✏️Edward R Tufte - Collected Quotes

"A good rule of thumb for deciding how long the analysis of the data actually will take is (1) to add up all the time for everything you can think of - editing the data, checking for errors, calculating various statistics, thinking about the results, going back to the data to try out a new idea, and (2) then multiply the estimate obtained in this first step by five." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

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

"[…] 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)

"Fitting lines to relationships between variables is the major tool of data analysis. Fitted lines often effectively summarize the data and, by doing so, help communicate the analytic results to others. Estimating a fitted line is also the first step in squeezing further information from the data." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"If two or more describing variables in an analysis are highly intercorrelated, it will be difficult and perhaps impossible to assess accurately their independent impacts on the response variable. As the association between two or more describing variables grows stronger, it becomes more and more difficult to tell one variable from the other. This problem, called 'multicollinearity' in the statistical jargon, sometimes causes difficulties in the analysis of nonexperimental data. […] No statistical technique can go very far to remedy the problem because the fault lies basically with the data rather than the method of analysis. Multicollinearity weakens inferences based on any statistical method - regression, path analysis, causal modeling, or cross-tabulations (where the difficulty shows up as a lack of deviant cases and as near-empty cells)."  (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

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

"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)

"Our inability to measure important factors does not mean either that we should sweep those factors under the rug or that we should give them all the weight in a decision. Some important factors in some problems can be assessed quantitatively. And even though thoughtful and imaginative efforts have sometimes turned the 'unmeasurable' into a useful number, some important factors are simply not measurable. As always, every bit of the investigator's ingenuity and good judgment must be brought into play. And, whatever un- knowns may remain, the analysis of quantitative data nonetheless can help us learn something about the world - even if it is not the whole story." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Quantitative techniques will be more likely to illuminate if the data analyst is guided in methodological choices by a substantive understanding of the problem he or she is trying to learn about. Good procedures in data analysis involve techniques that help to (a) answer the substantive questions at hand, (b) squeeze all the relevant information out of the data, and (c) learn something new about the world." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Random data contain no substantive effects; thus if the analysis of the random data results in some sort of effect, then we know that the analysis is producing that spurious effect, and we must be on the lookout for such artifacts when the genuine data are analyzed." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Sometimes clusters of variables tend to vary together in the normal course of events, thereby rendering it difficult to discover the magnitude of the independent effects of the different variables in the cluster. And yet it may be most desirable, from a practical as well as scientific point of view, to disentangle correlated describing variables in order to discover more effective policies to improve conditions. Many economic indicators tend to move together in response to underlying economic and political events." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The problem of multicollinearity involves a lack of data, a lack of information. […] Recognition of multicollinearity as a lack of information has two important consequences: (1) In order to alleviate the problem, it is necessary to collect more data - especially on the rarer combinations of the describing variables. (2) No statistical technique can go very far to remedy the problem because the fault lies basically with the data rather than the method of analysis. Multicollinearity weakens inferences based on any statistical method - regression, path analysis, causal modeling, or cross-tabulations (where the difficulty shows up as a lack of deviant cases and as near-empty cells)." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Statistical techniques do not solve any of the common-sense difficulties about making causal inferences. Such techniques may help organize or arrange the data so that the numbers speak more clearly to the question of causality - but that is all statistical techniques can do. All the logical, theoretical, and empirical difficulties attendant to establishing a causal relationship persist no matter what type of statistical analysis is applied." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

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

"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 matching procedure often helps inform the reader what is going on in the data […] Matching has some defects, chiefly that it is difficult to do a very good job of matching in complex situations without a large number of cases. […] One limitation of matching, then, is that quite often the match is not very accurate. A second limitation is that if we want to control for more than one variable using matching procedures, the tables begin to have combinations of categories without any cases at all in them, and they become somewhat more difficult for the reader to understand." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

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

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

"An especially effective device for enhancing the explanatory power of time-series displays is to add spatial dimensions to the design of the graphic, so that the data are moving over space (in two or three dimensions) as well as over time. […] Occasionally graphics are belligerently multivariate, advertising the technique rather than the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"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)

"Each part of a graphic generates visual expectations about its other parts and, in the economy of graphical perception, these expectations often determine what the eye sees. Deception results from the incorrect extrapolation of visual expectations generated at one place on the graphic to other places." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"For many people the first word that comes to mind when they think about statistical charts is 'lie'. No doubt some graphics do distort the underlying data, making it hard for the viewer to learn the truth. But data graphics are no different from words in this regard, for any means of communication can be used to deceive. There is no reason to believe that graphics are especially vulnerable to exploitation by liars; in fact, most of us have pretty good graphical lie detectors that help us see right through frauds." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Graphical excellence is the well-designed presentation of interesting data - a matter of substance, of statistics, and of design. Graphical excellence consists of complex ideas communicated with clarity, precision, and efficiency. Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space. Graphical excellence is nearly always multivariate. And graphical excellence requires telling the truth about the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Graphical competence demands three quite different skills: the substantive, statistical, and artistic. Yet now most graphical work, particularly at news publications, is under the direction of but a single expertise - the artistic. Allowing artist-illustrators to control the design and content of statistical graphics is almost like allowing typographers to control the content, style, and editing of prose. Substantive and quantitative expertise must also participate in the design of data graphics, at least if statistical integrity and graphical sophistication are to be achieved." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

" In time-series displays of money, deflated and standardized units of monetary measurement are nearly always better than nominal units." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Inept graphics also flourish because many graphic artists believe that statistics are boring and tedious. It then follows that decorated graphics must pep up, animate, and all too often exaggerate what evidence there is in the data. […] If the statistics are boring, then you've got the wrong numbers." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers even a very large set - is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Nearly all those who produce graphics for mass publication are trained exclusively in the fine arts and have had little experience with the analysis of data. Such experiences are essential for achieving precision and grace in the presence of statistics. [...] Those who get ahead are those who beautified data, never mind statistical integrity." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Of course, false graphics are still with us. Deception must always be confronted and demolished, even if lie detection is no longer at the forefront of research. Graphical excellence begins with telling the truth about the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"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)

"Relational graphics are essential to competent statistical analysis since they confront statements about cause and effect with evidence, showing how one variable affects another." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The conditions under which many data graphics are produced - the lack of substantive and quantitative skills of the illustrators, dislike of quantitative evidence, and contempt for the intelligence of the audience-guarantee graphic mediocrity. These conditions engender graphics that (1) lie; (2) employ only the simplest designs, often unstandardized time-series based on a small handful of data points; and (3) miss the real news actually in the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The interior decoration of graphics generates a lot of ink that does not tell the viewer anything new. The purpose of decoration varies - to make the graphic appear more scientific and precise, to enliven the display, to give the designer an opportunity to exercise artistic skills. Regardless of its cause, it is all non-data-ink or redundant data-ink, and it is often chartjunk."  (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The number of information-carrying (variable) dimensions depicted should not exceed the number of dimensions in the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"[…] the only worse design than a pie chart is several of them, for then the viewer is asked to compare quantities located in spatial disarray both within and between pies. […] Given their low data-density and failure to order numbers along a visual dimension, pie charts should never be used." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The problem with time-series is that the simple passage of time is not a good explanatory variable: descriptive chronology is not causal explanation. There are occasional exceptions, especially when there is a clear mechanism that drives the Y-variable." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numerical quantities represented." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The theory of the visual display of quantitative information consists of principles that generate design options and that guide choices among options. The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper. Most principles of design should be greeted with some skepticism, for word authority can dominate our vision, and we may come to see only though the lenses of word authority rather than with our own eyes." (Edward R Tufte, "The Visual Display of Quantitative Information", 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)

"Vigorous writing is concise. A sentence should contain no unnecessary words, a paragraph no unnecessary sentences, for the same reason that a drawing should have no unnecessary lines and a machine no unnecessary parts. This requires not that the writer make all his sentences short, or that heavoid all detail and treat his subjects only in outline, but that every word tell." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"At the heart of quantitative reasoning is a single question: Compared to what? Small multiple designs, multivariate and data bountiful, answer directly by visually enforcing comparisons of changes, of the differences among objects, of the scope of alternatives. For a wide range of problems in data presentation, small multiples are the best design solution." (Edward R Tufte, "Envisioning Information", 1990) 

"Confusion and clutter are failures of design, not attributes of information. And so the point is to find design strategies that reveal detail and complexity - rather than to fault the data for an excess of complication. Or, worse, to fault viewers for a lack of understanding. Among the most powerful devices for reducing noise and enriching the content of displays is the technique of layering and separation, visually stratifying various aspects of the data." (Edward R Tufte, "Envisioning Information", 1990)

"Consider this unsavory exhibit at right – chockablock with cliché and stereotype, coarse humor, and a content-empty third dimension. [...] Credibility vanishes in clouds of chartjunk; who would trust a chart that looks like a video game?" (Edward R Tufte, "Envisioning Information", 1990) [on diamond charts] 

"Gray grids almost always work well and, with a delicate line, may promote more accurate data reading and reconstruction than a heavy grid. Dark grid lines are chartjunk. When a graphic serves as a look-up table (rare indeed), then a grid may help with reading and interpolation. But even then the grid should be muted relative to the data." (Edward R Tufte, "Envisioning Information", 1990)

"Information consists of differences that make a difference." (Edward R Tufte, "Envisioning Information", 1990)

"Lurking behind chartjunk is contempt both for information and for the audience. Chartjunk promoters imagine that numbers and details are boring, dull, and tedious, requiring ornament to enliven. Cosmetic decoration, which frequently distorts the data, will never salvage an underlying lack of content. If the numbers are boring, then you've got the wrong numbers." (Edward R Tufte, "Envisioning Information", 1990)

"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)

"Visual displays rich with data are not only an appropriate and proper complement to human capabilities, but also such designs are frequently optimal. If the visual task is contrast, comparison, and choice - as so often it is - then the more relevant information within eyespan, the better. Vacant, low-density displays, the dreaded posterization of data spread over pages and pages, require viewers to rely on visual memory - a weak skill - to make a contrast, a comparison, a choice." (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)

"What about confusing clutter? Information overload? Doesn't data have to be ‘boiled down’ and  ‘simplified’? These common questions miss the point, for the quantity of detail is an issue completely separate from the difficulty of reading. Clutter and confusion are failures of design, not attributes of information. Often the less complex and less subtle the line, the more ambiguous and less interesting is the reading. Stripping the detail out of data is a style based on personal preference and fashion, considerations utterly indifferent to substantive content." (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)

"Audience boredom is usually a content failure, not a decoration failure." (Edward R Tufte, "The cognitive style of PowerPoint", 2003)

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

"A sparkline is a small, intense, simple, word-sized graphic with typographic resolution. Sparklines mean that graphics are no longer cartoonish special occasions with captions and boxes, but rather sparkline graphics can be everywhere a word or number can be: embedded in a sentence, table, headline, map, spreadsheet, graphic." (Edward R Tufte, "Beautiful Evidence", 2006)

"Areas surrounding data-lines may generate unintentional optical clutter. Strong frames produce melodramatic but content-diminishing visual effects. [...] A good way to assess a display for unintentional optical clutter is to ask 'Do the prominent visual effects convey relevant content?'" (Edward R Tufte, "Beautiful Evidence", 2006)

"By segregating evidence by mode (word, number, image, graph) , the current-day computer approach contradicts the spirit of sparklines, a spirit that makes no distinction among words, numbers, graphics, images. It is all evidence, after all. A good system for evidence display should be centered on evidence, not on a collection of application programs each devoted to a single mode of information." (Edward R Tufte, "Beautiful Evidence", 2006)

"By showing recent change in relation to many past changes, sparklines provide a context for nuanced analysis - and, one hopes, better decisions. [...] Sparklines efficiently display and narrate binary data (presence/absence, occurrence/non-occurrence, win/loss). [...] Sparklines can simultaneously accommodate several variables. [...] Sparklines can narrate on-going results detail for any process producing sequential binary outcomes." (Edward R Tufte, "Beautiful Evidence", 2006)

"Closely spaced lines produce moiré vibration, usually at its worst when data-lines (the figure) and spaces (the ground) between data-lines are approximately equal in size, and also when figure and ground contrast strongly in color value." (Edward R Tufte, "Beautiful Evidence", 2006)

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

"Documentation allows more effective watching, and we have the Fifth Principle for the analysis and presentation of data: 'Thoroughly describe the evidence. Provide a detailed title, indicate the authors and sponsors, document the data sources, show complete measurement scales, point out relevant issues.'" (Edward R Tufte, "Beautiful Evidence", 2006)

"Explanatory, journalistic, and scientific images should nearly always be mapped, contextualized, and placed on the universal grid. Mapped pictures combine representational images with scales, diagrams, overlays, numbers, words, images." (Edward R Tufte, "Beautiful Evidence", 2006)

"Evidence is evidence, whether words, numbers, images, din grams- still or moving. It is all information after all. For readers and viewers, the intellectual task remains constant regardless of the particular mode Of evidence: to understand and to reason about the materials at hand, and to appraise their quality, relevance. and integrity." (Edward R Tufte, "Beautiful Evidence", 2006)

"Excellent graphics exemplify the deep fundamental principles of analytical design in action. If this were not the case, then something might well be wrong with the principles." (Edward R Tufte, "Beautiful Evidence", 2006)

"Good design, however, can dispose of clutter and show all the data points and their names. [...] Clutter calls for a design solution, not a content reduction." (Edward R Tufte, "Beautiful Evidence", 2006)

"In general. statistical graphics should be moderately greater in length than in height. And, as William Cleveland discovered, for judging slopes and velocities up and down the hills in time-series, best is an aspect ratio that yields hill - slopes averaging 45°, over every cycle in the time-series. Variations in slopes are best detected when the slopes are around 45°, uphill or downhill." (Edward R Tufte, "Beautiful Evidence", 2006)

"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)

"Making an evidence presentation is a moral act as well as an intellectual activity. 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)

"Most techniques for displaying evidence are inherently multimodal, bringing verbal, visual. and quantitative elements together. Statistical graphics and maps arc visual-numerical fields labeled with words and framed by numbers. Even an austere image may evoke other images, new or remembered narrative, and perhaps a sense of scale and quantity. Words can simultaneously convey semantic and visual content, as the nouns on a map both name places and locate them in the two - space of latitude and longitude." (Edward R Tufte, "Beautiful Evidence", 2006)

"Principles of design should attend to the fundamental intellectual tasks in the analysis of evidence; thus we have the Second Principle for the analysis And presentation of data: Show causality, mechanism, explanation, systematic structure." (Edward R Tufte, "Beautiful Evidence", 2006)

"Sparklines are wordlike graphics, With an intensity of visual distinctions comparable to words and letters. [...] Words visually present both an overall shape and letter-by-letter detail; since most readers have seen the word previously, the visual task is usually one of quick recognition. Sparklines present an overall shape and aggregate pattern along with plenty of local detail. Sparklines are read the same way as words, although much more carefully and slowly." (Edward R Tufte, "Beautiful Evidence", 2006)

"Sparklines vastly increase the amount of data within our eyespan and intensify statistical graphics up to the everyday routine capabilities of the human eye-brain system for reasoning about visual evidence, seeing distinctions, and making comparisons. [...] Providing a straightforward and contextual look at intense evidence, sparkline graphics give us some chance to be approximately right rather than exactly wrong."  (Edward R Tufte, "Beautiful Evidence", 2006)

"Sparklines work at intense resolutions, at the level of good typography and cartography. [...] Just as sparklines are like words, so then distributions of sparklines on a page are like sentences and paragraphs. The graphical idea here is make it wordlike and typographic - an idea that leads to reasonable answers for most questions about sparkline arrangements." (Edward R Tufte, "Beautiful Evidence", 2006)

"[...] the First Principle for the analysis and presentation data: 'Show comparisons, contrasts, differences'. The fundamental analytical act in statistical reasoning is to answer the question "Compared with what?". Whether we are evaluating changes over space or time, searching big data bases, adjusting and controlling for variables, designing experiments , specifying multiple regressions, or doing just about any kind of evidence-based reasoning, the essential point is to make intelligent and appropriate comparisons. Thus visual displays, if they are to assist thinking, should show comparisons." (Edward R Tufte, "Beautiful Evidence", 2006)

"The only thing that is 2-dimensional about evidence is the physical flatland of paper and computer screen. Flatlandy technologies of display encourage flatlandy thinking. Reasoning about evidence should not be stuck in 2 dimensions, for the world seek to understand is profoundly multivariate. Strategies of design should make multivariateness routine, nothing out of the ordinary. To think multivariate. show multivariate; the Third Principle for the analysis and presentation of data: 

'Show multivariate data; that is, show more than 1 or 2 variables.'" (Edward R Tufte, "Beautiful Evidence", 2006)

"The principles of analytical design are universal - like mathematics, the laws of Nature, the deep structure of language - and are not tied to any particular language, culture, style, century, gender, or technology of information display." (Edward R Tufte, "Beautiful Evidence", 2006)

"The purpose of an evidence presentation is to assist thinking. Thus presentations should be constructed so as to assist with the fundamental intellectual tasks in reasoning about evidence: describing the data, making multivariate comparisons, understanding causality, integrating a diversity Of evidence, and documenting the analysis. Thus the Grand Principle of analytical design: 'The principles of analytical design are derived from the principles of analytical thinking.' Cognitive tasks are turned into principles of evidence presentation and design." (Edward R Tufte, "Beautiful Evidence", 2006)

"The Sixth Principle for the analysis and display of data: 'Analytical presentations ultimately stand or fall depending on the quality, relevance, and integrity of their content.' This suggests that the most effective way to improve a presentation is to get better content. It also suggests that design devices and gimmicks cannot salvage failed content." (Edward R Tufte, "Beautiful Evidence", 2006)

"These little data lines, because of their active quality over time, are named sparklines - small, high-resolution graphics usually embedded in a full context of words, numbers, images. Sparklines are datawords: data-intense, design-simple, word-sized graphics." (Edward R Tufte, "Beautiful Evidence", 2006)

"Words. numbers. pictures, diagrams, graphics, charts, tables belong together. Excellent maps, which are the heart and soul of good practices in analytical graphics, routinely integrate words, numbers, line-art, grids, measurement scales. Rarely is a distinction among the different modes of evidence useful for making sound inferences. It is all information after all. Thus the Fourth Principle for the analysis and presentation of data: 'Completely integrate words, numbers, images, diagrams.'" (Edward R Tufte, "Beautiful Evidence", 2006)

✏️Colin Ware - Collected Quotes

"Why should we be interested in visualization? Because the human visual system is a pattern seeker of enormous power and subtlety. The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers. At higher levels of processing, perception and cognition are closely interrelated, which is the reason why the words 'understanding' and 'seeing' are synonymous." (Colin Ware, 2000)

"A good visualization is not just a static picture or a 3D virtual environment that we can walk through and inspect like a museum full of statues. A good visualization is something that allows us to drill down and find more data about anything that seems important." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"Chernoff faces have not generally been adopted in practical visualization applications. The main reason for this may be the idiosyncratic nature of faces. When data is mapped to faces, many kinds of perceptual interactions can occur. Sometimes the combination of variables will result in a particular stereotypical face, perhaps a happy face or a sad face, and this will be identified more readily. In addition, there are undoubtedly great differences in our sensitivity to the different features. We may be more sensitive to the curvature of the mouth than to the height of the eyebrows, for example. This means that the perceptual space of Chernoff faces is likely to be extremely nonlinear. In addition, there are almost certainly many uncharted interactions between facial features, and these are likely to vary from one viewer to another." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"Diagrams are always hybrids of the conventional and the perceptual. Diagrams contain conventional elements, such as abstract labeling codes, that are difficult to learn but formally powerful. They also contain information that is coded according to perceptual rules, such as Gestalt principles. Arbitrary mappings may be useful, as in the case of mathematical notation, but a good diagram takes advantage of basic perceptual mechanisms that have evolved to perceive structure in the environment." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"It is useful to think of color as an attribute of an object rather than as its primary characteristic. It is excellent for labeling and categorization, but poor for displaying shape, detail, or space." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"Interactive visualization is a process made up of a number of interlocking feedback loops that fall into three broad classes. At the lowest level is the data manipulation loop, through which objects are selected and moved using the basic skills of eye–hand coordination. Delays of even a fraction of a second in this interaction cycle can seriously disrupt the performance of higher-level tasks. At an intermediate level is an exploration and navigation loop, through which an analyst finds his or her way in a large visual data space." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"The great advantage of the treemap over conventional tree views is that the amount of information on each branch of the tree can be easily visualized. Because the method is space-filling, it can show quite large trees containing thousands of branches. The disadvantage is that the hierarchical structure is not as clear as it is in a more conventional tree drawing, which is a specialized form of node–link diagram." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"The problem with the view that metadata and primary data are somehow essentially different is that all data is interpreted to some extent - there is no such thing as raw data. Every data gathering instrument embodies some particular interpretation in the way it is built. Also, from the practical viewpoint of the visualization designer, the problems of representation are the same for metadata as for primary data. In both cases, there are entities, relationships, and their attributes to be represented, although some are more abstract than others." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"[...] when data is presented in certain ways, the patterns can be readily perceived. If we can understand how perception works, our knowledge can be translated into rules for displaying information. Following perception‐based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"One reason design is difficult is that the designer already has the knowledge expressed in the design, has seen it develop from inception, and therefore cannot see it with fresh eyes. The solution is to be analytic and this is where this book is intended to add value. Effective design should start with a visual task analysis, determine the set of visual queries to be supported by a design, and then use color, form, and space to efficiently serve those queries." (Colin Ware, "Visual Thinking for Design", 2008)

✏️Anker V Andersen - Collected Quotes

"An economic justification for computer graphics is that the organization spends an enormous amount of money on data processing, often providing managers with too many reports, too many data, and an overload of information. The report output has to be condensed into a more usable form. The computer graph essentially is the data represented in a structured pictorial form. The role of the graph is to provide meaningful reports. To the extent that it does. it can be justified." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"Graphs are used to meet the need to condense all the available information into a more usable quantity. The selection process of combining and condensing will inevitably produce a less than complete study and will lead the user in certain directions, producing a potential for misleading." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"Graphs can present internal accounting data effectively. Because One of the main functions of the accountant is to communicate accounting information to users. accountants should use graphs, at least to the extent that they clarify the presentation of accounting data. present the data fairly, and enhance management's ability to make a more informed decision. It has been argued that the human brain can absorb and understand images more easily than words and numbers, and, therefore, graphs may be better communicative devices than written reports or tabular statements." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"Reliability is highly valued by accountants and has been defined as 'the faithfulness with which it (information) represents what it purports to represent'. The reason reliability is so important is that an essential characteristic of an accounting report is its acceptance, and if a report is considered to be misleading or superfluous, it and future reports will be disregarded." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"Understandability implies that the graph will mean something to the audience. If the presentation has little meaning to the audience, it has little value. Understandability is the difference between data and information. Data are facts. Information is facts that mean something and make a difference to whoever receives them. Graphic presentation enhances understanding in a number of ways. Many people find that the visual comparison and contrast of information permit relationships to be grasped more easily. Relationships that had been obscure become clear and provide new insights." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"The bar graph and the column graph are popular because they are simple and easy to read. These are the most versatile of the graph forms. They can be used to display time series, to display the relationship between two items, to make a comparison among several items, and to make a comparison between parts and the whole (total). They do not appear to be as 'statistical', which is an advantage to those people who have negative attitudes toward statistics. The column graph shows values over time, and the bar graph shows values at a point in time. bar graph compares different items as of a specific time (not over time)." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"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)

"There are several uses for which the line graph is particularly relevant. One is for a series of data covering a long period of time. Another is for comparing several series on the same graph. A third is for emphasizing the movement of data rather than the amount of the data. It also can be used with two scales on the vertical axis, one on the right and another on the left, allowing different series to use different scales, and it can be used to present trends and forecasts." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

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

Charles D Tupper - Collected Quotes

"An architecture is the response to the integrated collections of models and views within the problem area being examined." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

"An architecture represents combined perspectives in a structured format that is easily viewable and explains the context of the area being analyzed to all those viewing it." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

"Analyzing and defining an area must be done prior to doing any activity within that area. Without understanding all that must be done, incorrect assumptions can be reached. Short-term vision may handicap future development. Inappropriate scoping may produce artificial boundaries where there should be none." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

"Data architecture allows strategic development of flexible modular designs by insulating the data from the business as well as the technology process." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

"Methodologies provide guidelines for the application development process. They specify analysis and design techniques as well as the stages in which they occur. They also develop event sequencing. Lastly, they specify milestones and work products that must be created and the appropriate documentation that should be generated." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

"Data architectures are the heart of business functionality. Given the proper data architecture, all possible functions can be completed within the enterprise easily and expeditiously." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

"Processes that use data change far more frequently than the data structures themselves." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

"The enterprise architecture delineates the data according to the inherent structure within the organization rather than by organizational function or use. In this manner it makes the data dependent on business objects but independent of business processes." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

"Using architecture leads to foundational stability, not rigidity. As long as the appropriate characteristics are in place to ensure positive architectural evolution, the architecture will remain a living construct. Well-developed architectures are frameworks that evolve as the business evolves." (Charles D Tupper, "Data Architecture: From Zen to Reality", 2011)

✏️Mary H Briscoe - Collected Quotes

"A good chart delineates and organizes information. It communicates complex ideas, procedures, and lists of facts by simplifying, grouping, and setting and marking priorities. By spatial organization, it should lead the eye through information smoothly and efficiently." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"A graph is a system of connections expressed by means of commonly accepted symbols. As such, the symbols and symbolic forms used in making graphs are significant. To communicate clearly this symbolism must be acknowledged." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"A slide is usually seen for less than 30 seconds, so its impact has to be immediate. For this reason, figures for slides must be especially simple and succinct. A good slide makes no more than three points, and these points augment, emphasize, and explain the speaker's words." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"An axis is the ruler that establishes regular intervals for measuring information. Because it is such a widely accepted convention, it is often taken for granted and its importance overlooked. Axes may emphasize, diminish, distort, simplify, or clutter the information. They must be used carefully and accurately." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"Because 'reality' and 'truth' are essential in these figures, it is important to be straightforward and thoughtful in the selection of the areas to be used. Manipulation such as enlargement, reduction, and increase or decrease of contrast must not distort or change the information. Touch-up is permissible only to eliminate distracting artifacts. Labels should be used judiciously and sparingly, and should not hide or distract from important information." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"Good ideas do not communicate themselves. Ideas must be organized. Highly complex ideas need to be clarified and simplified whereas diffuse data may benefit from being combined. Ideas and data must be made interesting and comprehensible to those not familiar with them." (Mary H Briscoe, "Preparing Scientific Illustrations:  guide to better posters, presentations, and publications" 2nd ed., 1995)

"If you have a choice of presenting your information in tables or graphs, choose the graph. A graph conveys the information more quickly and easily than a table. It also shows the information more impressively and memorably. However, if the information can be said in one or two sentences or if the absolute numerical values are necessary in the presentation, use words or tables. To emphasize essential numerical data, use a graph with the table." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"Information for tables should be simplified as much as possible. Leave out data that have no bearing on the point you want to make. […] When simplifying by eliminating information, consider carefully the purpose of the table. If the intention is to summarize findings, the use of means and standard errors would be most effective. If the findings are to be compared and related, use only the pertinent data sets. Be selective about the number of data sets if documentation or facilitation of calculation is the goal. For reproduction of the experiment, two or more tables may be better than one if the information is long and complicated." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

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

"Often many tracings are shown together. Extraneous parts of the tracings must be eliminated and relevant tracings should be placed in a logical order. Repetitious labels should be eliminated and labels added that will fully clarify your information." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"Putting a box around items serves to isolate and emphasize them. Because a legend needs no emphasis, this is not a good idea. Do not add extra lines to a graph unless you have a good, functional reason for it. The simpler and less cluttered your graph is, the better it will communicate." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

✏️Ben Jones - Collected Quotes

"As presenters of data visualizations, often we just want our audience to understand something about their environment – a trend, a pattern, a breakdown, a way in which things have been progressing. If we ask ourselves what we want our audience to do with that information, we might have a hard time coming up with a clear answer sometimes. We might just want them to know something." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020) 

"Data is dirty. Let's just get that out there. How is it dirty? In all sorts of ways. Misspelled text values, date format problems, mismatching units, missing values, null values, incompatible geospatial coordinate formats, the list goes on and on." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020) 

"Data visualizations are either used (1) to help people complete a task, or (2) to give them a general awareness of the way things are, or (3) to enable them to explore the topic for themselves."  (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020) 

"I believe that the backlash against statistics is due to four primary reasons. The first, and easiest for most people to relate to, is that even the most basic concepts of descriptive and inferential statistics can be difficult to grasp and even harder to explain. […] The second cause for vitriol is that even well-intentioned experts misapply the tools and techniques of statistics far too often, myself included. Statistical pitfalls are numerous and tough to avoid. When we can't trust the experts to get it right, there's a temptation to throw the baby out with the bathwater. The third reason behind all the hate is that those with an agenda can easily craft statistics to lie when they communicate with us  […] And finally, the fourth cause is that often statistics can be perceived as cold and detached, and they can fail to communicate the human element of an issue." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020) 

"The first epistemic principle to embrace is that there is always a gap between our data and the real world. We fall headfirst into a pitfall when we forget that this gap exists, that our data isn't a perfect reflection of the real-world phenomena it's representing. Do people really fail to remember this? It sounds so basic. How could anyone fall into such an obvious trap?" (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020) 

"To make the best decisions in business and in life, we need to be adept at many different forms of thinking, including intuition, and we need to know how to incorporate many different types of inputs, including numerical data and statistics (analytics). Intuition and analytics don't have to be seen as mutually exclusive at all. In fact, they can be viewed as complementary." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020) 

"The way we explore data today, we often aren't constrained by rigid hypothesis testing or statistical rigor that can slow down the process to a crawl. But we need to be careful with this rapid pace of exploration, too. Modern business intelligence and analytics tools allow us to do so much with data so quickly that it can be easy to fall into a pitfall by creating a chart that misleads us in the early stages of the process." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020) 

"What is the purpose of collecting data? People gather and store data for at least three different reasons that I can discern. One reason is that they want to build an arsenal of evidence with which to prove a point or defend an agenda that they already had to begin with. This path is problematic for obvious reasons, and yet we all find ourselves traveling on it from time to time. Another reason people collect data is that they want to feed it into an artificial intelligence algorithm to automate some process or carry out some task. […] A third reason is that they might be collecting data in order to compile information to help them better understand their situation, to answer questions they have in their mind, and to unearth new questions that they didn't think to ask." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020)

✏️Leland Wilkinson - Collected Quotes

"A grammar of graphics facilitates coordinated activity in a set of relatively autonomous components. This grammar enables us to develop a system in which adding a graphic to a frame (say, a surface) requires no adjustments or changes in definitions other than the simple message 'add this graphic'. Similarly, we can remove graphics, transform scales, permute attributes, and make other alterations without redefining the basic structure."(Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"A graph is a set of points. A mathematical graph cannot be seen. It is an abstraction. A graphic, however, is a physical representation of a graph. This representation is accomplished by realizing graphs with aesthetic attributes such as size or color." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Comparing series visually can be misleading […]. Local variation is hidden when scaling the trends. We first need to make the series stationary (removing trend and/or seasonal components and/or differences in variability) and then compare changes over time. To do this, we log the series (to equalize variability) and difference each of them by subtracting last year’s value from this year’s value." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Coordinates are sets that locate points in space. These sets are usually numbers grouped in tuples, one tuple for each point. Because spaces can be defined as sets of geometric objects plus axioms defining their behavior, coordinates can be thought of more generally as schemes for mapping elements of sets to geometric objects." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Decision-makers process priors incorrectly in several ways. First, people tend to assess probability from the representativeness of an outcome rather than from its frequency. When supporting information is added to make an outcome more coherent and congruent with a representative mental image, people tend to judge the outcome more probable, even though the added qualifications and constraints by definition make it less probable. […] Second, humans often judge relative probability of outcomes by assessing similarity rather than frequency. […] Third, when given worthless evidence in a Bayesian framework, people tend to ignore prior probabilities and use the worthless evidence." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Estimating the missing values in a dataset solves one problem - imputing reasonable values that have well-defined statistical properties. It fails to solve another, however - drawing inferences about parameters in a model fit to the estimated data. Treating imputed values as if they were known (like the rest of the observed data) causes confidence intervals to be too narrow and tends to bias other estimates that depend on the variability of the imputed values (such as correlations)." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Human decision-making in the face of uncertainty is not only prone to error, it is also biased against Bayesian principles. We are not randomly suboptimal in our decisions. We are systematically suboptimal. (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"It is not always convenient to remember that the right model for a population can fit a sample of data worse than a wrong model - even a wrong model with fewer parameters. We cannot rely on statistical diagnostics to save us, especially with small samples. We must think about what our models mean, regardless of fit, or we will promulgate nonsense." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Taxonomies are useful to scientists when they lead to new theory or stimulate insights into a problem that previous theorizing might conceal. Classification for its own sake, however, is as unproductive in design as it is in science. In design, objects are only as useful as the system they support. And the test of a design is its ability to handle scenarios that include surprises, exceptions, and strategic reversals." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"The consequence of distinguishing statistical methods from the graphics displaying them is to separate form from function. That is, the same statistic can be represented by different types of graphics, and the same type of graphic can be used to display two different statistics. […] This separability of statistical and geometric objects is what gives a system a wide range of representational opportunities." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"The grammar of graphics takes us beyond a limited set of charts (words) to an almost unlimited world of graphical forms (statements). The rules of graphics grammar are sometimes mathematical and sometimes aesthetic. Mathematics provides symbolic tools for representing abstractions. Aesthetics, in the original Greek sense, offers principles for relating sensory attributes (color, shape, sound, etc.) to abstractions. In modern usage, aesthetics can also mean taste." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"The ordinary histogram is constructed by binning data on a uniform grid. Although this is probably the most widely used statistical graphic, it is one of the more difficult ones to compute. Several problems arise, including choosing the number of bins (bars) and deciding where to place the cutpoints between bars." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"The plot tells us the data are granular in the data source, something we could not ascertain with the histogram. There is an important lesson here. Statistics texts and statistical packages that recommend the histogram as the graphical starting point for a data analysis are giving bad advice. The same goes for kernel density estimates. These are appropriate second stages for graphical data analysis. The best starting point for getting a sense of the distribution of a variable is a tally, stem-and-leaf, or a dot plot. A dot plot is a special case of a tally (perhaps best thought of as a delta-neighborhood tally). Once we see that the data are not granular, we may move on to a histogram or kernel density, which smooths the data more than a dot plot." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 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)

"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)

✏️Felice C Frankel - Collected Quotes

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

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

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

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

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

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

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

✏️Alberto Cairo - Collected Quotes

"All graphics present data and allow a certain degree of exploration of those same data. Some graphics are almost all presentation, so they allow just a limited amount of exploration; hence we can say they are more infographics than visualization, whereas others are mostly about letting readers play with what is being shown, tilting more to the visualization side of our linear scale. But every infographic and every visualization has a presentation and an exploration component: they present, but they also facilitate the analysis of what they show, to different degrees." (Alberto Cairo, "The Functional Art", 2011)

"But if you don’t present your data to readers so they can see it, read it, explore it, and analyze it, why would they trust you?" (Alberto Cairo, "The Functional Art", 2011)

"By giving numbers a proper shape, by visually encoding them, the graphic has saved you time and energy that you would otherwise waste if you had to use a table that was not designed to aid your mind." (Alberto Cairo, "The Functional Art", 2011)

"Data always vary randomly because the object of our inquiries, nature itself, is also random. We can analyze and predict events in nature with an increasing amount of precision and accuracy, thanks to improvements in our techniques and instruments, but a certain amount of random variation, which gives rise to uncertainty, is inevitable." (Alberto Cairo, "The Functional Art", 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)

"For too many traditional journalists, infographics are mere ornaments to make the page look lighter and more attractive for audiences who grow more impatient with long-form stories every day. Infographics are treated not as devices that expand the scope of our perception and cognition, but as decoration." (Alberto Cairo, "The Functional Art", 2011)

"Good visualizations shouldn’t over-simplify information. They need to clarify it. In many cases, clarifying a subject requires increasing the amount of information, not reducing it." (Alberto Cairo, "The Functional Art", 2011)

"[...] graphical displays can be either figurative or non-figurative.[…] Other graphics that display abstract phenomena are non-figurative. In these ,there is no mimetic correspondence between what is being represented and its representation. The relationship between those two entities is conventional, no tnatural [...]." (Alberto Cairo, "The Functional Art", 2011)

"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)

"[...] if you want to show change through time, use a time-series chart; if you need to compare, use a bar chart; or to display correlation, use a scatter-plot - because some of these rules make good common sense." (Alberto Cairo, "The Functional Art", 2011) 

"In information graphics, what you show can be as important as what you hide." (Alberto Cairo, "The Functional Art", 2011)

"Information consumption can lead to higher knowledge on the part of the audience, if its members are able to perceive the patterns or meaning of data. It is not a passive process; our brains are not hard drives that store stuff uncritically .When people see, read, or listen, they assimilate content by relating it to their memories and experiences." (Alberto Cairo, "The Functional Art", 2011)

"It is not possible to be a good communicator if you have not developed a keen interest in almost everything as well as an urge to learn as much as you can about the strangest, most varied, unrelated topics. The life of a visual communicator should be one of systematic and exciting intellectual chaos." (Alberto Cairo, "The Functional Art", 2011)

"[...] it’s unrealistic to pretend that we can create a perfect model. But we can certainly come up with a good enough one." (Alberto Cairo, "The Functional Art", 2011)

" [...] the better defined the goals of an artifact, the narrower the variety of forms it can adopt." (Alberto Cairo, "The Functional Art", 2011)

"The fact that an information graphic is designed to help us complete certain intellectual tasks is what distinguishes it from fine art." (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)

"[...] the form of a technological object must depend on the tasks it should help with. This is one of the most important principles to remember when dealing with infographics and visualizations: The form should be constrained by the functions of your presentation. There may be more than one form a data set can adopt so that readers can perform operations with it and extract meanings, but the data cannot adopt any form. Choosing visual shapes to encode information should not be based on aesthetics and personal tastes alone." (Alberto Cairo, "The Functional Art", 2011)

"[...] the human brain is not good at calculating surface sizes. It is much better at comparing a single dimension such as length or height. [...] the brain is also a hopelessly lazy machine." (Alberto Cairo, "The Functional Art", 2011)

"The more adequately a model fits whatever it stands for without being needlessly complex, and the easier it is for its intended audience to interpret it correctly, the better it will be." (Alberto Cairo, "The Functional Art", 2011)

"[...] the relationship between forms and functions is bidirectional. Form doesn’t always follow function; in many cases, the function follows a form that previously followed another unrelated function." (Alberto Cairo, "The Functional Art", 2011)

"The overuse of bubble charts in news media is a good example of how infographics departments can become more worried about how their projects look than with how they work." (Alberto Cairo, "The Functional Art", 2011)

"The process of visually exploring data can be summarized in a single sentence: find patterns and trends lurking in the data and then observe the deviations from those patterns. Interesting stories may arise from both the norm - also called the smooth - and the exceptions." (Alberto Cairo, "The Functional Art", 2011)

"Thinking of graphics as art leads many to put bells and whistles over substance and to confound infographics with mere illustrations." (Alberto Cairo, "The Functional Art", 2011)

"This is what functional visualization means: choose graphic forms according to the tasks you wish to enable. The purpose of your graphics should somehow guide your decision of how to shape the information." (Alberto Cairo, "The Functional Art", 2011)

"We reach wisdom when we achieve a deep understanding of acquired knowledge, when we not only 'get it', but when new information blends with prior experience so completely that it makes us better at knowing what to do in other situations, even if they are only loosely related to the information from which our original knowledge came. Just as not all the information we absorb leads to knowledge, not all of the knowledge we acquire leads to wisdom." (Alberto Cairo, "The Functional Art", 2011)

"What is really important is to remember that no matter how creative and innovative you wish to be in your graphics and visualizations, the first thing you must do, before you put a finger on the computer keyboard, is ask yourself what users are likely to try to do with your tool." (Alberto Cairo, "The Functional Art", 2011)

"What you design is never exactly what your audience ends up interpreting, so reducing the chances for misinterpretation becomes crucial." (Alberto Cairo, "The Functional Art", 2011)

"[...] without conscious effort, the brain always tries to close the distance between observed phenomena and knowledge or wisdom that can help us survive. This is what cognition means. The role of an information architect is to anticipate this process and generate order before people’s brains try to do it on their own." (Alberto Cairo, "The Functional Art", 2011)

"Uncertainty confuses many people because they have the unreasonable expectation that science and statistics will unearth precise truths, when all they can yield is imperfect estimates that can always be subject to changes and updates." (Alberto Cairo, "How Charts Lie", 2019)

"An infographic is an edited, summarized presentation of data selected by a designer to tell a story. A visualization is a display designed to explore data so every reader will be able to extract his or her own stories" (Alberto Cairo)

✏️Andy Kirk - Collected Quotes

"A useful way to look at a data visualization challenge is to recognize that we are actually seeking to reduce choices. This is achieved through recognizing influential factors, by considering the desired function and tone of our work, familiarizing with our data and identifying stories. We are building clarity through selection and rejection. We are reducing the problem by enhancing our clarity." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"At its best, a static visualization is like a powerful photograph - a carefully conceived, arranged, and executed vision that manages to portray the sequence or motion of a story without the actual deployment of movement." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Data art is characterized by a lack of structured narrative and absence of any visual analysis capability. Instead, the motivation is much more about creating an artifact, an aesthetic representation or perhaps a technical/technique demonstration. At the extreme end, a design may be more guided by the idea of fun or playfulness or maybe the creation of ornamentation." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Data visualization is a means to an end, not an end in itself. It's merely a bridge connecting the messenger to the receiver and its limitations are framed by our own inherent irrationalities, prejudices, assumptions, and irrational tastes. All these factors can undermine the consistency and reliability of any predicted reaction to a given visualization, but that is something we can't realistically influence." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"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)

"Histograms are often mistaken for bar charts but there are important differences. Histograms show distribution through the frequency of quantitative values (y axis) against defined intervals of quantitative values(x axis). By contrast, bar charts facilitate comparison of categorical values. One of the distinguishing features of a histogram is the lack of gaps between the bars [...]" (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Explanatory data visualization is about conveying information to a reader in a way that is based around a specific and focused narrative. It requires a designer-driven, editorial approach to synthesize the requirements of your target audience with the key insights and most important analytical dimensions you are wishing to convey." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Sparklines aren't necessarily a variation on the line chart, rather, a clever use of them. [...] They take advantage of our visual perception capabilities to discriminate changes even at such a low resolution in terms of size. They facilitate opportunities to construct particularly dense visual displays of data in small space and so are particularly applicable for use on dashboards." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"The art side of the field [data visualization] refers to the scope for unleashing design flair and encouraging innovation, where you strive to design communications that appeal on an aesthetic level and then survive in the mind on an emotional one." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"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 process of visual analysis can potentially go on endlessly, with seemingly infinite combinations of variables to explore, especially with the rich opportunities bigger data sets give us. However, by deploying a disciplined and sensible balance between deductive and inductive enquiry you should be able to efficiently and effectively navigate towards the source of the most compelling stories." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"[...] there is never a single path towards a 'best' solution. The inherent creativity and individualism of design work ensures that. An idealistic desire for a single and simple set of rules to achieve a guaranteed effective solution is simply unreasonable [...] There is, however, an established body of theoretical and practical evidence that guides us to understand which techniques work better for certain situations and less well for others. Importantly, these guides transcend instinct or personal taste and help us frame many of our design options, influencing the choices we make." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Visual metaphors are about integrating a certain visual quality in your work that somehow conveys that extra bit of connection between the data, the design, and the topic. It goes beyond just the choice of visual variable, though this will have a strong influence. Deploying the best visual metaphor is something that really requires a strong design instinct and a certain amount of experience." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Visualization ethics relates to the potential deception that can be created, intentionally or otherwise, from an ineffective and inappropriate representation of data. Sometimes it can be through a simple lack of understanding of visual perception." (Andy Kirk, "Data Visualization: A successful design process", 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)

"[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)

"(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)

"If the goal of data visualization […] is to facilitate understanding, all judgements made through the design process have to contribute to accomplishing this." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

"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)

"The central premise in this book is that decision making is the key competency in data visualization: namely, effective decisions, efficiently made. To accomplish this you need to follow a design process that organizes your thinking and is underpinned by robust principles to optimize your thinking." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

"The experience offered by a visualization influences the interpreting phase of understanding. Whereas tone embodies a continuum, the judgement of the most suitable experience is more distinct and concerns different methods of enabling interpretation: explanatory, exhibitory or exploratory. […] Explanatory visualizations offer an experience characterized by the visualizer taking responsibility to present important observations and interpretations to help the viewer more quickly assimilate the meaning of what is presented. […] Exploratory visualizations differ from explanatory in that they are focused more on helping the viewers or – more specifically in this case – the users discover and form their own interpretations. Almost universally, these types of works will be digital and interactive in nature. […] Exhibitory visualizations are characterized by being neither explicitly explanatory nor functionally exploratory. With exhibitory visualizations the viewers have to do the work to interpret meaning, relying on their own capacity to perceive and translate the features of a visualization." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

"The term process contrasts considerably with procedure. The process […] provides a framework for thinking, rather than instructions to learn and follow. A good process should offer adaptability and remove the inflexibility of a defined procedure. In any visualization project, you will need to respond to revised requirements, additional data that emerges, or a shift in creative direction. A good process safeguards adaptability and cushions the impact of changing circumstances like these." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

"[…] the term visual representation is arguably the quintessential activity of data visualization. Representation involves making decisions about how you are going to portray your data visually so that the subject understanding it offers can be made accessible to your audience. In simple terms, this is all about charts and the act of selecting the right chart to show the features of your data that you think are most relevant." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

"There is an important distinction to make about the relationship between trust and truth. Achieving trust is an aim, presenting truth is an obligation. There should be no compromise here. You should never create work you know to be misleading, through either its content or its representation. You should never claim something presents the truth if it cannot be reasonably supported. The difference between a truth and an untruth should be beyond dispute. The fact that it is not, these days, is a sad indictment of modern society. Nevertheless, the imperative for truthfulness must be clear." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

"When consuming a visualization, a viewer will go through a process of understanding involving three phases: perceiving, interpreting and comprehending. […] The first phase is perceiving, and this concerns the act of reading a chart: ‘what do I see?’. […] Interpreting […] translates these observations into quantitative and/or qualitative meaning. Interpreting involves assimilating what you have observed against what you know about the subject. What does what you have seen mean, given the subject? […] comprehending […] is the consequence or reflective legacy of the communication experience. The viewers now consider what the interpretations mean to themselves. What can be inferred as being important to you about the interpretations you have made?" (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

✏️Peter Turchi - Collected Quotes

"A plot is a piece of ground, a plan (as in the plan of a building), or a scheme; to plot is to make a plan or, in geometry, to graph points on a grid. When we create a story, even a character-rather than event-based story, we make a plot or map out the narrative’s essential moments." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"But there is also beauty in the telling detail, the provocative glimpse, the perfectly framed snapshot. The question of what to include, how much to include, can only be answered with regard to what, precisely, we mean to create. A story isn’t as utilitarian as a map of bicycle paths, but like that map, it is defined by its purpose. To serve its purpose, a story might very well be stripped down to a few spare glittering parts; alternately, it might require, or benefit from, apparently useless observations, conversations, and excursions. Perhaps the only answer is that we can’t know what needs to be in, what needs to be out, until we know what it is that we’re making, toward what end." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"If we attempt to map the world of a story before we explore it, we are likely either to (a) prematurely limit our exploration, so as to reduce the amount of material we need to consider, or (b) explore at length but, recognizing the impossibility of taking note of everything, and having no sound basis for choosing what to include, arbitrarily omit entire realms of information. The opportunities are overwhelming." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"Our mental maps are often not terribly accurate, based as they are on our own selective experience, our knowledge and ignorance, and the information and misinformation we gain from others; nevertheless, these are the maps we depend on every day." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"The world of a story is not merely the sum of all the words we put on a page, or on many pages. When we talk about entering the world of a story as a reader we refer to things we picture, or imagine, and responses we form - to characters, events - all of which are prompted by, but not entirely encompassed by, the words on the page." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"The writer’s obligation is to make rewarding both the reader’s journey and his destination." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

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

✏️Cole N Knaflic - Collected Quotes

"Beyond annoying our audience by trying to sound smart, we run the risk of making our audience feel dumb. In either case, this is not a good user experience for our audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"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)

"Concentrate on the pearls, the information your audience needs to know." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Exploratory analysis is what you do to understand the data and figure out what might be noteworthy or interesting to highlight to others." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"First, to whom are you communicating? It is important to have a good understanding of who your audience is and how they perceive you. This can help you to identify common ground that will help you ensure they hear your message. Second, What do you want your audience to know or do?" (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Further develop the situation or problem by covering relevant background. Incorporate external context or comparison points. Give examples that illustrate the issue. Include data that demonstrates the problem. Articulate what will happen if no action is taken or no change is made. Discuss potential options for addressing the problem. Illustrate the benefits of your recommended solution." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Having all the information in the world at our fingertips doesn’t make it easier to communicate: it makes it harder. The more information you’re dealing with, the more difficult it is to filter." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

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

"I almost always use dark grey for the graph title. This ensures that it stands out, but without the sharp contrast you get from pure black on white (rather, I preserve the use of black for a standout color when I’m not using any other colors). A number of preattentive attributes are employed to draw attention to the Progress to date trend: color, thickness of line, presence of data marker and label on the final point, and the size of the corresponding text." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"If I had to pick a single go-to graph for categorical data, it would be the horizontal bar chart, which flips the vertical version on its side. Why? Because it is extremely easy to read. The horizontal bar chart is especially useful if your category names are long, as the text is written from left to right, as most audiences read, making your graph legible for your audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"If you do succeed in persuading them, you’ve only done so on an intellectual basis. That’s not good enough, because people are not inspired to act by reason alone." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"If you simply present data, it’s easy for your audience to say, Oh, that’s interesting, and move on to the next thing. But if you ask for action, your audience has to make a decision whether to comply or not. This elicits a more productive reaction from your audience, which can lead to a more productive conversation - one that might never have been started if you hadn’t recommended the action in the first place." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"In the field of design, experts speak of objects having 'affordances'. These are aspects inherent to the design that make it obvious how the product is to be used. For example, a knob affords turning, a button affords pushing, and a cord affords pulling. These characteristics suggest how the object is to be interacted with or operated. When sufficient affordances are present, good design fades into the background and you don’t even notice it." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

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

"One thing to keep in mind with a table is that you want the design to fade into the background, letting the data take center stage. Don’t let heavy borders or shading compete for attention. Instead, think of using light borders or simply white space to set apart elements of the table." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Short-term memory has limitations. Specifically, people can keep about four chunks of visual information in their short-term memory at a given time. This means that if we create a graph with ten different data series that are ten different colors with ten different shapes of data markers and a legend off to the side, we’re making our audience work very hard going back and forth between the legend and the data to decipher what they are looking at." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

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

"[...] tables interact with our verbal system, graphs interact with our visual system, which is faster at processing information." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"The 3-minute story is exactly that: if you had only three minutes to tell your audience what they need to know, what would you say? This is a great way to ensure you are clear on and can articulate the story you want to tell." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"The unique thing you get with a pie chart is the concept of there being a whole and, thus, parts of a whole. But if the visual is difficult to read, is it worth it?" (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 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)

"Using a table in a live presentation is rarely a good idea. As your audience reads it, you lose their ears and attention to make your point verbally." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"What do you need your audience to know or do? This is the point where you think through how to make what you communicate relevant for your audience and form a clear understanding of why they should care about what you say." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"What would a successful outcome look like? If you only had a limited amount of time or a single sentence to tell your audience what they need to know, what would you say? In particular, I find that these last two questions can lead to insightful conversation." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"When we’re at the point of communicating our analysis to our audience, we really want to be in the explanatory space, meaning you have a specific thing you want to explain, a specific story you want to tell - probably about those two pearls." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"When you have just a number or two that you want to communicate: use the numbers directly." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Will you be encountering each other for the first time through this communication, or do you have an established relationship? Do they already trust you as an expert, or do you need to work to establish credibility? These are important considerations when it comes to determining how to structure your communication and whether and when to use data, and may impact the order and flow of the overall story you aim to tell." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"You should always want your audience to know or do something. If you can't concisely articulate that, you should revisit whether you need to communicate in the first place." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)