📉Graphical Representation: Success (Just the Quotes)

"A graphic is an illustration that, like a painting or drawing, depicts certain images on a flat surface. The graphic depends on the use of lines and shapes or symbols to represent numbers and ideas and show comparisons, trends, and relationships. The success of the graphic depends on the extent to which this representation is transmitted in a clear and interesting manner." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"It is common for positive data to be skewed to the right: some values bunch together at the low end of the scale and others trail off to the high end with increasing gaps between the values as they get higher. Such data can cause severe resolution problems on graphs, and the common remedy is to take logarithms. Indeed, it is the frequent success of this remedy that partly accounts for the large use of logarithms in graphical data display." (William S Cleveland, "The Elements of Graphing Data", 1985)

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

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

"When a graph is constructed, quantitative and categorical information is encoded, chiefly through position, size, symbols, and color. When a person looks at a graph, the information is visually decoded by the person's visual system. A graphical method is successful only if the decoding process is effective. No matter how clever and how technologically impressive the encoding, it is a failure if the decoding process is a failure. Informed decisions about how to encode data can be achieved only through an understanding of the visual decoding process, which is called graphical perception." (William S Cleveland, "The Elements of Graphing Data", 1985)

"A chart is a bridge between you and your readers. It reveals your skills at comprehending the source information, at mastering presentation methods and at producing the design. Its success depends a great deal on your readers ' understanding of what you are saying, and how you are saying it. Consider how they will use your chart. Will they want to find out from it more information about the subject? Will they just want a quick impression of the data? Or will they use it as a source for their own analysis? Charts rely upon a visual language which both you and your readers must understand." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Humans may crave absolute certainty; they may aspire to it; they may pretend, as partisans of certain religions do, to have attained it. But the history of science - by far the most successful claim to knowledge accessible to humans - teaches that the most we can hope for is successive improvement in our understanding, learning from our mistakes, an asymptotic approach to the Universe, but with the proviso that absolute certainty will always elude us. We will always be mired in error. The most each generation can hope for is to reduce the error bars a little, and to add to the body of data to which error bars apply." (Carl Sagan, "The Demon-Haunted World: Science as a Candle in the Dark", 1995)

"Information needs representation. The idea that it is possible to communicate information in a 'pure' form is fiction. Successful risk communication requires intuitively clear representations. Playing with representations can help us not only to understand numbers" (describe phenomena) but also to draw conclusions from numbers" (make inferences). There is no single best representation, because what is needed always depends on the minds that are doing the communicating." (Gerd Gigerenzer, "Calculated Risks: How to know when numbers deceive you", 2002)

"Most dashboards fail to communicate efficiently and effectively, not because of inadequate technology (at least not primarily), but because of poorly designed implementations. No matter how great the technology, a dashboard's success as a medium of communication is a product of design, a result of a display that speaks clearly and immediately. Dashboards can tap into the tremendous power of visual perception to communicate, but only if those who implement them understand visual perception and apply that understanding through design principles and practices that are aligned with the way people see and think." (Stephen Few, "Information Dashboard Design", 2006)

"The key to the success of any visual, beautiful or not, is providing access to information so that the user may gain knowledge. A visual that does not achieve this goal has failed. Because it is the most important factor in determining overall success, the ability to convey information must be the primary driver of the design of a visual." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)

"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 offer a valuable perspective." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"With further similarities to small multiples, heatmaps enable us to perform rapid pattern matching to detect the order and hierarchy of different quantitative values across a matrix of categorical combinations. The use of a color scheme with decreasing saturation or increasing lightness helps create the sense of data magnitude ranking." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"By combining the visual and verbal, we set ourselves up for success when it comes to triggering the formation of long-term memories in our audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Key Performance Indicators (KPIs) in many organizations are a broken tool. The KPIs are often a random collection prepared with little expertise, signifying nothing. [...] KPIs should be measures that link daily activities to the organization’s critical success factors (CSFs), thus supporting an alignment of effort within the organization in the intended direction." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"One of the main problems with the visual approach to statistical data analysis is that it is too easy to generate too many plots: We can easily become totally overwhelmed by the shear number and variety of graphics that we can generate. In a sense, we have been too successful in our goal of making it easy for the user: Many, many plots can be generated, so many that it becomes impossible to understand our data." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"We see first what stands out. Our eyes go right to change and difference - peaks, valleys, intersections, dominant colors, outliers. Many successful charts - often the ones that please us the most and are shared and talked about - exploit this inclination by showing a single salient point so clearly that we feel we understand the chart’s meaning without even trying." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

📉Graphical Representation: Scales (Just the Quotes)

"For a curve the vertical scale. whenever practicable, should be so selected that the zero line will appear on the diagram. [...] If the zero line of the vertical scale will not normally appear on the curve diagram, the zero line should be shown by the use of a horizontal break in the diagram." (Joint Committee on Standards for Graphic Presentation, "Publications of the American Statistical Association" Vol.14 (112), 1915)

"If only one scale is used, it should be placed at the left-hand side of the chart. In very large charts it is sometimes desirable to repeat the scale at the right-hand side as well. Where two different units of measurement are used in the scales, the units should be carefully named so that there will be no danger of the reader's using the right-hand and the left-hand scales interchangeably as though they represented the same unit." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"It should be a strict rule for all kinds of curve plotting that the horizontal scale must be used. for the independent variable and the vertical scale for the dependent variable. When the curves are plotted by this rule the reader can instantly select a set of conditions from the horizontal scale and read the information from the vertical scale. If there were no rule relating to the arrangement of scales for the independent and dependent variables, the reader would never be able to tell whether he should approach a chart from the vertical scale and read the information from the horizontal scale, or the reverse." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"Sometimes the scales of these accompanying charts are so large that the reader is puzzled to get clearly in his mind what the whole chart is driving at. There is a possibility of making a simple chart on such a large scale that the mere size of the chart adds to its complexity by causing the reader to glance from one side of the chart to the other in trying to get a condensed visualization of the chart." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919) 

"The scales of any curve-chart should be so selected that the chart will not be exaggerated in either the horizontal or the vertical direction. It is possible to cause a visual exaggeration of data by carelessly or intentionally selecting a scale which unduly stretches the chart in either the horizontal or the vertical direction. Just as the English language can be used to exaggerate to the ear, so charts can exaggerate to the eye." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"The zero of the scale should appear on every chart, and should shown by a heavy line carried across the sheet. If this is not done the reader may assume the bottom of the sheet to be zero and so be mis- led. The scale should be graduated from zero to a little over the maximum figure to be plotted on the charts, so that there will be a space between the highest peak on the curve and the top of the chart." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"Under certain conditions, however, the ordinary form of graphic chart is slightly misleading. It will be conceded that its true function is to portray comparative fluctuations. This result is practically secured when the factors or quantities compared are nearly of the same value or volume, but analysis will show that this is not accomplished when the amounts compared differ greatly in value or volume. [...] The same criticism applies to charts which employ or more scales for various curve. If the different scale are in proper proportion, the result is the same as with one scale, but when two or more scales are used which are not proportional an indication may be given with respect to comparative fluctuations which is absolutely false." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"When dealing with very large quantities it is not always practicable to use a scale which starts at zero, and is carried up by even steps to a figure representing the highest peak on the curve. Such a chart would either be too large for convenient handling, or else the scale would have to be condensed so that only very large fluctuations would be indicated on the curve. In a ease of this kind the best practice is to start the at zero, and just above this point draw a wavy line across the sheet to indicate that the scale is broken at this point. This line can be very easily drawn with an ordinary serrated edge ruler as used by many accountants. The scale starts again on the upper side of the wavy line at a figure a little lower than the lowest point on the curve, and is carried up by even steps to a figure a little above the highest point to be shown on the curve." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"With the ordinary scale, fluctuations in large factors are very noticeable, while relatively greater fluctuations in smaller factors are barely apparent. The semi-logarithmic scale permits the graphic representation of changes in every quantity on the same basis, without respect to the magnitude of the quantity itself. At the same time, it shows the actual value by reference to the numbers in the scale column. By indicating both absolute and relative value and changes to one scale, it combines the advantages of both the natural and percentage scale, without the disadvantages of either." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"Admittedly a chart is primarily a picture, and for presentation purposes should be treated as such; but in most charts it is desirable to be able to read the approximate magnitudes by reference to the scales. Such reference is almost out of the question without some rulings to guide the eye. Second, the picture itself may be misleading without enough rulings to keep the eye 'honest'. Although sight is the most reliable of our senses for measuring (and most other) purposes, the unaided eye is easily deceived; and there are numerous optical illusions to prove it. A third reason, not vital, but still of some importance, is that charts without rulings may appear weak and empty and may lack the structural unity desirable in any illustration." (Kenneth W Haemer, "Hold That Line. A Plea for the Preservation of Chart Scale Ruling", The American Statistician Vol. 1 (1) 1947)

"[….] double-scale charts are likely to be misleading unless the two zero values coincide (either on or off the chart). To insure an accurate comparison of growth the scale intervals should be so chosen that both curves meet at some point. This treatment produces the effect of percentage relatives or simple index numbers with the point of juncture serving as the base point. The principal advantage of this form of presentation is that it is a short-cut method of comparing the relative change of two or more series without computation. It is especially useful for bringing together series that either vary widely in magnitude or are measured in different units and hence cannot be compared conveniently on a chart having only one absolute-amount scale. In general, the double scale treatment should not be used for presenting growth comparisons to the general reader." (Kenneth W Haemer, "Double Scales Are Dangerous", The American Statistician Vol. 2 (3) , 1948)

"[…] many readers are confused by the presence of two scales, and either use the wrong one or simply disregard both. Also, the general reader has the disconcerting habit of believing that because one curve is higher than another, it is also larger in magnitude. This leads to all sorts of misconceptions." (Kenneth W Haemer, "Double Scales Are Dangerous", The American Statistician Vol. 2 (3) , 1948)

"The ratio chart not only correctly represents relative changes but also indicates absolute amounts at the same time. Because of its distinctive structure, it is referred to as a semilogarithmic chart. The vertical axis is ruled logarithmically and the horizontal axis arithmetically. The continued narrowing of the spacings of the scale divisions on the vertical axis is characteristic of logarithmic rulings; the equal intervals on the horizontal axis are indicative of arithmetic rulings." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"Logging size transforms the original skewed distribution into a more symmetrical one by pulling in the long right tail of the distribution toward the mean. The short left tail is, in addition, stretched. The shift toward symmetrical distribution produced by the log transform is not, of course, merely for convenience. Symmetrical distributions, especially those that resemble the normal distribution, fulfill statistical assumptions that form the basis of statistical significance testing in the regression model." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Logging skewed variables also helps to reveal the patterns in the data. […] the rescaling of the variables by taking logarithms reduces the nonlinearity in the relationship and removes much of the clutter resulting from the skewed distributions on both variables; in short, the transformation helps clarify the relationship between the two variables. It also […] leads to a theoretically meaningful regression coefficient." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The logarithmic transformation serves several purposes: (1) The resulting regression coefficients sometimes have a more useful theoretical interpretation compared to a regression based on unlogged variables. (2) Badly skewed distributions - in which many of the observations are clustered together combined with a few outlying values on the scale of measurement - are transformed by taking the logarithm of the measurements so that the clustered values are spread out and the large values pulled in more toward the middle of the distribution. (3) Some of the assumptions underlying the regression model and the associated significance tests are better met when the logarithm of the measured variables is taken." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The scales used are important; contracting or expanding the vertical or horizontal scales will change the visual picture. The trend lines need enough grid lines to obviate difficulty in reading the results properly. One must be careful in the use of cross-hatching and shading, both of which can create illusions. Horizontal rulings tend to reduce the appearance. while vertical lines enlarge it. In summary, graphs must be reliable, and reliability depends not only on what is presented but also on how it is presented." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"The time-series plot is the most frequently used form of graphic design. With one dimension marching along to the regular rhythm of seconds, minutes, hours, days, weeks, months, years, centuries, or millennia, the natural ordering of the time scale gives this design a strength and efficiency of interpretation found in no other graphic arrangement." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"It is common for positive data to be skewed to the right: some values bunch together at the low end of the scale and others trail off to the high end with increasing gaps between the values as they get higher. Such data can cause severe resolution problems on graphs, and the common remedy is to take logarithms. Indeed, it is the frequent success of this remedy that partly accounts for the large use of logarithms in graphical data display." (William S Cleveland, "The Elements of Graphing Data", 1985)

"When magnitudes are graphed on a logarithmic scale, percents and factors are easier to judge since equal multiplicative factors and percents result in equal distances throughout the entire scale." (William S Cleveland, "The Elements of Graphing Data", 1985)

"When the data are magnitudes, it is helpful to have zero included in the scale so we can see its value relative to the value of the data. But the need for zero is not so compelling that we should allow its inclusion to ruin the resolution of the data on the graph." (William S Cleveland, "The Elements of Graphing Data", 1985)

"The logarithm is one of many transformations that we can apply to univariate measurements. The square root is another. Transformation is a critical tool for visualization or for any other mode of data analysis because it can substantially simplify the structure of a set of data. For example, transformation can remove skewness toward large values, and it can remove monotone increasing spread. And often, it is the logarithm that achieves this removal." (William S Cleveland, "Visualizing Data", 1993)

"The rule is that a graph of a change in a variable with time should always have a vertical scale that starts with zero. Otherwise, it is inherently misleading." (Douglas A Downing & Jeffrey Clark, "Forgotten Statistics: A Self-Teaching Refresher Course", 1996)

"The more clues to meaning that are supplied elsewhere, the less the need for cluttersome scales." (Eric Meyer, "Designing Infographics", 1997) 

"Choose scales wisely, as they have a profound influence on the interpretation of graphs. Not all scales require that zero be included, but bar graphs and other graphs where area is judged do require it." (Naomi B Robbins, "Creating More effective Graphs", 2005)

"The visual representation of a scale - an axis with ticks - looks like a ladder. Scales are the types of functions we use to map varsets to dimensions. At first glance, it would seem that constructing a scale is simply a matter of selecting a range for our numbers and intervals to mark ticks. There is more involved, however. Scales measure the contents of a frame. They determine how we perceive the size, shape, and location of graphics. Choosing a scale (even a default decimal interval scale) requires us to think about what we are measuring and the meaning of our measurements. Ultimately, that choice determines how we interpret a graphic." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Use a logarithmic scale when it is important to understand percent change or multiplicative factors. […] Showing data on a logarithmic scale can cure skewness toward large values." (Naomi B Robbins, "Creating More effective Graphs", 2005) 

"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)

"It is important to pay heed to the following detail: a disadvantage of logarithmic diagrams is that a graphical integration is not possible, i.e., the area under the curve (the integral) is of no relevance." (Manfred Drosg, "Dealing with Uncertainties: A Guide to Error Analysis", 2007)

"Another way to obscure the truth is to hide it with relative numbers. […] Relative scales are always given as percentages or proportions. An increase or decrease of a given percentage only tells us part of the story, however. We are missing the anchoring of absolute values." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"One way a chart can lie is through overemphasis of the size and scale of items, particularly when the dimension of depth isnʼt considered." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"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)

"Geographic maps have the advantage of being true to scale - great for walking. Diagrams have the advantage of being easily imaged and remembered, often true to a non-pedestrian experience, and the ability to open up congestion, reduce empty space, and use real estate efficiently. Hybrids 'mapograms' ? - often have the disadvantages of both map and diagram with none of the corresponding advantages." (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)

"Unfortunately, setting the scale at zero is the best recipe for creating dull charts, in both senses of the word: boring and with little variation. The solution is not to break the scale, but rather to find a similar message that can be communicated using alternative metrics." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"The use of dual-axis charts is a subtle form of graphical lie through which [...] a spurious relationship is established between variables. Considering that the author of a dual-axis chart tries to harmonize the representation, it’s natural to break some rules: The vertical scale is one of the first victims." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"Whichever scale is used to represent the data, it is important to keep it consistent in data presentations. The principles of clarity, precision, and efficiency are rarely met if the measurement scales change within tables." (John Hoffmann, "Principles of Data Management and Presentation", 2017) 

📉Graphical Representation: Good Graphics (Just the Quotes)

"A good graphic must give the impression that its various parts all belong together. They must be arranged in such a way that the illustration looks like a single entity. A good graphic chart should be more than just the sum of its individual lines, shapes, and shades. It should be more than the individual bars in a bar chart, more than the pieces of a pie chart, more than the boxes in a flow chart. Unity requires the establishment of coherent relationships among the component parts of the drawing. These relationships can be depicted in a very direct manner through the use of connecting lines that serve to connect shapes." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"Unlike some art forms. good graphics should be as concrete, geometrical, and representational as possible. A rectangle should be drawn as a rectangle, leaving nothing to the reader's imagination about what you are trying to portray. The various lines and shapes used in a graphic chart should be arranged so that it appears to be balanced. This balance is a result of the placement of shapes and lines in an orderly fashion." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"Generally speaking, a good display is one in which the visual impact of its components is matched to their importance in the context of the analysis. Consider the issue of overplotting." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Although arguments can be made that high data density does not imply that a graphic will be good, nor one with low density bad, it does reflect on the efficiency of the transmission of information. Obviously, if we hold clarity and accuracy constant, more information is better than less. One of the great assets of graphical techniques is that they can convey large amounts of information in a small space." (Howard Wainer, "How to Display Data Badly", The American Statistician Vol. 38(2), 1984) 

"Of course statistical graphics, just like statistical calculations, are only as good as what goes into them. An ill-specified or preposterous model or a puny data set cannot be rescued by a graphic (or by calculation), no matter how clever or fancy. A silly theory means a silly graphic." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Good graphics can be spoiled by bad annotation. Labels must always be subservient to the information to be conveyed, and legibility should never be sacrificed for style. All the information on the sheet should be easy to read, and more important, easy to interpret. The priorities of the information should be clearly expressed by the use of differing sizes, weights and character of letters." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"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)

"Good graphic design is not a panacea for bad copy, poor layout or misleading statistics. If any one of these facets are feebly executed it reflects poorly on the work overall, and this includes bad graphs and charts." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"Tailoring the message to the audience should not be synonymous with accepting its prejudices, routines, and the usual ways of doing things. Many of what we believe to be good data visualization principles are opposite to what is practiced within organizations. When presenting a chart type the audience is unfamiliar with, or when breaking a rule, the author must argue for its advantages. Annotating the chart, showing how to read it, drawing aˆention to key points, and making direct comparisons with alternative representations will help the audience feel safer in their reading and possible adoption of the new chart." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"(1) Good data visualization is trustworthy: Is it reliable? Is the portrayal of the data and the subject faithful? Do the representation and presentation design have integrity? (2) Good data visualization is accessible: Is it usable? Is the portrayal of the data and the subject relevant? Is the representation and presentation design suitably understandable? (3) Good data visualization is elegant: Is it aesthetic? Is the representation and presentation design appealing?" (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

"Graphic design is not just about making things look good. It is a powerful combination of form and function that uses visual elements to communicate a message. Form refers to the physical appearance of a design, such as its shape, color, and typography. Function refers to the purpose of a design, such as what it is trying to communicate or achieve. A good graphic design is both visually appealing and functional. It uses the right combination of form and function to communicate its message effectively. Graphic design is also a strategic and thoughtful craft. It requires careful planning and execution to create a design that is both effective and aesthetically pleasing." (Faith Aderemi, "The Essential Graphic Design Handbook", 2024)

📉Graphical Representation: Objectivity (Just the Quotes)

"Indeed the language of statistics is rarely as objective as we imagine. The way statistics are presented, their arrangement in a particular way in tables, the juxtaposition of sets of figures, in itself reflects the judgment of the author about what is significant and what is trivial in the situation which the statistics portray." (Ely Devons, "Essays in Economics", 1961)

"The more complex the shape of any object. the more difficult it is to perceive it. The nature of thought based on the visual apprehension of objective forms suggests, therefore, the necessity to keep all graphics as simple as possible. Otherwise, their meaning will be lost or ambiguous, and the ability to convey the intended information and to persuade will be inhibited." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"Data is transformed into graphics to understand. A map, a diagram are documents to be interrogated. But understanding means integrating all of the data. In order to do this it’s necessary to reduce it to a small number of elementary data. This is the objective of the 'data treatment' be it graphic or mathematic." (Jacques Bertin [interview], 2003)

"Making a presentation is a moral act as well as an intellectual activity. The use of corrupt manipulations and blatant rhetorical ploys in a report or presentation - outright lying, flagwaving, personal attacks, setting up phony alternatives, misdirection, jargon-mongering, evading key issues, feigning disinterested objectivity, willful misunderstanding of other points of view - suggests that the presenter lacks both credibility and evidence. To maintain standards of quality, relevance, and integrity for evidence, consumers of presentations should insist that presenters be held intellectually and ethically responsible for what they show and tell. Thus consuming a presentation is also an intellectual and a moral activity." (Edward R Tufte, "Beautiful Evidence", 2006)

"The use of computers shouldn't ignore the objectives of graphics, that are: (1) Treating data to get information. (2) Communicating, when necessary, the information obtained." (Jacques Bertin, [interview] 2003)"

"The best advice for guiding your decisions about using color is to refer to the two key rules [...] - make sure it is used unobtrusively and it does not mislead by implying representation when it shouldn't be. As with all design layers, the sensible objective here should be to strive for elegance rather than novelty, eye-candy, or attractiveness. To achieve this, it is important to be aware of the different functions, choices, and potential issues surrounding color deployment." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"The main difference between journalistic and artistic infographics is that, while in the first information must try to be as objective as possible, the second supports a complete subjectivity and can lend itself to different interpretations, all of them valid. That’s the concept of 'subjective infographic', something apparently contradictory." (Jaime Serra, [interviewed] 2012)

"Another problem is that while data visualizations may appear to be objective, the designer has a great deal of control over the message a graphic conveys. Even using accurate data, a designer can manipulate how those data make us feel. She can create the illusion of a correlation where none exists, or make a small difference between groups look big." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Numbers are ideal vehicles for promulgating bullshit. They feel objective, but are easily manipulated to tell whatever story one desires. Words are clearly constructs of human minds, but numbers? Numbers seem to come directly from Nature herself. We know words are subjective. We know they are used to bend and blur the truth. Words suggest intuition, feeling, and expressivity. But not numbers. Numbers suggest precision and imply a scientific approach. Numbers appear to have an existence separate from the humans reporting them." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"As data communicators, it is therefore our responsibility to treat our work and our data as carefully and objectively as possible. It is also our responsibility to recognize where our data may suffer from underlying bias or error, or even implicit bias that data creators may themselves not even be aware of." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"I agree that data visualizations should be visually appealing, driving and utilizing the appeal and power for individuals to utilize it effectively, but sometimes this can take too much time, taking it away from more valuable uses in data. Plus, if the data visualization is not moving the needle of a business goal or objective, how effective is that visualization?" (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"When integrating written text with charts in a functionally aesthetic way, the reader should be able to find the key takeaways from the chart or dashboard, taking into account the context, constraints, and reading objectives of the overall message. " (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

📉Graphical Representation: Trellis (Just the Quotes)

"In general, Trellis displays consist of one or more panels, arranged in a regular grid-like structure of columns, rows, and pages. Simple displays are usually easy to create; multi-panel displays take little more effort. A wide range of graphs can be drawn inside each panel, although all panels in a particular Trellis display must be alike. Each panel displays a subset of the data, determined by the values of the given variables." (Richard A Becker et al, "A Tour of Trellis Graphics", 1996)

"Trellis displays are plots which contain one or more panels, arranged in a regular grid-like structure (a trellis). Each panel graphs a subset of the data. All panels in a Trellis display contain the same type of graph but these graphs are general enough to encompass a wide variety of 2-D and 3-D displays: histogram, scatter plot, dot plot, contour plot, wireframe, 3-D point cloud and more. The data subsets are chosen in a regular manner, conditioning on continuous or discrete variables in the data, thus providing a coordinated series of views of high-dimensional data." (Richard A Becker et al, "A Tour of Trellis Graphics", 1996)

"Trellis display is a framework for the visualization of data. Its most prominent aspect is an overall visual design, reminiscent of a garden trelliswork, in which panels are laid out into rows, columns, and pages. On each panel of the trellis, a subset of the data is graphed by a display method such as a scatterplot, curve plot, boxplot, 3-D wireframe, normal quantile plot, or dot plot. Each panel shows the relationship of certain variables conditional on the values of other variables. A number of display methods employed in the visual design of Trellis display enable it to succeed in uncovering the structure of data even when the structure is quite complicated." (Richard A Becker et al, "The Visual Design and Control of Trellis Display", Journal of Computational and Graphical Statistics Vol. 5 (2), 1996)

"The salient visual aspect of Trellis display is a three-way rectangular array of panels with columns, rows, and pages. [...] Each panel of a trellis display shows a subset of the values of panel variables; these values are formed by conditioning on the values of conditioning variables." (Richard A Becker et al, "The Visual Design and Control of Trellis Display", Journal of Computational and Graphical Statistics Vol. 5 (2), 1996)

"Shingling is the process of dividing a continuous variable into - possibly overlapping - intervals in order to convert a continuous variable into a discrete variable. Shingling is quite different from conditioning on categorical variables. Overlapping shingles/intervals lead to multiple representation of data within a trellis display, which is not the case for categorical variables. Furthermore, it is challenging to judge which intervals/cases have been chosen to build a shingle. Trellis displays represent the shingle interval visually by an interval of the strip label. Although no plotting space is wasted, the information on the intervals is difficult to read from the strip label. Despite these drawbacks, there is a valid motivation for shingling […]." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Trellis displays introduce the concept of shingling. Shingling is the process of dividing a continuous variable into - possibly overlapping - intervals in order to convert a continuous variable into a discrete variable. Shingling is quite different from conditioning on categorical variables. Overlapping shingles/intervals lead to multiple representation of data within a trellis display, which is not the case for categorical variables. Furthermore, it is challenging to judge which intervals/cases have been chosen to build a shingle. Trellis displays represent the shingle interval visually by an interval of the strip label. Although no plotting space is wasted, the information on the intervals is difficult to read from the strip label. Despite these drawbacks, there is a valid motivation for shingling [...]" (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Trellis displays use a lattice-like arrangement to place plots onto so-called panels. Each plot in a trellis display is conditioned upon at least one other variable. The same scales are used in all the panel plots in order to make them comparable across rows and columns. […] Trellis displays are an ideal tool to compare models for different subsets. " (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"The trellis plot allows examination of heterogeneity within databases by holding method constant and looking down each column. The plot also allows for side-by-side comparison of different methods, which enables examination of how methods vary both in point estimate and standard error. The trellis forest plot provides a multidimensional approach to sensitivity analysis that should allow more comprehensive examination of heterogeneity, a more robust assessment of key factors influencing an observation, and better context for drawing inferences when interpreting effect estimates. (Patrick Ryan, "Using Exploratory Visualization in the Analysis of Medical Product Safety in Observational Healthcare Data" [in "A Picture is Worth a Thousand Tables: Graphics in Life Sciences", Andreas Krause & Michael O’Connell], 2012)

"One problem for visualizing multiple views is that of laying out the plots. Indeed, there are some plots, such as scatterplot matrixes and trellis displays, that are formed just by arranging simpler plots according to certain rules. Scatterplot matrices, for example, arrange scatterplots side by side so that each variable in a dataset is graphed against the other variables, with the graphs being displayed as a row or a column of the matrix. This lets the user rapidly inspect all of the bivariate relationships among the variables, permitting the detection of outliers, nonlinearities, and other features of the data." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"A trellis is a graph whose nodes are ordered into vertical slices (time) with every node at almost every time connected to at least one node at an earlier and at least one node at a later time. The earliest and latest times in the trellis have only one node (hence the 'almost in the preceding sentence)." (Wikipedia) [link]

📉Graphical Representation: Venn Diagrams (Just the Quotes)

"[...] for merely theoretical purposes the rule of formation would be very simple. It would merely be to begin by drawing any closed figure, and then proceed [sic] to draw others, subject to the one condition that each is to intersect once and once only all the existing subdivisions produced by those which had gone before." (John Venn, "On the Diagrammatic and Mechanical Representation of Propositions and Reasonings", 1880)

"[…] it must be noticed that these diagrams do not naturally harmonize with the propositions of ordinary life or ordinary logic. […] The great bulk of the propositions which we commonly meet with are founded, and rightly founded, on an imperfect knowledge of the actual mutual relations of the implied classes to one another. […] one very marked characteristic about these circular diagrams is that they forbid the natural expression of such uncertainty, and are therefore only directly applicable to a very small number of such propositions as we commonly meet with." (John Venn, "On the Diagrammatic and Mechanical Representation of Propositions and Reasonings", 1880)

"[...] we can not readily break up a complicated problem into successive steps which can be taken independently. We have, in fact, to solve the problem first, by determining what are the actual mutual relations of the classes involved, and then to draw the circles to represent this final result; we cannot work step-by-step towards the conclusion by aid of our figures." (John Venn, "On the Diagrammatic and Mechanical Representation of Propositions and Reasonings", 1880)

"Whereas the Eulerian plan endeavoured at once and directly to represent propositions, or relations of class terms to one another, we shall find it best to begin by representing only classes, and then proceed to modify these in some way so as to make them indicate what our propositions have to say. How, then, shall we represent all the subclasses which two or more class terms can produce? Bear in mind that what we have to indicate is the successive duplication of the number of subdivisions produced by the introduction of each successive term. and we shall see our way to a very important departure from the Eulerian conception. All that we have to do is to draw our figures, say circles, so that each successive one which we introduce shall intersect once, and once only, all the subdivisions already existing, and we then have what may be called a general framework indicating every possible combination producible by the given class terms." (John Venn, "On the Diagrammatic and Mechanical Representation of Propositions and Reasonings", 1880)

"We endeavour to employ only symmetrical figures, such as should not only be an aid to reasoning, through the sense of sight, but should also be to some extent elegant in themselves." (John Venn, "Symbolic Logic", 1881)

"At the basis of our Symbolic Logic, however represented, whether by words by letters or by diagrams, we shall always find the same state of things. What we ultimately have to do is to break up the entire field before us into a definite number of classes or compartments which are mutually exclusive and collectively exhaustive." (John Venn, "Symbolic Logic" 2nd Ed., 1894)

"The best way of introducing this question will be to enquire a little more strictly whether it is really classes that we thus represent, or merely compartments into which classes may be put? […] The most accurate answer is that our diagrammatic subdivisions, or for that matter our symbols generally, stand for compartments and not for classes. We may doubtless regard them as representing the latter, but if we do so we should never fail to keep in mind the proviso, 'if there be such things in existence'. And when this condition is insisted upon, it seems as if we expressed our meaning best by saying that what our symbols stand for are compartments which may or may not happen to be occupied." (John Venn, "Symbolic Logic" 2nd Ed., 1894)

"A Venn diagram is a simple representation of the sample space, that is often helpful in seeing 'what is going on'. Usually the sample space is represented by a rectangle, with individual regions within the rectangle representing events. It is often helpful to imagine that the actual areas of the various regions in a Venn diagram are in proportion to the corresponding probabilities. However, there is no need to spend a long time drawing these diagrams - their use is simply as a reminder of what is happening." (Graham Upton & Ian Cook, "Introducing Statistics", 2001)

"Two types of graphic organizers are commonly used for comparison: the Venn diagram and the comparison matrix [...] the Venn diagram provides students with a visual display of the similarities and differences between two items. The similarities between elements are listed in the intersection between the two circles. The differences are listed in the parts of each circle that do not intersect. Ideally, a new Venn diagram should be completed for each characteristic so that students can easily see how similar and different the elements are for each characteristic used in the comparison." (Robert J. Marzano et al, "Classroom Instruction that Works: Research-based strategies for increasing student achievement, 2001)

"The notion of outcomes covering a space is a very useful mental image, as it ties in strongly with the use of Venn diagrams and tables for clarifying the nature of possible events resulting from a trial. There are two important aspects to this. First, when enumerating the various outcomes that comprise an event, the number of (equally. likely) outcomes should correspond, visually, with the area of that part of the diagram represented by the event in question - the greater the probability, the larger the area. Secondly, where events overlap (for example, when rolling a die, consider the two events 'getting an even score' and 'getting a score greater than 2' ), the various regions in the Venn diagram help to clarify the various combinations of events that might occur." (Alan Graham, "Developing Thinking in Statistics", 2006)

📉Graphical Representation: Tools (Just the Quotes)

"Recognize effective results. Does the type of chart selected give a comprehensive picture of the situation? Does the size of chart and visual aid used satisfy all audience requirements? Do materials meet all reproduction problems? Is the layout well balanced and style of lettering uniform? Does the chart as a whole accurately present the facts? Is the projected idea an effective visual tool?" (Mary E Spear, "Charting Statistics", 1952)

"The grid with the vertical ruling carrying the logarithmic scale and the horizontal ruling carrying the arithmetic scale denoting time is the most common. The reverse may be used, and the horizontal ruling may carry the log scale. Charts of this type are frequently referred to as 'semilog charts'. [...] The full or double log scale (with the log grid carried on both horizontal and vertical rulings) is used mostly for statistical study and economic analysis and is not a good tool for popular presentation of data." (Mary E Spear, "Charting Statistics", 1952)

"Graphic forms help us to perform and influence two critical functions of the mind: the gathering of information and the processing of that information. Graphs and charts are ways to increase the effectiveness and the efficiency of transmitting information in a way that enhances the reader's ability to process that information. Graphics are tools to help give meaning to information because they go beyond the provision of information and show relationships, trends, and comparisons. They help to distinguish which numbers and which ideas are more important than others in a presentation." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"The square has always had a no-nonsense sort of image. Stable, solid, and - well - square. Perhaps that's why it is the shape used in business visuals in those rare cases where a visual is even bothered with. Flip through most business books and you'll find precious few places for your eye to stop and your visual brain to engage. But when you do, the shape of the graphic, chart, matrix, table, or diagram is certainly square. It's a comfortable shape, which makes it a valuable implement in your kit of visual communication tools." (Terry Richey, "The Marketer's Visual Tool Kit", 1994)

"The triangle is one of the best tools for visualizing a problem. Every difficult problem I've encountered in business breaks down into pieces, which carry different weight and importance. The pieces with the most importance sit at the top of the triangle, which progresses down to the sometimes thorny but less important piece at the base." (Terry Richey, "The Marketer's Visual Tool Kit", 1994)

"Visual thinking can begin with the three basic shapes we all learned to draw before kindergarten: the triangle, the circle, and the square. The triangle encourages you to rank parts of a problem by priority. When drawn into a triangle, these parts are less likely to get out of order and take on more importance than they should. While the triangle ranks, the circle encloses and can be used to include and/or exclude. Some problems have to be enclosed to be managed. Finally, the square serves as a versatile problem-solving tool. By assigning it attributes along its sides or corners, we can suddenly give a vague issue a specific place to live and to move about." (Terry Richey, "The Marketer's Visual Tool Kit", 1994)

"When visualization tools act as a catalyst to early visual thinking about a relatively unexplored problem, neither the semantics nor the pragmatics of map signs is a dominant factor. On the other hand, syntactics (or how the sign-vehicles, through variation in the visual variables used to construct them, relate logically to one another) are of critical importance." (Alan M MacEachren, "How Maps Work: Representation, Visualization, and Design", 1995)

"Good numeric representation is a key to effective thinking that is not limited to understanding risks. Natural languages show the traces of various attempts at finding a proper representation of numbers. [...] The key role of representation in thinking is often downplayed because of an ideal of rationality that dictates that whenever two statements are mathematically or logically the same, representing them in different forms should not matter. Evidence that it does matter is regarded as a sign of human irrationality. This view ignores the fact that finding a good representation is an indispensable part of problem solving and that playing with different representations is a tool of creative thinking." (Gerd Gigerenzer, "Calculated Risks: How to know when numbers deceive you", 2002)

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

"To analyze means to untangle. Even when we 'let the data speak for themselves', we need to untangle some aspect of the data before displaying things in a graphic. The more analytics we can include in the process of displaying graphics, the more flexibility our tools will have." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Graphics, charts, and maps aren’t just tools to be seen, but to be read and scrutinized. The first goal of an infographic is not to be beautiful just for the sake of eye appeal, but, above all, to be understandable first, and beautiful after that; or to be beautiful thanks to its exquisite functionality." (Alberto Cairo, "The Functional Art", 2011)

"The first and main goal of any graphic and visualization is to be a tool for your eyes and brain to perceive what lies beyond their natural reach." (Alberto Cairo, "The Functional Art", 2011)

"[...] communicating with data is less often about telling a specific story and more like starting a guided conversation. It is a dialogue with the audience rather than a monologue. While some data presentations may share the linear approach of a traditional story, other data products (analytical tools, in particular) give audiences the flexibility for exploration. In our experience, the best data products combine a little of both: a clear sense of direction defined by the author with the ability for audiences to focus on the information that is most relevant to them. The attributes of the traditional story approach combined with the self-exploration approach leads to the guided safari analogy." (Zach Gemignani et al, "Data Fluency", 2014)

"Creating a data fluent organization doesn’t just happen. It starts with people who love using data as a tool to improve their job performance - people who have learned to converse with others in the language of data. It needs people who expect and demand better, more useful data products from themselves and others. It starts with you." (Zach Gemignani et al, "Data Fluency", 2014)

"Key Performance Indicators (KPIs) in many organizations are a broken tool. The KPIs are often a random collection prepared with little expertise, signifying nothing. [...] KPIs should be measures that link daily activities to the organization’s critical success factors (CSFs), thus supporting an alignment of effort within the organization in the intended direction." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"There is a story in your data. But your tools don’t know what that story is. That’s where it takes you - the analyst or communicator of the information - to bring that story visually and contextually to life." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Commonly, data do not make a clear and unambiguous statement about our world, often requiring tools and methods to provide such clarity. These methods, called statistical data analysis, involve collecting, manipulating, analyzing, interpreting, and presenting data in a form that can be used, understood, and communicated to others." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"Exploring data generates hypotheses about patterns in our data. The visualizations and tools of dynamic interactive graphics ease and improve the exploration, helping us to 'see what our data seem to say'." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"A performance dashboard is a practical tool to improve management effectiveness and efficiency, not just a pretty retrospective picture in an annual report." (Pearl Zhu, "Performance Master: Take a Holistic Approach to Unlock Digital Performance", 2017)

"Color is difficult to use effectively. A small number of well-chosen colors can be highly distinguishable, particularly for categorical data, but it can be difficult for users to distinguish between more than a handful of colors in a visualization. Nonetheless, color is an invaluable tool in the visualization toolbox because it is a channel that can carry a great deal of meaning and be overlaid on other dimensions. […] There are a variety of perceptual effects, such as simultaneous contrast and color deficiencies, that make precise numerical judgments about a color scale difficult, if not impossible." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Maps also have the disadvantage that they consume the most powerful encoding channels in the visualization toolbox - position and size - on an aspect that is held constant. This leaves less effective encoding channels like color for showing the dimension of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

📉Graphical Representations: Dashboards (Just the Quotes)

"The real value of dashboard products lies in their ability to replace hunt‐and‐peck data‐gathering techniques with a tireless, adaptable, information‐flow mechanism. Dashboards transform data repositories into consumable information." (Gregory L Hovis, "Stop Searching for InformationMonitor it with Dashboard Technology," DM Direct, 2002)

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

“Dashboards aren't all that different from some of the other means of presenting information, but when properly designed the single-screen display of integrated and finely tuned data can deliver insight in an especially powerful way.” (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"An effective dashboard is the product not of cute gauges, meters, and traffic lights, but rather of informed design: more science than art, more simplicity than dazzle. It is, above all else, about communication." (Stephen Few, "Information Dashboard Design", 2006)

"Most dashboards fail to communicate efficiently and effectively, not because of inadequate technology (at least not primarily), but because of poorly designed implementations. No matter how great the technology, a dashboard's success as a medium of communication is a product of design, a result of a display that speaks clearly and immediately. Dashboards can tap into the tremendous power of visual perception to communicate, but only if those who implement them understand visual perception and apply that understanding through design principles and practices that are aligned with the way people see and think." (Stephen Few, "Information Dashboard Design", 2006) 

"Having a purposeless or poorly performing dashboard is more common than not. This happens when the underlying architecture is not designed properly to support the needs of dashboard interaction. There is an obvious disconnect between the design of the data warehouse and the design of the dashboards. The people who design the data warehouse do not know what the dashboard will do; and the people who design the dashboards do not know how the data warehouse was designed, resulting in a lack of cohesion between the two. A similar disconnect can also exist between the dashboard designer and the business analyst, resulting in a dashboard that may look beautiful and dazzling but brings very little business value." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)

"In general, it still holds true that 'there is no such thing as a free lunch'. What this means is that the most advanced dashboard solutions with the most features and flexibility are generally also the technologies that require more setup and more skill sets from the administrators and the end users. In some cases companies 'dumb down' their dashboard application in the initial stages of deployment so as not to scare their users with too many options. Later, when a dashboard culture has developed, they open up more of the functionality." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)

"There are myriad questions that we can ask from data today. As such, it’s impossible to write enough reports or design a functioning dashboard that takes into account every conceivable contingency and answers every possible question." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"A dashboard is like the executive summary of a report. We read executive summaries and skip the body of the report if the summary is more or less in line with our expectations. Trouble is, measurement is never exhaustive. It is only when we dive in that we realize what areas may have been missed." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"[…] an overall green status indicator doesn’t mean anything most of the time. All it says is that the things under measurement seem okay. But there always will be many more things not under measurement. To celebrate green indicators is to ignore the unknowns. […] The tendency to roll up metrics into dashboards promotes ignorance of the real situation on the ground. We forget that we only see what is under measurement. We only act when something is not green." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Rolling up fine-grained metrics to create high-level dashboards puts pressure on teams to keep the fine-grained metrics green even when it might not be the best use of their time." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"A performance dashboard is a practical tool to improve management effectiveness and efficiency, not just a pretty retrospective picture in an annual report." (Pearl Zhu, "Performance Master: Take a Holistic Approach to Unlock Digital Performance", 2017)

"All human storytellers bring their subjectivity to their narratives. All have bias, and possibly error. Acknowledging and defusing that bias is a vital part of successfully using data stories. By debating a data story collaboratively and subjecting it to critical thinking, organizations can get much higher levels of engagement with data and analytics and impact their decision making much more than with reports and dashboards alone." (James Richardson, 2017)

"Dashboards are a type of multiform visualization used to summarize and monitor data. These are most useful when proxies have been well validated and the task is well understood. This design pattern brings a number of carefully selected attributes together for fast, and often continuous, monitoring - dashboards are often linked to updating data streams. While many allow interactivity for further investigation, they typically do not depend on it. Dashboards are often used for presenting and monitoring data and are typically designed for at-a-glance analysis rather than deep exploration and analysis." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Infographics combine art and science to produce something that is not unlike a dashboard. The main difference from a dashboard is the subjective data and the narrative or story, which enhances the data-driven visual and engages the audience quickly through highlighting the required context." (Travis Murphy, "Infographics Powered by SAS®: Data Visualization Techniques for Business Reporting", 2018)

"Dashboards are collections of several linked visualizations all in one place. The idea is very popular as part of business intelligence: having current data on activity summarized and presented all inone place. One danger of cramming a lot of disparate information into one place is that you will quickly hit information overload. Interactivity and small multiples are definitely worth considering as ways of simplifying the information a reader has to digest in a dashboard. As with so many other visualizations, layering the detail for different readers is valuable." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"[Dashboards] are popular methods for displaying multiple visualizations and statistical information. Dashboards often take the form of some organizational instrument that offers both at-a-glance and detailed views of many different analytical and information dimensions. Dashboards are not a unique chart type themselves, but rather should be considered compositions that comprise multiple chart types." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

"Understanding the entire data ecosystem, from the production of a data point to its consumption in a dashboard or a visualization, provides the ability to invoke action, which is more valuable than the mere sum of its parts." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"A well-designed dashboard needs to provide a similar experience; information cannot be placed just anywhere on the dashboard. Charts that relate to one another are usually positioned close to one another. Important charts often appear larger and more visually prominent than less important ones. In other words, there are natural sizes for how a dashboard comprises charts based on the task and context." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"As we enter into certain types of analytical conversations, we expect the conversations to flow in a predictable and cohesive manner. A KPI dashboard, for example, uses redundant structures across specific dimensions or measures to convey information. A dashboard with a top-down exposition style provides high-level information first and clarifies downward, while a bottom-up dashboard starts with the details and clarifies them against the larger picture." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Chart choices can also create weight within the entire composition. Presenting information as a comprehensive visualization, such as in a dashboard, requires thinking beyond individual charts. In writing, we not only craft sentences, but write the composition as an entire piece. Certain sentences may drive the writing more, but all sentences play a role in conveying the message." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"The sizes of charts in space reflect how we convey information to a reader. In a dashboard context, the content, size, and space that the various charts occupy should reflect the form and function of the main message. As you saw with the bento box metaphor from the introduction, there needs to be deliberate thought put into the placement and size of each individual chart so that they all work together in harmony." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"When integrating written text with charts in a functionally aesthetic way, the reader should be able to find the key takeaways from the chart or dashboard, taking into account the context, constraints, and reading objectives of the overall message."  (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

📉Graphical Representation: Information Design (Just the Quotes)

"The ducks of information design are false escapes from flatland, adding pretend dimensions to impoverished data sets, merely fooling around with information." (Edward R Tufte, "Envisioning Information", 1990)

"We envision information in order to reason about, communicate, document, and preserve that knowledge - activities nearly always carried out on two-dimensional paper and computer screen. Escaping this flatland and enriching the density of data displays are the essential tasks of information design." (Edward R Tufte, "Envisioning Information", 1990)

"Good information design is clear thinking made visible, while bad design is stupidity in action." (Edward Tufte, "Visual Explanations" , 1997)

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

"Information design is defined as the art and science of preparing information so that can be used by human beings with efficiency and effectiveness. Its primary objectives are:To develop documents that are comprehensible, rapidly and accurately retrievable, and easy to translate into effective actions [...]" (Sheila Pontis, "La historia de la esquematica en la visualization de datos", 2007)

"I feel that every day, all of us now are being blasted by information design. It's being poured into our eyes through the Web, and we're all visualizers now; we're all demanding a visual aspect to our information. There's something almost quite magical about visual information. It's effortless; it literally pours in." (David McCandless, "The beauty of data visualization", TEDGlobal, 2010) 

"The composing of intelligible patterns from the noise of raw data is a hallmark of a good information designer. The most successful examples extract and present essential relationships in a coherent manner while limiting the obtrusiveness of accessory relationships. Effective results are self-evident whereby the information graphic is absorbed by the mind holistically." (William A Anderson & William M Bevington, "Complications and Adjacencies: An Organizing Logic for Information Graphics", Parsons Journal of Information Mapping Vol. II(3), 2010)

"Information design, when successful - whether in print, on the web, or in the environment - represents the functional balance of the meaning of the information, the skills and inclinations of the designer, and the perceptions, education, experience, and needs of the audience." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Successful information design in movement systems gives the user the information he needs - and only the information he needs - at every decision point." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"Information design is a design practice concerned with the presentation of information. It is often associated with the activities of data visualization; indeed sometimes it is presented as the major field in which data visualization belongs. Unquestionably, both share an underlying motive to facilitate understanding. However, in my view, information design has a much broader application concerned with the design of many different forms of visual communication, particularly those with an instructional or functional slant, such as way-finding devices like hospital building maps or in the design of utility bills." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

📉Graphical Representation: Explanation (Just the Quotes)

"Wherever unusual peaks or valleys occur on a curve it is a good plan to mark these points with a small figure inside a circle. This figure should refer to a note on the back of the chart explaining the reason for the unusual condition. It is not always sufficient to show that a certain item is unusually high or low; the executive will want to know why it is that way." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"The preliminary examination of most data is facilitated by the use of diagrams. Diagrams prove nothing, but bring outstanding features readily to the eye; they are therefore no substitutes for such critical tests as may be applied to the data, but are valuable in suggesting such tests, and in explaining the conclusions founded upon them." (Sir Ronald A Fisher, "Statistical Methods for Research Workers", 1925)

"Try telling the story in words different from those on the charts. […] If the chart shows a picture, describe the picture. Tell what it shows and why it is shown. If it is a diagram, explain it. Don't leave the audience to figure it out. No matter how simple the story shown, tell it in your own words: but remember that explaining a chart doesn't mean reading it out loud." (Edward J Hegarty, "How to Use a Set of Display Charts", The American Statistician Vol. 2" (5), 1948)

"Charts and graphs represent an extremely useful and flexible medium for explaining, interpreting, and analyzing numerical facts largely by means of points, lines, areas, and other geometric forms and symbols. They make possible the presentation of quantitative data in a simple, clear, and effective manner and facilitate comparison of values, trends, and relationships. Moreover, charts and graphs possess certain qualities and values lacking in textual and tabular forms of presentation." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"It is almost impossible to define 'time-sequence chart' in a clear and unambiguous manner because of the many forms and adaptations open to this type of chart. However. it might be said that, in essence, time-sequence chart portrays a chain of activities through time, indicates the type of activity in each link of the chain, shows clearly the position of the link in the total sequence chain, and indicates the duration of each activity. The time sequence chart may also contain verbal elements explaining when to begin an activity, how long to continue the activity, and a description of the activity. The chart may also indicate when to blend a given activity with another and the point at which a given activity is completed. The basic time-sequence chart may also be accompanied by verbal explanations and by secondary or contributory charts." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

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

"Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity. Write out explanations of the data on the graphic itself. Label important events in the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Graphical methodology provides powerful diagnostic tools for conveying properties of the fitted regression, for assessing the adequacy of the fit, and for suggesting improvements. There is seldom any prior guarantee that a hypothesized regression model will provide a good description of the mechanism that generated the data. Standard regression models carry with them many specific assumptions about the relationship between the response and explanatory variables and about the variation in the response that is not accounted for by the explanatory variables. In many applications of regression there is a substantial amount of prior knowledge that makes the assumptions plausible; in many other applications the assumptions are made as a starting point simply to get the analysis off the ground. But whatever the amount of prior knowledge, fitting regression equations is not complete until the assumptions have been examined." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Maps used as charts do not need fine cartographic detail. Their purpose is to express ideas, explain relationships, or store data for consultation. Keep your maps simple. Edit out irrelevant detail. Without distortion, try to present the facts as the main feature of your map, which should serve only as a springboard for the idea you're trying to put across." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Diagrams are a means of communication and explanation, and they facilitate brainstorming. They serve these ends best if they are minimal. Comprehensive diagrams of the entire object model fail to communicate or explain; they overwhelm the reader with detail and they lack meaning." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Statistics can certainly pronounce a fact, but they cannot explain it without an underlying context, or theory. Numbers have an unfortunate tendency to supersede other types of knowing. […] Numbers give the illusion of presenting more truth and precision than they are capable of providing." (Ronald J Baker, "Measure what Matters to Customers: Using Key Predictive Indicators", 2006)

"Presentation graphics face the challenge to depict a key message in - usually a single - graphic which needs to fit very many observers at a time, without the chance to give further explanations or context. Exploration graphics, in contrast, are mostly created and used only by a single researcher, who can use as many graphics as necessary to explore particular questions. In most cases none of these graphics alone gives a comprehensive answer to those questions, but must be seen as a whole in the context of the analysis." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Bear in mind is that the use of color doesn’t always help. Use it sparingly and with a specific purpose in mind. Remember that the reader’s brain is looking for patterns, and will expect both recurrence itself and the absence of expected recurrence to carry meaning. If you’re using color to differentiate categorical data, then you need to let the reader know what the categories are. If the dimension of data you’re encoding isn’t significant enough to your message to be labeled or explained in some way - or if there is no dimension to the data underlying your use of difference colors - then you should limit your use so as not to confuse the reader." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Communication is the primary goal of data visualization. Any element that hinders - rather than helps - the reader, then, needs to be changed or removed: labels and tags that are in the way, colors that confuse or simply add no value, uncomfortable scales or angles. Each element needs to serve a particular purpose toward the goal of communicating and explaining information. Efficiency matters, because if you’re wasting a viewer’s time or energy, they’re going to move on without receiving your message." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Don’t rush to write a headline or an entire story or to design a visualization immediately after you find an interesting pattern, data point, or fact. Stop and think. Look for other sources and for people who can help you escape from tunnel vision and confirmation bias. Explore your information at multiple levels of depth and breadth, looking for extraneous factors that may help explain your findings. Only then can you make a decision about what to say, and how to say it, and about what amount of detail you need to show to be true to the data." (Alberto Cairo, "The Functional Art", 2011)

"Done well, annotation can help explain and facilitate the viewing and interpretive experience. It is the challenge of creating a layer of user assistance and user insight: how can you maximize the clarity and value of engaging with this visualization design?" (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Readability in visualization helps people interpret data and make conclusions about what the data has to say. Embed charts in reports or surround them with text, and you can explain results in detail. However, take a visualization out of a report or disconnect it from text that provides context" (as is common when people share graphics online), and the data might lose its meaning; or worse, others might misinterpret what you tried to show." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Ranks do not explain how much one item varies from another. Ranked data is ordinal; that is, the data is categorical and has a sequence (e.g., who finished the race first, second, and third). That’s it! Ranked data can be used for showing the order of the data points. […] When working with ranked data, you cannot make inferences about the variance in the data; all you can say with certainty is which item is ranked higher than the others, not how much higher." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Using a question as a title is a great way to guide the audience. The question helps you ensure that your charts respond directly to the question and when they do not, you can remove them. And that is the main point: You need to answer the question. If the data is not conclusive, say so. Give an explanation that relates back to your title and close the loop so that your audience is informed and gets the complete picture included in your analysis." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"A map by itself requires little explanation, but once data are superimposed, readers will probably need labels on the maps, and legends explaining encodings like the color of markers." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

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

"When the colors are dull and neutral, they can communicate a sense of uniformity and an aura of calmness. Grays do a great job of mapping out the context of your story so that the more sharp colors highlight what you’re trying to explain. The power of gray comes in handy for all of our supporting details such as the axis, gridlines, and nonessential data that is included for comparative purposes. By using gray as the primary color in a visualization, we automatically draw our viewers’ eyes to whatever isn’t gray. That way, if we are interested in telling a story about one data point, we can do so quite easily." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

📉Graphical Representation: Charts vs. Thousand Words (Just the Quotes)

"The drawing shows me at a glance what would be spread over ten pages in a book." (Ivan Turgenev, 1862) [2]

"Sometimes, half a dozen figures will reveal, as with a lighting-flash, the importance of a subject which ten thousand labored words with the same purpose in view, had left at last but dim and uncertain." (Mark Twain, "Life on the Mississippi", 1883) 

"One good picture is worth many pages of written description." (William Sproston Caine, 1891) [2]

"One look is worth a thousand words" (Kathleen Caffyn, 1903) 

"Use a picture. It's worth a thousand words." (Arthur Brisbane, The Post-Standard, 1911)

"One Look Is Worth A Thousand Words" ([advertisement] 1913)

"A picture is worth ten thousand words. If you can’t see the truth in these pictures you are among the vast majority that must learn only by experience." (Arthur Brisbane, 1915)

"One picture is worth ten thousand words." (Frederick R Barnard, Printer’s Ink, 1921)

"One Picture Worth Ten Thousand Words" ([Chinese proverb] 1927)

"In many instances, a picture is indeed worth a thousand words. To make this true in more diverse circumstances, much more creative effort is needed to pictorialize the output from data analysis. Naive pictures are often extremely helpful, but more sophisticated pictures can be both simple and even more informative." (John W Tukey & Martin B Wilk, "Data Analysis and Statistics: An Expository Overview", 1966)

"Graphic charts are ways of presenting quantitative as well as qualitative information in an efficient and effective visual form. Numbers and ideas presented graphically are often more easily understood. remembered. and integrated than when they are presented in narrative or tabular form. Descriptions. trends. relationships, and comparisons can be made more apparent. Less time is required to present and comprehend information when graphic methods are employed. As the old truism states, 'One picture is worth a thousand words.'" (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"One word is worth a thousand pictures. If it's the right word." (Edward Abbey, "Beyond the Wall: Essays from the Outside", 1984)

"A picture may be worth a thousand words, a formula is worth a thousand pictures." (Edsger Dijkstra, [conference at ETH Zurich] 1994)

"A magnificent picture is never worth a thousand perfect words." (John Dunning, "The Bookman's Wake", 1995)

"A picture tells a thousand words. But you get a thousand pictures from someone's voice." (Paul Fleischman, "Seek", 2001)

"If a picture is worth a thousand words, a metaphor is worth a thousand pictures." (Daniel H Pink, "A Whole New Mind: Why Right-Brainers Will Rule the Future", 2005)

"The amount of information rendered in a single financial graph is easily equivalent to thousands of words of text or a page-sized table of raw values. A graph illustrates so many characteristics of data in a much smaller space than any other means. Charts also allow us to tell a story in a quick and easy way that words cannot." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"Visual reports exploit the idea that a picture is worth a thousand words and, in particular, for many tasks a picture is more useful than a large table of numbers." (Stephen G Eick, "Graph Drawing for Data Analytics" [in "Handbook of Graph Drawing and Visualization"] , 2013)

"Graphs can help us interpret data and draw inferences. They can help us see tendencies, patterns, trends, and relationships. A picture can be worth not only a thousand words, but a thousand numbers. However, a graph is essentially descriptive - a picture meant to tell a story. As with any story, bumblers may mangle the punch line and the dishonest may lie." (Gary Smith, "Standard Deviations", 2014)

"The caption should explain what is shown, possibly also giving the data source. Captions should be detailed enough that the graphic can pretty well stand on its own. Longer is usually better than shorter. A picture may be worth a thousand words, but you need at least some words to describe and explain it." (Antony Unwin, "Graphical Data Analysis with R", 2015)

"A picture may be worth a thousand words, but not all pictures are readable, interpretable, meaningful, or relevant." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"A recurring theme in machine learning is combining predictions across multiple models. There are techniques called bagging and boosting which seek to tweak the data and fit many estimates to it. Averaging across these can give a better prediction than any one model on its own. But here a serious problem arises: it is then very hard to explain what the model is (often referred to as a 'black box'). It is now a mixture of many, perhaps a thousand or more, models." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"'A picture is worth a thousand words' is definitely true, and graphs can help you tell a story about your data that would otherwise go untold with only numerical summaries and statistics. While inferential statistics and effect size measures can help us draw relatively reliable conclusions from our data, graphs and visualizations can help make the scientific findings accessible to virtually anyone, even with minimal coursework in statistics or data science." (Daniel J Denis, "Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science, 2020)

"Although a picture may be worth a thousand words, a single static picture is in most cases insufficient for a valid analysis and for understanding of a complex subject. It is usual that an analyst needs to see different aspects or parts of data and look at the data from different perspectives. This means that the analyst needs to interact with the data and with the system that generates visual displays of the data: select data components and subsets for viewing, select and tune visualization techniques, transform the views, transform the data, and so on." (Natalia Andrienko et al, "Visual Analytics for Data Scientists", 2020)

"A picture really can be worth a thousand words, and human beings are adept at extracting useful information from visual presentations. Modern data analysis increasingly relies on graphical presentations to uncover meaning and convey results." (Robert I Kabacoff, "R in Action: Data analysis and graphics with R and Tidyverse", 2022)

"A good metaphor is worth a thousand pictures." (Anon) 

"As the Chinese say, 1001 words is worth more than a picture." (John McCarthy [source]) 

References:
[1] Wikipedia (2024) A picture is worth a thousand words [link]
[2] Quote Investigator (2022) A Picture Is Worth Ten Thousand Words [link]