🧭💹Business Intelligence: Perspectives (Part XXI: Data Visualization Revised)

Data Visualization Series

Creating data visualizations nowadays became so easy that anybody can do it with a minimum of effort and knowledge, which on one side is great for the creators but can be easily become a nightmare for the readers, respectively users. Just dumping data in visuals can be barely called data visualization, even if the result is considered as such. The problems of visualization are multiple – the lack of data culture, the lack of understanding processes, data and their characteristics, the lack of being able to define and model problems, the lack of educating the users, the lack of managing the expectations, etc.

There are many books on data visualization though they seem an expensive commodity for the ones who want rapid enlightenment, and often the illusion of knowing proves maybe to be a barrier. It's also true that many sets of data are so dull, that the lack of information and meaning is compensated by adding elements that give a kitsch look-and-feel (aka chartjunk), shifting the attention from the valuable elements to decorations. So, how do we overcome the various challenges? 

Probably, the most important step when visualizing data is to define the primary purpose of the end product. Is it to inform, to summarize or to navigate the data, to provide different perspectives at macro and micro level, to help discovery, to explore, to sharpen the questions, to make people think, respectively understand, to carry a message, to be artistic or represent truthfully the reality, or maybe is just a filler or point of attraction in a textual content?

Clarifying the initial purpose is important because it makes upfront the motives and expectations explicit, allowing to determine the further requirements, characteristics, and set maybe some limits in what concern the time spent and the qualitative and/or qualitative criteria upon which the end result should be eventually evaluated. Narrowing down such aspects helps in planning and the further steps performed. 

Many of the steps are repetitive and past experience can help reduce the overall effort. Therefore, professionals in the field, driven by intuition and experience probably don't always need to go through the full extent of the process. Conversely, what is learned and done poorly, has high chances of delivering poor quality. 

A visualization can be considered as effective when it serves the intended purpose(s), when it reveals with minimal effort the patterns, issues or facts hidden in the data, when it allows people to explore the data, ask questions and find answers altogether. One can talk also about efficiency, especially when readers can see at a glance the many aspects encoded in the visualization. However, the more the discovery process is dependent on data navigation via filters or other techniques, the more difficult it becomes to talk about efficiency.

Better criteria to judge visualizations is whether they are meaningful and useful for the readers, whether the readers understood the authors' intent, the further intrinsic implication, though multiple characteristics can be associated with these criteria: clarity, specificity, correctedness, truthfulness, appropriateness, simplicity, etc. All these are important in lower or higher degree depending on the broader context of the visualization.

All these must be weighted in the bigger picture when creating visualizations, though there are probably also exceptions, especially on the artistic side, where artists can cut corners for creating an artistic effect, though also in here the authors need to be truthful to the data and make sure that their work don't distort excessively the facts. Failing to do so might not have an important impact on the short term,considerably. though in time the effects can ripple with unexpected effects.

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📉Graphical Representation: Effectiveness (Just the Quotes)

"Though accurate data and real facts are valuable, when it comes to getting results the manner of presentation is ordinarily more important than the facts themselves. The foundation of an edifice is of vast importance. Still, it is not the foundation but the structure built upon the foundation which gives the result for which the whole work was planned. As the cathedral is to its foundation so is an effective presentation of facts to the data." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"Graphic charts have often been thought to be tools of those alone who are highly skilled in mathematics, but one needs to have a knowledge of only eighth-grade arithmetic to use intelligently even the logarithmic or ratio chart, which is considered so difficult by those unfamiliar with it. […] If graphic methods are to be most effective, those who are unfamiliar with charts must give some attention to their fundamental structure. Even simple charts may be misinterpreted unless they are thoroughly understood. For instance, one is not likely to read an arithmetic chart correctly unless he also appreciates the significance of a logarithmic chart." (John R Riggleman & Ira N Frisbee, "Business Statistics", 1938)

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

"Correct emphasis is basic to effective graphic presentation. Intensity of color is the simplest method of obtaining emphasis. For most reproduction purposes black ink on a white page is most generally used.  Screens, dots and lines can, of course, be effectively used to give a gradation of tone from light grey to solid black. When original charts are the subjects of display presentation, use of colors is limited only by the subject and the emphasis desired." (Anna C Rogers, "Graphic Charts Handbook", 1961)

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

"The bar or column chart is the easiest type of graphic to prepare and use in reports. It employs a simple form: four straight lines that are joined to construct a rectangle or oblong box. When the box is shown horizontally it is called a bar; when it is shown vertically it is called a column. [...] The bar chart is an effective way to show comparisons between or among two or more items. It has the added advantage of being easily understood by readers who have little or no background in statistics and who are not accustomed to reading complex tables or charts." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"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)

"The effective communication of information in visual form, whether it be text, tables, graphs, charts or diagrams, requires an understanding of those factors which determine the 'legibility', 'readability' and 'comprehensibility', of the information being presented. By legibility we mean: can the data be clearly seen and easily read? By readability we mean: is the information set out in a logical way so that its structure is clear and it can be easily scanned? By comprehensibility we mean: does the data make sense to the audience for whom it is intended? Is the presentation appropriate for their previous knowledge, their present information needs and their information processing capacities?" (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"One graph is more effective than another if its quantitative information can be decoded more quickly or more easily by most observers. […] This definition of effectiveness assumes that the reason we draw graphs is to communicate information - but there are actually many other reasons to draw graphs." (Naomi B Robbins, "Creating More effective Graphs", 2005)

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

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

"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)

"For a visual to qualify as beautiful, it must be aesthetically pleasing, yes, but it must also be novel, informative, and efficient. [...] For a visual to truly be beautiful, it must go beyond merely being a conduit for information and offer some novelty: a fresh look at the data or a format that gives readers a spark of excitement and results in a new level of understanding. Well-understood formats (e.g., scatterplots) may be accessible and effective, but for the most part they no longer have the ability to surprise or delight us. Most often, designs that delight us do so not because they were designed to be novel, but because they were designed to be effective; their novelty is a byproduct of effectively revealing some new insight about the world." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)

"A graph is considered effective if it conveys the intended information in a way that can be understood quickly and without ambiguity by most consumers." (Sanjay Matange  & Dan Neath, "Statistical Graphics Procedures by Example: Effective Graphs Using SAS", 2011)

"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)

"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)

"A great infographic leads readers on a visual journey, telling them a story along the way. Powerful infographics are able to capture people’s attention in the first few seconds with a strong title and visual image, and then reel them in to digest the entire message. Infographics have become an effective way to speak for the creator, conveying information and image simultaneously." (Justin Beegel, "Infographics For Dummies", 2014)

"The effectiveness principle dictates that the importance of the attribute should match the salience of the channel; that is, its noticeability. In other words, the most important attributes should be encoded with the most effective channels in order to be most noticeable, and then decreasingly important attributes can be matched with less effective channels. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

[…] 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)

"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)

"Understanding language goes hand in hand with the ability to integrate complex contextual information into an effective visualization and being able to converse with the data interactively, a term we call analytical conversation. It also helps us think about ways to create artifacts that support and manage how we converse with machines as we see and understand data."(Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Unlike text, visual communication is governed less by an agreed-upon convention between 'writer' and 'reader' than by how our visual systems react to stimuli, often before we’re aware of it. And just as composers use music theory to create music that produces certain predictable effects on an audience, chart makers can use visual perception theory to make more-effective visualizations with similarly predictable effects." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

"Good design isn’t just choosing colors and fonts or coming up with an aesthetic for charts. That’s styling - part of design, but by no means the most important part. Rather, people with design talent develop and execute systems for effective visual communication. They understand how to create and edit visuals to focus an audience and distill ideas." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

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

"When deeply complex charts work, we find them effective and beautiful, just as we find a symphony beautiful, which is another marvelously complex arrangement of millions of data points that we experience as a coherent whole." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

⛩️Edmond Lau - Collected Quotes

"A work environment that iterates quickly provides a faster feedback cycle and enables you to learn at a faster rate. Lengthy release cycles, formalized product approvals, and indecisive leadership slow down iteration speed; automation tools, lightweight approval processes, and a willingness to experiment accelerate progress." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Adopting a mindset of instrumentation means ensuring we have a set of dashboards that surface key health metrics and that enable us to drill down to the relevant data. However, many of the questions we want to answer tend to be exploratory, since we often don’t know everything that we want to measure ahead of time. Therefore, we need to build flexible tools and abstractions that make it easy to track additional metrics." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"At fast-growing teams and companies, the number of problems to solve exceeds available resources, providing ample opportunities to make a big impact and to increase your responsibilities. The growth also makes it easier to attract strong talent and build a strong team, which feeds back to generate even more growth. A lack of growth, on the other hand, leads to stagnation and politics. Employees might squabble over limited opportunities, and it becomes harder to find and retain talent." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Because we spend so much of our time at work, one of the most powerful leverage points for increasing our learning rate is our choice of work environment." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"But like many other aspects of code quality, building an abstraction for a problem comes with tradeoffs. Building a generalized solution takes more time than building one specific to a given problem. To break even, the time saved by the abstraction for future engineers needs to outweigh the time invested." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Continuous deployment is but one of many powerful tools at your disposal for increasing iteration speed. Other options include investing in time-saving tools, improving your debugging loops, mastering your programming workflows, and, more generally, removing any bottlenecks that you identify." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Developing the skills to automate takes time, whether they be using shell scripts, browser extensions, or something else. But the cost of mastering these skills gets smaller the more often you do it and the better you get at it." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Fine-tuning the efficiency of simple actions and saving a second here or there may not seem worth it at first glance. It requires an upfront investment, and you’ll very likely be slower the first few times you try out a new and unfamiliar workflow. But consider that you’ll repeat those actions at least tens of thousands of times during your career: minor improvements easily compound over time." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Good metrics accomplish a number of goals. First, they help you focus on the right things. […] Second, when visualized over time, good metrics help guard against future regressions. […] Third, good metrics can drive forward progress. […] Fourth, a good metric lets you measure your effectiveness over time and compare the leverage of what you’re doing against other activities you could be doing instead." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"How can we reduce integration risk? One effective strategy is to build end-to-end scaffolding and do system testing earlier. Stub out incomplete functions and modules, and assemble an end-to-end system as soon as possible, even if it’s only partly functional. Front-loading the integration work provides a number of benefits. First, it forces you to think more about the necessary glue between different pieces and how they interact, which can help refine the integration estimates and reduce project risk. Second, if something breaks the end-to-end system during development, you can identify and fix it along the way, while dealing with much less code complexity, rather than scrambling to tackle it at the end. Third, it amortizes the cost of integration throughout the development process, which helps build a stronger awareness of how much integration work is actually left." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"In any engineering discipline (and in life), there will always be more tasks to do than you have time for. Working on one task means not working on another. Therefore, regular prioritization is a high-leverage activity, because it determines the leverage of the rest of your time." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"It’s wishful thinking to believe that all the code we write will be bug-free and work the first time. In actuality, much of our engineering time is spent either debugging issues or validating that what we’re building behaves as expected. The sooner we internalize this reality, the sooner we will start to consciously invest in our iteration speed in debugging and validation loops." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Many of us are familiar with the concept of a minimal, reproducible test case. This refers to the simplest possible test case that exercises a bug or demonstrates a problem. A minimal, reproducible test case removes all unnecessary distractions so that more time and energy can be spent on the core issue, and it creates a tight feedback loop so that we can iterate quickly." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Project estimation and project planning are extremely difficult to get right, and many engineers (myself included) have learned this the hard way. The only way to get better is by practicing these concepts, especially on smaller projects where the cost of poor estimations is lower. The larger the project, the higher the risks, and the more leverage that good project planning and estimation skills will have on your success." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Project estimation is one of the hardest skills that an effective engineer needs to learn. But it’s crucial to master: businesses need accurate estimates to make long-term plans for their products. They need to know when resources might free up to work on upcoming features or when they can promise feature requests to customers. And even when we don’t have pressure to ship against a deadline, how long we think a project will take affects our decisions of what to work on." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Projects fail from under-communicating, not over-communicating. Even if resource constraints preclude the dependency that you want from being delivered any sooner, clarifying priorities and expectations enables you to plan ahead and work through alternatives." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Sometimes, we build things in a way that makes sense in the short-term but that can be costly in the long-term. We work around design guidelines because it’s faster and easier than following them. We punt on writing test cases for a new feature because there’s too much work to finish before the deadline. We copy, paste, and tweak small chunks of existing code instead of refactoring it to support our use cases. Each of these tradeoffs, whether they’re made from laziness or out of a conscious decision to ship sooner, can increase the amount of technical debt in our codebase." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"To be effective engineers, we need to be able to identify which activities produce more impact with smaller time investments. Not all work is created equal. Not all efforts, however well-intentioned, translate into impact." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"The best strategy for optimizing your iteration speed is the same as for optimizing the performance of any system: identify the biggest bottlenecks, and figure out how to eliminate them. What makes this difficult is that while tools, debugging workflows, and programming environments might be the areas most directly under your control as an engineer, sometimes they’re not the only bottlenecks slowing you down." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"The faster that you can iterate, the more that you can learn about what works and what doesn’t work. You can build more things and try out more ideas. Not every change will produce positive value and growth, of course." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"[…] the first heuristic for prioritizing high-leverage activities is to focus on what directly produces value. At the end of the day (or when it comes time for performance reviews), what matters is how much value you’ve created. […] the first heuristic for prioritizing high-leverage activities is to focus on what directly produces value. At the end of the day (or when it comes time for performance reviews), what matters is how much value you’ve created." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"The freedom to choose what to work on and how to do it drives our ability to learn—as long as we have the support that we need to use that freedom effectively. At established companies, employees tend to work on specialized projects, but they also have access to more coaching and structure. At smaller companies, you’ll end up wielding significantly more autonomy over the total surface area of product features and responsibilities, but you’ll also need to take more ownership of your own learning and growth." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"The next time you find yourself repeatedly going through the same motions when you’re fixing a bug or iterating on a feature, pause. Take a moment to think through whether you might be able to tighten that testing loop. It could save you time in the long run." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"To invest in your own growth, you should carve out your own 20% time. It’s more effective to take it in one- or two-hour chunks each day rather than in one full day each week, because you can then make a daily habit out of improving your skills. Your productivity may decrease at first (or it might not change much if you’re taking time away from web surfing or other distractions), but the goal is to make investments that will make you more effective in the long run." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Ultimately, software quality boils down to a matter of tradeoffs, and there’s no one universal rule for how to do things. […] Rigidly adhering to a notion of building something 'the right way' can paralyze discussions about tradeoffs and other viable options. Pragmatism - thinking in terms of what does and doesn’t work for achieving our goals - is a more effective lens through which to reason about quality." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"When establishing our goals, it’s important to choose carefully what core metrics to measure (or not measure) and optimize. When it comes to day-to-day operations, however, you should be less discriminatory: measure and instrument as much as possible. Although these two principles may seem contradictory, they actually complement each other. The first describes a high-level, big-picture activity, whereas the second is about gaining insight into what’s going on with systems that we’ve built." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"When you write to teach other people, you gain a deeper understanding of ideas you’re already familiar with and pinpoint the details that you didn’t fully understand." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015) 

"Why is continuous deployment such a powerful tool? Fundamentally, it allows engineers to make and deploy small, incremental changes rather than the larger, batched changes typical at other companies. That shift in approach eliminates a significant amount of overhead associated with traditional release processes, making it easier to reason about changes and enabling engineers to iterate much more quickly." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)