abstract and the concrete, such that a simple model can be relied on as a source of relevantly truthful information about the complex reality." (Uskali Mäki, "Fact and Fiction in Economics: Models, Realism and Social Construction", 2002)

"Entropy is not about speeds or positions of particles, the way temperature and pressure and volume are, but about our lack of information." (Hans C von Baeyer," Information, The New Language of Science", 2003)

"The use of computers shouldn't ignore the objectives of graphics, that are: 

 1) Treating data to get information. 

 2) Communicating, when necessary, the information obtained." (Jacques Bertin, [interview] 2003)

"There is no end to the information we can use. A 'good' map provides the information we need for a particular purpose - or the information the mapmaker wants us to have. To guide us, a map’s designers must consider more than content and projection; any single map involves hundreds of decisions about presentation." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"While in theory randomness is an intrinsic property, in practice, randomness is incomplete information." (Nassim N Taleb, "The Black Swan", 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)

"Access to more information isn’t enough - the information needs to be correct, timely, and presented in a manner that enables the reader to learn from it. The current network is full of inaccurate, misleading, and biased information that often crowds out the valid information. People have not learned that 'popular' or 'available' information is not necessarily valid." (Gene Spafford, 2010) 

"We face danger whenever information growth outpaces our understanding of how to process it. The last forty years of human history imply that it can still take a long time to translate information into useful knowledge, and that if we are not careful, we may take a step back in the meantime." (Nate Silver, "The Signal and the Noise", 2012)

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

"Complexity scientists concluded that there are just too many factors - both concordant and contrarian - to understand. And with so many potential gaps in information, almost nobody can see the whole picture. Complex systems have severe limits, not only to predictability but also to measurability. Some complexity theorists argue that modelling, while useful for thinking and for studying the complexities of the world, is a particularly poor tool for predicting what will happen." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"One of the most powerful transformational catalysts is knowledge, new information, or logic that defies old mental models and ways of thinking" (Elizabeth Thornton, "The Objective Leader", 2015)

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

🔭Data Science: Data Analysis (Just the Quotes)

"As in Mathematics, so in Natural Philosophy, the Investigation of difficult Things by the Method of Analysis, ought ever to precede the Method of Composition. This Analysis consists in making Experiments and Observations, and in drawing general Conclusions from them by Induction, and admitting of no Objections against the Conclusions but such as are taken from Experiments, or other certain Truths." (Sir Isaac Newton, "Opticks", 1704)

"The errors which arise from the absence of facts are far more numerous and more durable than those which result from unsound reasoning respecting true data." (Charles Babbage, "On the Economy of Machinery and Manufactures", 1832)

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

"Not even the most subtle and skilled analysis can overcome completely the unreliability of basic data." (Roy D G Allen, "Statistics for Economists", 1951)

"The technical analysis of any large collection of data is a task for a highly trained and expensive man who knows the mathematical theory of statistics inside and out. Otherwise the outcome is likely to be a collection of drawings - quartered pies, cute little battleships, and tapering rows of sturdy soldiers in diversified uniforms - interesting enough in the colored Sunday supplement, but hardly the sort of thing from which to draw reliable inferences." (Eric T Bell, "Mathematics: Queen and Servant of Science", 1951)

"If data analysis is to be well done, much of it must be a matter of judgment, and ‘theory’ whether statistical or non-statistical, will have to guide, not command." (John W Tukey, "The Future of Data Analysis", Annals of Mathematical Statistics, Vol. 33 (1), 1962)

"If one technique of data analysis were to be exalted above all others for its ability to be revealing to the mind in connection with each of many different models, there is little doubt which one would be chosen. The simple graph has brought more information to the data analyst’s mind than any other device. It specializes in providing indications of unexpected phenomena." (John W Tukey, "The Future of Data Analysis", Annals of Mathematical Statistics Vol. 33 (1), 1962)

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

"The first step in data analysis is often an omnibus step. We dare not expect otherwise, but we equally dare not forget that this step, and that step, and other step, are all omnibus steps and that we owe the users of such techniques a deep and important obligation to develop ways, often varied and competitive, of replacing omnibus procedures by ones that are more sharply focused." (John W Tukey, "The Future of Processes of Data Analysis", 1965)

"The basic general intent of data analysis is simply stated: to seek through a body of data for interesting relationships and information and to exhibit the results in such a way as to make them recognizable to the data analyzer and recordable for posterity. Its creative task is to be productively descriptive, with as much attention as possible to previous knowledge, and thus to contribute to the mysterious process called insight." (John W Tukey & Martin B Wilk, "Data Analysis and Statistics: An Expository Overview", 1966)

"Comparable objectives in data analysis are (l) to achieve more specific description of what is loosely known or suspected; (2) to find unanticipated aspects in the data, and to suggest unthought-of-models for the data's summarization and exposure; (3) to employ the data to assess the (always incomplete) adequacy of a contemplated model; (4) to provide both incentives and guidance for further analysis of the data; and (5) to keep the investigator usefully stimulated while he absorbs the feeling of his data and considers what to do next." (John W Tukey & Martin B Wilk, "Data Analysis and Statistics: An Expository Overview", 1966)

"Data analysis must be iterative to be effective. [...] The iterative and interactive interplay of summarizing by fit and exposing by residuals is vital to effective data analysis. Summarizing and exposing are complementary and pervasive." (John W Tukey & Martin B Wilk, "Data Analysis and Statistics: An Expository Overview", 1966)

"Every student of the art of data analysis repeatedly needs to build upon his previous statistical knowledge and to reform that foundation through fresh insights and emphasis." (John W Tukey, "Data Analysis, Including Statistics", 1968)

"[...] bending the question to fit the analysis is to be shunned at all costs." (John W Tukey, "Analyzing Data: Sanctification or Detective Work?", 1969)

"Statistical methods are tools of scientific investigation. Scientific investigation is a controlled learning process in which various aspects of a problem are illuminated as the study proceeds. It can be thought of as a major iteration within which secondary iterations occur. The major iteration is that in which a tentative conjecture suggests an experiment, appropriate analysis of the data so generated leads to a modified conjecture, and this in turn leads to a new experiment, and so on." (George E P Box & George C Tjao, "Bayesian Inference in Statistical Analysis", 1973)

"Almost all efforts at data analysis seek, at some point, to generalize the results and extend the reach of the conclusions beyond a particular set of data. The inferential leap may be from past experiences to future ones, from a sample of a population to the whole population, or from a narrow range of a variable to a wider range. The real difficulty is in deciding when the extrapolation beyond the range of the variables is warranted and when it is merely naive. As usual, it is largely a matter of substantive judgment - or, as it is sometimes more delicately put, a matter of 'a priori nonstatistical considerations'." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"[…] it is not enough to say: 'There's error in the data and therefore the study must be terribly dubious'. A good critic and data analyst must do more: he or she must also show how the error in the measurement or the analysis affects the inferences made on the basis of that data and analysis." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The use of statistical methods to analyze data does not make a study any more 'scientific', 'rigorous', or 'objective'. The purpose of quantitative analysis is not to sanctify a set of findings. Unfortunately, some studies, in the words of one critic, 'use statistics as a drunk uses a street lamp, for support rather than illumination'. Quantitative techniques will be more likely to illuminate if the data analyst is guided in methodological choices by a substantive understanding of the problem he or she is trying to learn about. Good procedures in data analysis involve techniques that help to (a) answer the substantive questions at hand, (b) squeeze all the relevant information out of the data, and (c) learn something new about the world." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Typically, data analysis is messy, and little details clutter it. Not only confounding factors, but also deviant cases, minor problems in measurement, and ambiguous results lead to frustration and discouragement, so that more data are collected than analyzed. Neglecting or hiding the messy details of the data reduces the researcher's chances of discovering something new." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

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

"[...] exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as for those we believe might be there. Except for its emphasis on graphs, its tools are secondary to its purpose." (John W Tukey, [comment] 1979)

"[...] any hope that we are smart enough to find even transiently optimum solutions to our data analysis problems is doomed to failure, and, indeed, if taken seriously, will mislead us in the allocation of effort, thus wasting both intellectual and computational effort." (John W Tukey, "Choosing Techniques for the Analysis of Data", 1981)

"The fact must be expressed as data, but there is a problem in that the correct data is difficult to catch. So that I always say 'When you see the data, doubt it!' 'When you see the measurement instrument, doubt it!' [...]For example, if the methods such as sampling, measurement, testing and chemical analysis methods were incorrect, data. […] to measure true characteristics and in an unavoidable case, using statistical sensory test and express them as data." (Kaoru Ishikawa, Annual Quality Congress Transactions, 1981)

"Exploratory data analysis, EDA, calls for a relatively free hand in exploring the data, together with dual obligations: (•) to look for all plausible alternatives and oddities - and a few implausible ones, (graphic techniques can be most helpful here) and (•) to remove each appearance that seems large enough to be meaningful - ordinarily by some form of fitting, adjustment, or standardization [...] so that what remains, the residuals, can be examined for further appearances." (John W Tukey, "Introduction to Styles of Data Analysis Techniques", 1982)

"A competent data analysis of an even moderately complex set of data is a thing of trials and retreats, of dead ends and branches." (John W Tukey, Computer Science and Statistics: Proceedings of the 14th Symposium on the Interface, 1983)

"Data in isolation are meaningless, a collection of numbers. Only in context of a theory do they assume significance […]" (George Greenstein, "Frozen Star", 1983)

"Iteration and experimentation are important for all of data analysis, including graphical data display. In many cases when we make a graph it is immediately clear that some aspect is inadequate and we regraph the data. In many other cases we make a graph, and all is well, but we get an idea for studying the data in a different way with a different graph; one successful graph often suggests another." (William S Cleveland, "The Elements of Graphing Data", 1985)

"There are some who argue that a graph is a success only if the important information in the data can be seen within a few seconds. While there is a place for rapidly-understood graphs, it is too limiting to make speed a requirement in science and technology, where the use of graphs ranges from, detailed, in-depth data analysis to quick presentation." (William S Cleveland, "The Elements of Graphing Data", 1985)

"A first analysis of experimental results should, I believe, invariably be conducted using flexible data analytical techniques - looking at graphs and simple statistics - that so far as possible allow the data to 'speak for themselves'. The unexpected phenomena that such a approach often uncovers can be of the greatest importance in shaping and sometimes redirecting the course of an ongoing investigation." (George Box, "Signal to Noise Ratios, Performance Criteria, and Transformations", Technometrics 30, 1988)

"Data analysis is an art practiced by individuals who are skilled at quantitative reasoning and have much experience in looking at numbers and detecting  patterns in data. Usually these individuals have some background in statistics." (David Lubinsky, Daryl Pregibon , "Data analysis as search", Journal of Econometrics Vol. 38 (1–2), 1988)

"Like a detective, a data analyst will experience many dead ends, retrace his steps, and explore many alternatives before settling on a single description of the evidence in front of him." (David Lubinsky & Daryl Pregibon , "Data analysis as search", Journal of Econometrics Vol. 38 (1–2), 1988)

"[…] data analysis in the context of basic mathematical concepts and skills. The ability to use and interpret simple graphical and numerical descriptions of data is the foundation of numeracy […] Meaningful data aid in replacing an emphasis on calculation by the exercise of judgement and a stress on interpreting and communicating results." (David S Moore, "Statistics for All: Why, What and How?", 1990)

"Data analysis is rarely as simple in practice as it appears in books. Like other statistical techniques, regression rests on certain assumptions and may produce unrealistic results if those assumptions are false. Furthermore it is not always obvious how to translate a research question into a regression model." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"Data analysis typically begins with straight-line models because they are simplest, not because we believe reality is inherently linear. Theory or data may suggest otherwise [...]" (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"90 percent of all problems can be solved by using the techniques of data stratification, histograms, and control charts. Among the causes of nonconformance, only one-fifth or less are attributable to the workers." (Kaoru Ishikawa, The Quality Management Journal Vol. 1, 1993)

"Probabilistic inference is the classical paradigm for data analysis in science and technology. It rests on a foundation of randomness; variation in data is ascribed to a random process in which nature generates data according to a probability distribution. This leads to a codification of uncertainly by confidence intervals and hypothesis tests." (William S Cleveland, "Visualizing Data", 1993)

"Visualization is an approach to data analysis that stresses a penetrating look at the structure of data. No other approach conveys as much information. […] Conclusions spring from data when this information is combined with the prior knowledge of the subject under investigation." (William S Cleveland, "Visualizing Data", 1993)

"When the distributions of two or more groups of univariate data are skewed, it is common to have the spread increase monotonically with location. This behavior is monotone spread. Strictly speaking, monotone spread includes the case where the spread decreases monotonically with location, but such a decrease is much less common for raw data. Monotone spread, as with skewness, adds to the difficulty of data analysis. For example, it means that we cannot fit just location estimates to produce homogeneous residuals; we must fit spread estimates as well. Furthermore, the distributions cannot be compared by a number of standard methods of probabilistic inference that are based on an assumption of equal spreads; the standard t-test is one example. Fortunately, remedies for skewness can cure monotone spread as well." (William S Cleveland, "Visualizing Data", 1993)

"A careful and sophisticated analysis of the data is often quite useless if the statistician cannot communicate the essential features of the data to a client for whom statistics is an entirely foreign language." (Christopher J Wild, "Embracing the ‘Wider view’ of Statistics", The American Statistician 48, 1994)

"Science is not impressed with a conglomeration of data. It likes carefully constructed analysis of each problem." (Daniel E Koshland Jr, Science Vol. 263 (5144), [editorial] 1994)

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

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

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

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

"The purpose of analysis is insight. The best analysis is the simplest analysis which provides the needed insight." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Without meaningful data there can be no meaningful analysis. The interpretation of any data set must be based upon the context of those data." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Statistical analysis of data can only be performed within the context of selected assumptions, models, and/or prior distributions. A statistical analysis is actually the extraction of substantive information from data and assumptions. And herein lies the rub, understood well by Disraeli and others skeptical of our work: For given data, an analysis can usually be selected which will result in 'information' more favorable to the owner of the analysis then is objectively warranted." (Stephen B Vardeman & Max D Morris, "Statistics and Ethics: Some Advice for Young Statisticians", The American Statistician vol 57, 2003)

"Exploratory Data Analysis is more than just a collection of data-analysis techniques; it provides a philosophy of how to dissect a data set. It stresses the power of visualisation and aspects such as what to look for, how to look for it and how to interpret the information it contains. Most EDA techniques are graphical in nature, because the main aim of EDA is to explore data in an open-minded way. Using graphics, rather than calculations, keeps open possibilities of spotting interesting patterns or anomalies that would not be apparent with a calculation (where assumptions and decisions about the nature of the data tend to be made in advance)." (Alan Graham, "Developing Thinking in Statistics", 2006)

"It is the aim of all data analysis that a result is given in form of the best estimate of the true value. Only in simple cases is it possible to use the data value itself as result and thus as best estimate." (Manfred Drosg, "Dealing with Uncertainties: A Guide to Error Analysis", 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)

"Data analysis is careful thinking about evidence." (Michael Milton, "Head First Data Analysis", 2009)

"Doing data analysis without explicitly defining your problem or goal is like heading out on a road trip without having decided on a destination." (Michael Milton, "Head First Data Analysis", 2009)

"The discrepancy between our mental models and the real world may be a major problem of our times; especially in view of the difficulty of collecting, analyzing, and making sense of the unbelievable amount of data to which we have access today." (Ugo Bardi, "The Limits to Growth Revisited", 2011)

"Data analysis is not generally thought of as being simple or easy, but it can be. The first step is to understand that the purpose of data analysis is to separate any signals that may be contained within the data from the noise in the data. Once you have filtered out the noise, anything left over will be your potential signals. The rest is just details." (Donald J Wheeler," Myths About Data Analysis", International Lean & Six Sigma Conference, 2012)

"The four questions of data analysis are the questions of description, probability, inference, and homogeneity. Any data analyst needs to know how to organize and use these four questions in order to obtain meaningful and correct results. [...] 

THE DESCRIPTION QUESTION: Given a collection of numbers, are there arithmetic values that will summarize the information contained in those numbers in some meaningful way?

THE PROBABILITY QUESTION: Given a known universe, what can we say about samples drawn from this universe? [...] 

THE INFERENCE QUESTION: Given an unknown universe, and given a sample that is known to have been drawn from that unknown universe, and given that we know everything about the sample, what can we say about the unknown universe? [...] 

THE HOMOGENEITY QUESTION: Given a collection of observations, is it reasonable to assume that they came from one universe, or do they show evidence of having come from multiple universes?" (Donald J Wheeler," Myths About Data Analysis", International Lean & Six Sigma Conference, 2012)

"Each systems archetype embodies a particular theory about dynamic behavior that can serve as a starting point for selecting and formulating raw data into a coherent set of interrelationships. Once those relationships are made explicit and precise, the 'theory' of the archetype can then further guide us in our data-gathering process to test the causal relationships through direct observation, data analysis, or group deliberation." (Daniel H Kim, "Systems Archetypes as Dynamic Theories", The Systems Thinker Vol. 24 (1), 2013)

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

"The random element in most data analysis is assumed to be white noise - normal errors independent of each other. In a time series, the errors are often linked so that independence cannot be assumed (the last examples). Modeling the nature of this dependence is the key to time series." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

"Statistics is an integral part of the quantitative approach to knowledge. The field of statistics is concerned with the scientific study of collecting, organizing, analyzing, and drawing conclusions from data." (Kandethody M Ramachandran & Chris P Tsokos, "Mathematical Statistics with Applications in R" 2nd Ed., 2015)

"Too often there is a disconnect between the people who run a study and those who do the data analysis. This is as predictable as it is unfortunate. If data are gathered with particular hypotheses in mind, too often they (the data) are passed on to someone who is tasked with testing those hypotheses and who has only marginal knowledge of the subject matter. Graphical displays, if prepared at all, are just summaries or tests of the assumptions underlying the tests being done. Broader displays, that have the potential of showing us things that we had not expected, are either not done at all, or their message is not able to be fully appreciated by the data analyst." (Howard Wainer, Comment, Journal of Computational and Graphical Statistics Vol. 20(1), 2011)

"The dialectical interplay of experiment and theory is a key driving force of modern science. Experimental data do only have meaning in the light of a particular model or at least a theoretical background. Reversely theoretical considerations may be logically consistent as well as intellectually elegant: Without experimental evidence they are a mere exercise of thought no matter how difficult they are. Data analysis is a connector between experiment and theory: Its techniques advise possibilities of model extraction as well as model testing with experimental data." (Achim Zielesny, "From Curve Fitting to Machine Learning" 2nd Ed., 2016)

"Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational or otherwise empirical domain of interest. 'Structure' has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Symmetries directly point to invariants, which pinpoint intrinsic properties of the data and of the background empirical domain of interest. As our data models change, so too do our perspectives on analysing data." (Fionn Murtagh, "Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics", 2018)

"[…] the data itself can lead to new questions too. In exploratory data analysis (EDA), for example, the data analyst discovers new questions based on the data. The process of looking at the data to address some of these questions generates incidental visualizations - odd patterns, outliers, or surprising correlations that are worth looking into further." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Analysis is a two-step process that has an exploratory and an explanatory phase. In order to create a powerful data story, you must effectively transition from data discovery (when you’re finding insights) to data communication (when you’re explaining them to an audience). If you don’t properly traverse these two phases, you may end up with something that resembles a data story but doesn’t have the same effect. Yes, it may have numbers, charts, and annotations, but because it’s poorly formed, it won’t achieve the same results." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"While visuals are an essential part of data storytelling, data visualizations can serve a variety of purposes from analysis to communication to even art. Most data charts are designed to disseminate information in a visual manner. Only a subset of data compositions is focused on presenting specific insights as opposed to just general information. When most data compositions combine both visualizations and text, it can be difficult to discern whether a particular scenario falls into the realm of data storytelling or not." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"If the data that go into the analysis are flawed, the specific technical details of the analysis don’t matter. One can obtain stupid results from bad data without any statistical trickery. And this is often how bullshit arguments are created, deliberately or otherwise. To catch this sort of bullshit, you don’t have to unpack the black box. All you have to do is think carefully about the data that went into the black box and the results that came out. Are the data unbiased, reasonable, and relevant to the problem at hand? Do the results pass basic plausibility checks? Do they support whatever conclusions are drawn?" (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"We all know that the numerical values on each side of an equation have to be the same. The key to dimensional analysis is that the units have to be the same as well. This provides a convenient way to keep careful track of units when making calculations in engineering and other quantitative disciplines, to make sure one is computing what one thinks one is computing. When an equation exists only for the sake of mathiness, dimensional analysis often makes no sense." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

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

[Murphy’s Laws of Analysis:] "(1) In any collection of data, the figures that are obviously correct contain errors. (2) It is customary for a decimal to be misplaced. (3) An error that can creep into a calculation, will. Also, it will always be in the direction that will cause the most damage to the calculation." (G C Deakly)

"We must include in any language with which we hope to describe complex data-processing situations the capability for describing data. We must also include a mechanism for determining the priorities to be applied to the data. These priorities are not fixed and are indicated in many cases by the data." (Grace Hopper)