🧭💹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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🧭💹Business Intelligence: Perspectives (Part XIX: Data Visualization between Art, Pragmatism and Kitsch)

Business Intelligence Series

The data visualizations (aka dataviz) presented in the media, especially the ones coming from graphical artists, have the power to help us develop what is called graphical intelligence, graphical culture, graphical sense, etc., though without a tutor-like experience the process is suboptimal because it depends on our ability of identifying what is important and which are the steps needed for decoding and interpreting such work, respectively for integrating their messages in our overall understanding about the world.

When such skillset is lacking, without explicit annotations or other form of support, the reader might misinterpret or fail to observe important visual cues even for simple visualizations, with all the implications deriving from this – a false understanding, and further aspects deriving from it, this being probably the most important aspect to consider. Unfortunately, even the most elaborate work can fail if the reader doesn’t have a basic understanding of all that’s implied in the process.

The books of Willard Brinton, Ana Rogers, Jacques Bertin, William Cleveland, Leland Wilkinson, Stephen Few, Albert Cairo, Soctt Berinato and many others can help the readers build a general understanding of the dataviz process and how data visualizations or simple graphics can be used/misused effectively, though each reader must follow his/her own journey. It’s also true that the basics can be easily learned, though the deeper one dives, the more interesting and nontrivial the journey becomes. Fortunately, the average reader can stick to the basics and many visualizations are simple enough to be understood.

To grasp the full extent of the implications, one can make comparisons with the domain of poetry where the author uses basic constructs like metaphor, comparisons, rhythm and epithets to create, communicate and imprint in reader’s mind old and new meanings, images and feelings altogether. Artistic data visualizations tend to offer similar charge as poetry does, even if the impact might not appeal so much to our artistic sensibility. Though dataviz from this perspective is or at least resembles an art form.

Many people can write verses, though only a fraction can write good meaningful poetry, from which a smaller fraction get poems, respectively even fewer get books published. Conversely, not everything can be expressed in verses unless one finds good metaphors and other aspects that can be leveraged in the process. Same can be said about good dataviz.

One can argue that in dataviz the author can explore and learn especially by failing fast (seeing what works and what doesn’t). One can also innovate, though the creator has probably a limited set of tools and rules for communication. Enabling readers to see the obvious or the hidden in complex visualizations or contexts requires skill and some kind of mastery of the visual form.

Therefore, dataviz must be more pragmatic and show the facts. In art one has the freedom to distort or move things around to create new meanings, while in dataviz it’s important for the meaning to be rooted in 'truth', at least by definition. The more the creator of a dataviz innovates, the higher the chances of being misunderstood. Moreover, readers need to be educated in interpreting the new meanings and get used to their continuous use.

Kitsch is a term applied to art and design that is perceived as naïve imitation to the degree that it becomes a waste of resources even if somebody pays the tag price. There’s a trend in dataviz to add elements to visualizations that don’t bring any intrinsic value – images, colors and other elements can be misused to the degree that the result resembles kitsch, and the overall value of the visualization is diminished considerably.

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📊Graphical Representation: Graphics We Live By II (Discount Rates in MS Excel)

Graphical Representation Series

It's difficult, if not impossible, to give general rules on how data visualizations should be built. However, the data professional can use a set of principles, which are less strict than rules, and validate one's work against them. Even then one might need to make concessions and go against the principles or common sense, though such cases should be few, at least in theory. One of such important principles is reflected in Tufte's statement that "if the statistics are boring, then you've got the wrong numbers" [1].

So, the numbers we show should not be boring, but that's the case with most of the numbers we need to show, or we consume in news and other media sources. Unfortunately, in many cases we need to go with the numbers we have and find a way to represent them, ideally by facilitating the reader to make sense of the respective data. This should be our first goal when visualizing data. Secondly, because everybody talks about insights nowadays, one should identify the opportunity for providing views into the data that go beyond the simple visualization, even if this puts more burden on data professional's shoulder. Frankly, from making sense of a set of data and facilitating an 'Aha' moment is a long leap. Thirdly, one should find or use the proper tools and channels for communicating the findings. 

A basic requirement for the data professional to be able to address these goals is to have clear definitions of the numbers, have a good understanding of how the numbers reflect the reality, respectively how the numbers can be put into the broader context. Unfortunately, all these assumptions seem to be a luxury. On the other side, the type of data we work with allows us to address at least the first goal. Upon case, our previous experience can help, though there will be also cases in which we can try to do our best. 

Let's consider a simple set of data retrieved recently from another post - Discount rates (in percentage) per State, in which the values for 5 neighboring States are considered (see the first two columns from diagram A). Without knowing the meaning of data, one could easily create a chart in Excel or any other visualization tool. Because the State has categorical values, probably some visualization tools will suggest using bar and not column charts. Either by own choice or by using the default settings, the data professional might end up with a column chart (see diagram B), which is Ok for some visualizations. 

One can start with a few related questions:

(1) Does it make sense to use a chart to represent 5 values which have small variability (the difference between the first and last value is of only 6%)? 

(2) Does it make sense to use a chart only for the sake of visualizing the data?

(3) Where is the benefit for using a chart as long there's no information conveyed? 

One can see similar examples in the media where non-aggregated values are shown in a chart just for the sake of visualizing the data. Sometimes the authors compensate for the lack of meaning with junk elements, fancy titles or other tricks. Usually, sense-making in a chart takes longer than looking at the values in a table as there are more dimensions or elements to consider. For a table there's the title, headers and the values, nothing more! For a chart one has in addition the axes and some visualization elements that can facilitate or complicate visualization's decoding. Where to add that there are also many tricks to distort the data. 

Tables tend to maximize the amount of digital ink used to represent the data, and minimize the amount used to represent everything else not important to understanding. It's what Tufte calls the data-to-ink ratio (see [1]), a second important principle. This can be translated in (a) removing the border of the chart area, (b) minimizing the number of gridlines shown, (c) minimizing the number of ticks on the axis without leading to information lost, (d) removing redundant information, (e) or information that doesn't help the reader. 

However, the more data is available in the table, the more difficult it becomes to navigate the data. But again, if the chart shows the individual data without any information gained, a table might be still more effective. One shouldn't be afraid to show a table where is the case!

(4) I have a data visualization, what's next?

Ideally, the data professional should try to obtain the maximum of effect with minimum of elements. If this principle aims for the efficiency of design, a fourth related principle aims for the efficiency of effort - one should achieve a good enough visualization with a minimum of effort. Therefore, it's enough maybe if we settle to any of the two above results. 

On the other side, maybe by investing a bit more effort certain aspects can be improved. In this area beginners start playing with the colors, formatting the different elements of the chart. Unfortunately, even if color plays a major role in the encoding and decoding of meaning, is often misused/overused. 

(5) Is there any meaning in the colors used?

In the next examples taken from the web (diagram C and D), the author changed the color of the column with the minimal value to red to contrast it with the other values. Red is usually associated with danger, error, warning, or other similar characteristics with negative impact. The chances are high that the reader will associate the value with a negative connotation, even if red is used also for conveying important information (usually in text). Moreover, the reader will try to interpret the meaning of the other colors. In practice, the color grey has a neutral tone (and calming effect on the mind). Therefore, it's safe to use grey in visualization (see diagram D in contrast with diagram C). Some even advise setting grey as default for the visualization and changing the colors as needed later. 

In these charts, the author signalized in titles that red denotes the lowest value, though it just reduces the confusion. One can meet titles in which several colors are used, reminding of a Christmas tree. Frankly, this type of encoding is not esthetically pleasing, and it can annoy the reader. 

(6) What's in a name?

The titles and, upon case, the subtitles are important elements in communicating what the data reflects. The title should be in general short and succinct in the information it conveys, having the role of introducing, respectively identifying the chart, especially when multiple charts are used. Some charts can also use a subtitle, which can be longer than the title and have more of a storytelling character by highlighting the message and/or the finding in the data. In diagrams C and D the subtitles were considered as tiles, which is not considerably wrong. 

In the media and presentations with influencing character, subtitles help the user understand the message or the main findings, though it's not appropriate for hardcoding the same in dynamic dashboards. Even if a logic was identified to handle the various scenarios, this shifts users' attention, and the chance is high that they'll stop further investigating the visualization. A data professional should present the facts with minimal interference in how the audience and/or users perceive the data. 

As a recommendation, one should aim for clear general titles and avoid transmitting own message in charts. As a principle this can be summarized as "aim for clarity and equidistance".

(7) What about meaning?

Until now we barely considered the meaning of data. Unfortunately, there's no information about what the Discount rate means. It could be "the minimum interest rate set by the US Federal Reserve (and some other national banks) for lending to other banks" or "a rate used for discounting bills of exchange", to use the definitions given by the Oxford dictionary. Searching on the web, the results lead to discount rates for royalty savings, resident tuitions, or retail for discount transactions. Most probably the Discount rates from the data set refer to the latter.

We need a definition of the Discount rate to understand what the values represent when they are ordered. For example, when Texas has a value of 25% (see B), does this value have a negative or a positive impact when compared with other values? It depends on how it's used in the associated formula. The last two charts consider that the minimum value has a negative impact, though without more information the encoding might be wrong! 

Important formulas and definitions should be considered as side information in the visualization, accompanying text or documentation! If further resources are required for understanding the data, then links to the required resources should be provided as well. At least this assures that the reader can acquire the right information without major overhead. 

(8) What do readers look for? 

Frankly, this should have been the first question! Readers have different expectations from data visualizations. First of all, it's the curiosity - how the data look in row and/or aggregated form, or in more advanced form how are they shaped (e.g. statistical characteristics like dispersion, variance, outliers). Secondly, readers look in the first phase to understand mainly whether the "results" are good or bad, even if there are many shades of grey in between. Further on, there must be made distinction between readers who want to learn more about the data, models, and processes behind, respectively readers who just want a confirmation of their expectations, opinions and beliefs (aka bias). And, in the end, there are also people who are not interested in the data and what it tells, where the title and/or subtitle provide enough information. 

Besides this there are further categories of readers segmented by their role in the decision making, the planning and execution of operational, tactical, or strategic activities. Each of these categories has different needs. However, this exceeds the scope of our analysis. 

Returning to our example, one can expect that the average reader will try to identify the smallest and highest Discount rates from the data set, respectively try to compare the values between the different States. Sorting the data and having the values close to each other facilitates the comparison and ranking, otherwise the reader needing to do this by himself/herself. This latter aspect and the fact that bar charts better handle the display of categorical data such as length and number, make from bar charts the tool of choice (see diagram E). So, whenever you see categorical data, consider using a bar chart!

Despite sorting the data, the reader might still need to subtract the various values to identify and compare the differences. The higher the differences between the values, the more complex these operations become. Diagram F is supposed to help in this area, the comparison to the minimal value being shown in orange. Unfortunately, small variances make numbers' display more challenging especially when the visualization tools don't offer display alternatives.

For showing the data from Diagram F were added in the table the third and fourth columns (see diagram A). There's a fifth column which designates the percentage from a percentage (what's the increase in percentages between the current and minimal value). Even if that's mathematically possible, the gain from using such data is neglectable and can create confusion. This opens the door for another principle that applies in other areas as well: "just because you can, it doesn't mean you should!". One should weigh design decisions against common sense or one's intuition on how something can be (mis)used and/or (mis)understood!

The downside of Diagram F is that the comparisons are made only in relation to the minimum value. The variations are small and allow further comparisons. The higher the differences, the more challenging it becomes to make further comparisons. A matrix display (see diagram G) which compares any two values will help if the number of points is manageable. The upper side of the numbers situated on and above the main diagonal were grayed (and can be removed) because they are either nonmeaningful, or the negatives of the numbers found below the diagram. Such diagrams are seldom used, though upon case they prove to be useful.

Choropleth maps (diagram H) are met almost everywhere data have a geographical dimension. Like all the other visuals they have their own advantages (e.g. relative location on the map) and disadvantages (e.g. encoding or displaying data). The diagram shows only the regions with data (remember the data-to-ink ratio principle).

(9) How about the shape of data?

When dealing with numerical data series, it's useful to show aggregated summaries like the average, quartiles, or standard deviation to understand how the data are shaped. Such summaries don't really make sense for our data set given the nature of the numbers (five values with small variance). One can still calculate them and show them in a box plot, though the benefit is neglectable. 

(10) Which chart should be used?

As mentioned above, each chart has advantages and disadvantages. Given the simplicity and the number of data points, any of the above diagrams will do. A table is simple enough despite not using any visualization effects. Also, the bar charts are simple enough to use, with a plus maybe for diagram F which shows a further dimension of the data. The choropleth map adds the geographical dimension, which could be important for some readers. The matrix table is more appropriate for technical readers and involves more effort to understand, at least at first sight, though the learning curve is small. The column charts were considered only for exemplification purposes, though they might work as well. 

In the end one should go with own experience and consider the audience and the communication channels used. One can also choose 2 different diagrams, especially when they are complementary and offer an additional dimension (e.g. diagrams F and H), though the context may dictate whether their use is appropriate or not. The diagrams should be simple to read and understand, but this doesn't mean that one should stick to the standard visuals. The data professional should explore other means of representing the data, a fresh view having the opportunity of catching the reader's attention.

As a closing remark, nowadays data visualization tools allow building such diagrams without much effort. Conversely, it takes more effort to go beyond the basic functionality and provide more value for thyself and the readers. One should be able to evaluate upfront how much time it makes sense to invest. Hopefully, the few methods, principles and recommendations presented here will help further!

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Resources:
[1] Edward R Tufte (1983) "The Visual Display of Quantitative Information"

📉Graphical Representation: Meaning (Just the Quotes)

"It is desirable in all chart work to have certain conventions by which colors would be understood to have certain definite meanings. Thus, following railroad practice, red could generally be used in chart work to indicate dangerous or unfavorable conditions, and green to indicate commended features or favorable conditions. Where neither commendation nor adverse criticism is intended, colors such as blue, yellow, brown, etc., could be used." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

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

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

"Understanding is accomplished through: (a) the use of relative size of the shapes used in the graphic; (b) the positioning of the graphic-line forms; (c) shading; (d) the use of scales of measurement; and (e) the use of words to label the forms in the graphic. In addition, in order for a person to attach meaning to a graphic it must also be simple, clear, and appropriate." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

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

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

"There is a technical difference between a bar chart and a histogram in that the number represented is proportional to the length of bar in the former and the area in the latter. This matters if non-uniform binning is used. Bar charts can be used for qualitative or quantitative data, whereas histograms can only be used for quantitative data, as no meaning can be attached to the width of the bins if the data are qualitative." (Roger J Barlow, "Statistics: A guide to the use of statistical methods in the physical sciences", 1989)

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

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

"To keep accuracy and efficiency of your diagrams appealing to a potential audience, explicitly describe the encoding principles we used. Titles, labels, and legends are the most common ways to define the meaning of the diagram and its elements." (Vasily Pantyukhin, "Principles of Design Diagramming", 2015)

"Essentially, magnitude is the size of the effect. It’s a way to determine if the results are meaningful. Without magnitude, it’s hard to get a sense of how much something matters. […] the magnitude of an effect can change, depending on the relationship." (John H Johnson & Mike Gluck, "Everydata: The misinformation hidden in the little data you consume every day", 2016)

"Are your insights based on data that is accurate and reliable? Trustworthy data is correct or valid, free from significant defects and gaps. The trustworthiness of your data begins with the proper collection, processing, and maintenance of the data at its source. However, the reliability of your numbers can also be influenced by how they are handled during the analysis process. Clean data can inadvertently lose its integrity and true meaning depending on how it is analyzed and interpreted." (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)

"When narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important. Ample context and commentary are often needed to fully appreciate an analysis finding. The narrative element adds structure to the data and helps to guide the audience through the meaning of what’s being shared." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)