🧭💹Business Intelligence: Perspectives (Part XVIII: There’s More to Noise)

Business Intelligence Series

Visualizations should be built with an audience's characteristics in mind! Upon case, it might be sufficient to show only values or labels of importance (minima, maxima, inflexion points, exceptions, trends), while other times it might be needed to show all or most of the values to provide an accurate extended perspective. It even might be useful to allow users switching between the different perspectives to reduce the clutter when navigating the data or look at the patterns revealed by the clutter. 

In data-based storytelling are typically shown the points, labels and further elements that support the story, the aspects the readers should focus on, though this approach limits the navigability and users’ overall experience. The audience should be able to compare magnitudes and make inferences based on what is shown, and the accurate decoding shouldn’t be taken as given, especially when the audience can associate different meanings to what’s available and what’s missing. 

In decision-making, selecting only some well-chosen values or perspectives to show might increase the chances for a decision to be made, though is this equitable? Cherry-picking may be justified by the purpose, though is in general not a recommended practice! What is not shown can be as important as what is shown, and people should be aware of the implications!

One person’s noise can be another person’s signal. Patterns in the noise can provide more insight compared with the trends revealed in the "unnoisy" data shown! Probably such scenarios are rare, though it’s worth investigating what hides behind the noise. The choice of scale, the use of special types of visualizations or the building of models can reveal more. If it’s not possible to identify automatically such scenarios using the standard software, the users should have the possibility of changing the scale and perspective as seems fit. 

Identifying patterns in what seems random can prove to be a challenge no matter the context and the experience in the field. Occasionally, one might need to go beyond the general methods available and statistical packages can help when used intelligently. However, a presenter’s challenge is to find a plausible narrative around the findings and communicate it further adequately. Additional capabilities must be available to confirm the hypotheses framed and other aspects related to this approach.

It's ideal to build data models and a set of visualizations around them. Most probable some noise may be removed in the process, while other noise will be further investigated. However, this should be done through adjustable visual filters because what is removed can be important as well. Rare events do occur, probably more often than we are aware and they may remain hidden until we find the right perspective that takes them into consideration. 

Probably, some of the noise can be explained by special events that don’t need to be that rare. The challenge is to identify those parameters, associations, models and perspectives that reveal such insights. One’s gut feeling and experience can help in this direction, though novel scenarios can surprise us as well.

Not in every set of data one can find patterns, respectively a story trying to come out. Whether we can identify something worth revealing depends also on the data available at our disposal, respectively on whether the chosen data allow identifying significant patterns. Occasionally, the focus might be too narrow, too wide or too shallow. It’s important to look behind the obvious, to look at data from different perspectives, even if the data seems dull. It’s ideal to have the tools and knowledge needed to explore such cases and here the exposure to other real-life similar scenarios is probably critical!

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🔭Data Science: Data Visualization (Just the Quotes)

"No matter how clever the choice of the information, and no matter how technologically impressive the encoding, a visualization fails if the decoding fails. Some display methods lead to efficient, accurate decoding, and others lead to inefficient, inaccurate decoding. It is only through scientific study of visual perception that informed judgments can be made about display methods." (William S Cleveland, "The Elements of Graphing Data", 1985)

"The greatest possibilities of visual display lie in vividness and inescapability of the intended message. A visual display can stop your mental flow in its tracks and make you think. A visual display can force you to notice what you never expected to see. One should see the intended at once; one should not even have to wait for it to appear." (John W Tukey, "Data-based graphics: Visual display in the decades to come", Statistical Science 5, 1990)

"Data that are skewed toward large values occur commonly. Any set of positive measurements is a candidate. Nature just works like that. In fact, if data consisting of positive numbers range over several powers of ten, it is almost a guarantee that they will be skewed. Skewness creates many problems. There are visualization problems. A large fraction of the data are squashed into small regions of graphs, and visual assessment of the data degrades. There are characterization problems. Skewed distributions tend to be more complicated than symmetric ones; for example, there is no unique notion of location and the median and mean measure different aspects of the distribution. There are problems in carrying out probabilistic methods. The distribution of skewed data is not well approximated by the normal, so the many probabilistic methods based on an assumption of a normal distribution cannot be applied." (William S Cleveland, "Visualizing Data", 1993)

"Many of the applications of visualization in this book give the impression that data analysis consists of an orderly progression of exploratory graphs, fitting, and visualization of fits and residuals. Coherence of discussion and limited space necessitate a presentation that appears to imply this. Real life is usually quite different. There are blind alleys. There are mistaken actions. There are effects missed until the very end when some visualization saves the day. And worse, there is the possibility of the nearly unmentionable: missed effects." (William S Cleveland, "Visualizing Data", 1993)

"One important aspect of reality is improvisation; as a result of special structure in a set of data, or the finding of a visualization method, we stray from the standard methods for the data type to exploit the structure or the finding." (William S Cleveland, "Visualizing Data", 1993)

"There are two components to visualizing the structure of statistical data - graphing and fitting. Graphs are needed, of course, because visualization implies a process in which information is encoded on visual displays. Fitting mathematical functions to data is needed too. Just graphing raw data, without fitting them and without graphing the fits and residuals, often leaves important aspects of data undiscovered." (William S Cleveland, "Visualizing Data", 1993)

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

"Visualization is an effective framework for drawing inferences from data because its revelation of the structure of data can be readily combined with prior knowledge to draw conclusions. By contrast, because of the formalism of probablistic methods, it is typically impossible to incorporate into them the full body of prior information." (William S Cleveland, "Visualizing Data", 1993)

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

"The nature of maps and of their use in science and society is in the midst of remarkable change - change that is stimulated by a combination of new scientific and societal needs for geo-referenced information and rapidly evolving technologies that can provide that information in innovative ways. A key issue at the heart of this change is the concept of ‘visualization’." (Alan M MacEachren, "Exploratory cartographic visualization: advancing the agenda", 1997)

"Visualization for large data is an oxymoron - the art is to reduce size before one visualizes. The contradiction (and challenge) is that we may need to visualize first in order to find out how to reduce size." (Peter Huber, "Massive datasets workshop: Four years after", Journal of Computational and Graphical Statistics Vol 8, 1999)

"Functional visualizations are more than innovative statistical analyses and computational algorithms. They must make sense to the user and require a visual language system that uses color, shape, line, hierarchy and composition to communicate clearly and appropriately, much like the alphabetic and character-based languages used worldwide between humans." (Matt Woolman, "Digital Information Graphics", 2002)

"Visualizations can be used to explore data, to confirm a hypothesis, or to manipulate a viewer. [...] In exploratory visualization the user does not necessarily know what he is looking for. This creates a dynamic scenario in which interaction is critical. [...] In a confirmatory visualization, the user has a hypothesis that needs to be tested. This scenario is more stable and predictable. System parameters are often predetermined." (Usama Fayyad et al, "Information Visualization in Data Mining and Knowledge Discovery", 2002) 

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

"Merely drawing a plot does not constitute visualization. Visualization is about conveying important information to the reader accurately. It should reveal information that is in the data and should not impose structure on the data." (Robert Gentleman, "Bioinformatics and Computational Biology Solutions using R and Bioconductor", 2005)

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

"Data visualization [...] expresses the idea that it involves more than just representing data in a graphical form (instead of using a table). The information behind the data should also be revealed in a good display; the graphic should aid readers or viewers in seeing the structure in the data. The term data visualization is related to the new field of information visualization. This includes visualization of all kinds of information, not just of data, and is closely associated with research by computer scientists." (Antony Unwin et al, "Introduction" [in "Handbook of Data Visualization"], 2008) 

"The main goal of data visualization is its ability to visualize data, communicating information clearly and effectively. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex dataset by communicating its key aspects in a more intuitive way. Yet designers often tend to discard the balance between design and function, creating gorgeous data visualizations which fail to serve its main purpose - communicate information." (Vitaly Friedman, "Data Visualization and Infographics", Smashing Magazine, 2008)

"The purpose of visualization is insight, not pictures." (Ben Shneiderman, "Extreme visualization: squeezing a billion records into a million pixels",  SIGMOD ’08: Proceedings of the 2008 ACM SIGMOD, 2008)

"With the ever increasing amount of empirical information that scientists from all disciplines are dealing with, there exists a great need for robust, scalable and easy to use clustering techniques for data abstraction, dimensionality reduction or visualization to cope with and manage this avalanche of data."  (Jörg Reichardt, "Structure in Complex Networks", 2009)

"So what is the difference between a chart or graph and a visualization? […] a chart or graph is a clean and simple atomic piece; bar charts contain a short story about the data being presented. A visualization, on the other hand, seems to contain much more ʻchart junkʼ, with many sometimes complex graphics or several layers of charts and graphs. A visualization seems to be the super-set for all sorts of data-driven design." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"The goal of visualization is to aid our understanding of data by leveraging the human visual system’s highly tuned ability to see patterns, spot trends, and identify outliers." (J Heer et al, "A tour through the visualization zoo", Queue 8, 2010) 

"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)

"Exploratory data visualizations are appropriate when you have a whole bunch of data and you’re not sure what’s in it. […] By contrast, explanatory data visualization is appropriate when you already know what the data has to say, and you are trying to tell that story to somebody else." (Noah Iliinsky & Julie Steele, "Designing Data Visualizations", 2011)

"In data visualization, the number one rule of thumb to bear is mind is: Function first, suave second." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 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)

"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)

"[...] the terms data visualization and information visualization (casually, data viz and info viz) are useful for referring to any visual representation of data that is: (•) algorithmically drawn (may have custom touches but is largely rendered with the help of computerized methods); (•) easy to regenerate with different data (the same form may be repurposed to represent different datasets with similar dimensions or characteristics); (•) often aesthetically barren (data is not decorated); and (•) relatively data-rich (large volumes of data are welcome and viable, in contrast to infographics)." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Visualizations act as a campfire around which we gather to tell stories." (Al Shalloway, 2011)

"Good infographic design is about storytelling by combining data visualization design and graphic design." (Randy Krum, "Good Infographics: Effective Communication with Data Visualization and Design", 2013)

"Good visualization is a winding process that requires statistics and design knowledge. Without the former, the visualization becomes an exercise only in illustration and aesthetics, and without the latter, one of only analyses. On their own, these are fine skills, but they make for incomplete data graphics. Having skills in both provides you with the luxury - which is growing into a necessity - to jump back and forth between data exploration and storytelling." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"The biggest thing to know is that data visualization is hard. Really difficult to pull off well. It requires harmonization of several skills sets and ways of thinking: conceptual, analytic, statistical, graphic design, programmatic, interface-design, story-telling, journalism - plus a bit of 'gut feel'. The end result is often simple and beautiful, but the process itself is usually challenging and messy." (David McCandless, 2013)

"Visualization can be appreciated purely from an aesthetic point of view, but it’s most interesting when it’s about data that’s worth looking at. That’s why you start with data, explore it, and then show results rather than start with a visual and try to squeeze a dataset into it. It’s like trying to use a hammer to bang in a bunch of screws. […] Aesthetics isn’t just a shiny veneer that you slap on at the last minute. It represents the thought you put into a visualization, which is tightly coupled with clarity and affects interpretation." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

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

"What is good visualization? It is a representation of data that helps you see what you otherwise would have been blind to if you looked only at the naked source. It enables you to see trends, patterns, and outliers that tell you about yourself and what surrounds you. The best visualization evokes that moment of bliss when seeing something for the first time, knowing that what you see has been right in front of you, just slightly hidden. Sometimes it is a simple bar graph, and other times the visualization is complex because the data requires it." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Just because data is visualized doesn’t necessarily mean that it is accurate, complete, or indicative of the right course of action. Exhibiting a healthy skepticism is almost always a good thing." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"To be sure, data doesn’t always need to be visualized, and many data visualizations just plain suck. Look around you. It’s not hard to find truly awful representations of information. Some work in concept but fail because they are too busy; they confuse people more than they convey information [...]. Visualization for the sake of visualization is unlikely to produce desired results - and this goes double in an era of Big Data. Bad is still bad, even and especially at a larger scale." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"We are all becoming more comfortable with data. Data visualization is no longer just something we have to do at work. Increasingly, we want to do it as consumers and as citizens. Put simply, visualizing helps us understand what’s going on in our lives - and how to solve problems." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"Data visualization is marketed today as the miracle cure that will open the doors to success, whatever its shape. We have enough experience to realize that in reality it’s not always easy to distinguish between real usefulness and zealous marketing. After the initial excitement over the prospects of data visualization comes disillusionment, and after that the possibility of a balanced assessment. The key is to get to this point quickly, without disappointments and at a lower cost." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"[...] data visualization [is] a tool that, by applying perceptual mechanisms to the visual representation of abstract quantitative data, facilitates the search for relevant shapes, order, or exceptions. [...]  We must think of data visualization as a generic field where several (combinations of) perspectives, processes, technologies, and objectives (not forgetting the subjective component of personal style) can coexist. In this sense, data art, infographics, and business visualization are branches of data visualization." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"Data visualization is not a science; it is a crossroads at which certain scientific knowledge is used to justify and frame subjective choices. Ÿis doesn’t mean that rules don’t count. Rules exist and are effective when applied within the context for which they were designed." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"Creating effective visualizations is hard. Not because a dataset requires an exotic and bespoke visual representation - for many problems, standard statistical charts will suffice. And not because creating a visualization requires coding expertise in an unfamiliar programming language [...]. Rather, creating effective visualizations is difficult because the problems that are best addressed by visualization are often complex and ill-formed. The task of figuring out what attributes of a dataset are important is often conflated with figuring out what type of visualization to use. Picking a chart type to represent specific attributes in a dataset is comparatively easy. Deciding on which data attributes will help answer a question, however, is a complex, poorly defined, and user-driven process that can require several rounds of visualization and exploration to resolve." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"[…] no single visualization is ever quite able to show all of the important aspects of our data at once - there just are not enough visual encoding channels. […] designing effective visualizations to make sense of data is not an art - it is a systematic and repeatable process." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

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

"The field of [data] visualization takes on that goal more broadly: rather than attempting to identify a single metric, the analyst instead tries to look more holistically across the data to get a usable, actionable answer. Arriving at that answer might involve exploring multiple attributes, and using a number of views that allow the ideas to come together. Thus, operationalization in the context of visualization is the process of identifying tasks to be performed over the dataset that are a reasonable approximation of the high-level question of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

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

"As a first principle, any visualization should convey its information quickly and easily, and with minimal scope for misunderstanding. Unnecessary visual clutter makes more work for the reader’s brain to do, slows down the understanding (at which point they may give up) and may even allow some incorrect interpretations to creep in." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Data storytelling can be defined as a structured approach for communicating data insights using narrative elements and explanatory visuals." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling involves the skillful combination of three key elements: data, narrative, and visuals. Data is the primary building block of every data story. It may sound simple, but a data story should always find its origin in data, and data should serve as the foundation for the narrative and visual elements of your story." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 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)

"In addition to managing how the data is visualized to reduce noise, you can also decrease the visual interference by minimizing the extraneous cognitive load. In these cases, the nonrelevant information and design elements surrounding the data can cause extraneous noise. Poor design or display decisions by the data storyteller can inadvertently interfere with the communication of the intended signal. This form of noise can occur at both a macro and micro level." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"One very common problem in data visualization is that encoding numerical variables to area is incredibly popular, but readers can’t translate it back very well." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"There is often no one 'best' visualization, because it depends on context, what your audience already knows, how numerate or scientifically trained they are, what formats and conventions are regarded as standard in the particular field you’re working in, the medium you can use, and so on. It’s also partly scientific and partly artistic, so you get to express your own design style in it, which is what makes it so fascinating." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019) 

"When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs. Many interesting patterns and outliers in the data would remain hidden in the rows and columns of data tables without the help of data visualizations. They connect with our visual nature as human beings and impart knowledge that couldn’t be obtained as easily using other approaches that involve just words or numbers." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

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

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

"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 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)

"Much of the data visualization that bombards us today is decoration at best, and distraction or even disinformation at worst. The decorative function is surprisingly common, perhaps because the data visualization teams of many media organizations are part of the art departments. They are led by people whose skills and experience are not in statistics but in illustration or graphic design. The emphasis is on the visualization, not on the data. It is, above all, a picture." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"A data visualization, or dashboard, is great for summarizing or describing what has gone on in the past, but if people don’t know how to progress beyond looking just backwards on what has happened, then they cannot diagnose and find the ‘why’ behind it." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Data literacy is for the masses, and data visualization is powerful to simplify what could be very complicated." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

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

"Data visualization is a simplified approach to studying data." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Data visualization is a mix of science and art. Sometimes we want to be closer to the science side of the spectrum - in other words, use visualizations that allow readers to more accurately perceive the absolute values of data and make comparisons. Other times we may want to be closer to the art side of the spectrum and create visuals that engage and excite the reader, even if they do not permit the most accurate comparisons." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

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

"Data becomes more useful once it’s transformed into a data visualization or used in a data story. Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context and inspire action from your audience. Color can be very helpful when you are trying to make information stand out within your data visualizations." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data visualization is the practice of taking insights found in data analysis and turning them into numbers, graphs, charts, and other visual concepts to make them easier to grasp, understand, learn from, and utilize.[...] The visualization of data can be thought of as both a science and an art in that the way it is displayed is often as important to its understanding as the actual information that is being displayed." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

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

"The best approach is to build visualizations in the most digestible form, fitted to how that executive thinks. You will have to interact with executives, show them different visualizations, and see how they react in order to learn which forms work best for them. Be ready to fail often and learn fast, particularly with visualizations." (John Lucker)

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

"We often think of visualization as a design and programming task, but the process starts further back with the data. You have to understand the data - its trends and patterns, along with its flaws and imperfections - and the rest follows." (Nathan Yau)

🔭Data Science: Data Stories (Just the Quotes)

"Stories have a marvelous way of focusing our attention and helping us to discern why the data presented is important or relevant to some part of our lives. It is only inside of a context that data is meaningful, and using the data as part of a story is an excellent way of allowing the data to make a lasting impact. The most effective information visualizations will make themselves a pivotal point in a story or narrative within the viewers’ (or users’) minds." (Matthias Shapiro, "Once Upon a Stacked Time Series", [in "Beautiful Visualization"] 2010)

"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 stories are a subset of the much broader concept (or buzzword) of storytelling. […] Stories, or narratives, are useful in data visualization because they force us to recognize the limited value of a single chart in a complex environment. Stories also force us to recognize the need for a better integration of our displays, as we move away from strings of siloed charts." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"A data story starts out like any other story, with a beginning and a middle. However, the end should never be a fixed event, but rather a set of options or questions to trigger an action from the audience. Never forget that the goal of data storytelling is to encourage and energize critical thinking for business decisions." (James Richardson, 2017)

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

"A random collection of interesting but disconnected facts will lack the unifying theme to become a data story - it may be informative, but it won’t be insightful." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

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

"Before you can even consider creating a data story, you must have a meaningful insight to share. One of the essential attributes of a data story is a central or main insight. Without a main point, your data story will lack purpose, direction, and cohesion. A central insight is the unifying theme (telos appeal) that ties your various findings together and guides your audience to a focal point or climax for your data story. However, when you have an increasing amount of data at your disposal, insights can be elusive. The noise from irrelevant and peripheral data can interfere with your ability to pinpoint the important signals hidden within its core." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling gives your insight the best opportunity to capture attention, be understood, be remembered, and be acted on. An effective data story helps your insight reach its full potential: inspiring others to act and drive change." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling involves the skillful combination of three key elements: data, narrative, and visuals. Data is the primary building block of every data story. It may sound simple, but a data story should always find its origin in data, and data should serve as the foundation for the narrative and visual elements of your story." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Even with a solid narrative and insightful visuals, a data story cannot overcome a weak data foundation. As the master architect, builder, and designer of your data story, you play an instrumental role in ensuring its truthfulness, quality, and effectiveness. Because you are responsible for pouring the data foundation and framing the narrative structure of your data story, you need to be careful during the analysis process. Because all of the data is being processed and interpreted by you before it is shared with others, it can be exposed to cognitive biases and logical fallacies that distort or weaken the data foundation of your story." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

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

"So what does it mean to tell an honest story? Numbers should be presented in ways that allow meaningful comparisons." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"To tell an honest story, it is not enough for numbers to be correct. They need to be placed in an appropriate context so that a reader or listener can properly interpret them." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Good data stories have three key components: data, narrative, and visuals. [...] The data part is fairly obvious - data has to be accurate for the correct insights to be achieved. The narrative has to give a voice to the data in simple language, turning each data point into a character in the story with its own tale to tell. The visuals are what we are most concerned about. They have to allow us to be able to find trends and patterns in our datasets and do so easily and specifically. The last thing we want is for the most important points to be buried in rows and columns." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"One of the most challenging components of data visualization is that oftentimes the story you need to tell is complex, dynamic, and multidimensional. However, the standard tools we have are flat, static, and designed for paper. Decomposing the dynamic nature of the narrative embedded within your data into a storyboard format is one of the best ways to ensure your key points are effectively received by your intended audience." (Thomas Rhodes)

♜Strategic Management: Storytelling (Definitions)

"Narrating sequences of events in an artistic manner for a group of audience orally or in written words." (Mehrak Rahimi, "Digital Storytelling in Language Classes", Encyclopedia of Information Science and Technology 4th Ed., 2018)

"The act of sharing a tale or a series of events, often in a trajectory that give perspective on context and culture." (Catherine Hayes & Ian Corrie, "Learner-Centred Pedagogy Framing Authentic Identity and Positionality in Higher Education", 2020)

"Art of conveying events or phenomena in different formats, in order to entertain, inform, instruct, demonstrate, or persuade." (Tamara E Martin et al, "The Use of Storytelling to Promote Literacy Skills in Biology Education: An Intervention Proposal", 2021)

"Storytelling is a communication tool that creates meanings and emotions on audience. It is a tool to create and maintain bonds for humans as well as organizations." Beris A Özoran, "Digital Storytelling and Public Relations: An Analysis Through Case Studies", 2021)

"Storytelling is one of the most effective communication methods in telling the organizations themselves. Corporate stories are powerful and permanent narratives that express the corporate identity. In stories, perceptions are reshaped. The vision, mission, and values of the organization are narrated." (Deniz Özer, "Corporate Cults: Corporate Identity and Storytelling in the Context of Archetypal Symbolism", 2021)

"The art of telling tales and a great tool to teach language incorporating culture." (Carmela B Scala, "How to Foster Equality in the Language Classroom", 2021)

"The process of using fact and narrative to communicate something to your audience. Some stories are factual, and some are embellished or improvised in order to better explain the core message." (Stavroula Kalogeras, "Transmedia Storytelling Edutainment and the New Testament Lesson", 2021)

"The social and cultural activity of creating and sharing stories. Today, it is re-considered as an important communication tool useful in many domains such as organizational leadership and branding." (Laura R Grünberg, "In Need for More Tailored Feminist Stories in a Time of Crisis", 2021)

📊Business Intelligence: Data Storytelling (Definitions)

"A narrative way of describing a scenario, product idea, or strategy intended to provide a real-world context to promote decision making and better understanding." (Steven Haines, "The Product Manager's Desk Reference", 2008)

[storytelling:] "A method of communicating and sharing ideas, experiences and knowledge in a specific context." (Darren Dalcher, "Making Sense of IS Failures", Encyclopedia of Information Science and Technology 2nd Ed., 2009)

"A method of explaining a series of events through narrative." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"using a combination of data facts and a qualitative 'story' that provides effective communication of a business message." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Data storytelling can be defined as a structured approach for communicating data insights using narrative elements and explanatory visuals." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

[storytelling:] "The social and cultural activity of sharing stories, with great application to journalism." (Georgios Vassis et al, "Review and Evaluation of Systems Supporting Data Journalism", 2021)

"Data storytelling forms a compelling narrative by putting data in context to show the challenges, insights and solutions of a specific business problem. It normally highlights a series of changes or trends over time through linked visualizations that combine to tell a story." (Sisense) [source]

"Data storytelling is a method of visually presenting data to make it more understandable and easy to digest. Visualizations such as charts and graphs guide users toward a conclusion about their data and empower them to make a decision based on that conclusion." (Logi Analytics) [source]

"Data storytelling is a methodology for communicating information, tailored to a specific audience, with a compelling narrative. It is the last ten feet of your data analysis and arguably the most important aspect." (Nugit) [source]

"Data storytelling is the practice of building a narrative around a set of data and its accompanying visualizations to help convey the meaning of that data in a powerful and compelling fashion." (TDWI)

📉Graphical Representations: Storytelling (Just the Quotes)

"Storytelling reveals meaning without committing the error of defining it." (Hannah Arendt, "Men in Dark Times", 1968)

"Scientific practice may be considered a kind of storytelling practice [...]" (Donna Haraway, "Primate Visions", 1989)

"Storytelling is the art of unfolding knowledge in a way that makes each piece contribute to a larger truth." (Philip Gerard, "Writing a Book That Makes a Difference", 2000)

"The human mind is a wanton storyteller and even more, a profligate seeker after pattern. We see faces in clouds and tortillas, fortunes in tea leaves and planetary movements. It is quite difficult to prove a real pattern as distinct from a superficial illusion." (Richard Dawkins, "A Devil's Chaplain", 2003)

"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)

"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)

"We have, as human beings, a storytelling problem. We're a bit too quick to come up with explanations for things we don't really have an explanation for." (Malcolm Gladwell, "Blink: The Power of Thinking Without Thinking", 2005)

"It is indisputable that successful communication with the increasingly important group of non-professional customers requires that statistical offices go far beyond the simple provision of tables and other purely static information. The visual presentation of data through comprehensible and flexible graphical tools, possibly embedded in a storytelling environment and connected with maps for the presentation of spatial data, crucially contributes to meeting the needs of the non-expert." (Hans-Joachim Mittag "Educating the Public, The Role of E-Learning and Visual Communication of Official Data", ECE/CES, 2006)

"Graphics should be planned, written and developed to stand alone. Even when a graphic is accompanied by a story, we can’t always count on the reader to get that far. Scanning readers often don’t engage with stories at all. Rather, they browse the page, often reading only display type and visual elements. And, even those who intend to read the story often engage with the graphics first because they tend to be more eye-catching. In both cases, you simply can’t create a graphic that isn’t complete without the story. Readers should finish an information graphic feeling confident that they understand the information it presents. This isn’t to say that you must tell the entire story with the graphic. However, the portions of the story that are represented in the graphic must be complete and clear." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"There is an extraordinary power in storytelling that stirs the imagination and makes an indelible impression on the mind." (Brennan Manning, "The Ragamuffin Gospel: Good News for the Bedraggled, Beat-Up, and Burnt Out", 2008)

"Mostly we rely on stories to put our ideas into context and give them meaning. It should be no surprise, then, that the human capacity for storytelling plays an important role in the intrinsically human-centered approach to problem solving, design thinking." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"The purpose of a storyteller is not to tell you how to think, but to give you questions to think upon." (Brandon Sanderson, "The Way of Kings", 2010)

"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)

"Visualizations act as a campfire around which we gather to tell stories." (Al Shalloway, 2011)

"The storytelling mind is allergic to uncertainty, randomness, and coincidence. It is addicted to meaning. If the storytelling mind cannot find meaningful patterns in the world, it will try to impose them. In short, the storytelling mind is a factory that churns out true stories when it can, but will manufacture lies when it can't." (Jonathan Gottschall, "The Storytelling Animal: How Stories Make Us Human", 2012)

"We are, as a species, addicted to story. Even when the body goes to sleep, the mind stays up all night, telling itself stories." (Jonathan Gottschall, "The Storytelling Animal", 2012)

"Good visualization is a winding process that requires statistics and design knowledge. Without the former, the visualization becomes an exercise only in illustration and aesthetics, and without the latter, one of only analyses. On their own, these are fine skills, but they make for incomplete data graphics. Having skills in both provides you with the luxury - which is growing into a necessity - to jump back and forth between data exploration and storytelling." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"At its most basic level, a story is a description of something happening that contains some form of sensation, or drama. It is, in other words, an explanation of cause and effect that is soaked in emotion (...) We are natural-born storytellers who have a propension to believe our own tales." (Will Storr, "The Unpersuadables", 2014)

"The fact of storytelling hints at a fundamental human unease, hints at human imperfection. Where there is perfection there is no story to tell." (Ben Okri, "A Way of Being Free", 2014)

"Data stories are a subset of the much broader concept (or buzzword) of storytelling. […] Stories, or narratives, are useful in data visualization because they force us to recognize the limited value of a single chart in a complex environment. Stories also force us to recognize the need for a better integration of our displays, as we move away from strings of siloed charts." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"There is no such thing as a fact. There is only how you saw the fact, in a given moment. How you reported the fact. How your brain processed that fact. There is no extrication of the storyteller from the story." (Jodi Picoult, "Small Great Things", 2016)

"A data story starts out like any other story, with a beginning and a middle. However, the end should never be a fixed event, but rather a set of options or questions to trigger an action from the audience. Never forget that the goal of data storytelling is to encourage and energize critical thinking for business decisions." (James Richardson, 2017)

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

"Stories can begin with a question or line of inquiry." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Data storytelling can be defined as a structured approach for communicating data insights using narrative elements and explanatory visuals." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling gives your insight the best opportunity to capture attention, be understood, be remembered, and be acted on. An effective data story helps your insight reach its full potential: inspiring others to act and drive change." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling involves the skillful combination of three key elements: data, narrative, and visuals. Data is the primary building block of every data story. It may sound simple, but a data story should always find its origin in data, and data should serve as the foundation for the narrative and visual elements of your story." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling is transformative. Many people don’t realize that when they share insights, they’re not just imparting information to other people. The natural consequence of sharing an insight is change. Stop doing that, and do more of this. Focus less on them, and concentrate more on these people. Spend less there, and invest more here. A poignant insight will drive an enlightened audience to think or act differently. So, as a data storyteller, you’re not only guiding the audience through the data, you’re also acting as a change agent. Rather than just pointing out possible enhancements, you’re helping your audience fully understand the urgency of the changes and giving them the confidence to move forward." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling provides a bridge between the worlds of logic and emotion. A data story offers a safe passage for your insights to travel around emotional pitfalls and through analytical resistance that typically impede facts." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"[...] just because we act, and something changes, it doesn’t mean we were responsible for the result. Humans seem to find this simple truth difficult to grasp - we are always keen to construct an explanatory narrative, and even keener if we are at its centre. Of course sometimes this interpretation is true - if you flick a switch, and the light comes on, then you are usually responsible. But sometimes your actions are clearly not responsible for an outcome: if you don’t take an umbrella, and it rains, it is not your fault (although it may feel that way). But the consequences of many of our actions are less clear-cut. [...] We have a strong psychological tendency to attribute change to intervention, and this makes before-and-after comparisons treacherous." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

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

"Data becomes more useful once it’s transformed into a data visualization or used in a data story. Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context and inspire action from your audience. Color can be very helpful when you are trying to make information stand out within your data visualizations." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data storytelling is a method of communicating information that is custom-fit for a specific audience and offers a compelling narrative to prove a point, highlight a trend, make a sale, or all of the above. [...] Data storytelling combines three critical components, storytelling, data science, and visualizations, to create not just a colorful chart or graph, but a work of art that carries forth a narrative complete with a beginning, middle, and end." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories." (Steven Levitt)

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

✏️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)