25 September 2022

Felice C Frankel - Collected Quotes

"A viewer’s eye must be guided to 'read' the elements in a logical order. The design of an exploratory graphic needs to allow for the additional component of discovery - guiding the viewer to first understand the overall concept and then engage her to further explore the supporting information." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"It is important to remember that a visual representation of a scientific concept (or data) is a re-presentation, and not the thing itself - some interpretation or translation is always involved. There are many parallels between creating a graphic and writing an article. First, you must carefully plan what to 'say', and in what order you will 'say it'. Then you must make judgments to determine a hierarchy of information - what must be included and what could be left out? The process of making a visual representation requires you to clarify your thinking and improve your ability to communicate with others. Furthermore, the process of making an effective graphic often leads to new insights into your work; when you make decisions about how to depict your data and underlying concepts, you must often clarify your basic assumptions." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"Color can tell us where to look, what to compare and contrast, and it can give us a visual scale of measure. Because color can be so effective, it is often used for multiple purposes in the same graphic - which can create graphics that are dazzling but difficult to interpret. Separating the roles that color can play makes it easier to apply color specifically for encouraging different kinds of visual thinking. [...] Choose colors to draw attention, to label, to show relationships (compare and contrast), or to indicate a visual scale of measure." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"Processes take place over time and result in change. However, we’re often constrained to depict processes in static graphics, perhaps even a single image. Luckily, a good static graphic can be just as successful, perhaps even more so, than an animation. Giving the reader the ability to see each 'frame' of time can of f er a valuable perspective." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"When various types of data are layered directly on top of one another, the viewer is able to spatially correlate multiple features. This is immediately intuitive in the case of spatial relationships […]" (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"When you decide how to depict your data, you decide on the abstraction. Will you present a graph? A cartoon? An accurate molecular model? And which features will you include in these representations? Your preferred abstraction should include all necessary information, exclude unnecessary information, and make use of your reader’s preexisting knowledge without being confined by it." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"The final step in creating your graphic is to refine it. Step back and look at it with fresh eyes. Is there anything that could be removed? Or anything that should be removed because it is distracting? Consider each element in your figure and question whether it contributes enough to your overall goal to justify its contribution. Also consider whether there is anything that could be represented more clearly. Perhaps you have been so effective at simplifying your graphic that you could now include another point in the same figure. Another method of refinement is to check the placement and alignment of your labels. They should be unobtrusive and clearly indicate which object they refer to. Consistency in fonts and alignment of labels can make the difference between something that is easy and pleasant to read, and something that is cluttered and frustrating." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

24 September 2022

Data Science: Color (Just the Quotes)

"If your words or images are not on point, making them dance in color won't make them relevant." (Edward R Tufte, "The cognitive style of PowerPoint", 2003)

"Design has the power to enrich our lives by engaging our emotions through image, form, texture, color, sound, and smell. The intrinsically human-centered nature of design thinking points to the next step: we can use our empathy and understanding of people to design experiences that create opportunities for active engagement and participation." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Color can modify - and possibly even contradict – our intuitive response to value, because of its own powerful connotations." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Color can tell us where to look, what to compare and contrast, and it can give us a visual scale of measure. Because color can be so effective, it is often used for multiple purposes in the same graphic - which can create graphics that are dazzling but difficult to interpret. Separating the roles that color can play makes it easier to apply color specifically for encouraging different kinds of visual thinking. [...] Choose colors to draw attention, to label, to show relationships (compare and contrast), or to indicate a visual scale of measure." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"Violating established and functional color conventions makes it more difficult for the audience to understand an information graphic or a map. Respecting them gives the user that much less on which to expend unnecessary energy." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Context (information that lends to better understanding the who, what, when, where, and why of your data) can make the data clearer for readers and point them in the right direction. At the least, it can remind you what a graph is about when you come back to it a few months later. […] Context helps readers relate to and understand the data in a visualization better. It provides a sense of scale and strengthens the connection between abstract geometry and colors to the real world." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Data is more than numbers, and to visualize it, you must know what it represents. Data represents real life. It’s a snapshot of the world in the same way that a photograph captures a small moment in time. […] The connection between data and what it represents is key to visualization that means something. It is key to thoughtful data analysis. It is key to a deeper understanding of your data. Computers do a bulk of the work to turn numbers into shapes and colors, but you must make the connection between data and real life, so that you or the people you make graphics for extract something of value." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Visualization is what happens when you make the jump from raw data to bar graphs, line charts, and dot plots. […] In its most basic form, visualization is simply mapping data to geometry and color. It works because your brain is wired to find patterns, and you can switch back and forth between the visual and the numbers it represents. This is the important bit. You must make sure that the essence of the data isn’t lost in that back and forth between visual and the value it represents because if you can’t map back to the data, the visualization is just a bunch of shapes." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"A signal is a useful message that resides in data. Data that isn’t useful is noise. […] When data is expressed visually, noise can exist not only as data that doesn’t inform but also as meaningless non-data elements of the display (e.g. irrelevant attributes, such as a third dimension of depth in bars, color variation that has no significance, and artificial light and shadow effects)." (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

12 September 2022

​​​​​​Jack Welch - Collected Quotes

"Good business leaders create a vision, articulate the vision, passionately own the vision, and relentlessly drive it to completion." (Jack Welch, Harvard Business Review, 1989)

"Business success is less a function of grandiose predictions than it is a result of being able to respond rapidly to real changes as they occur." (Jack Welch, "Jack: Straight from the Gut", 2001)

"Getting the right people in the right jobs is a lot more important than developing a strategy." (Jack Welch, "Jack: Straight from the Gut", 2001)

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

"The best way to support dreams and stretch is to set apart small ideas with big potential, then give people positive role models and the resources to turn small projects into big businesses." (Jack Welch, "Jack: Straight from the Gut", 2001)

"The binders, the charts, the grids may seem formidable, but the meetings themselves are built around informality, trust, emotion and humor." (Jack Welch, "Jack: Straight from the Gut", 2001)

"Achieving work-life balance is a process. Getting it right is iterative. You get better at it with experience and observation, and eventually, after some time passes, you notice it’s not getting harder anymore. It’s just what you do." (Jack Welch, "Winning", 2005)

"At the end of the day, effective mission statements balance the possible and the impossible. They give people a clear sense of the direction to profitability and the inspiration to feel they are part of something big and important." (Jack Welch, "Winning", 2005)

"Forget the arduous, intellectualized number crunching and data grinding that gurus say you have to go through to get strategy right. Forget the scenario planning, yearlong studies, and hundred-plus-page reports. They’re time-consuming and expensive, and you just don’t need them. In real life, strategy is actually very straightforward. You pick a general direction and implement like hell." (Jack Welch, "Winning", 2005)

"In my experience, an effective mission statement basically answers one question: How do we intend to win in this business?" (Jack Welch, "Winning", 2005)

"It sounds awful, but a crisis rarely ends without blood on the floor. That’s not easy or pleasant. But sadly, it is often necessary so the company can move forward again." (Jack Welch, "Winning", 2005)

"No vision is worth the paper it's printed on unless it is communicated constantly and reinforced with rewards." (Jack Welch, "Winning", 2005)

"An organization’​​​​​​s ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage."​​​​ (​​​​​​Jack Welch)

02 September 2022

Data Science: Torturing the Data in Statistics

Statistics, through its methods, techniques and models rooted in mathematical reasoning, allows exploring, analyzing and summarizing a given set of data, being used to support decision-making, experiments, theories and ultimately to gain and communicate insights. When used adequately, statistics can prove to be a useful toolset, however as soon its use deviates from the mathematical rigor and principles on which it was built, it can be easily misused. Moreover, the results obtained with the help of statistics, can be easily denatured in communication, even when the statistical results are valid. 

The easiness with which statistics can be misused is probably best reflected in sayings like 'if you torture the data long enough it will confess'.  The formulation is attributed by several sources to the economist Ronald H Coase, however according to Coase the reference made by him in the 1960’s was slightly different: 'if you torture the data enough, nature will always confess' (see [1]). The latter formulation is not necessarily negative if one considers the persistence needed by researchers in revealing nature’s secrets. In exchange, the former formulation seems to stress only the negative aspect. 

The word 'torture' seems to be used instead of 'abuse', though metaphorically it has more weight, it draws the attention and sticks with the reader or audience. As the Quotes Investigator remarks [1], ‘torturing the data’ was employed as metaphor much earlier. For example, a 1933 article contains the following passage: 

"The evidence submitted by the committee from its own questionnaire warrants no such conclusion. To torture the data given in Table I into evidence supporting a twelve-hour minimum of professional training is indeed a statistical feat, but one which the committee accomplishes to its own satisfaction." ("The Elementary School Journal" Vol. 33 (7), 1933)

More than a decade earlier, in a similar context with Coase's quote, John Dewey remarked:

"Active experimentation must force the apparent facts of nature into forms different to those in which they familiarly present themselves; and thus make them tell the truth about themselves, as torture may compel an unwilling witness to reveal what he has been concealing." (John Dewey, "Reconstruction in Philosophy", 1920)

Torture was used metaphorically from 1600s, if we consider the following quote from Sir Francis Bacon’s 'Advancement of Learning':

"Another diversity of Methods is according to the subject or matter which is handled; for there is a great difference in delivery of the Mathematics, which are the most abstracted of knowledges, and Policy, which is the most immersed […], yet we see how that opinion, besides the weakness of it, hath been of ill desert towards learning, as that which taketh the way to reduce learning to certain empty and barren generalities; being but the very husks and shells of sciences, all the kernel being forced out and expulsed with the torture and press of the method." (Sir Francis Bacon, Advancement of Learning, 1605)

However a similar metaphor with closer meaning can be found almost two centuries later:

"One very reprehensible mode of theory-making consists, after honest deductions from a few facts have been made, in torturing other facts to suit the end proposed, in omitting some, and in making use of any authority that may lend assistance to the object desired; while all those which militate against it are carefully put on one side or doubted." (Henry De la Beche, "Sections and Views, Illustrative of Geological Phaenomena", 1830)

Probably, also the following quote from Goethe deservers some attention:

"Someday someone will write a pathology of experimental physics and bring to light all those swindles which subvert our reason, beguile our judgement and, what is worse, stand in the way of any practical progress. The phenomena must be freed once and for all from their grim torture chamber of empiricism, mechanism, and dogmatism; they must be brought before the jury of man's common sense." (Johann Wolfgang von Goethe)

Alternatives to Coase’s formulation were used in several later sources, replacing 'data' with 'statistics' or 'numbers':

"Beware of the problem of testing too many hypotheses; the more you torture the data, the more likely they are to confess, but confessions obtained under duress may not be admissible in the court of scientific opinion." (Stephen M Stigler, "Neutral Models in Biology", 1987)

"Torture numbers, and they will confess to anything." (Gregg Easterbrook, New Republic, 1989)

"[…] an honest exploratory study should indicate how many comparisons were made […] most experts agree that large numbers of comparisons will produce apparently statistically significant findings that are actually due to chance. The data torturer will act as if every positive result confirmed a major hypothesis. The honest investigator will limit the study to focused questions, all of which make biologic sense. The cautious reader should look at the number of ‘significant’ results in the context of how many comparisons were made." (James L Mills, "Data torturing", New England Journal of Medicine, 1993)

"This is true only if you torture the statistics until they produce the confession you want." (Larry Schweikart, "Myths of the 1980s Distort Debate over Tax Cuts", 2001) [source

"Even properly done statistics can’t be trusted. The plethora of available statistical techniques and analyses grants researchers an enormous amount of freedom when analyzing their data, and it is trivially easy to ‘torture the data until it confesses’." (Alex Reinhart, "Statistics Done Wrong: The Woefully Complete Guide", 2015)

There is also a psychological component attached to data or facts' torturing to fit the reality, tendency derived from the way the human mind works, the limits and fallacies associated with mind's workings. 

"What are the models? Well, the first rule is that you’ve got to have multiple models - because if you just have one or two that you’re using, the nature of human psychology is such that you’ll torture reality so that it fits your models, or at least you’ll think it does." (Charles Munger, 1994)

Independently of the formulation and context used, the fact remains: statistics (aka data, numbers) can be easily abused, and the reader/audience should be aware of it!

Previously published on quotablemath.blogspot.com.

01 September 2022

Norman R Augustine - Collected Quotes

Law of Apocalyptic Costing: "Ninety percent of the time things will turn out worse than you expect. The other ten percent of the time you had no right to expect so much."  (Norman R Augustine, "Augustine's Laws", 1983)

Law of Definitive Imprecision: "The weaker the data available upon which to base one's position, the greater the precision which should be quoted in order to give that data authenticity." (Norman R Augustine, "Augustine's Laws", 1983)

Comprehensive Law of Incomprehensibility: "Profound concepts are often characterized by their difficulty of being understood; therefore persons unfamiliar with Greek or Latin should give intellectual depth to their ideas by utilizing acronyms to a degree more or less proportionate with the lack of sophistication of the ideas being presented." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Insatiable Appetites: "The last 10 percent of the performance sought generates one-third of the cost and two-thirds of the problems." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Conservation of Input: "The features incorporated into any system will continue to increase until the unit cost of the system in dollars approximates the Threshold of Intolerance, which is defined as 10^10/N^1.2, where N is the quantity of the item which is to be purchased." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Undiminished Expectations: "It is very expensive to achieve high degrees of unreliability. It Is not uncommon to increase the cost of an item by a factor of ten for each factor of ten degradation accomplished." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Propagation of Misery: "If a sufficient number of management layers are superimposed on top of each other, it can be assured that disaster is not left to chance." (Norman R Augustine, "Augustine's Laws", 1983)

"Big Bang" Theory of Software Development: "Software is like entropy. It is difficult to grasp, weighs nothing, and obeys the Second Law of Thermodynamics; i.e., it always increases." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Universal Agitation: "There are only three kinds of programs which suffer incessant budget tampering: those which are behind schedule, those which are on schedule, and those which used to be ahead of schedule." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Inconstancy of Time: "In a noncompetitive process, time expands to fit the work prescribed." (Norman R Augustine, "Augustine's Laws", 1983)

Second Law of Averages: "One-tenth of the participants produce at least one-third of the output, and increasing the number of participants merely reduces the average output." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Unmitigated Optimism: "Any task can be completed In only one-third more time than is currently estimated." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Economic Unipolarity: "The only thing more costly than stretching the schedule of an established development program Is accelerating it, which is itself the most costly action known to man." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Coinplicational Simplicity: "Truly simple systems are not feasible because they require infinite testing." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Limited Liability: "The problem with the acquisition process is that by the time the people at the top are ready for the answer the people at the bottom have forgotten the question" (Norman R Augustine, "Augustine's Laws", 1983).

Law of Amplification of Agony: "One should expect that the expected can be prevented but that the unexpected should have been expected." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Hardware Belligerency: "Hardware works best when it matters the least." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Analytical Alchemy: "Hiring advisors to conduct studies can be an excellent means of turning problems into gold: your problems into their gold." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Inverse Contributions: "It is true that complex systems may be expensive, but it must be remembered that they don't contribute much." (Norman R Augustine, "Augustine's Laws", 1983)

Law of Consternation of Energy: "The ubiquitous regulation, created as a management surrogate, takes on a life of its own and exhibits a growth pattern which closely parallels that of selected other living entities observed in nature; most specifically, weeds." (Norman R Augustine, "Augustine's Laws", 1983)

"Whenever parameters can be quantified, it is usually desirable to do so." (Norman R Augustine, "Augustine's Laws", 1983)

30 August 2022

Harold Kerzner - Collected Quotes

"The acceptance of project management has not been easy, however. Many executives are not willing to accept change and are inflexible when it comes to adapting to a different environment." (Harold Kerzner, "Project Management", 1979)

"There are always 'class or prestige' gaps between various levels of management. There are also functional gaps between working units of the organization. If we superimpose the management gaps on top of the functional gaps, we find that companies are made up of small operational islands that refuse to communicate with one another for fear that giving up information may strengthen their opponents. The project manager’s responsibility is to get these islands to communicate cross-functionally toward common goals and objectives." (Harold Kerzner, "Project Management: A systems approach to planning, scheduling, and controlling", 1979)

"There is no such thing as a good or bad organizational structure; there are only appropriate or inappropriate ones." (Harold Kerzner, "Project Management: A systems approach to planning, scheduling, and controlling", 1979)

"Project management is the planning, organizing, directing, and controlling of company resources for a relatively short-term objective that has been established to complete specific goals and objectives. Furthermore, project management utilises the systems approach to management by having functional personnel (the vertical hierarchy) assigned to a specific project (the horizontal hierarchy)." (Harold Kerzner, "Project Management for Executives", 1982)

"The acceptance of project management has not been easy, however. Many executives are not willing to accept change and are inflexible when it comes to adapting to a different environment." (Harold Kerzner, "Project Management for Executives", 1982)

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

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

"Today, excellent companies realize that project failures have more to do with behavioral shortcomings - poor employee morale, negative human relations, low productivity, and lack of commitment." (Harold Kerzner, "In search of excellence in project management", 1998)

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

"When unmeetable expectations are formed, failure is virtually assured, since we have defined failure as unmet expectations. This is called a planning failure and is the difference between what was planned to be accomplished and what was, in fact, achievable. The second component of failure is poor performance or actual failure. This is the difference between what was achievable and what was actually accomplished." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"Project management is the art of creating the illusion that any outcome is the result of a series of predetermined, deliberate acts when, in fact, it was dumb luck." (Harold Kerzner, "Project Management: A Systems Approach to Planning, Scheduling, and Controlling", 2009)

28 August 2022

Kevin Forsberg - Collected Quotes

"A model is a representation of the real thing used to depict a process, investigate an opportunity or a risk, or evaluate an attribute. Properly constructed models are valuable tools because they focus attention on critical issues while stripping away less important details that tend to obscure what is needed to understand and to manage. Because they idealize a complex situation, a variety of different models can be constructed to represent the same situation. A useful model will be simple, but it must retain the essence of the situation to be managed [...]" (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

"Being temporary, projects often bring together people unknown to each other. The newly formed group usually includes specialists motivated by the work itself and by their individual contributions. Teams of highly skilled technicians can make costly errors - even fatal ones - simply because the members fail to understand or internalize a systematic approach for applying best practices to project management. A major factor critical to project success is the availability of an effective and intuitive management process - one the group will quickly buy into and build their team upon." (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

"Developers often focus on what is possible technically regardless of the constraints of cost, a limiting schedule, or what the customer requires." (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

"Failure usually results from a lack of a common approach to accomplish the work as a team. Inadequate leadership fails to create the environment in which teams can flourish. Furthermore, potential team members are seldom trained in how to share their efforts to accomplish team goals. The team may also assume they know more about teamwork than they actually do. So we need to be able to differentiate between superficial teamwork and the real thing." (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

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

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

"Project failures can frequently be traced to unrealistic technical, cost, or schedule targets. Such targets may be entirely arbitrary or based on bad assumptions - setting team members up for failure. Furthermore, the goals that motivate one team member may not motivate another member. All tasks don’t have to be inherently motivating - that’s not sensible. But there have to be motivating factors, if by nothing more than participating in goal determination. This also helps ensure adequate opportunity and risk identification, analysis, and management." (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

"The appropriate models help avoid costly errors that can lead to failure. One of the major sources of project failure is f lawed requirements and scope management. Models of the project environment, therefore, need to address the development and management of project requirements. Continuing to work on the project solution with an insufficient understanding of stakeholder requirements and a deficient requirements development process often leads to expensive time delays and redesigns. This doesn’t have to be the case. A strong requirements development and management process model can provide that ounce of prevention." (Kevin Forsberg et al, "Visualizing Project Management: Models and frameworks for mastering complex systems" 3rd Ed., 2005)

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

"When we fail to grasp the systemic source of problems, we are left to treat symptoms rather than eliminate underlying causes. Without systemic thinking, the best we can ever do is adapt or react. Systems thinking, powered by visual models, stimulates creative - rather than adaptive - behavior. [...] To benefit from systems thinking, the project team needs to extend that viewpoint upward to the bigger picture of the project’s overall environment."

Project Management: Risk (Just the Quotes)

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

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

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

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

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

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

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

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

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

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

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

Paul Gibbons - Collected Quotes

"According to the traditional distinction from economics, risk is measurable, whereas uncertainty is indefinite or incalculable. In truth, risk can never be measured precisely except in dice rolls and games of chance, called a priori probability. Risk can only be estimated from observations in the real world, but to do that, we need to take a sample, and estimate the underlying distribution. In a sense, our estimates of real-world volatility are themselves volatile. Failure to realize this fundamental untidiness of the real world is called the ludic fallacy from the Latin for games. […] However, when the term risk measurement is used as opposed to risk estimation, a degree of precision is suggested that is unrealistic, and the choice of language suggests that we know more than we do. Even the language '​​​​​​risk management'​​​​​​ implies we can do more than we can." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

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

"Change strategy is, by this definition, the way a business (1) manages the portfolio of change to make sure that the parts deliver the whole business strategy, (2) creates the context for change, and (3) monitors change risk and change performance across the entire business." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"Culture is an emergent phenomenon produced by structures, practices, leadership behavior, incentives, symbols, rituals, and processes. All those levers have to be pulled to have any chance of success. However, one driver of culture change is more important than the others. Culture change fails when the most visible symbols of it fail to change. Those key symbols are almost always the top leader’​​​​​​s behavior, which speaks much louder than anything they might say." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"One way of managing complexity is to constrain the freedom of the parts: to hold some of those nonlinear interactions still. Businesses accomplish this with tight rules, processes, hierarchies, policies, and rigid strategies. Gathering people together under a corporate roof reduces complexity by constraining individual autonomy. The upside, of course, is collaboration, alignment of goals, and faster exchange of information." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

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

"Strategic coherence is more important than strategic precision in an uncertain world. It is impossible to get everything right because of market volatility, but we can ensure strategies do not collide. In large, complex organizations where many executives are empowered to launch major change, strategic incoherence can be a big problem." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"Strategy that takes no account of tactical practicalities is doomed, and great tactics without strategy produce incoherence and nonalignment. Despite this, the strategy-tactics dialogue happens too rarely in organizations." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"The more complex the system, the more variable (risky) the outcomes. The profound implications of this essential feature of reality still elude us in all the practical disciplines. Sometimes variance averages out, but more often fat-tail events beget more fat-tail events because of interdependencies. If there are multiple projects running, outlier (fat-tail) events may also be positively correlated - one IT project falling behind will stretch resources and increase the likelihood that others will be compromised." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"The planning fallacy is the systematic tendency for project plans and budgets to undershoot. […] The reasons for the planning fallacy are partly psychological, partly cultural, and partly to do with our limited ability to think probabilistically." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"What is commonly called change strategy is not very strategic because strategy properly focuses on goals and not on how to deliver those goals." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

Systems Engineering: Problem Solving (Just the Quotes)

"Even these humble objects reveal that our reality is not a mere collocation of elemental facts, but consists of units in which no part exists by itself, where each part points beyond itself and implies a larger whole. Facts and significance cease to be two concepts belonging to different realms, since a fact is always a fact in an intrinsically coherent whole. We could solve no problem of organization by solving it for each point separately, one after the other; the solution had to come for the whole. Thus we see how the problem of significance is closely bound up with the problem of the relation between the whole and its parts. It has been said: The whole is more than the sum of its parts. It is more correct to say that the whole is something else than the sum of its parts, because summing is a meaningless procedure, whereas the whole-part relationship is meaningful." (Kurt Koffka, "Principles of Gestalt Psychology", 1935)

"By some definitions 'systems engineering' is suggested to be a new discovery. Actually it is a common engineering approach which has taken on a new and important meaning because of the greater complexity and scope of problems to be solved in industry, business, and the military. Newly discovered scientific phenomena, new machines and equipment, greater speed of communications, increased production capacity, the demand for control over ever-extending areas under constantly changing conditions, and the resultant complex interactions, all have created a tremendously accelerating need for improved systems engineering. Systems engineering can be complex, but is simply defined as 'logical engineering within physical, economic and technical limits' - bridging the gap from fundamental laws to a practical operating system." (Instrumentation Technology, 1957)

"Systems engineering embraces every scientific and technical concept known, including economics, management, operations, maintenance, etc. It is the job of integrating an entire problem or problem to arrive at one overall answer, and the breaking down of this answer into defined units which are selected to function compatibly to achieve the specified objectives. [...] Instrument and control engineering is but one aspect of systems engineering - a vitally important and highly publicized aspect, because the ability to create automatic controls within overall systems has made it possible to achieve objectives never before attainable, While automatic controls are vital to systems which are to be controlled, every aspect of a system is essential. Systems engineering is unbiased, it demands only what is logically required. Control engineers have been the leaders in pulling together a systems approach in the various technologies." (Instrumentation Technology, 1957)

"Systems engineering is the name given to engineering activity which considers the overall behavior of a system, or more generally which considers all factors bearing on a problem, and the systems approach to control engineering problems is correspondingly that approach which examines the total dynamic behavior of an integrated system. It is concerned more with quality of performance than with sizes, capacities, or efficiencies, although in the most general sense systems engineering is concerned with overall, comprehensive appraisal." (Ernest F Johnson, "Automatic process control", 1958)

"[System dynamics] is an approach that should help in important top-management problems [...] The solutions to small problems yield small rewards. Very often the most important problems are but little more difficult to handle than the unimportant. Many [people] predetermine mediocre results by setting initial goals too low. The attitude must be one of enterprise design. The expectation should be for major improvement [...] The attitude that the goal is to explain behavior; which is fairly common in academic circles, is not sufficient. The goal should be to find management policies and organizational structures that lead to greater success." (Jay W Forrester, "Industrial Dynamics", 1961)

"Systems engineering is most effectively conceived of as a process that starts with the detection of a problem and continues through problem definition, planning and designing of a system, manufacturing or other implementing section, its use, and finally on to its obsolescence. Further, Systems engineering is not a matter of tools alone; It is a careful coordination of process, tools and people." (Arthur D. Hall, "Systems Engineering from an Engineering Viewpoint" In: Systems Science and Cybernetics. Vol.1 Issue.1, 1965)

"System theory is basically concerned with problems of relationships, of structure, and of interdependence rather than with the constant attributes of objects. In general approach it resembles field theory except that its dynamics deal with temporal as well as spatial patterns. Older formulations of system constructs dealt with the closed systems of the physical sciences, in which relatively self-contained structures could be treated successfully as if they were independent of external forces. But living systems, whether biological organisms or social organizations, are acutely dependent on their external environment and so must be conceived of as open systems." (Daniel Katz, "The Social Psychology of Organizations", 1966)

"Only if mathematical rigor is adhered to, can systems problems be dealt with effectively, and so it is that the systems engineer must, at least, develop an appreciation for mathematical rigor if not also considerable mathematical competence." (A Wayne Wymore, "A Mathematical Theory of Systems Engineering", 1967)

"The systems approach to problems focuses on systems taken as a whole, not on their parts taken separately. Such an approach is concerned with total - system performance even when a change in only one or a few of its parts is contemplated because there are some properties of systems that can only be treated adequately from a holistic point of view. These properties derive from the relationship between parts of systems: how the parts interact and fit together." (Russell L Ackoff, "Towards a System of Systems Concepts", 1971)

"It remains an unhappy fact that there is no best method for finding the solution to general nonlinear optimization problems. About the best general procedure yet devised is one that relies upon imbedding the original problem within a family of problems, and then developing relations linking one member of the family to another. If this can be done adroitly so that one family member is easily solvable, then these relations can be used to step forward from the solution of the easy problem to that of the original problem. This is the key idea underlying dynamic programming, the most flexible and powerful of all optimization methods." (John L Casti, "Five Golden Rules", 1995)

"In sharp contrast (with the traditional social planning) the systems design approach seeks to understand a problem situation as a system of interconnected, interdependent, and interacting issues and to create a design as a system of interconnected, interdependent, interacting, and internally consistent solution ideas." (Béla H Bánáthy, "Designing Social Systems in a Changing World", 1996)

"It [system dynamics] focuses on building system dynamics models with teams in order to enhance team learning, to foster consensus and to create commitment with a resulting decision […] System dynamics can be helpful to elicit and integrate mental models into a more holistic view of the problem and to explore the dynamics of this holistic view […] It must be understood that the ultimate goal of the intervention is not to build a system dynamics model. The system dynamics model is a means to achieve other ends […] putting people in a position to learn about a messy problem … create a shared social reality […] a shared understanding of the problem and potential solutions … to foster consensus within the team [..]" (Jac A M Vennix, "Group Model Building: Facilitating Team Learning Using System Dynamics", 1996)

"It is, however, fair to say that very few applications of swarm intelligence have been developed. One of the main reasons for this relative lack of success resides in the fact that swarm-intelligent systems are hard to 'program', because the paths to problem solving are not predefined but emergent in these systems and result from interactions among individuals and between individuals and their environment as much as from the behaviors of the individuals themselves. Therefore, using a swarm-intelligent system to solve a problem requires a thorough knowledge not only of what individual behaviors must be implemented but also of what interactions are needed to produce such or such global behavior." (Eric Bonabeau et al, "Swarm Intelligence: From Natural to Artificial Systems", 1999)

"True systems thinking, on the other hand, studies each problem as it relates to the organization’s objectives and interaction with its entire environment, looking at it as a whole within its universe. Taking your organization from a partial systems to a true systems state requires effective strategic management and backward thinking." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"Systems thinking means the ability to see the synergy of the whole rather than just the separate elements of a system and to learn to reinforce or change whole system patterns. Many people have been trained to solve problems by breaking a complex system, such as an organization, into discrete parts and working to make each part perform as well as possible. However, the success of each piece does not add up to the success of the whole. to the success of the whole. In fact, sometimes changing one part to make it better actually makes the whole system function less effectively." (Richard L Daft, "The Leadership Experience", 2002)

"Self-organization can be seen as a spontaneous coordination of the interactions between the components of the system, so as to maximize their synergy. This requires the propagation and processing of information, as different components perceive different aspects of the situation, while their shared goal requires this information to be integrated. The resulting process is characterized by distributed cognition: different components participate in different ways to the overall gathering and processing of information, thus collectively solving the problems posed by any perceived deviation between the present situation and the desired situation." (Carlos Gershenson & Francis Heylighen, "How can we think the complex?", 2004)

"System Thinking is a common concept for understanding how causal relationships and feedbacks work in an everyday problem. Understanding a cause and an effect enables us to analyse, sort out and explain how changes come about both temporarily and spatially in common problems. This is referred to as mental modelling, i.e. to explicitly map the understanding of the problem and making it transparent and visible for others through Causal Loop Diagrams (CLD)." (Hördur V. Haraldsson, "Introduction to System Thinking and Causal Loop Diagrams", 2004)

"In engineering, a self-organizing system would be one in which elements are designed to dynamically and autonomously solve a problem or perform a function at the system level. In other words, the engineer will not build a system to perform a function explicitly, but elements will be engineered in such a way that their behaviour and interactions will lead to the system function. Thus, the elements need to divide, but also to integrate, the problem." (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"Swarm intelligence can be effective when applied to highly complicated problems with many nonlinear factors, although it is often less effective than the genetic algorithm approach [...]. Swarm intelligence is related to swarm optimization […]. As with swarm intelligence, there is some evidence that at least some of the time swarm optimization can produce solutions that are more robust than genetic algorithms. Robustness here is defined as a solution’s resistance to performance degradation when the underlying variables are changed. (Michael J North & Charles M Macal, Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, 2007) 

"A systems approach is one that focuses on the system as a whole, specifically linking value judgments (what is desired) and design decisions (what is feasible). A true systems approach means that the design process includes the 'problem' as well as the solution. The architect seeks a joint problem–solution pair and understands that the problem statement is not fixed when the architectural process starts. At the most fundamental level, systems are collections of different things that together produce results unachievable by the elements alone."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"Taking a systems approach means paying close attention to results, the reasons we build a system. Architecture must be grounded in the client’s/user’s/customer’s purpose. Architecture is not just about the structure of components. One of the essential distinguishing features of architectural design versus other sorts of engineering design is the degree to which architectural design embraces results from the perspective of the client/user/customer. The architect does not assume some particular problem formulation, as 'requirements'  is fixed. The architect engages in joint exploration, ideally directly with the client/user/customer, of what system attributes will yield results worth paying for."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)


Systems Engineering: Issues (Just the Quotes)

"[Disorganized complexity] is a problem in which the number of variables is very large, and one in which each of the many variables has a behavior which is individually erratic, or perhaps totally unknown. However, in spite of this helter-skelter, or unknown, behavior of all the individual variables, the system as a whole possesses certain orderly and analyzable average properties. [...] [Organized complexity is] not problems of disorganized complexity, to which statistical methods hold the key. They are all problems which involve dealing simultaneously with a sizable number of factors which are interrelated into an organic whole. They are all, in the language here proposed, problems of organized complexity." (Warren Weaver, "Science and Complexity", American Scientist Vol. 36, 1948)

"The fundamental problem today is that of organized complexity. Concepts like those of organization, wholeness, directiveness, teleology, and differentiation are alien to conventional physics. However, they pop up everywhere in the biological, behavioral and social sciences, and are, in fact, indispensable for dealing with living organisms or social groups. Thus a basic problem posed to modern science is a general theory of organization. General system theory is, in principle, capable of giving exact definitions for such concepts and, in suitable cases, of putting them to quantitative analysis." (Ludwig von Bertalanffy, "General System Theory", 1968)

"Technology can relieve the symptoms of a problem without affecting the underlying causes. Faith in technology as the ultimate solution to all problems can thus divert our attention from the most fundamental problem - the problem of growth in a finite system." (Donella A Meadows, "The Limits to Growth", 1972)

"When a mess, which is a system of problems, is taken apart, it loses its essential properties and so does each of its parts. The behavior of a mess depends more on how the treatment of its parts interact than how they act independently of each other. A partial solution to a whole system of problems is better than whole solutions of each of its parts taken separately." (Russell L Ackoff, "The future of operational research is past", The Journal of the Operational Research Society Vol. 30 (2), 1979)

"The world is a complex, interconnected, finite, ecological–social–psychological–economic system. We treat it as if it were not, as if it were divisible, separable, simple, and infinite. Our persistent, intractable global problems arise directly from this mismatch." (Donella Meadows,"Whole Earth Models and Systems", 1982)

"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)

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

"All forms of complex causation, and especially nonlinear transformations, admittedly stack the deck against prediction. Linear describes an outcome produced by one or more variables where the effect is additive. Any other interaction is nonlinear. This would include outcomes that involve step functions or phase transitions. The hard sciences routinely describe nonlinear phenomena. Making predictions about them becomes increasingly problematic when multiple variables are involved that have complex interactions. Some simple nonlinear systems can quickly become unpredictable when small variations in their inputs are introduced." (Richard N Lebow, "Forbidden Fruit: Counterfactuals and International Relations", 2010)

"The problem of complexity is at the heart of mankind's inability to predict future events with any accuracy. Complexity science has demonstrated that the more factors found within a complex system, the more chances of unpredictable behavior. And without predictability, any meaningful control is nearly impossible. Obviously, this means that you cannot control what you cannot predict. The ability ever to predict long-term events is a pipedream. Mankind has little to do with changing climate; complexity does." (Lawrence K Samuels, "The Real Science Behind Changing Climate", 2014)

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


27 August 2022

Software Engineering: Risks (Just the Quotes)

"The major distinguishing feature of the spiral model is that it creates a risk-driven approach to the software process rather than a primarily document-driven or code-driven process. It incorporates many of the strengths of other models and resolves many of their difficulties." (Barry Boehm, "A spiral model of software development and enhancement", IEEE, 1988)

"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)

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

"Developing fewer features allows you to conserve development resources and spend more time refining those features that users really need. Fewer features mean fewer things to confuse users, less risk of user errors, less description and documentation, and therefore simpler Help content. Removing any one feature automatically increases the usability of the remaining ones." (Jakob Nielsen, "Prioritizing Web Usability", 2006)

"Duplication is the primary enemy of a well-designed system. It represents additional work, additional risk, and additional unnecessary complexity."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Modeling is the creation of abstractions or representations of the system to predict and analyze performance, costs, schedules, and risks and to provide guidelines for systems research, development, design, manufacture, and management. Modeling is the centerpiece of systems architecting - a mechanism of communication to clients and builders, of design management with engineers and designers, of maintaining system integrity with project management, and of learning for the architect, personally."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"In essence, Continuous Integration is about reducing risk by providing faster feedback. First and foremost, it is designed to help identify and fix integration and regression issues faster, resulting in smoother, quicker delivery, and fewer bugs. By providing better visibility for both technical and non-technical team members on the state of the project, Continuous Integration can open and facilitate communication channels between team members and encourage collaborative problem solving and process improvement. And, by automating the deployment process, Continuous Integration helps you get your software into the hands of the testers and the end users faster, more reliably, and with less effort." (John F Smart, "Jenkins: The Definitive Guide", 2011)

"Systems with high risks must be tested more thoroughly than systems that do not generate big losses if they fail. The risk assessment must be done for the individual system parts, or even for single error possibilities. If there is a high risk for failures by a system or subsystem, there must be a greater testing effort than for less critical (sub)systems. International standards for production of safety-critical systems use this approach to require that different test techniques be applied for software of different integrity levels." (Andreas Spillner et al, "Software Testing Foundations: A Study Guide for the Certified Tester Exam" 4th Ed., 2014)

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

Strategic Management: Risk (Just the Quotes)

"The decision which achieves organization objectives must be both (1) technologically sound and (2) carried out by people. If we lose sight of the second requirement or if we assume naively that people can be made to carry out whatever decisions are technically sound - we run the risk of decreasing rather than increasing the effectiveness of the organization." (Douglas McGregor, "The Human Side of Enterprise", 1960)

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

"Management theory is obsessed with risks. Top executives bemoan the lack of risk-taking initiative among their young. Politicians and stockholders are advised (by directors) to make directors rich, so that they can afford to take risks. Theorists teach how to construct decision trees, heraldic devices of scientific management; and how to marry the trees with probability theory, so that the degree of risk along each branch (each branch and twig representing alternative results of alternative courses of action) can be metered. But the measuring is spurious, and, anyway, the best management doesn't take risks. It avoids them. It goes for the sure thing.(Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"Taking no action to solve these problems is equivalent of taking strong action. Every day of continued exponential growth brings the world system closer to the ultimate limits of that growth. A decision to do nothing is a decision to increase the risk of collapse." (Donella Meadows et al, "The Limits to Growth", 1972) 

"Overly optimistic goals nearly always result in one of two extremes. If the goal is seen as a must, then the division manager must 'go for broke. This can result in reckless risk taking. More commonly [...] ultraconservative action. The reasoning is: "Why take any chances to achieve an unattainable goal."(Bruce Henderson, "Henderson on Corporate Strategy", 1979)

"Risk is a function of how poorly a strategy will perform if the 'wrong' scenario occurs." (Michael Porter, "Competitive Advantage: Creating and Sustaining Superior Performance", 1985)

"The risk of making a decision that's wrong is so enormous that sometimes it just crushes people so that they can't make any decision at all because they're afraid of making the wrong decision." (James M McPherson, "An Exchange With a Civil War Historian", 1995)

"Until we can distinguish between an event that is truly random and an event that is the result of cause and effect, we will never know whether what we see is what we'll get, nor how we got what we got. When we take a risk, we are betting on an outcome that will result from a decision we have made, though we do not know for certain what the outcome will be. The essence of risk management lies in maximizing the areas where we have some control over the outcome while minimizing the areas where we have absolutely no control over the outcome and the linkage between effect and cause is hidden from us." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)

"Risk management is the explicit quantitative declaration of uncertainty. But in some corporate cultures, people aren’t allowed to be uncertain. They’re allowed to be wrong, but they can’t be uncertain. They are obliged to look their bosses and clients in the face and lie rather than show uncertainty about outcomes. Uncertainty is for wimps." (Tom DeMarco, "Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency", 2001)

"Risk mitigation is the set of actions you will take to reduce the impact of a risk should it materialize. There are two not-immediately-obvious aspects to risk mitigation: The plan has to precede materialization. Some of the mitigation activities must also precede materialization." (Tom DeMarco, "Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency", 2001)

"According to the traditional distinction from economics, risk is measurable, whereas uncertainty is indefinite or incalculable. In truth, risk can never be measured precisely except in dice rolls and games of chance, called a priori probability. Risk can only be estimated from observations in the real world, but to do that, we need to take a sample, and estimate the underlying distribution. In a sense, our estimates of real-world volatility are themselves volatile. Failure to realize this fundamental untidiness of the real world is called the ludic fallacy from the Latin for games. […] However, when the term risk measurement is used as opposed to risk estimation, a degree of precision is suggested that is unrealistic, and the choice of language suggests that we know more than we do. Even the language '​​​​​​risk management'​​​​​​ implies we can do more than we can." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"Change strategy is, by this definition, the way a business (1) manages the portfolio of change to make sure that the parts deliver the whole business strategy, (2) creates the context for change, and (3) monitors change risk and change performance across the entire business." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"After you think, you act. After you act, you learn. Make decisions, but decisions will have risks of mistakes. But make sure you avoid disastrous mistakes and avoid making the same mistake twice." (Sukanto Tanoto, [Keynote speech] 2015)

"Governance and leadership are the yin and the yang of successful organisations. If you have leadership without governance you risk tyranny, fraud and personal fiefdoms. If you have governance without leadership you risk atrophy, bureaucracy and indifference." (Mark Goyder, "What Matters in Corporate Governance?", 2015)

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

"Often greater risk is involved in postponement than in making a wrong decision." (Harry A Hopf)

22 August 2022

Data Science: Regression toward the Mean (Just the Quotes)

"Whenever we make any decision based on the expectation that matters will return to 'normal', we are employing the notion of regression to the mean." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)

"Regression to the mean occurs when the process produces results that are statistically independent or negatively correlated. With strong negative serial correlation, extremes are likely to be reversed each time (which would reinforce the instructors' error). In contrast, with strong positive dependence, extreme results are quite likely to be clustered together." (Dan Trietsch, "Statistical Quality Control : A loss minimization approach", 1998) 

"Unfortunately, people are poor intuitive scientists, generally failing to reason in accordance with the principles of scientific method. For example, people do not generate sufficient alternative explanations or consider enough rival hypotheses. People generally do not adequately control for confounding variables when they explore a novel environment. People’s judgments are strongly affected by the frame in which the information is presented, even when the objective information is unchanged. People suffer from overconfidence in their judgments (underestimating uncertainty), wishful thinking (assessing desired outcomes as more likely than undesired outcomes), and the illusion of control (believing one can predict or influence the outcome of random events). People violate basic rules of probability, do not understand basic statistical concepts such as regression to the mean, and do not update beliefs according to Bayes’ rule. Memory is distorted by hindsight, the availability and salience of examples, and the desirability of outcomes. And so on."  (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

 "People often attribute meaning to phenomena governed only by a regression to the mean, the mathematical tendency for an extreme value of an at least partially chance-dependent quantity to be followed by a value closer to the average. Sports and business are certainly chancy enterprises and thus subject to regression. So is genetics to an extent, and so very tall parents can be expected to have offspring who are tall, but probably not as tall as they are. A similar tendency holds for the children of very short parents." (John A Paulos, "A Mathematician Plays the Stock Market", 2003)

"'Regression to the mean' […] says that, in any series of events where chance is involved, very good or bad performances, high or low scores, extreme events, etc. tend on the average, to be followed by more average performance or less extreme events. If we do extremely well, we're likely to do worse the next time, while if we do poorly, we're likely to do better the next time. But regression to the mean is not a natural law. Merely a statistical tendency. And it may take a long time before it happens." (Peter Bevelin, "Seeking Wisdom: From Darwin to Munger",  2003)

"Another aspect of representativeness that is misunderstood or ignored is the tendency of regression to the mean. Stochastic phenomena where the outcomes vary randomly around stable values (so-called stationary processes) exhibit the general tendency that extreme outcomes are more likely to be followed by an outcome closer to the mean or mode than by other extreme values in the same direction. For example, even a bright student will observe that her or his performance in a test following an especially outstanding outcome tends to be less brilliant. Similarly, extremely low or extremely high sales in a given period tend to be followed by sales that are closer to the stable mean or the stable trend." (Hans G Daellenbach & Donald C McNickle, "Management Science: Decision making through systems thinking", 2005)

"Behavioural research shows that we tend to use simplifying heuristics when making judgements about uncertain events. These are prone to biases and systematic errors, such as stereotyping, disregard of sample size, disregard for regression to the mean, deriving estimates based on the ease of retrieving instances of the event, anchoring to the initial frame, the gambler’s fallacy, and wishful thinking, which are all affected by our inability to consider more than a few aspects or dimensions of any phenomenon or situation at the same time." (Hans G Daellenbach & Donald C McNickle, "Management Science: Decision making through systems thinking", 2005)

"regression to the mean: The fact that unexpectedly high or low numbers from the mean are an exception and are usually followed by numbers that are closer to the mean. Over the long haul, we tend to get relatively more numbers that are near the mean compared to numbers that are far from the mean." (Hari Singh, "Framed! Solve an Intriguing Mystery and Master How to Make Smart Choices", 2006)

 "A naive interpretation of regression to the mean is that heights, or baseball records, or other variable phenomena necessarily become more and more 'average' over time. This view is mistaken because it ignores the error in the regression predicting y from x. For any data point xi, the point prediction for its yi will be regressed toward the mean, but the actual yi that is observed will not be exactly where it is predicted. Some points end up falling closer to the mean and some fall further." (Andrew Gelman & Jennifer Hill, "Data Analysis Using Regression and Multilevel/Hierarchical Models", 2007)

"Regression toward the mean. That is, in any series of random events an extraordinary event is most likely to be followed, due purely to chance, by a more ordinary one." (Leonard Mlodinow, "The Drunkard’s Walk: How Randomness Rules Our Lives", 2008)

"Regression does not describe changes in ability that happen as time passes […]. Regression is caused by performances fluctuating about ability, so that performances far from the mean reflect abilities that are closer to the mean." (Gary Smith, "Standard Deviations", 2014)

"We encounter regression in many contexts - pretty much whenever we see an imperfect measure of what we are trying to measure. Standardized tests are obviously an imperfect measure of ability. [...] Each experimental score is an imperfect measure of “ability,” the benefits from the layout. To the extent there is randomness in this experiment - and there surely is - the prospective benefits from the layout that has the highest score are probably closer to the mean than was the score." (Gary Smith, "Standard Deviations", 2014)

"When a trait, such as academic or athletic ability, is measured imperfectly, the observed differences in performance exaggerate the actual differences in ability. Those who perform the best are probably not as far above average as they seem. Nor are those who perform the worst as far below average as they seem. Their subsequent performances will consequently regress to the mean." (Gary Smith, "Standard Deviations", 2014)

"The term shrinkage is used in regression modeling to denote two ideas. The first meaning relates to the slope of a calibration plot, which is a plot of observed responses against predicted responses. When a dataset is used to fit the model parameters as well as to obtain the calibration plot, the usual estimation process will force the slope of observed versus predicted values to be one. When, however, parameter estimates are derived from one dataset and then applied to predict outcomes on an independent dataset, overfitting will cause the slope of the calibration plot (i.e., the shrinkage factor ) to be less than one, a result of regression to the mean. Typically, low predictions will be too low and high predictions too high. Predictions near the mean predicted value will usually be quite accurate. The second meaning of shrinkage is a statistical estimation method that preshrinks regression coefficients towards zero so that the calibration plot for new data will not need shrinkage as its calibration slope will be one." (Frank E. Harrell Jr., "Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis" 2nd Ed, 2015)

"Often when people relate essentially the same variable in two different groups, or at two different times, they see this same phenomenon - the tendency of the response variable to be closer to the mean than the predicted value. Unfortunately, people try to interpret this by thinking that the performance of those far from the mean is deteriorating, but it’s just a mathematical fact about the correlation. So, today we try to be less judgmental about this phenomenon and we call it regression to the mean. We managed to get rid of the term 'mediocrity', but the name regression stuck as a name for the whole least squares fitting procedure - and that’s where we get the term regression line." (Richard D De Veaux et al, "Stats: Data and Models", 2016)

"Regression toward the mean is pervasive. In sports, excellent performance tends to be followed by good, but less outstanding, performance. [...] By contrast, the good news about regression toward the mean is that very poor performance tends to be followed by improved performance. If you got the worst score in your statistics class on the first exam, you probably did not do so poorly on the second exam (but you were probably still below the mean)." (Alan Agresti et al, Statistics: The Art and Science of Learning from Data" 4th Ed., 2018)

21 August 2022

ERP Implementations: It’s all about Partnership II

When starting an ERP implementation project an organization needs to fill the existing knowledge gaps in respect to whatever it takes to achieve the goals associated with the respective project. Therefore, it makes sense to work with a implementer that can help cover the gaps directly or indirectly. Moreover, it makes sense to establish a long-term relationship that would allow to harness ERP system’s capabilities after project’s end, increase the ROI and, why not, find other areas of cooperation. It’s in theory what a partner does, and a strategic technology partnership is about – providing any kind of technological expertise the customer doesn't have in-house. 

Unfortunately, from being a ‘service provider’ to becoming a ‘partner’ is a challenging road for many organizations, especially when this type of relationship is not understood and managed accordingly. Partnership’s management may resume in defining common goals, principles, values and processes, establishing a communication strategy and a common understanding of the challenges and the steps ahead, providing visibility into the cost estimates, billing, resources’ availability and utilization. Addressing these aspects would offer a framework on which the partnerships can nourish. Without considering these topics, the implementer remains just a 'service provider', no matter of the names used to characterize the relationship. 

Now, the use of the word ‘partner’ would make someone think that only one partner is considered, typically a big to middle-sized organization that would have this kind of resources. The main reason behind this reasoning is that the number of functional areas and volume of skillset required for filling the requirements of an implementation are high compared with other projects, the resources needing to be available on-demand without affecting the other constraints: costs, quality, time. This can be challenging, therefore can be met scenarios in which two or more external organizations are involved in the partnership, ideally organizations that complement each other. 

It is common in ERP implementations to appeal also to individual consultants for specific areas or the whole project. The principles and values of a partnership, as well the framework behind, can be applied to individual consultants as well. Independently of resources’ provenience more important is the partnership ‘mindset’ - being together in the same boat, working together on a shared and understood strategy, with clear goals and objectives.

Moreover, the people participating in the project must have a ‘partner's mindset’ as well. Without this, the project will likely get different impulses in the wrong direction(s), as a group’s interests will take priority over the ones of the organization. Ideally, this mindset should extend to the whole organization as topics like Data Quality and Process Improvement must be an organization’s effort, deep imprinted in organization’s culture.

More like ever, it’s important for the business to see and treat the IT department as a ‘partner’ and not as a ‘service provider’ by providing the needed level of transparency in requirements, issues, practices and processes, by treating the IT department as equal party in the decision-making and addressing its current and future strategical requirements. Ideally, this partnership should happen long before the implementation starts, given that it takes time for mentalities and practices to change, for knowledge to be acquired and used appropriately. 

Building a partnership takes time, effort and strategic thinking, this on top of the actual implementation, increasing thus the overall complexity, at least at the beginning. Does it pay off? Like in a marriage, it’s useful to have somebody you can trust, who knows you, whom you can rely upon, and talk with to find solutions. However, only time will tell whether such expectations are met and kept till the end. 

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