📉Graphical Representation: Taboos in Data Visualization (Just the Quotes)

"The essential quality of graphic representations is clarity. If the diagram fails to give a clearer impression than the tables of figures it replaces, it is useless. To this end, we will avoid complicating the diagram by including too much data." (Armand Julin, "Summary for a Course of Statistics, General and Applied", 1910)

"Comparison between circles of different size should be absolutely avoided. It is inexcusable when we have available simple methods of charting so good and so convenient from every point of view as the horizontal bar." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"In general, the comparison of two circles of different size should be strictly avoided. Many excellent works on statistics approve the comparison of circles of different size, and state that the circles should always be drawn to represent the facts on an area basis rather than on a diameter basis. The rule, however, is not always followed and the reader has no way of telling whether the circles compared have been drawn on a diameter basis or on an area basis, unless the actual figures for the data are given so that the dimensions may be verified." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"Judgment must be used in the showing of figures in any chart or numerical presentation, so that the figures may not give an appearance of greater accuracy than their method of collection would warrant. Too many otherwise excellent reports contain figures which give the impression of great accuracy when in reality the figures may be only the crudest approximations. Except in financial statements, it is a safe rule to use ciphers whenever possible at the right of all numbers of great size. The use of the ciphers greatly simplifies the grasping of the figures by the reader, and, at the same time, it helps to avoid the impression of an accuracy which is not warranted by the methods of collecting the data." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"Many people use statistics as a drunkard uses a street lamp - for support rather than illumination. It is not enough to avoid outright falsehood; one must be on the alert to detect possible distortion of truth. One can hardly pick up a newspaper without seeing some sensational headline based on scanty or doubtful data." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"Simplicity, accuracy, appropriate size, proper proportion, correct emphasis, and skilled execution - these are the factors that produce the effective chart. To achieve simplicity your chart must be designed with a definite audience in mind, show only essential information. Technical terms should be absent as far as possible. And in case of doubt it is wiser to oversimplify than to make matters unduly complex. Be careful to avoid distortion or misrepresentation. Accuracy in graphics is more a matter of portraying a clear reliable picture than reiterating exact values. Selecting the right scales and employing authoritative titles and legends are as important as precision plotting. The right size of a chart depends on its probable use, its importance, and the amount of detail involved." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"If two or more data paths ate to appear on the graph. it is essential that these lines be labeled clearly, or at least a reference should be provided for the reader to make the necessary identifications. While clarity seems to be a most obvious goal. graphs with inadequate or confusing labeling do appear in publications, The user should not find identification of data paths troublesome or subject to misunderstanding. The designer normally should place no more than three data paths on the graph to prevent confusion - particularly if the data paths intersect at one or more points on the Cartesian plane." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"There are two kinds of misrepresentation. In one, the numerical data do not agree with the data in the graph, or certain relevant data are omitted. This kind of misleading presentation, while perhaps hard to determine, clearly is wrong and can be avoided. In the second kind of misrepresentation, the meaning of the data is different to the preparer and to the user." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"Do not allow data labels in the data region to interfere with the quantitative data or to clutter the graph. […] Avoid putting notes, keys, and markers in the data region. Put keys and markers just outside the data region and put notes in the legend or in the text." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Make the data stand out and avoid superfluity are two broad strategies that serve as an overall guide to the specific principles […] The data - the quantitative and qualitative information in the data region - are the reason for the existence of the graph. The data should stand out. […] We should eliminate superfluity in graphs. Unnecessary parts of a graph add to the clutter and increase the difficulty of making the necessary elements - the data - stand out." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Labels should be complete but succinct. Long and complicated labels will defeat the viewer and therefore the purpose of the graph. Treat a label as a cue to jog the memory or to complete comprehension. Shorten long labels; avoid abbreviations unless they are universally understood; avoid repetition on the same graph. A title, for instance, should not repeat what is already in the axis labels. Be consistent in terminology." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"Principal components and factor analysis are methods for data reduction. They seek a few underlying dimensions that account for patterns of variation among the observed variables underlying dimensions imply ways to combine variables, simplifying subsequent analysis. For example, a few combined variables could replace many original variables in a regression. Advantages of this approach include more parsimonious models, improved measurement of indirectly observed concepts, new graphical displays, and the avoidance of multicollinearity." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"Graphical illustrations should be simple and pleasing to the eye, but the presentation must remain scientific. In other words, we want to avoid those graphical features that are purely decorative while keeping a critical eye open for opportunities to enhance the scientific inference we expect from the reader. A good graphical design should maximize the proportion of the ink used for communicating scientific information in the overall display." (Phillip I Good & James W Hardin, "Common Errors in Statistics" (and How to Avoid Them)", 2003)

"These questions can be applied to every kind of problem. They measure the usefulness of whatever construction or graphical invention allowing you to avoid useless graphics." (Jacques Bertin [interview], 2003)

"Use a scale break only when necessary. If a break cannot be avoided, use a full scale break. Taking logs can cure the need for a break." (Naomi B Robbins, "Creating More effective Graphs", 2005)

"Conflicting with the idea of integrating evidence regardless of its these guidelines provoke several issues: First, labels are data. even intriguing data. [...] Second, when labels abandon the data points, then a code is often needed to relink names to numbers. Such codes, keys, and legends are impediments to learning, causing the reader's brow to furrow. Third, segregating nouns from data-dots breaks up evidence on the basis of mode" (verbal vs. nonverbal), a distinction lacking substantive relevance. Such separation is uncartographic; contradicting the methods of map design often causes trouble for any type of graphical display. Fourth, design strategies that reduce data-resolution take evidence displays in the wrong direction. Fifth, what clutter? Even this supposedly cluttered graph clearly shows the main ideas: brain and body mass are roughly linear in logarithms, and as both variables increase, this linearity becomes less tight." (Edward R Tufte, "Beautiful Evidence", 2006) [argumentation against Cleveland's recommendation of not using words on data plots]

"Generally pie charts are to be avoided, as they can be difficult to interpret particularly when the number of categories is greater than five. Small proportions can be very hard to discern […] In addition, unless the percentages in each of the individual categories are given as numbers it can be much more diff i cult to estimate them from a pie chart than from a bar chart […]." (Jenny Freeman et al, "How to Display Data", 2008)

"Numerical precision should be consistent throughout and summary statistics such as means and standard deviations should not have more than one extra decimal place" (or significant digit) compared to the raw data. Spurious precision should be avoided although when certain measures are to be used for further calculations or when presenting the results of analyses, greater precision may sometimes be appropriate." (Jenny Freeman et al, "How to Display Data", 2008)

"The data [in tables] should not be so spaced out that it is difficult to follow or so cramped that it looks trapped. Keep columns close together; do not spread them out more than is necessary. If the columns must be spread out to fit a particular area, such as the width of a page, use a graphic device such as a line or screen to guide the reader’s eye across the row." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"Dealing with a circular visualization and trying to compare its radial portions is always problematic. When designing with data, the story should always be told as clearly as possible. To do so, it is often best to avoid round charts and graphs." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"[...] you should not rely on social or cultural conventions to convey information. However, these conventions can be very powerful, and you should be aware that your reader brings them to the table. Making use of them, when possible, to reinforce your message will help you convey information efficiently. Avoid countering conventions where possible in order to avoid creating cognitive dissonance, a clash of habitual interpretation with the underlying message you are sending." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"We naturally draw conclusions from what we see […]. We should also think about what we do not see […]. The unseen data may be just as important, or even more important, than the seen data. To avoid survivor bias, start in the past and look forward." (Gary Smith, "Standard Deviations", 2014)

"Highlighting one aspect can make other things harder to see one word of warning in using preattentive attributes: when you highlight one point in your story, it can actually make other points harder to see. When you’re doing exploratory analysis, you should mostly avoid the use of preattentive attributes for this reason. When it comes to explanatory analysis, however, you should have a specific story you are communicating to your audience. Leverage preattentive attributes to help make that story visually clear." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"My base color is grey, not black, to allow for greater contrast since color stands out more against grey than black. For my attention-grabbing color, I often use blue for a number of reasons: (1) I like it, (2) you avoid issues of colorblindness that we’ll discuss momentarily, and (3) it prints well in black-and-white. That said, blue is certainly not your only option (and you’ll see many examples where I deviate from my typical blue for various reasons)." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Sometimes bar charts are avoided because they are common. This is a mistake. Rather, bar charts should be leveraged because they are common, as this means less of a learning curve for your audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"A semantic approach to visualization focuses on the interplay between charts, not just the selection of charts themselves. The approach unites the structural content of charts with the context and knowledge of those interacting with the composition. It avoids undue and excessive repetition by instead using referential devices, such as filtering or providing detail-on-demand. A cohesive analytical conversation also builds guardrails to keep users from derailing from the conversation or finding themselves lost without context. Functional aesthetics around color, sequence, style, use of space, alignment, framing, and other visual encodings can affect how users follow the script." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"One tip to keep an audience focused on your story without overwhelming them is to reduce the saturation of the colors [...] When you lower the brightness and intensity, you are reducing the cognitive load that your audience has to bear. [...] Regardless of what combinations you decide on, you need to avoid pure colors that are bright and saturated." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

🧭Business Intelligence: Perspectives (Part 28: Cutting through Complexity)

Business Intelligence Series

Independently of the complexity of the problems, one should start by framing the problem(s) correctly and this might take several steps and iterations until a foundation is achieved, upon which further steps can be based. Ideally, the framed problem should reflect reality and should provide a basis on which one can build something stable, durable and sustainable. Conversely, people want quick low-cost fixes and probably the easiest way to achieve this is by focusing on appearances, which are often confused with more.

In many data-related contexts, there’s the tendency to start with the "solution" in mind, typically one or more reports or visualizations which should serve as basis for further exploration. Often, the information exchange between the parties involved (requestor(s), respectively developer(s)) is kept to a minimum, though the formalized requirements barely qualify for the minimum required. The whole process starts with a gap that can create further changes as the development process progresses, with all the consequences deriving from this: the report doesn’t satisfy the needs, more iterations are needed - requirements’ reevaluation, redesign, redevelopment, retesting, etc.

The poor results are understandable, all parties start with a perspective based on facts which provide suboptimal views when compared with the minimum basis for making the right steps in the right direction. That’s not only valid for reports’ development but also for more complex endeavors – data models, data marts and warehouses, and other software products. Data professionals attempt to bridge the gaps by formalizing and validating the requirements, building mock-ups and prototypes, testing, though that’s more than many organizations can handle!

There are simple reports or data visualizations for which not having prior knowledge of the needed data sources, processes and the business rules has a minimal impact on the further steps of the processes involved in building the final product(s). However, "all generalizations are false" to some degree, and there’s a critical point after which minimal practices tend to create more waste than companies can afford. Consequently, applying the full extent of the processes can lead to waste when the steps aren’t imperative for the final product.

Even if one is aware of all the implications, one’s experience and the application of best practices doesn’t guarantee the quality of the results as long as some kind of novelty, unknown, fuzziness or complexity is involved. Novelty can appear in different ways – process, business rules, data or problem formulations, particularities that aren’t easily perceived or correctly understood. Each important minor piece of information can have an exponential impact under the wrong circumstances.

The unknown can encompass novelty, though can be also associated with the multitude of facts not explicitly and/or directly considered. "The devil is in details" and it’s so easy for important or minor facts to remain hidden under the veil of suppositions, expectations, respectively under the complex and fuzzy texture of logical aspects. Many processes operate under strict rules, though there are various consequences, facts or unnecessary information that tend to increase the overall complexity and fuzziness.

Predefined processes, procedures and practices can help cut and illuminate through this complex structure associated with the various requirements and aspects of problems. Plunging headfirst can be occasionally associated with the need to evaluate what is known and unknown from facts and data’s perspective, to identify the gaps and various factors that can weigh in the final solution. Unfortunately, too often it’s nothing of this!  

Besides the multitude of good/best practices and problem-solving approaches, all one has is his experience and intuition to cut through the overall complexity. False steps are inevitable for finding the approachable path(s) from the requirements to the solution.

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🧭Business Intelligence: Why Data Projects Fail to Deliver Real-Life Impact (Part III: Failure through the Looking Glass)

Business Intelligence Series

There’s a huge volume of material available on project failure – resources that document why individual projects failed, why in general projects fail, why project members, managers and/or executives think projects fail, and there seems to be no other more rewarding activity at the end of a project than to theorize why a project failed, the topic culminating occasionally with the blaming game. Success may generate applause, though it's failure that attracts and stirs the most waves (irony, disapproval, and other similar behavior) and everybody seems to be an expert after the consumed endeavor. 

The mere definition of a project failure – not fulfilling project’s objectives within the set budget and timeframe - is a misnomer because budgets and timelines are estimated based on the information available at the beginning of the project, the amount of uncertainty for many projects being considerable, and data projects are no exceptions from it. The higher the uncertainty the less probable are the two estimates. Even simple projects can reveal uncertainty especially when the broader context of the projects is considered. 

Even if it’s not a common practice, one way to cope with uncertainty is to add a tolerance for the estimates, though even this practice probably will not always accommodate the full extent of the unknown as the tolerances are usually small. The general expectation is to have an accurate and precise landing, which for big or exploratory projects is seldom possible!

Moreover, the assumptions under which the estimates hold are easily invalidated in praxis – resources’ availability, first time right, executive’s support to set priorities, requirements’ quality, technologies’ maturity, etc. If one looks beyond the reasons why projects fail in general, quite often the issues are more organizational than technological, the lack of knowledge and experience being some of the factors. 

Conversely, many projects will not get approved if the estimates don’t look positive, and therefore people are pressured in one way or another to make the numbers fit the expectations. Some projects, given their importance, need to be done even if the numbers don’t look good or can’t be quantified correctly. Other projects represent people’s subsistence on the job, respectively people's self-occupation to create motion, though they can occasionally have also a positive impact for the organizations. These kinds of aspects almost never make it in statistics or surveys. Neither do the big issues people are afraid to talk about. Where to consider that in the light of politics and office’s grapevine the facts get distorted!

Data projects reflect all the symptoms of failure projects have in general, though when words like AI, Statistics or Machine Learning are used, the chances for failure are even higher given that the respective fields require a higher level of expertise, the appropriate use of technologies and adherence to the scientific process for the results to be valid. If projects can benefit from general recipes, respectively established procedures and methods, their range of applicability decreases when the mentioned areas are involved. 

Many data projects have an exploratory nature – seeing what’s possible - and therefore a considerable percentage will not reach production. Moreover, even those that reach that far might arrive to be stopped or discarded sooner or later if they don’t deliver the expected value, and probably many of the models created in the process are biased, irrelevant, or incorrectly apply the theory. Where to add that the mere use of tools and algorithms is not Data Science or Data Analysis. 

The challenge for many data projects is to identify which Project Management (PM) best practices to consider. Following all or no practices at all just increases the risks of failure!

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🧭Business Intelligence: Data Culture (Part I: Generative AI - No Silver Bullet)

Business Intelligence Series

Talking about holy grails in Data Analytics, another topic of major importance for an organization’s "infrastructure" is data culture, that can be defined as the collective beliefs, values, behaviors, and practices of an organization’s employees in harnessing the value of data for decision-making, operations, or insight. Rooted in data literacy, data culture is an extension of an organization’s culture in respect to data that acts as enabler in harnessing the value of data. It’s about thinking critically about data and how data is used to create value. 

The current topic was suggested by PowerBI.tips’s webcast from today [3] and is based on Brent Dykes’ article from Forbes ‘Why AI Isn’t Going to Solve All Your Data Culture Problems’ [1]. Dykes’ starting point for the discussion is Wavestone's annual data executive survey based on which the number of companies that reported they had "created a data-driven organization" rose sharply from 23.9 percent in 2023 to 48.1 percent in 2024 [2]. The report’s authors concluded that the result is driven by the adoption of Generative AI, the capabilities of OpenAI-like tools to generate context-dependent meaningful text, images, and other content in response to prompts. 

I agree with Dykes that AI technologies can’t be a silver bullet for an organization data culture given that AI either replaces people’s behaviors or augments existing ones, being thus a substitute and not a cure [1]. Even for a disruptive technology like Generative AI, it’s impossible to change so much employees’ mindset in a so short period of time. Typically, a data culture matures over years with sustained effort. Therefore, the argument that the increase is due to respondent’s false perception is more than plausible. There’s indeed a big difference between thinking about an organization as being data-driven and being data-driven. 

The three questions-based evaluation considered in the article addresses this difference, thinking vs. being. Changes in data culture don’t occur just because some people or metrics say so, but when people change their mental models based on data, when the interpersonal relations change, when the whole dynamics within the organization changes (positively). If people continue the same behavior and practices, then there are high chances that no change occurred besides the Brownian movement in a confined space of employees, that’s just chaotic motion.  

Indeed, a data culture should encourage the discovery, exploration, collaboration, discussions [1] respectively knowledge sharing and make people more receptive and responsive about environmental or circumstance changes. However, just involving leadership and having things prioritized and funded is not enough, no matter how powerful the drive. These can act as enablers, though more important is to awaken and guide people’s interest, working on people’s motivation and supporting the learning process through mentoring. No amount of brute force can make a mind move and evolve freely unless the mind is driven by an inborn curiosity!

Driving a self-driving car doesn’t make one a better driver. Technology should challenge people and expand their understanding of how data can be used in different contexts rather than give solutions based on a mass of texts available as input. This is how people grow meaningfully and how an organization’s culture expands. Readily available answers make people become dull and dependent on technology, which in the long-term can create more problems. Technology can solve problems when used creatively, when problems and their context are properly understood, and the solutions customized accordingly.

Unfortunately, for many organizations data culture will be just a topic to philosophy about. Data culture implies a change of mindset, perception, mental models, behavior, and practices based on data and not only consulting the data to confirm one’s biases on how the business operates!

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Resources:
[1] Forbes (2024) Why AI Isn’t Going To Solve All Your Data Culture Problems, by Brent Dykes (link)
[2] Wavestone (2024) 2024 Data and AI Leadership Executive Survey (link)
[3] Power BI tips (2024) Ep.299: AI & Data Culture Problems (link)

🛡️Information Security: Cybersecurity (Definitions)

 "The art of ensuring the existence and continuity of the Information Society of a nation, guaranteeing and protecting, in Cyberspace, its information assets and critical infrastructure." (Claudia Canongia & Raphael Mandarino, "Cybersecurity: The New Challenge of the Information Society", 2012)

"The act of protecting technology, information, and networks from attacks." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"The practice of protecting computers and electronic communication systems as well as the associated information." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"Cybersecurity deals with damage to, unauthorized use of, exploitation of electronic information and communications systems that ensure confidentiality, integrity and availability." (Sanjukta Pookulangara, "Cybersecurity: What Matters to Consumers - An Exploratory Study", 2016)

"Focuses on protecting computers, networks, programs and data from unintended or unauthorized access, change or destruction." (Kimberly Lukin, "Russian Cyberwarfare Taxonomy and Cybersecurity Contradictions between Russia and EU", 2016)

"The activity or process, ability or capability, or state whereby information and communications systems and the information contained therein are protected from and/or defended against damage, unauthorized use or modification, or exploitation." (Olivera Injac & Ramo Šendelj, "National Security Policy and Strategy and Cyber Security Risks", 2016)

"The ability to protect against the unauthorized use of electronic data and malicious activity. This electronic data can be personal customer information such as names, addresses, social security numbers, credit cards, and debit cards, to name a few." (Brittany Bullard, "Style and Statistics", 2016)

"A trustworthiness property concerned with the protection of systems from cyberattacks." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"Information security (infosec) but broadly referring to technology and human systems that are built around the secure exchange, storage, and management of information." (Shalin Hai-Jew, "Safe Distances: Online and RL Hyper-Personal Relationships as Potential Attack Surfaces", 2018)

"Is defined as the collection of tools, policies, security concepts, security safeguards, guidelines, risk management approaches, actions, training, best practices, assurance and technologies that can be used to protect the cyber environment, organization, and user assets." (Thokozani I Nzimakwe, "Government's Dynamic Approach to Addressing Challenges of Cybersecurity in South Africa", 2018)

"Protection against criminal access to one’s data and information and against criminal manipulation of computer networks/data/systems." (Shalin Hai-Jew, "Beware!: A Multimodal Analysis of Cautionary Tales in Strategic Cybersecurity Messaging Online", 2018)

"The collection of tools, policies, security concepts, security safeguards, guidelines, risk management approaches, actions, training, best practices, assurance, and technologies that can be used to protect the cyberspace environment and organization and users’ assets." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"The organization and collection of resources, processes, and structures used to protect cyberspace from occurrences that misalign de jure from de facto property rights." (Mika Westerlund et al, "A Three-Vector Approach to Blind Spots in Cybersecurity", 2018)

"A computing-based discipline involving technology, people, information, and processes to enable assured operations. It involves the creation, operation, analysis, and testing of secure computer systems. It is an interdisciplinary course of study, including aspects of law, policy, human factors, ethics, and risk management in the context of adversaries." (Matt Bishop et al, "Cybersecurity Curricular Guidelines", 2019)

"Acts taken, technologies created and deployed, policies written and enacted, to protect computer systems and networks against misuse, intrusion, and exploitation." (Shalin Hai-Jew, "The Electronic Hive Mind and Cybersecurity: Mass-Scale Human Cognitive Limits to Explain the “Weakest Link” in Cybersecurity", 2019)

"Also known as computer security or IT security, is the protection of computer systems from the theft or damage to the hardware, software or the information on them, as well as from disruption or misdirection of the services they provide." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2019)

"Includes process, procedures, technologies, and controls designed to protect systems, networks, and data." (Sandra Blanke et al, "How Can a Cybersecurity Student Become a Cybersecurity Professional and Succeed in a Cybersecurity Career?", 2019)

"The protection of computer systems from theft and damage to their assets and from manipulation and distraction of their services." (Viacheslav Izosimov & Martin Törngren, "Security Awareness in the Internet of Everything", 2019)

"The protection of internet-connected systems including hardware, software, and data from cyberattacks."  (Semra Birgün & Zeynep Altan, "A Managerial Perspective for the Software Development Process: Achieving Software Product Quality by the Theory of Constraints", 2019)

"Cybersecurity is seen where security alerts and cyber-attacks are becoming more frequent and malicious, these threats include private access attempts and exploitation software or phishing, malware, web application attacks, and network penetration." (Theunis G Pelser & Garth Gaffley, "Implications of Digital Transformation on the Strategy Development Process for Business Leaders", 2020)

"Is the protection of internet-connected systems, including hardware, software and data, from cyberattacks. In a computing context, security comprises cybersecurity and physical security - both are used by enterprises to protect against unauthorized access to data centers and other computerized systems." (Alexander A Filatov, "Sovereign Bureaucrats vs. Global Tech Companies: Ethical and Regulatory Challenges", 2020)

"It is a general term which describes technologies, processes, methods, and practices for the purpose of protection of internet-connected information systems from attacks, i.e., cyberattacks. Cybersecurity can refer to security of data, software or hardware within information systems." (Ana Gavrovska & Andreja Samčović, "Intelligent Automation Using Machine and Deep Learning in Cybersecurity of Industrial IoT: CCTV Security and DDoS Attack Detection", 2020)

"Cybersecurity is an act to protect data, devices, applications, servers, network from the malicious attack through various tools and techniques. The process also ensures the confidentiality, integrity, availability, and non-repudiation of the content." (Shafali Agarwal, "Preserving Information Security Using Fractal-Based Cryptosystem", 2021)

"Cybersecurity refers to the set of technologies, processes, and practices designed to safeguard networks, devices, programs, and data from attack, threats, or unauthorized access." (Sanjeev Rao et al, "Online Social Networks Misuse, Cyber Crimes, and Counter Mechanisms", 2021)

"It is the organization and collection of resources, processes, and structures used to protect cyberspace from security events." (Carlos A M S Teles et al, "A Black-Box Framework for Malicious Traffic Detection in ICT Environments", Handbook of Research on Cyber Crime and Information Privacy, 2021)

"Prevention of damage to, protection of, and restoration of computers, electronic communications systems, electronic communications services, wire communication, and electronic communication, including information contained therein, to ensure its availability, integrity, authentication, confidentiality, and nonrepudiation." (CNSSI 4009-2015)

"The ability to protect or defend the use of cyberspace from cyber attacks." (NISTIR 8170)

"The prevention of damage to, unauthorized use of, exploitation of, and - if needed - the restoration of electronic information and communications systems, and the information they contain, in order to strengthen the confidentiality, integrity and availability of these systems." (NISTIR 8074 Vol. 2)

"The process of protecting information by preventing, detecting, and responding to attacks." (NISTIR 8183)

🧱IT: Information Technology Information Library [ITIL] (Definitions)

"A series of documents used to aid the implementation of a framework for IT service management (ITSM). This framework defines how service management is applied in specific organizations. Being a framework, it is completely customizable for an application within any type of business or organization that has a reliance on IT infrastructure." (Tilak Mitra et al, "SOA Governance", 2008)

"A framework and set of standards for IT governance based on best practices." (Judith Hurwitz et al, "Service Oriented Architecture For Dummies" 2nd Ed., 2009)

"A framework of supplier independent best practice management procedures for delivery of high quality IT services." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"a set of guidelines for developing and managing IT operations and services." (Bill Holtsnider & Brian D Jaffe, "IT Manager's Handbook" 3rd Ed., 2012)

"A framework and set of standards for IT governance based on best practices." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A group of books written and released by the United Kingdom’s Office of Government and Commerce (OGC). ITIL documents best practices organizations can implement to provide consistent IT services. The library includes five books." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"A set of process-oriented best practices and guidance originally developed in the United Kingdom to standardize delivery of informational technology service management." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"Best practices for information technology services management processes developed by the United Kingdom’s Office of Government Commerce." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"The IT Infrastructure Library; a set of best practice publications for IT service management." (by Brian Johnson & Leon-Paul de Rouw, "Collaborative Business Design", 2017)

"The Information Technology Infrastructure Library (ITIL) presents pre-defined processes for IT service management. The fourth edition of ITIL depicts two key elements ITIL Service-Value-System (SVS) and a four dimensions model." (Anna Wiedemann et al, "Transforming Disciplined IT Functions: Guidelines for DevOps Integration", 2021)

"set of best practices guidance" (ITIL)

💼Project Management: Planning Correctly Misunderstood - Part I

It is sometimes helpful to take a step back, observe, and then logically generalize the extremes of the observed facts; if possible, without judging people’s behavior as there’s more to it as the eyes can perceive. In some cases however one can feel that the observed situations are really close to extreme. It’s the case of some tendencies met in project planning - not planning, planning for the sake of planning, expecting a plan to be perfect, setting a plan as fix, without the possibility of changing it in utile time, respectively changing the plan too often.

There are situations in which it’s better to be spontaneous and go with the flow. Managing a project isn’t one of these situations. As Lakein’s Law formulates it succinctly: "failing to plan is planning to fail", or paraphrasing Eisenhower (1) and Clausewitz (2) - plans are useless as no plan ever survived contact with the enemy (reality), but planning is indispensable - as a plan increases awareness about project’s scope, actions, challenges, risks and opportunities, and allows devising the tactics and logistics needed to reach the set goals. Even if the plan doesn’t reflect anymore the reality, it can still be adapted to fit the new requirements. The more planning experience one has the more natural it becomes to close the gap between the initial plan and reality, and of adapting the plan as needed.

There’s an important difference between doing something because one is forced to do it and doing it because one sees and understands the value of planning. There's the tendency to plan for the sake of planning, because there's the compel to do it. Besides the fact that it documents the what, when, why and who, and that is used as a basis for action, the plan must reflect project’s current status and the activities planed for the next reporting cycle. As soon a plan is not able to reflect these aspects it becomes thus in time unusable.

The enemy of a good plan can prove to be the dream of a perfect plan (3). Some may think that the holy grail of planning is the perfect plan, that the project can’t start until all the activities were listed to the lowest detail and the effort thoroughly assigned. Few plans actually survive the contact with the reality and there can be lot of energy lost by working on the perfect plan.

Another similar behavior,  rooted mainly in the methodologies used, is that of not allowing a plan to be changed for a part or whole duration of the project. Publilius Syrus recognized more than two millennia ago that a plan that admits no modification is a bad plan (4) per se. Methodologies and practices that don’t allow a flexible way of changing the plan make no service to projects. Often changes need to occur immediately and not at an ideal point in time, when maybe the effect is lost.

Modern Project Management tools allow building the dependencies between the various activities and it’s inevitable that a change in one place will cause a chain reaction and lead to a contraction or dilatation of the plan, and this can happen with each planning iteration. In extremis the end date will alternate as the lines of a seismograph during an earthquake. It’s natural for this to happen in projects in a first phase, however it’s in Project Manager’s attribution to mitigate such variations.

The project plan is a reflection of the project and how it’s managed, therefore, one needs to give it the proper focus, how often and how detailed required.

Referenced quotes:
(1) "In preparing for battle I have always found that plans are useless, but planning is indispensable." (Eisenhower quoted by Nixon)
(2) "No plan ever survived contact with the enemy. " (Carl von Clausewitz)
(3) "The enemy of a good plan is the dream of a perfect plan." (Carl von Clausewitz)
(4) "It's a bad plan that admits of no modification." (Publilius Syrus)

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

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

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

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

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

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

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

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

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

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

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

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

♜Strategic Management: Culture (Definitions)

"(a) A perception of the critical success factors shared by a unit of the firm. (b) Norms and values applied to selection of strategic projects." (H Igor Ansoff et al, "Implanting Strategic Management" 3rd Ed., 1990)

"(1) The shared methods in which people of an organization think and behave. (2) The 'personality' of an organization." (Margaret Y Chu, "Blissful Data ", 2004)

"In the Framework for Information Quality, a company’s attitudes, values, customs, practices, and social behavior, including both official policies and unofficial 'ways of doing things', 'how things get done', and 'how decisions get made'." (Danette McGilvray, "Executing Data Quality Projects", 2008)

"The collective set of attitudes, activities, and behaviors that, collectively, tend to give an organization its personality." (Steven Haines, "The Product Manager's Desk Reference", 2008)

[adaptive culture:] "Adaptive cultures engage in at least five practices. They (1) name the elephants in the room, (2) share responsibility for the organization’s future, (3) exercise independent judgment, (4) develop leadership capacity, and (5) institutionalize reflection and continuous learning." (Alexander Grashow et al, "The Practice of Adaptive Leadership", 2009)

[participatory culture:] "An environment in which information is made available to support individuals in making appropriate decisions, and where decisions are shifted to the most appropriate location in the organization so that those affected by a decision participate in, or are represented in, the process of making it." (Sally A Miller et al, "People CMM: A Framework for Human Capital Management" 2nd Ed., 2009)

"This is the name given to the collection of basic assumptions, values, norms and artefacts that are shared by and influence the behaviour of an organisation’s members." (Bernard Burnes, "Managing change : a strategic approach to organisational dynamics" 5th Ed., 2009)

"Includes the customary beliefs, forms of expression, and material traits of a particular racial group situated within certain geographical location and within certain time." (Irina Kondratova & Ilia Goldfarb, "Culturally Appropriate Web User Interface Design Study: Research Methodology and Results", 2011)

[corporate culture:] "A collection of beliefs, expectations, and values learned and shared by a corporation’s members and transmitted from one generation of employees to another." (Thomas L Wheelen & J David Hunger., "Strategic management and business policy: toward global sustainability" 13th Ed., 2012)

"A shared system of values, beliefs, and behaviors that characterize a group of organization." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"Distinctive heritage shared by a group of people. It passes on beliefs, norms, and customs." (Barry Berman & Joel R Evans, "Retail Management: A Strategic Approach" 12th Ed., 2013)

"The set of shared attitudes, values, goals, and practices that characterize a company or an organization." (Jim Davis & Aiman Zeid, "Business Transformation: A Roadmap for Maximizing Organizational Insights", 2014)

"The beliefs, customs, practices, and social behavior of a particular nation or people; a group of people whose shared beliefs and practices identify a particular place, class, or time to which they belong; a particular set of attitudes that characterizes a group of people." (Ken Sylvester, "Negotiating in the Leadership Zone", 2015)

"defined as a set of shared attitudes, values, goals and practices that characterize an institution, organization or group." (Thomas C Wilson, "Value and Capital Management", 2015)

"An organization’s values, traditions, behavioral norms, symbols, and social characteristics." (Robert M Grant, "Contemporary Strategy Analysis" 10th Ed., 2018)

"Is the set of assumptions, beliefs, values, and norms shared by an organization's members." (Justína Mikulášková et al, "Spiral Management: New Concept of the Social Systems Management", 2020)

"A set of shared values and beliefs that drive behavior." (Forrester)

"set of values shared by a group of people, including expectation about how people should behave, their ideas, beliefs and practices" (ITIL)

📊Business Intelligence: Reporting (Definitions)

"An automated business process or related functionality that provides a detailed, formal account of relevant or requested information." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

[enterprise reporting:] "1.The process of producing reports using unified views of enterprise data. 2.A category of software tools used to produce reports; a term for what were simply known as reporting tools." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

[ad hoc reporting:] "A reporting system that enables end users to run queries and create custom reports without having to know the technicalities of the underlying database schema and query syntax." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A process by which insight is presented in a visually appealing and informative manner." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"The practice of reporting what has happened, analyzing contributing data to determine why it happened, and monitoring new data to determine what is happening now. Also known as descriptive analytics and business intelligence." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"The process of collecting data from various sources and presenting it to business people in an understandable way." (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A common interaction with an organizing system." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

"The function or activity for generating documents that contain information organized in a narrative, graphic, or tabular form, often in a repeatable and regular fashion." (Jonathan Ferrar et al., 2017)

"Business intelligence reporting, or BI reporting, is the process of gathering data by utilizing different software and tools to extract relevant insights. Ultimately, it provides suggestions and observations about business trends, empowering decision-makers to act." (Data Pine) [source] 

"When we talk about reporting in business intelligence (BI), we are talking about two things. One is reporting strictly defined. The other is 'reporting' taken in a more general meaning. In the first case, reporting is the art of collecting data from various data sources and presenting it to end-users in a way that is understandable and ready to be analyzed. In the second sense, reporting means presenting data and information, so it also includes analysis–in other words, allowing end-users to both see and understand the data, as well as act on it." (Logi Analytics) [source] 

🔦Process Management: Just in Time (Definitions)

"A manufacturing method in which product parts and components arrive at the manufacturing facility as needed for production of ordered product, rather than being stockpiled on site. This method requires strong supply chain management." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"A theory in materials management that calls for delivering materials at time of installation only, thus, not having any materials stored on-site." (Christopher Carson et al, "CPM Scheduling for Construction: Best Practices and Guidelines", 2014)

"Information delivered at the time it will be used, not before and not after." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An inventory scheduling system in which material and parts arrive at a work place when needed, minimizing inventory, waste, and interruptions." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"An inventory management practice where inventory items are supplied just in time for use to minimize inventory levels." (Rod Stephens, "Beginning Software Engineering", 2015)

"An approach of sequencing the arrival of material to a work center just prior to consumption to avoid large work-in-process inventories." (Gartner)

"Producing or conveying only the items that are needed by the next process when they are needed and in the quantity needed." (Lean Enterprise Institute)

🔦Process Management: Business Process Modeling [BPM] (Definitions)

"A set of practices or tasks that companies can perform to visually depict or describe all the aspects of a business process, including its flow, control and decision points, triggers and conditions for activity execution, the context in which an activity runs, and associated resources." (Nicolai M Josuttis, "SOA in Practice", 2007)

"A technique for transforming how business operates into a codified source in code so that it can be translated into software." (Judith Hurwitz et al, "Service Oriented Architecture For Dummies 2nd Ed.", 2009)

"An activity similar to drafting a blueprint for a house; it includes techniques and activities used as part of the larger business process management discipline." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"A technique for transforming how business operates into a codified source so that it can be translated into software." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"Business process modeling (BPM) is the data visualization of companies’ workflows and business processes to provide insight and identify areas for improvement. The field focuses on creating detailed graphic representations of these processes to reduce waste, enhance cycle speed, improve upon existing workflows, uncover inefficiencies, and remove redundancies. Business process modeling focuses on representing business flows 'as-is' - in their current state, with no modifications - as well as predictive models that highlight potential improvements in the process." (Sisense) [source]

"Business process modeling (BPM) links business strategy to IT systems development to ensure business value. It combines process/workflow, functional, organizational and data/resource views with underlying metrics such as costs, cycle times and responsibilities to provide a foundation for analyzing value chains, activity-based costs, bottlenecks, critical paths and inefficiencies." (Gartner)

🏗️Software Engineering: Heuristic (Just the Quotes)

"Heuristic reasoning is reasoning not regarded as final and strict but as provisional and plausible only, whose purpose is to discover the solution of the present problem. We are often obliged to use heuristic reasoning. We shall attain complete certainty when we shall have obtained the complete solution, but before obtaining certainty we must often be satisfied with a more or less plausible guess. We may need the provisional before we attain the final. We need heuristic reasoning when we construct a strict proof as we need scaffolding when we erect a building." (George Pólya, "How to Solve It", 1945)

"The aim of heuristics is to study the methods and rules of discovery and invention. [...] Heuristic, as an adjective, means 'serving to discover'." (George Pólya, "How to Solve It", 1945)

"An algorithm gives you the instructions directly. A heuristic tells you how to discover the instructions for yourself, or at least where to look for them." (Steve McConnell, "Code Complete", 1993)

"Heuristic (it is of Greek origin) means discovery. Heuristic methods are based on experience, rational ideas, and rules of thumb. Heuristics are based more on common sense than on mathematics. Heuristics are useful, for example, when the optimal solution needs an exhaustive search that is not realistic in terms of time. In principle, a heuristic does not guarantee the best solution, but a heuristic solution can provide a tremendous shortcut in cost and time." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Heuristic methods may aim at local optimization rather than at global optimization, that is, the algorithm optimizes the solution stepwise, finding the best solution at each small step of the solution process and 'hoping' that the global solution, which comprises the local ones, would be satisfactory." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Models of bounded rationality describe how a judgement or decision is reached (that is, the heuristic processes or proximal mechanisms) rather than merely the outcome of the decision, and they describe the class of environments in which these heuristics will succeed or fail." (Gerd Gigerenzer & Reinhard Selten [Eds., "Bounded Rationality: The Adaptive Toolbox", 2001)

"A heuristic is a rule applied to an existing solution represented in a perspective that generates a new (and hopefully better) solution or a new set of possible solutions." (Scott E Page, "The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies", 2008)

"There are two parts to learning craftsmanship: knowledge and work. You must gain the knowledge of principles, patterns, practices, and heuristics that a craftsman knows, and you must also grind that knowledge into your fingers, eyes, and gut by working hard and practicing." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"A second class of metaphors - mathematical algorithms, heuristics, and models - brings us closer to the world of computer science programs, simulations, and approximations of the brain and its cognitive processes." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"[...] heuristics are simple, efficient rules - either hard-wired in our brains or learned - that kick in especially when we're facing problems with incomplete information." (David DiSalvo, "What Makes Your Brain Happy and Why You Should Do the Opposite", 2011)

"This is the essence of intuitive heuristics: when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution." (Daniel Kahneman, "Thinking, Fast and Slow", 2011)

"Heuristics are simplified rules of thumb that make things simple and easy to implement. But their main advantage is that the user knows that they are not perfect, just expedient, and is therefore less fooled by their powers. They become dangerous when we forget that." (Nassim N Taleb, "Antifragile: Things that gain from disorder", 2012)

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

"Heuristic decision making is fast and frugal and is often based on the evaluation of one or two salient bits of information." (Amitav Chakravarti, "Why People (Don’t) Buy: The Go and Stop Signals", 2015)

"A heuristic is a strategy we derive from previous experience with a similar problem." (Darius Foroux, "Think Straight", 2017)

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

🏗️Software Engineering: Practices (Just the Quotes)

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

"Refactoring is risky. It requires changes to working code that can introduce subtle bugs. Refactoring, if not done properly, can set you back days, even weeks. And refactoring becomes riskier when practiced informally or ad hoc." (Erich Gamma, 2002)

"If the design, or some central part of it, does not map to the domain model, that model is of little value, and the correctness of the software is suspect. At the same time, complex mappings between models and design functions are difficult to understand and, in practice, impossible to maintain as the design changes. A deadly divide opens between analysis and design so that insight gained in each of those activities does not feed into the other." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Software design patterns are what allow us to describe design fragments, and reuse design ideas, helping developers leverage the expertise of others. Patterns give a name and form to abstract heuristics, rules and best practices of object-oriented techniques." (Philippe Kruchten, [foreword] 2004)

"[Corporate programming] is often done to the point where the individual is completely submerged in corporate 'culture' with no outlet for unique talents and skills. Corporate practices can be directly hostile to individuals with exceptional skills and initiative in technical matters. I consider such management of technical people cruel and wasteful." ( Bjarne Stroustrup, ["The Problem with Programming", MIT Technology Review, [interview] ] 2006)

"Design thinking taps into capacities we all have but that are overlooked by more conventional problem-solving practices. It is not only human-centered; it is deeply human in and of itself. Design thinking relies on our ability to be intuitive, to recognize patterns, to construct ideas that have emotional meaning as well as functionality, to express ourselves in media other than words or symbols." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"You do deliberate practice to improve your ability to perform a task. It’s about skill and technique. Deliberate practice means repetition. It means performing the task with the aim of increasing your mastery of one or more aspects of the task. It means repeating the repetition. Slowly, over and over again, until you achieve your desired level of mastery. You do deliberate practice to master the task, not to complete the task." (Jon Jagger, [in Kevlin Henney’s "97 Things Every Programmer Should Know", 2010])

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

"The fact that software engineering is not like other forms of engineering should really come as no surprise. Medicine is not like the law. Carpentry is not like baking. Software development is like one thing, and one thing only: software development. We need practices that make what we do more efficient, more verifiable, and easier to change. If we can do this, we can slash the short-term cost of building software, and all but eliminate the crippling long-term cost of maintaining it." (David S Bernstein, "Beyond Legacy Code", 2015)

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

"Programming is the immediate act of producing code. Software engineering is the set of policies, practices, and tools that are necessary to make that code useful for as long as it needs to be used and allowing collaboration across a team." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)

"Documentation is a practice concerned with all the processes involved in transferring documents from sources to users." (Brian C Vickery)

🌁Software Engineering: DevOps (Definitions)

"An application delivery philosophy that stresses communication, collaboration, and integration between software developers and their information technology (IT) counterparts in operations. DevOps is a response to the interdependence of software development and IT operations. It aims to help an organization rapidly produce software products and services." (Pierre Pureur & Murat Erder, "Continuous Architecture", 2015)

DevOps is an approach based on lean and agile principles in which business owners and the development, operations, and quality assurance departments collaborate to deliver software in a continuous manner that enables the business to more quickly seize market opportunities and reduce the time to include customer feedback. Indeed, enterprise (Sanjeev Sharma & Bernie Coyne, "DevOps For Dummies" 2nd Ed, 2015)

"Is a method for software development and management that integrates the development and deployment cycles to achieve a more agile, continuous evolution of software-based products and services" (Diego R López & Pedro A. Aranda, "Network Functions Virtualization: Going beyond the Carrier Cloud", 2015)

"DevOps is a mindset, a culture, and a set of technical practices. It provides communication, integration, automation, and close cooperation among all the people needed to plan, develop, test, deploy, release, and maintain a Solution." (Dean Leffingwell, "SAFe 4.5 Reference Guide: Scaled Agile Framework for Lean Enterprises" 2nd Ed., 2018)

"Short for development operations, an information technology environment in which development and operations are tightly tied together, yielding small incremental releases to gain user feedback." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"The practice of incorporating developers and members of operations and quality assurance (QA) staff into software development projects to align their incentives and enable frequent, efficient, and reliable releases of software products." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"The tighter integration between the developers of applications and the IT department that tests and deploys them. DevOps is said to be the intersection of software engineering, quality assurance, and operations." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"A software engineering practice that aims at unifying software development (Dev) and software operation (Ops)." (Jun Bi et al, "Automatic Address Scheduling and Management for Broadband IP Networks", Emerging Automation Techniques for the Future Internet, 2019)

"Develop operations, or DevOps, is an agile methodology that merges the functions of software development and operations in the enterprise software development domain. This approach has been adopted in the networking world to facilitate a programmable approach to network operations. Often when applied to networking the term is changed to NetOps." (Patrick Moore, "Model-Centric Fulfillment Operations and Maintenance Automation", Emerging Automation Techniques for the Future Internet, 2019)

"Practices and technologies that promote tighter coupling of software development (Dev) and operations (Ops) - typically marked by more automation, continuous monitoring, shorter development cycles and higher deployment frequencies. A key driver for security policy automation. DevSecOps is a related term that refers to practices and technologies that aim to embed security in DevOps practices." (Myo Zarny et al, "Network Security Policy Automation: Enterprise Use Cases and Methodologies", 2019)

"Development and operations is an abbreviation for 'development' and 'operations'; is a software engineering methodology for managing software development (Dev) and technology operations (Ops). The main aim of DevOps is to enable automation and tracing for all phases of software implementation, from integration, testing, releasing to deployment and infrastructure management." (Antoine Trad & Damir Kalpić, "Using Applied Mathematical Models for Business Transformation", 2020)

"Development and operations (DevOps) has been adopted by prominent software and service companies (e.g., IBM) to support enhanced collaboration across the company and its value chain partners. In this way, DevOps facilitates uninterrupted delivery and coexistence between development and operation facilities, enhances the quality and performance of software applications, improving end-user experience, and help to simultaneous deployment of software across different platforms." (Kamalendu Pal & Bill Karakostas, "Software Testing Under Agile, Scrum, and DevOps", 2021)

"DevOps is a sprint-based approach that can catch coding flaws during the development of code due to security reviews, rework on previous sprint cycles, and testing." (David A Bird, "Hacker and Non-Attributed State Actors", Real-Time and Retrospective Analyses of Cyber Security, 2021)

"It is a set of practices emerging to bridge the gaps between operation and developer teams to achieve a better collaboration." (Mirna Muñoz, "Boosting the Competitiveness of Organizations With the Use of Software Engineering", 2021)

"It is a way to work were the software is rapidly developed and immediately deployed for operating in a computational productive environment. It is continuous delivery product development lifecycle. It must automate the development process. DevOps is both a culture and a set of technologies and tools used for automation." Laura C Rodriguez-Martinez et al, "Service-Oriented Computing Applications (SOCA) Development Methodologies: A Review of Agility-Rigor Balance", 2021)

"People from software development and operations work together to enhance the speed of delivery of new software features. It is a concept for bridging the gap between software development and software operations and integrating the logic of common responsibility for the complete software delivery lifecycle into one cross-functional team." (Anna Wiedemann et al, "Transforming Disciplined IT Functions: Guidelines for DevOps Integration", 2021)

"DevOps is a set of tools and processes that help automate IT operations." (Aniruddha Deswandikar,"Engineering Data Mesh in Azure Cloud", 2024)

"DevOps is a catch‑all term for the blending of roles between developers and operations engineers. As the barriers between roles such as database administrator, systems administrator, and software engineer have eroded, the term DevOps has emerged as a way of describing the intersection of responsibilities from all these camps, and their increasing interrelation in the lifecycle of a product. A crucial enabling aspect of this movement is the increased use of automation in building, deploying, and monitoring large applications." (NGINX) [source]

"DevOps is a collection of best practices and working methods for the software development process whose cumulative goal is to shorten the development life cycle and support practice such as continuous integration, continuous delivery and continuous deployment." (Sum Logic) [source]

"DevOps is a set of practices that works to automate and integrate the processes between software development and IT teams, so they can build, test, and release software faster and more reliably." Atlassian [source] 

"DevOps is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity: evolving and improving products at a faster pace than organizations using traditional software development and infrastructure management processes." (Amazon) [source]

"DevOps refers to a broad range of practices related to the development and operation of software code in production in cloud data centers. DevOps is centered in Agile project management techniques and microservice support. DevOps approaches the entire software development lifecycle with automation based around version control standards." (VMWare) [source]

"The cultural movement that stresses communication, collaboration and integration between software developers and IT operations." (Global Knowledge)

🖌️Peter M Senge - Collected Quotes

"Few, if any, forces in human affairs are as powerful as shared vision." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"In great teams, conflict becomes productive. The free flow of conflicting ideas is critical for creative thinking, for discovering new solutions no one individual would have come to on his own." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"Many leaders have personal visions that never get translated into shared visions that galvanize an organization. What has been lacking is a discipline for translating individual vision into shared vision." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"Personal mastery is the discipline of continually clarifying and deepening our personal vision, of focusing our energies, of developing patience, and of seeing reality objectively." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"Real learning gets to the heart of what it means to be human. Through learning we re-create ourselves. Through learning we become able to do something we never were able to do. Through learning we reperceive the world and our relationship to it. Through learning we extend our capacity to create, to be part of the generative process of life." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

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

"Systems thinking is a discipline for seeing wholes. It is a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static 'snapshots'." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"Systems thinking is a framework for seeing interrelationships rather than things, for seeing patterns rather than static snapshots. It is a set of general principles spanning fields as diverse as physical and social sciences, engineering and management." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"The discipline of dialogue also involves learning how to recognize the patterns of interaction in teams that undermine learning. The patterns of defensiveness are often deeply engrained in how a team operates. If unrecognized, they undermine learning. If recognized and surfaced creatively, they can actually accelerate learning. Team learning is vital because teams, not individuals, are the fundamental learning unit in modern organizations. This where "the rubber meets the road"; unless teams can learn, the organization cannot learn." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"The organizations that will truly excel in the future will be the organizations that discover how to tap people's commitment and capacity to learn at all levels in an organization." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"The real leverage in most management situations lies in understanding dynamic complexity, not detail complexity. […] Unfortunately, most 'systems analyses' focus on detail complexity not dynamic complexity. Simulations with thousands of variables and complex arrays of details can actually distract us from seeing patterns and major interrelationships. In fact, sadly, for most people 'systems thinking' means 'fighting complexity with complexity', devising increasingly 'complex' (we should really say 'detailed') solutions to increasingly 'complex' problems. In fact, this is the antithesis of real systems thinking." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"We often spend so much time coping with problems along our path that we forget why we are on that path in the first place. The result is that we only have a dim, or even inaccurate, view of what's really important to us." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)