AI is the new sensation. Will it be helpful in creating infographics? What are the dos and don’ts?

I think that AI as an aid to design is very useful to save time in tedious processes. But something very different is to expect a totally original result or to achieve something that accurately emulates what we have in mind.

Infographics is an informative need that is built on existing information, data processing, and representation of elements. Infographics must be understood by different audiences, with different levels of complexity and meeting different needs.

Infographics must be a user-centered design that reflect a creative process. Visual explanations must “understand” the audience, that is, they must efficiently show what the user is willing and interested in exploring in less than 20 seconds.

When an infographic department evolves in the ways of creating visual explanations, it is much more difficult to standardize the working methods. Artificial intelligence would need many references to achieve a fresh result, with a stamp of originality.

AI can be expected to cooperate in refining certain contents or to support us in specific processes. What cannot be expected, at least these days, is to achieve a brilliant, original, functional and instantaneous result. Besides, human information design still has an irreplaceable emotional richness, it is generated from experience and comes close to what the reader expects to see.

“What cannot be expected [ from AI], at least these days, is to achieve a brilliant, original, functional and instantaneous result…Human information design still has an irreplaceable emotional richness, it is generated from experience and comes close to what the reader expects to see.

There is another aspect that I would like to mention. The authors of infographics have the intention of expressing and sharing their own ideas when creating a visual piece. And the result can be stimulating when we notice a good reception from the audience and our peers, or it can be frustrating when it is not. It’s all about the ego of the creator. When finishing a graphic, the expectation is to achieve something as close as possible to what we have conceived in our mind. Automating that journey through AI doesn’t seem to be an option that many of us seriously consider. 

South China Morning Post is one of those publications that promotes infographics. Can you explain how the print readers and web viewers have responded to them?

Our printed infographics have a good reputation among our audience. Our newsroom is always open to give us the space to publish our material, and that is a privilege. There is also something we have noticed, on the day of the publishing of a printed infographic, we always post our printed page on social media (Twitter or Instagram), which gives us good traffic and great feedback. That tells us that people still appreciate seeing all the information in one static frame. This encourages us to continue publishing full page infographics and to continue exploring the many possibilities that paper offers.

For our online pieces, we have a very solid post-production system connected to multiple areas of the newsroom, which allows our work to be promoted on the web. Obviously the exposure time [to infographics] is limited, since the publication’s offerings are massive and there are many other articles to highlight on our front page, but when the numbers (visits) are good, and they often are, the permanence of our work extends for a few days.

It is a way to understand the audience as well, by reviewing the number of page views and the behaviour of a visual story over time.

There are stories, which are the result of a long working process, involving resources, time, discussions and planning, finally receiving satisfactory results.

On other occasions, we create very simple stories that have required little effort and production time, which achieve surprising numbers and which maintain a very acceptable level of visits over time.

Which work thrills you more—print or web? And how different are they?

I have a strong background in print, however, I think the online platform offers many more possibilities and obviously has a wider reach. That said, I can’t lean towards one in particular. Making infographics for print has a charm from start to finish (because obviously there is an end point to the process of infographics for print, not so for online), and it always connects me to the beginnings of my career in visual information.

The exciting thing about print is that time, space, and resources are limited so it demands more creativity and practical thinking.

Online infographics, on the other hand, allow the development of topics in different dimensions, it imposes a wide range of skills as in content, form and functionality, but it does not limit the expressions and creativity, on the contrary, it expands them.

Online pieces require a permanent tracking and solid testing processes to ensure the good performance of the information for all users, all the time.

Even though print infographics are more permanent and tangible than online infographics, I like the immediacy and the capacity for permanent editing and expansion that online work allows when it comes to sharing my work with the audience.

Can you explain how you or your team visualised the project “Fly at your own risk: Nepal’s poor air safety record”?

After the plane crash in Pokhara, multiple videos and images of the catastrophe emerged, our idea at first was to explain the accident in a breaking news format, but after doing some research, we realized that it was more important to explain the reasons for the increasing number of accidents in the skies of Nepal. The infographic was published three weeks after the accident. And it includes mainly general statistics on Nepalese civil aviation, the existing standards, and the complexity of the conditions imposed by the geography on the most experienced pilots.

We started the project by understanding how the accident happened and explaining the characteristics of the crash site.

We include a detail of the ATR72-500 model, in those days a theory of the cause of the accident arose, based on the observation of some videos made from the cockpit, where you can see that the flaps were not properly deployed at the time of the approach, causing the speed of the aircraft to not decrease. This point is explained in the diagram.

 An interior layout of the aircraft and dimensional references were also included.

In civil aeronautics there is fortunately a lot of information available, which helped us to define a series of visualizations, we only included in the project the most relevant ones.

An example is a graph that shows which are the most critical phases during a flight in Nepal, we realized that accidents occur towards the end of the trip, practically in 50% of the occurrences, as shown in the following diagram:

In all our projects, the usual thing is to create an image that opens the piece, this image is also used for the promotion of the infographics in social networks. It is part of our process, and I can affirm that it is one of the tasks we enjoy the most.

Do you work on a template or approach every graphic independently?

That depends on the type of graph we are working on. For the simplest and daily coverage we use templates, for those projects in which we can invest more time and resources, we always look for an original and different approach. Each topic represents an expectation to accomplish, as well as each artist has a particular vision of how to tell a story and we respect that before anything else.

What are the most important considerations for infographic designers?

Nowadays the word “infographics” is not enough to describe what we information designers or visual journalists are currently developing. Visual storytelling is too broad, and stories can be told in multiple ways. In that sense, infographics departments have evolved towards more dynamic results, but essentially connected to a creative way of captivating the audience using unique visual narratives, and originating from direct experience (field reporting, first-person research) rather than relying on more common formulas such as isometric representation of a space, or the use of a set of graphs and diagrams. It has been a long time since it required some expertise to create this type of element, now there are many tools available that deliver quite acceptable results.

You also have to consider what the audience is willing to explore. My suspicion is that more than some readers when seeing an isometric drawing (just as an example of a widely used method of representing a space in an infographic) may feel some aversion because it tends to be an overly recurrent and technical representation of reality, and to put any minor obstacle between the information and the reader these days is always a risk.

For me, the most important consideration to keep at the top of the list, is to enhance the ability to discover stories that deserve to be told visually. The technical aspects, interactivity and virtuosity are very important but without the first ability it will be hard to produce interesting visual explainers.

Can you explain how you visualised the award-winning project “Life in a Shoe Box”?

Hong Kong is a vibrant and cosmopolitan city, full of attractions and contrasts, one of them is represented by the great difference in the living conditions of its inhabitants. It is considered one of the most expensive cities in the world, which is partially true in my experience. What is really expensive here is housing. The monthly rent for half of the apartments is at least US$2,250, while Hong Kong households’ monthly median income is US$3,600.

This problem is exacerbated for those living below the poverty line (20% of the population, or 1.65 million people), who face serious difficulties in finding decent housing.

A practice among many landlords is to subdivide an apartment into modules, it can be three or four modules in one flat, but there are also cases where the situation is extreme and the space is forced to accommodate up to 20 small modules (which sometimes means having 20 sq.ft. spaces for one person). Obviously the conditions offered are deplorable.

In general, these types of configurations are found in old buildings, with low maintenance, poor thermal insulation and non-existent security measures. The inhabitants are generally older men with very low incomes and no family to help them.

This is one of the aspects that has been widely covered previously by the media, in the form of reports and documentaries, but the challenge we had as a team was to provide more direct spatial references and represent in a more vivid way (without using photographs) this reality, to put the reader in a new perspective in front of this situation.

From this starting point we proposed to consider any detail that would help to understand the limitations in space and comfort of the residents of these cubicles.

We planned several interviews and a round of visits to apartments where the landlords maintained inhabited cubicles.

With the help of a local NGO that was visiting many of these places to distribute medicine, food or some legal assistance, we were able to enter the flats for field research, in order to cover technical aspects such as materiality, dimensions, lighting, ventilation, use of common and private spaces, etc., and human aspects such as coexistence, urgent needs, sanitation problems, daily routines, experiences and opinions of the tenants.

During each visit the team was equipped with cameras, notebooks, measuring instruments, etc. in order to capture the maximum information, we knew that the chance to visit the places again in the short term was very hard.

The team decided to use mainly illustrations; this was a decision discussed in the planning stage. We completely ruled out the use of photographs or videos in our piece because we didn’t want to expose real homes, and the intention was to respect people’s privacy. In addition, the attractiveness of an illustration, based on analysis and made from videos, images, quick sketches, and 3D modelling tools helped to visualise the elements, and to show more accurately the composition and structure of the small rooms.

Here’s the sketching from original idea to final visual analysis:

A 3D model allowed us to displace-rotate the walls of the cubicle in order to avoid hidden objects and angles and see how every surface of the limited space is used by the resident.

By combining digital and traditional drawing techniques, the final product enriches the user experience and delivers a more immersive result. Animations were included in the beginning to contextualise the location of these houses. Various illustrative styles were combined to accurately depict the critical living situations of these people as witnessed by each artist.

Official figures or data reports from local universities were the complement of the field investigation, in order to show a complete panorama of housing solutions in Hong Kong at all levels. This information allowed us to add references of area and cost per square foot.

Pencil sketches at each location were followed by 3D models and animations. These served only as a base to build the final appearance of the piece, which preserves well-differentiated illustrative styles and follows defined functions. An informative animation at the top gives a very close idea of the real appearance of the places, another line-drawing style done in Procreate helps to explain the cubicles, and a few larger, more generic, illustrations were used to separate and represent the following explanation of each housing type.

After the production of the assets and writing the story, it took us several weeks to make the online piece fully operational for all platforms. We had several rounds of revision, correction and polishing of visual details. The published piece gives readers an updated and realistic portrayal of many Hongkongers’ living conditions.

The full research process and the round of interviews helped us to build one big infographic and three long read stories with illustrations and charts. The active role of our reporter Fiona Sun in all the process of gathering information combined with our field work and research, helped us to have original content and an unique approach to a well-known issue of this city.

As an infographic expert, how do you visualise the growth of infographics and the new platforms on which it can spread?

The growth and [unification] of infographics on the new platforms will necessarily depend on the degree of functionality they offer. It does not mean that complex infographics or those that are a very personal expression of the author will disappear. All kinds of infographics will continue to exist; Just as today it is possible to find a wide range of them, from the unreadable one to a super-efficient piece. At the end of the day, the audiences will always be very diverse and there will continue to be products for specific groups of people.

But the visual journalist, which seeks to inform well and quickly, will need to put the user at the centre of his or her priorities.

If we ask ourselves, “What is a successful infographic?”, today we can have multiple opinions, according to our principles, aesthetic beliefs, experiences, or needs. Maybe in the future, we will say something totally different. But my feeling is that the answer related to the number of visits or clicks will be the one at the top of the list. 

European, American, and Spanish approaches to infographics look very different. How do you differentiate them?

It is well known that information graphics departments around the world are very different in their nature, functions, and origin.

There are some teams with 30 talented people ready to cover different needs, and others with only four designers trying to give their best, and in many cases achieve amazing results. What determines the approach to infographics of each team is the relevance they have in the newsroom, the autonomy and support they receive, the resources they are provided with, and the topics they are allowed to cover, among other factors

I don’t think there is a big difference in how topics are explained, the visualization formulas or explanatory techniques used in Europe or America are similar—what obviously changes is the story they want to tell, but the most relevant factor to make a huge difference is the resources available backing the infographic work.

Infographics or data visualisation. Which one do you love most? And why?

Today and during the past, the term infographics has been widely used to describe products that are not infographics, when searching in Google, practically 100% of the results are wrong, and then it is necessary to refine the search to find a real one.

Both disciplines are in my heart, but my love is much greater for infographics (which also include clear and clean data visualizations, useful for a big audience).

Since my childhood I was intrigued and captivated by the brilliant way that diagrams, thematic maps, scientific illustrations, and later, infographics explain complex phenomena, structures, places, and situations.

I started in infographics in 1996, back then it was a different scenario, different urges and concerns, different skills to learn, different things to feel proud of, and different motivations. And the constant evolution that infographics experienced along all these years keeps me amazed. I would say, the Malofiej Awards made our beloved profession grow and change to reach unexpected limits. I really miss that event, same as many other colleagues

Infographics is something that wraps you from the beginning and shows you multiple paths; it can be kind but it also can be harsh. It can show you all the fields of knowledge, it gives you the chance to learn, it can take you to many places if you want, and it can introduce you to plenty of interesting people.

At this point, I think it is a kind of unconditional love.

The post Marcelo Duhalde’s Graphics Bring Journalistic Investigations to Life appeared first on Nightingale.

Now, almost a decade into my data visualization design journey, I have begun to question this practice and consider what happens when designers make the complex appear simple. In this article, I discuss the problem of oversimplification in data visualization and show how it can obscure (data) complexities that should be communicated.

The problem of oversimplification

We live in a complex world, and the visualizations we design should accurately and empathetically represent and celebrate the phenomena being explored (or explained). However, many practitioners in the field argue that the primary goal of data visualization is to present data in a way that is easy to understand and interpret. Now, such a goal seems not only productive but also laudable. But there is a seedy side to simplification that can result in designs that conceal important information, encourage overgeneralizations, and constrain creative expression. 

Giorgia Lupi, an information designer and data humanist, explores the theme of oversimplification, among others, in her article Data Humanism: The Revolutionary Future of Data Visualization for PRINT. She argues that, for many, part of the allure of visual design and, by extension, data visualization is its ability to simplify data. Indeed, there is something to be said for being able to reduce data into easily digestible visual representations. But do not be fooled by a designer’s “effortless” ability to make data look simpler than they are. Every decision a designer makes is deliberate and influences how their audience perceives the data and the real-life stories the data represent. 

Looking at several examples will help to illustrate this point. For brevity, I focus on three types of oversimplification: data aggregation, chart choice, and artistic license.

Data aggregation

Data aggregation is the process of expressing data in a summary form. When aggregating data, choices are made about what data elements should be minimized, emphasized, or removed altogether. Although data aggregation often occurs before a designer gains access to a dataset, data visualization designers and developers are increasingly being tasked with cleaning and preparing datasets before analysis begins. 

Thoughtful aggregation can make data easier to analyze—for instance, when your data are too granular or large to answer a question. However, if not approached with care, data aggregation can severely limit your ability to meaningfully make sense of your data. As an example, consider the case of “Underrepresented Minorities” (URMs) in Science, Technology, Engineering, and Mathematics (STEM).

According to the National Science Foundation’s National Center for Science and Engineering Statistics (2021), URMs are persons from groups whose representation in science and engineering education or employment (in the United States) is smaller than their representation in the United States (US) population. This includes individuals who identify as

• Black, 
• Hispanic,
• Latinx, or
• American Indian or Alaska Native.

Say you are a newly appointed Vice Provost for Institutional Analysis and Planning at a university in the US. Your first goal as Vice Provost is to better understand the state of racial (in)equity in undergraduate engineering degree completion. You ask your lead analyst to calculate degree completion rates for 2020-21 by race and ethnicity for the following groups: White Students, Asian Students, and URMs (which is standard practice). Your analyst provides the following summary information:

• URM Completion Rate: 54 percent
  (920 out of 1,700 Black, Latinx, and Indigenous students graduated on time in 2021)
• Asian Completion Rate: 76 percent
  (1,520 out of 2,000 Asian students completed their degrees on time in 2021)
• White Completion Rate: 71 percent
  (2,130 out of 3,000 White students completed their degrees on time in 2021)

The analyst also points out that when they performed a quality check, they noticed disparities in completion rates by individual URM groups. Curious to learn more; you request that information as well. After generating a report that presents degree completion rates disaggregated by all available race and ethnicity groups, a slightly different picture emerges:

• Black Completion Rate:  40 percent
  (200 out of 500 Black students completed their degrees on time in 2021)
• Latinx Completion Rate: 60 percent
  (720 out of 1,200 Latinx students completed their degrees on time in 2021)
• Indigenous Completion Rate: N/A
  (The university did not graduate any Indigenous students in 2021, nor do they currently have any Indigenous students enrolled.)
• Asian Completion Rate: 76 percent
  (1,520 out of 2,000 Asian students completed their degrees on time in 2021)
• White Completion Rate: 71 percent
  (2,130 out of 3,000 White students completed their degrees on time in 2021)

Here, creating an aggregate “URM” designation that groups Black, Latinx, and Indigenous students together masks variability in completion rates between students from different backgrounds. If the Vice Provost had decided to use data from the first report produced by the analyst, they would have never known that the engineering department did not graduate any Indigenous students during the 2020-21 school year AND that the Black student completion rate is substantially lower than the completion rate for Latinx students. 

By combining data for different subgroups into one larger one, a false sense of understanding is created about all students who are lumped into the “URM” category. This, in effect, erases the diversity of experiences, perspectives, and (potential) needs of those students. Indeed, by that line of reasoning, one could make an argument for further disaggregating the “Asian” category so as not to marginalize those Asian ethnicities that are often overlooked or not prioritized in conversations about STEM equity. 

I want to be clear that careful data (dis)aggregation is not a means to an end but a step in the sensemaking process. And the process of (dis)aggregation should not occur in a vacuum, nor should it be understood as static. Rather, it should be a (context-specific) liberatory practice that facilitates more comprehensive data stories and creates opportunities for differentiation and insight.

Now, I will turn to discuss chart choice.

Chart choice

Choosing the best visualization type is a challenge all designers face. Fortunately, there is a long history of research (e.g., Cleveland & Robert 1984  (paywalled), 1985  (paywalled); Pandey et al. 2015  (paywalled)) showing that certain types of charts and graphs are easier for audiences to understand. However, a designer can also choose a chart—intentionally or irresponsibly—that conceals data complexity or implies a misleading pattern or trend. Eli Holder and Cindy Xiong’s (2022) recent study explores how design choices can inappropriately convey and reinforce discriminatory messages. 

Through four experiments, Holder & Xiong (2022) show how visualization design can influence viewers’ perceptions about the subject presented. More specifically, they found that study participants were more likely to attribute differences in (social) outcomes to personal characteristics (e.g., people with better outcomes work harder than people with worse outcomes) when presented with a visualization that hides within-group variability (like a bar chart). On the other hand, participants were less likely to agree that differences in the outcomes presented were due to personal characteristics when they saw a visualization that emphasized within-group variability (like a jitter plot). 

Let me offer an example to illustrate this critical finding.

For more than half a century, ethnic and racial differences in educational outcomes have been the subject of much debate (Coleman 1968 (paywalled); Jencks & Meredith 2011  (paywalled)). These educational disparities (like the test score gap) have largely been framed in Black-White terms, with many (falsely) interpreting these differences to mean that Black students are academically and intellectually inferior to students from other backgrounds. One question that is often of interest to parents, educators, and policymakers is whether there are differences in reading test scores between students who identify as Black versus those who identify as White. 

Imagine you are the lead educational analyst for a school district. Your boss, the Director of Research and Policy Analysis, wants to know whether there are differences in the third grade reading test scores of Black and White students in the district. You run the numbers and find that the average reading score for Black third graders in the district is 148.48, and for White students, it is 172.90. You produce the following (horizontal) bar graph and present it to your boss:

Your boss looks at the chart and offhandedly remarks, “Looks like Black third graders can’t read.” You pipe up and say, “That is not true.” Your boss turns to you and asks, “How do you know?” Being the amazing analyst you are, you also produced a jitter plot showing the distribution of reading scores for both Black and White students, where the full range of values (e.g., minimum, maximum, and mean) can be seen: 

Now, your boss sees the fuller picture and realizes that, yes, on average, Black third graders scored lower in reading than their White counterparts. However, some Black students scored higher and others lower. 

Bar charts conceal how spread out a dataset is and overstate the appearance of differences between groups. And while their simplicity and widespread familiarity make them easy to digest at a glance, bar charts can be an irresponsible choice for presenting quantitative data that are grouped into discrete categories, especially if there is considerable variability in the outcome being displayed. In other words, visualizations that reduce a dataset to a single number can have the unfortunate consequence of misleading audiences at best and reinforcing stereotypes and societal biases at worst. 

Although the implications of Holder & Xiong’s (2022) study are significant for research and practice, they should have a profound impact on how educators and experts approach training future data visualization designers. Choosing visuals that oversimplify a dataset is often the result of a lack of experience with data and knowledge of the visual expression of data. All designers should be exposed to and have a thorough understanding of the variety of ways data can be (re)presented and encoded. This includes design considerations beyond conventional visualization approaches and popular charts that are standard in analytics and business intelligence tools but do not always fully capture patterns or trends or are not well suited for telling complex stories. 

My goal here is not to convince all of you reading to replace your bar charts with a jitter plot. Rather, like Holder & Xiong, I hope to impress upon you the “duty of care” designers owe not only to themselves but also to the public when it comes to (re)presenting data.

One final topic I will talk about in this article is artistic license. And by artistic license, I mean creative expression.

Artistic license

Creative expression in data visualization is a touchy subject. Many believe that creative expression and data visualization are inherently incompatible and design choices that deviate from “best practices” should be banished to the realm of data (or computational) artistry. For instance, Stephen Few, an information designer, has written about this topic on his blog, Perceptual Edge, in a now infamous post titled Does Art Play a Role in Data Visualization?. In the piece, Few argues that we, as data designers, should use the term “art” when referencing data visualization with caution. And I agree. But where he and I differ is in our understanding of visual design conventions or what he calls “aesthetics.” 

According to Few, there is no place for artistic license in “effective” data visualization; the creation of visuals and graphics should remain an objective, science-informed endeavor. That said, Few does recognize the importance of aesthetics in data visualization, but only because the design conventions (i.e., what works and does not) he employs in his work are rooted in “scientific research.” But what Few and others who adhere to this philosophy fail to acknowledge is that nothing operates in a vacuum. Everything—including scientific research—is influenced by time, culture, and current understandings of how the world should work. So, who is to say that data artists and data designers who experiment with more artistic forms of visual communication are not simply at the forefront of scientific developments that will lead to new norms in visualizing (or envisioning) information?

The tension between creative expression (form) and usability (function) extends beyond the theoretical to the practical realm. Current data visualization practices encourage sameness—copies of what others have designed. This has resulted in some designers becoming de facto technicians, blindly following “best practices” without questioning or tailoring them to fit their specific needs. Now, I am not suggesting that we throw caution to the wind and create works of art that do not communicate a story or allow the audience to discover their own story. What I am arguing for is a data visualization design practice that relies on evidence-based techniques grounded in scientific research— that acknowledges different ways of knowing and being— but embraces creative expression, such as experimenting with different encodings and visualization types. 

An example will help to illuminate this point.

Children living in immigrant families continue to be a growing segment of the US child population. In 2018, one in four—or 18.4 million—children in the US were born in another country or lived in a family with at least one foreign-born parent. 

Say you work as an information designer at an organization focused on immigration policy in the US. Your executive director requests that you create a visual that allows viewers to see where most children in immigrant families live. Using Kids Count Data Center data from 2005 to 2018, you produce two tile grid maps (one map using 2005 data, the other 2018) and stitch them together in a GIF animation:

Original Design: GIF of Two Tile Grid Maps

A tile grid map is a popular and effective way to present geographic data while giving each region (or state, in this case) equal visual weight. But there are other ways to present trend data that do not require animated transitions between two static visualizations.

In keeping with the “tile grid theme,” your next design brings an interactive element (i.e., scroll bar) to the visualization as well as a small multiples area graph. Users can click on the arrows of the scroll bar or use the slider to “activate” a red vertical line that highlights the current year’s percentage. Each tile also has a label showing the state’s name and the current year’s percentage.

Alternative #1: Interactive Tile Grid Map with Area Graphs

While interactivity can increase understanding, designers should not assume that viewers will (or should) click or hover to make sense of the presented data. Further, because many data points are displayed, forcing interaction via a scroll bar, in this case, can become burdensome for people with mobility impairments. Not to mention, the interactive element adds a level of granularity without much value. In other words, even without a data value label (that changes with each year), viewers can still see how values in the outcome change over time and judge how much using the map legend.

For your final piece, you produce a static visualization that uses the tile grid map as a foundation and blends popular visual encodings with a non-traditional chart type

Alternative #2: Tile Grid Map with Radial Column Charts

Here, a radial column chart is placed in each tile, and individual bars on said chart(s) represent a single year in the dataset, ranging from 2005 (one o’clock position) to 2018 (twelve). Instead of displaying values for specific years, each chart has a series of concentric rings representing 10 percent, ranging from 0 percent (innermost ring) to 50 percent (outermost ring). Moreover, the length of each bar is proportional to the percentage it represents, and viewers do not have to rely solely on bar length to decode the visualization. Color is strategically used to highlight and define regions with more (or fewer) children living in immigrant families.

My goal in discussing creative expression is not to suggest that all designers should aim to be more “artistic” in their approach or that visualizations with an “artistic” aesthetic are inherently better. Rather, the simplified setting of the example underscores how a dataset can be visually (re)presented in different ways. Some designs may resonate with your audience. Others, not so much. Experimentation, however, offers an opportunity to thoughtfully examine our design choices and decision-making and create more meaningful visualizations that will help your audience uncover new knowledge or come to a new understanding of the presented data.

We are at a critical juncture in the history of information design. Rising interest in data and growing familiarity with the tools and skills needed to present data offers the opportunity for, as Lupi puts it, a second wave of visualization that is experimental, unique, and “connect[s] numbers and graphics to what they really stand for: knowledge, behaviors, people.” This article only scratches the surface of the issue of oversimplification and the influence it can have on how we—as designers—choose to visually (re)present life. I will leave you with a quote from an essay titled Seeing Your Life in Data (paywalled), penned by Nathan Yau, a statistician and data visualization expert. One line in the piece perfectly captures my attitude towards the current moment and perhaps was (at the time) a foreshadowing of what was to come, “Data often can be sterile, but only if we present it that way.”

The post When Oversimplification Obscures appeared first on Nightingale.

Data visualization artists are responsible for making large amounts of information easily accessible and digestible for a wide array of readers. This includes the 17 percent of the global population who have been diagnosed as neurodiverse, as reported by the Oxford University British Medical Bulletin. For those in this community, having a visual representation of data can be invaluable, but there are still ways to make data more accessible. Taking extra care to consider elements such as emphasis, balance, proportion, typography, and color can make a noticeable difference.

Here are a few guidelines designers should consider when creating visualizations to make them more accessible to neurodiverse readers, from a neurodivergent designer.

Typography

Font selection is one of the most important decisions a designer makes. It’s the key to ensuring the data you’re attempting to communicate is understood beyond your design. While it may be a go-to decision to select a font which looks professional, such as Times New Roman, research shows you may want to avoid serif fonts altogether when attempting to appeal to neurodiverse audiences. 

Serif fonts can be identified by their tails and ticks on the ends of most strokes. While serif fonts like Times New Roman have been a standard for professional writing for decades, they have been found to be far less readable among neurodiverse audiences, according to the British Dyslexia Association. As an alternative, sans-serif fonts prove to be much easier to comprehend. While fonts resembling handwriting appeal to neurodiverse audiences, such as Comic Sans, there is limited usage for them in the vast majority of data visualization projects. Sans-serif fonts continue to offer multiple variations to add dimension to your design, where using different weights and sizes can make for a visually interesting piece.

Color

Color can be a data visualization artist’s best friend, offering an easy way to divide information and adding variables to a chart or dashboard. While it is both reasonable and natural to feel the need to use drastically different colors to differentiate areas, it can be overstimulating for a large percentage of people with dyslexia who suffer from Scotopic Sensitivity Syndrome, according to the National Library of Medicine. This is where the usage of single-hue scales can be a perfect solution.

Single-hue color scales limit the number of hues in use while still providing plenty of variations. While this won’t work for projects breaking down a large number of variables where color is your only option for variation, it surely has its place in many others.

Selecting a background color is also crucial for contrast. A designer should not overlay two highly contrasted colors on top of each other, causing an unpleasant viewing experience for those who are neurodivergent. For many neurodivergent audiences, there is a preference for muted and pastel hues and neutral tones.This may mean a matte-black background with two-to-three pastel hues to depict data, or a neutral tan, gray or white background, to prevent colors from overwhelming the reader.

Visual hierarchy

Having a clear path for a reader to follow is essential among neurodiverse audiences. When looking at the project as a whole, it should allow for the eyes to follow a path to easily find the title, description, primary graphic, and key. This greatly aids comprehension, ensuring information isn’t overlooked, nor is a strain to find.

Another way to ensure your project has an effective visual hierarchy is to break up large bodies of text with visual elements. When there are too many large blocks of text, it can be overwhelming and harder for neurodiverse audiences to comprehend. Breaking up the text into smaller, more digestible pieces will considerably increase comprehension. 

A visual hierarchy ensures balance in our work; by using an even amount of symmetry or asymmetry, a strong visual stability is enforced. 

Patterns

The use of patterns, both organic and geometric, appeases a neurodivergent audiences’ need for predictability and repetition, according to a study conducted by the Association for Psychological Science. This technique is known as fractals structures, and while they naturally help the neurodivergent understand, manage, and navigate the world, when used in data visualization projects, patterns can improve understanding, management, and navigation of large amounts of information. These patterns, while still used in moderation to avoid overstimulation, can add another element to visual storytelling efforts.

Many of those who are neurodivergent depend on repetition and predictability to feel in control and comfortable with what they’re attempting to comprehend. Having a pattern combined with a well-executed visual hierarchy can keep a reader who is neurodiverse more engaged.

Data visualization artists are storytellers with the unique ability to take information that requires skills – and patience – to dissect and turn it into beautiful designs. By designing data visualization projects to accommodate a variety of neurodivergent disorders, dataviz practitioners only further ensure our work is more accessible for all.

The post Designing for Neurodivergent Audiences appeared first on Nightingale.

Kenneth’s new book Thematic Mapping: 101 Inspiring Ways to Visualise Empirical Data is currently available for purchase as an e-book. 

The conversation has been summarised below.

Oscar So: Where did the idea for Thematic Mapping come from? What lessons have you learnt or imparted from your first book into this one?

Kenneth Field: That’s an interesting question, and one I have written up as part of the preface in the book. 

Sometime in early 2018, Alberto Cairo, who is a great information designer in his own right, visited and I was showing him the draft of Cartography. His first question to me was, “What are you going to write for your second book?” I really had not thought about that because I had spent so long writing the first book, and I thought I put everything I learnt into it. 

But, something I have been doing at Esri ever since I started working there is making maps with election data, partly for my own interest, but also to generate a collection of maps for teaching. I am interested in taking a single dataset and representing it in multiple ways. Electoral data tends to pique people’s interest in a way that maps of tourism statistics, for instance, do not. I rifle through 20 of these maps at workshops to show people that you do not have to use a choropleth map or a proportional symbol map, but can instead try to do something a little inventive or experimental. 

At some point I realized: there is a book here. Why not turn a gallery of 30+ maps into a book where you go through the same tutorial process? I already had 30 maps built, but I figured I’d better make more than 30 or the book would not be very long. I decided on 100, and then because 100 is too nice a number, I decided to make it 101. Surprisingly, it was pretty easy to make 70-80 maps! 

So the intent of the book is to create a glossary of map techniques, where the dataset is static. This allows you to look at the way a specific technique changes the message of the dataset. It is not a new idea, though. Jacques Bertin did something similar in Semiology of Graphics. He made 100 maps of France using three pieces of information, which I reference in the book. It is important to reference what has gone before, but maybe by redoing it for a new audience it is timely?

OS: Why did you choose to use the 2016 US election dataset?

KF: Well, there are practical reasons. I started talking about this book back in mid-to-late 2018, and I already had 30-to-35 maps in a fairly advanced state of production. The practicality of having some of the work done helped because it gave me a good starting point. 

Secondly, as I said earlier, when you are using a single dataset, it needs to make people want to look at the maps. I think maps of elections are interesting to people because they provide us with a snapshot of a particular point in time. They change every four years in the US and, four-to-five years in the UK. Sometimes the shift in results is insignificant from year to year, and sometimes it is hugely significant. So, that was it—practical reasons, plus I think the dataset has a good chance of being of interest to a wide audience.

OS: Some map techniques, like choropleths, cartograms, and proportional symbols, are commonplace for election maps. Were there other techniques that surprised or intrigued you?

KF: I am not sure that there was anything that surprised me, but in general I think that techniques that are not so commonplace get less attention partly because they are harder to construct and partly because they are harder to read. Normally, you want someone to look at a map and be able to interpret and understand the message easily. There are a few maps in the book that are challenging, and people will have to spend quite a bit of time with them to understand the message. I am not saying that is a bad thing, but it depends on your goal. Are you making a map for a poster? Are you making a map for a statistical report? Are you making a map to persuade? Are you making a map for the front page of a newspaper’s website or page 48 of a print newspaper? These are all variables that will affect the techniques you use. 

In the book, I tried to recreate maybe half-a-dozen of some really good maps we have seen in the media over the last decade. There is a famous one in the Washington Post where they use mountain symbology and vary the width of the base, the height of the peak, the color and line thickness. 

I’ve also included a modified version of Tim Wallace’s Islands of the US. I riffed on that idea and created something along the same lines. 

In total, I’ve created a collection of old standards plus a few modern classics, as well as some maps that are not used often. There are Chernoff face maps in there, and Dorling Cartograms, and Dorling Cartograms with Chernoff faces (see below). There are 3,000+ counties and they each have a mini Chernoff face, but it is a Dorling cartogram as well. It is quite the bizarre looking thing! Now, is it useful? I do not know—it looks interesting, but may not have practical value. What I have tried to do, though, is share my editorial thinking and process. So in the book you will see my notes and annotations highlighting certain aspects of each map.

What I tried to do is exhibit the thinking behind each technique and what that technique means for the dataset that is being mapped.

OS: For those of us who are well versed in cartography, how does your book build on existing thematic mapping knowledge established by classics such as Thematic Cartography and Geovisualization by Slocum, Howard, Kessler and McMaster and Cartography: Thematic Map Design by Dent. What avenues does your book build on that these books have not?

KF: Those two books are both great books; I have used them for years. I would suggest that both of those are more similar to my first book, Cartography. They dive deeply into cartography in a conceptual way, including theory and cognitive background. What I have tried to do with my second book, Thematic Mapping is get away from that and provide a glossary of practical examples. 

Some people used to ask me about election maps: “Which map should I choose?” Well, I cannot tell you that because it depends on what message you want to impart. But you could flick through my book and stumble across a map and say, “Oh I want to make a map that looks like that.” The write-up of that particular map will give you a broad idea of when to use that type of map, why it works, and why it may not always work. But this book does not go into detail about the How To’s, the principles of symbology, and stuff like that. People can buy my first book for that — and maybe the other two you mentioned as well — and then see this second book as a practical implementation of those concepts.

OS: Any final words of wisdom you want to share with the readers? 

KF: Yeah, go buy the book! If you want to reach out, I am on Twitter; my DMs are open. 

As for words of wisdom? I think they are all in the two books I have written! But ultimately, enjoy what you do, have fun with cartography, and do not be afraid to pick up a phone and talk to people because the cartography community is actually quite friendly and helpful. We may not always agree, but if you can have a disagreement about a visualization and still go for a coffee or beer with that person, that is professional respect. That is when you can really talk about a map or discuss the techniques you have used.

–To learn more or purchase ‘Thematic Mapping: 101 ways to visualise empirical data,’ be sure to check out the book page.

The post “What are you going to write for your second?” – An interview with Kenneth Field appeared first on Nightingale.

These reports, each of which takes years for teams of scientists to develop, provide an overview of the current state of climate science. They are a go-to reference for policymakers to make science-informed decisions that tackle climate change. The 2021 IPCC report details global changes that illustrate exactly how worried we should be about our future and how responsible we humans are for that situation. (Spoiler alert: pretty worried, and pretty responsible.)

But, despite representing the latest knowledge in climate science, the IPCC reports have not historically been recognized for the quality of their graphics. Critics of previous reports have pointed to dense scientific figures that often necessitate subject area expertise to parse. While likely effective for scientist-to-scientist communication, these figures did not consistently embrace best practices in dataviz, limiting their effectiveness as communication tools for broader audiences.

That changed with the most recent report.

The 2021 SPM includes a suite of beautiful, intuitively designed data graphics. They are clearer, cleaner, more approachable, and more consistent in style than in previous reports. Moreover, they are making an impact: versions of these graphics were shared by global news media during the summer of 2021 and were a key tool for advancing discussions about the climate crisis.

So when I stumbled across this tweet from climate scientist Ed Hawkins, who is himself known for creating iconic and approachable climate visuals like the Warming Stripes, I was immediately intrigued and reached out to the individuals he named to ask about their design process and philosophy.

As it turns out, the impact of the AR6 graphics is the result of a multi-year effort to implement a human-centered “co-design” approach to the dataviz process.

What follows is a distillation of my communications with four members of the core design team:

• Angela Morelli and Tom Johansen, co-founders of InfoDesignLab, who developed and led the co-design process, including drafting the visualizations that appear in the SPM
• Melissa Gomis, Senior Science Officer at IPCC, who served as a liaison between the InfoDesignLab team and the AR6 chapter authors, while also providing a style guide and individualized feedback on visualizations to the authors
• Jordan Harold, an applied cognitive psychologist at the Tyndall Centre for Climate Change Research, who contributed to the development of evidence-based best practices and led user testing of the AR6 visualizations

Overview of the co-design process

When I first reached out to the team, I asked if they would share background on the IPCC data visualization design process. “Co-design process,” they corrected me, emphasizing the close collaboration between information designers, content experts (the scientists who author each report chapter), cognitive scientists, the target audience for the report (global policymakers), and other stakeholders. The co-design process itself, they pointed out, is the result of multiple iterations—it has been used in some form for three IPCC reports: the 2018 Special Report on Global Warming of 1.5 °C; the 2020 Special Report on Climate Change and Land; and now, the most recent report, the Working Group I contribution to the AR6. The process has been refined each time based on lessons learned during work on the previous reports.

“The way we collectively managed to push for a really participatory and human-centered approach for the AR6 report—I’m extremely proud of that. It’s a very tangible change from previous reports.” – Angela Morelli

At a high level, the AR6 process worked like this (note: the core design team published a detailed account of the co-design process used to develop the IPCC graphics in the journal Climatic Change last week; read it here):

• Each figure in the SPM was developed by a distinct group that initially included the core design team plus a subset of chapter authors; as the report moved closer to finalization, this group grew in order to make sure the suite of figures in the SPM worked well together to advance the desired narrative.
• The primary audience for the SPM – and the visualizations within – is policymakers: specifically, hundreds of delegates from 195 UN member countries.
• The co-design process began with research and study, with the design team reading the draft report to understand the science, organize the content, and identify questions.
• The design team then conducted in-depth interviews with country representatives from all regions of the world to understand the policymakers’ needs, preferences, and anticipated use of the visualizations.
• In meetings facilitated by InfoDesignLab, the chapter authors suggested figures (for example, those already created for the full report) as candidates to include in the SPM; the suggested figures were the starting point for the co-design process. Because the co-design process began while the text of the SPM was still being written, the visualizations and the text evolved in parallel. These initial meetings were largely about coming to a common understanding of what to visualize: the content, variables, and themes to illustrate in each figure.
• Together, the co-design team then began to think about how to visualize each figure through an iterative cycle of prototyping, dialogue, and revision. As the SPM text evolved and approached finalization, so too did the visualizations.
• Draft visualizations went through multiple rounds of review by the authors and policymakers, including formal comment periods and a survey to evaluate how the figures were perceived in terms of clarity, accessibility, and usability.
• Ultimately, the figures were discussed in detail and revised in real time at IPCC’s final approval session, which included the chapter authors, country delegates, and the core design team.

Altogether, the process spanned nearly 18 months, from initial in-depth interviews in February 2020 to final approval in July 2021.

“For the first two IPCC reports that we worked on, the drafting time was much shorter. The co-design of figures was unprecedented at the time. [For the AR6] it was really about understanding, trying to get into the shoes of the people that would use the figures before ever starting the design process.” – Melissa Gomis

The following sections elaborate on key insights from the co-design process.

Start with intent

One of the central components of the co-design process was for the authors to agree early on the “intent” for each figure, or a plain-language, headline sentence summarizing the figure’s main message. Although figure intents continued to evolve throughout the design and report development process, starting with the intent helped achieve two key benefits:

• It gave the chapter authors and the design team a shared understanding of the goal of the visualization.
• It provided a benchmark for evaluating the effectiveness of each figure at communicating its intended message.

“Don’t think about the visual before knowing what you want to communicate. First, write down your message in one sentence: what do you want to communicate with your data? Then construct your visual and your visual narrative and all the elements you decide to put in your figure based on that intent.” – Melissa Gomis

The stronger focus on figure intent for the AR6 came about, in part, because of challenges during the design process for previous IPCC reports. As one example, Tom recounted the frustration realized during the process of developing the 1.5ºC report that one country was consistently misunderstanding the intent of a particular figure, even as the working group continued debating colors and formats. Since then, InfoDesignLab revised the co-design process to focus more heavily on developing figure intent. The chapter authors have now had several years of exposure to that idea, and as a result, they often independently referred back to figure intent when debating design decisions.

This focus on intent was particularly important because the policymakers who are the target audience, while well-versed in climate science, are not content experts and often need to communicate complex information from the IPCC reports to broader audiences that include government ministers, businesses, and citizens. The policymakers, therefore, still benefit from plain-language interpretations of the complex and often nuanced science underlying the report figures.

“If you are creating a visualization to be a decision-making tool, you have to build in a reflective moment; you have to build up the narrative. It’s only through reflection that you can achieve knowledge and action and change.” – Tom Johansen

“Sometimes we have to unpack the science in a way that scientists would not because everything is so tacit in their minds that ours has to be the voice of beginners. We try to unpack everything before we get to figure intent and prototypes.” – Angela Morelli

Embrace best practices

Once the intent of a figure was clear, the next step was to develop a prototype. In contrast to previous IPCC reports, the figures of the AR6 moved away from the visually dense style of scientific journals and towards something cleaner and more approachable. The Tyndall Centre advocated for this shift based on their research into cognitive psychology and good dataviz practice—which is encapsulated in their ‘MADE’ principle for designing effective scientific visuals. The MADE principle defines effective visuals as ones that consider:

• Message: Does the visual communicate a clear message?
• Audience: Is the visual appropriate for the intended audience(s)?
• Design: Does the visual use evidence-based design principles?
• Evaluation: Has the visual been tested with the audience(s)?

To help scientific visualizations achieve these goals, the Tyndall Centre recommends following 12 design guidelines that should be familiar to most dataviz practitioners—things like “Identify your main message,” and “Build-up information to provide visual structure.”

“IPCC authors are extremely busy, so we needed a way to make the core principles memorable, hence the ‘MADE’ acronym.  Of course the principle doesn’t just apply to climate change information—it could be used with scientific data visualizations in any discipline.” – Jordan Harold

Use unconventional chart types carefully

One insight from the Tyndall Centre’s research on best practices is that familiar chart types are generally easier for people to understand, even if unconventional chart types may grab more attention. Policymakers also like to see similar charts in every IPCC report so that they can easily evaluate changes over time. As a result, the design team generally considered conventional chart types to be the starting point, with decisions to use unconventional charts guided by the message the chart intended to convey.

For example, one of the most striking visualizations in the SPM is an unconventional hexagon tile map showing changing climate risks across the world. Jordan described the thought process behind this choice of map:

“The message that authors wanted to convey was that climate change is affecting every inhabited region of the world. The hexagons give each region equal representation and support the message. When we user tested the hexagons, some people didn’t realize straight away that it represented a world map, but many people saw the benefits in terms of communication. Hence, the choice to go with hexagons was an informed one, acknowledging the fine balance between these aspects.” – Jordan Harold

Engage your audience

One of the benefits of identifying the intent of the figure early on is that you have a benchmark to evaluate its effectiveness with your target audience. The AR6 visualizations achieved this goal through in-depth interviews with policymakers, a user testing survey developed by the Tyndall Centre, and IPCC’s formal comment process. (That comment process can yield tens of thousands of comments, each of which requires a response!)

The results of user testing activities influenced both the form and aesthetics of the visualizations. For example, one consideration that emerged during initial interviews was the need to create visuals that the policymakers could easily explain to different audiences and re-use in presentations or online. The user testing survey focused on eliciting more detailed feedback on the draft visualizations through both open- and closed-ended questions. Because the drafts of the IPCC reports are confidential and the figures could not be tested with broader audiences, this feedback was critical.

“We had applied the MADE principle to re-design previously published IPCC data visuals and tested those with wider audiences, which gave us confidence in our approach. Critically, we tested actual comprehension, rather than just people’s views—people sometimes say they like a data visual from its aesthetics but then struggle to understand the message or misinterpret the information.” – Jordan Harold

“User testing helped during the report approval process because the policymakers were already involved – more than in past reports – so the figures didn’t change much during approval. We had done a lot of the work already, integrating the users’ needs.” – Melissa Gomis

Choose tools to support the process

According to InfoDesignLab, 80 percent of the design process is “process, process, process!”; the rest is visual design work. That 80 percent of “process” includes everything from exchanging and understanding the data, to identifying the figure intent, to creating basic visualizations to aid understanding.

This sort of back-to-the-basics process does not require sophisticated tools but does require careful facilitation. Over the past 15 years, InfoDesignLab has developed and refined a “toolkit” of facilitation tools central to the co-design process. This toolkit includes figure intent, but also considerations such as visual narrative, storytelling, and user adoption.

“As information designers our goal is to communicate useful and meaningful information to our readers. We do that by leaning on our experience, research, collaboration, and our toolkit. These tools are powerful—because they help us facilitate the process, and also because they turn into a learning journey for our clients.” – InfoDesignLab

InfoDesignLab noted that most of their initial data work and basic visualizations happen in Excel, although they may use other tools depending on the scientists’ preferences. As they start creating draft visualizations, they sketch and build wireframes with a variety of tools—Excel, Keynote, Google Slides, Illustrator, and even good ol’ pen and paper. Importantly, though, the InfoDesignLab team creates slidebooks that record the history of every figure to facilitate evaluation and revisions.

“Recording the history of the figures has been one of the most powerful design tools in the co-design process, because you can have 10 teams of 25 scientists, plus co-chairs and other working groups that come in and out. One figure in particular had 160 slides—the number of visual ideas that are generated for a figure can be incredible.” – Angela Morelli

Moreover, the tools used need to align with the user’s needs. For the IPCC visualizations, policymakers were clear that they needed graphics that could be used in different contexts—for example, in presentations as well as on paper. To facilitate this for the AR6, the co-design team shifted away from the full-page infographics of previous reports to paneled images with fewer text annotations that could be shown as stand-alone charts in a presentation. But, knowing what their audience needed, they made some sacrifices—notably, the visualizations do not work well on mobile.

“The one thing that we didn’t manage is to optimize the visualizations for mobile, but you cannot do everything. We had to make them work in the PDF for print, and in PowerPoint, so we had to make some choices.” – Melissa Gomis

Advice for aspiring information designers

Although IPCC follows a prescribed co-design process, much of that process can be applied to other data visualization work. The primary difference between the IPCC work and other projects, InfoDesignLab explained, is the magnitude of the effort. The key questions to ask clients and partners are universal:

• What are we trying to achieve with this visualization? (What is the intent of the figure?)
• Who is it for?

The answers to these questions will be different for every figure, so the process of creating those figures – including, often, the tools used – must be different as well. Each one should be thought of as a project in and of itself. (You can read more about the philosophy behind InfoDesignLab’s co-design process in this 2019 article.)

So, what advice can we take from the IPCC co-design process and core design team?

“There’s sometimes a bit of a challenge in that people have their own views and personal experiences when interpreting a data visual. When you become so close to the design process it can become difficult to look at a visual from the perspective of a fresh reader looking at it for the first time – in psychology we call this the curse of knowledge! User testing really helps to complement the process.” – Jordan Harold

—

“You’re not designing figures for yourself; you’re designing them to communicate something to someone else, to another person with their own prior knowledge and abilities. You cannot infer what they know. You think people will get it, but you don’t realize that you’re a step ahead of some of your audience. Whenever you prepare a figure, test it on someone else.” – Melissa Gomis

—

“Dive in; try things; mess up! Don’t just think about visualizing data; start doing it. Know that, in the end, the data you are visualizing are often about people, even if you’re visualizing CO₂ or sea level; understand the weight of that, but then just take courses, mess up, try again.” – Tom Johansen

—

“Stop sometimes and ask yourself why you’re doing what you’re doing. The answer doesn’t have to be, ‘Because I want to change the world’; it can also be, ‘Because I want to have fun’; or ‘I want to explore visually’; or ‘I love aesthetics.’ Reflecting on why we are using design gives so much clarity on what we want to achieve.” – Angela Morelli

For more information on the IPCC co-design process and data visualization best practices:

• “Co-designing engaging and accessible data visualisations: a case study of the IPCC reports” (2021): https://link.springer.com/article/10.1007/s10584-021-03171-4
• “Designing for Tuesday” (2019): https://medium.com/infodesignlabposts/designing-for-tuesday-e82d2f184447
• “IPCC Visual Style Guide for Authors” (2018): https://www.ipcc.ch/site/assets/uploads/2019/04/IPCC-visual-style-guide.pdf
• “Enhancing the accessibility of climate change data visuals: Recommendations to the IPCC and guidance for researchers.” (2017): http://guidance.climatesciencecognition.com/

The post ​​Communicating Climate Risk: Insights from the IPCC Information Design Team appeared first on Nightingale.

I found myself focusing deeply on the word “dashboard,” wondering if that’s indeed what my client had in mind and whether that type of display would truly be persuasive. You see, when someone says dashboard, I think of COVID dashboards like the one above that we’ve all become accustomed to seeing (and, admittedly for some of us, to nervously checking at regular intervals). 

In addition, there are the modular-looking dashboard displays used for internal monitoring purposes  – for example, a business dashboard to summarize KPIs (key performance indicators) for senior management.

In the health field, in particular, dashboards have become commonplace. This very journal, for example, featured a post by Tricia Aung on the “The Global Health Dashboard Epidemic” – and that blog post was published about a year before the pandemic brought to us the proliferation of COVID dashboards. 

So, you may ask: What’s my issue with dashboards? Some may point out that dashboards are a terrific way to display findings, certainly better than showing a singular graph to summarize a complex topic. 

I agree that dashboards have their place, but here are a few things that I find problematic with a typical dashboard display: 

• Dashboards prioritize the graphs, meaning there’s typically no room for context or explanation about the significance of the overall findings.
  
• Dashboards don’t often have enough space to explain the meaning of individual visualizations. It’s as if we’re supposed to be familiar with what each indicator is conveying, including its acronyms and other inside-baseball terminology.
  
• There’s often no sense of hierarchy with dashboards. All visualizations are given equal prominence. 
  
• And some common elements that we leverage in data storytelling to help readers better absorb and find meaning in the information – narratives about individuals, quotes, photos – are foreign to most dashboard displays.

That’s not to say that all dashboards should be banished. With COVID, for example, the use of dashboards is appropriate, given that we all quickly became familiar with the pandemic’s data terminology. For that same reason, a car dashboard works well; we collectively understand what a fuel gauge or speedometer is communicating.

The same reasoning applies to dashboards used internally in business settings – for example, at the start of monthly meetings to help a division of a company know where to focus efforts. After all, when everyone on the team is well-acquainted with the measures, context isn’t necessary, so dashboards work well.

But what about that new employee who needs to be onboarded? It would be helpful to educate them about the significance of the measures on the company’s dashboard. 

You see, in so many instances, our work as data visualization practitioners require us to explain concepts that are not as familiar as a car’s speed or COVID hospitalizations. In other words, much of the time when we communicate data, we don’t have the luxury of the user knowing what we know, because we’re talking about a topic our audiences don’t understand. There are many occasions, too, when we won’t always be able to control where, or how, our data is shared.

The bottom line is that we want to be able to compel people to take an action with the information we’re sharing, even if we’re not there in the room or on the Zoom call to describe the data, and that means that we need to explain and unpack the concepts for the visualizations we’re sharing. 

It’s in these settings that it would be helpful for us to realize that dashboards may not be the best display format. In fact, I’d go one step further: 

Dashboards ≠ Data Stories

The sooner we recognize that dashboards don’t adequately address the assignment we’re often given as visualization specialists – “create a story from the data” – the more willing we’ll be to consider formats beyond the dashboard. Sure, our colleagues not steeped in dataviz may say they want a dashboard – as my client above did – but that’s often because dashboards have become shorthand for data display these days.

It’s our job as data practitioners to find the right format by interviewing end-users and lifting up the actions our clients want audiences to take with the data. If we always fall back to the familiar dashboard display, our efforts may fail. The end user won’t understand the significance of the findings and won’t be persuaded to take an action, which means our work to find, analyze, and visualize the data will have been for nought.

Fortunately, we have many options for how to display data. To assist with this work of expanding beyond dashboards, I recently catalogued other display formats for a continuing education course I’m teaching through George Washington University, “From Spreadsheet to Story: A Step-by-Step Guide to Communicating Data.”

Here are some data story formats I highlighted for that course that can be more helpful than a dashboard can at driving towards action: 

The Fact Sheet: Often geared for print consumption, this one-pager highlights – and explains – some key findings that can be shared as a print handout or attached to an email.

A Slideshow: The emphasis here is on chunking out data findings into snack-size bites of information, so that you’re providing helpful context without describing too much at once.

An infographic: Sometimes cartoon-like in its presentation of information, an infographic often will dive into a specific topic by providing an abbreviated summary of key data findings.

The Persuasive Narrative: In my mind, the purest form of data storytelling is when you relate a story about an individual and use data to describe how this issue impacts more than just this one person. A persuasive narrative is one such way to humanize the data, telling an individual’s story from problem to potential solution. 

A Q&A: I particularly appreciate how the story linked below uses a combination of questions, then small blurbs + data, to answer them. The questions provide a useful cadence that leads the reader through the material.

A List: People engage with lists, such as the ‘top five things to know’ about a topic. Perhaps when we know the end point (e.g., you are on fact, seven out of 10), we’re more likely to digest the findings by reading a paragraph of explanation.

A Data Game: When we transform data into an intellectual exercise or quiz, it’s more likely that our information will be remembered and shared. Simply put, people like participating; it’s often far better than just talking at people with data.

Long-Form Scrollytell or Data Movie: Sometimes we have a lot to say, and need digital space to let the data breathe. There are some wonderful examples of long-form data stories, including “The Desperate Journeys of Rohingya Refugees.” By leveraging a combination of photos, artwork, audio clips, data maps, and graphs, this data story from kontinentalist educates us about the plight of 2,400 Rohingya who have fled Myanmar and Bangladesh. 

Another one of my favorites is this poignant data movie about World War II.

I’m sure this is an incomplete list. Know of any other data storytelling formats? Please share. I’d love to see what other forms of data stories exist out there in the wild.

The good news for those of us attached to our dashboards is that it actually doesn’t need to be an either/or choice between data stories like the examples above and dashboards. I often advise my clients that they can start with a data story to educate and explain. Then, you can let readers explore the findings on their own through an interactive dashboard that allows them to find their own narrative.

So rather than: 

Dashboards ≠ Data Stories

Let’s think of it as:

Dashboards & Data Stories

What did I end up doing for that community health collaborative project, you may ask? Well, the project is still very much in progress, but the template is taking shape as a fact sheet, one that leaves space for quotes, poignant photos, and other forms of content that help paint the data on a broader canvas than a dashboard can and help steer audiences to take an action that’s important to the health collaboratives.

The post Dashboards Are Not Data Stories appeared first on Nightingale.

The Nightingale team had the opportunity to connect with Darren Long, Marcelo Duhalde, and Adolfo Arranz to discuss their unique approach to visual journalism. The following interview has been lightly edited.

Jason Forrest: Would you all like to do a quick introduction?

Marcelo Duhalde: I have been working in newsrooms since 1996 and based in Hong Kong since 2016. My progression to online infographics actually started here in Hong Kong. Before, I specialized in print graphics and visualizations, providing solutions for other people’s content. I started to produce infographics from my own reporting and research around 2010.

I have developed different kinds of skills to deliver my investigations and I’m still exploring new ways to visualize data. We strive to produce diverse and interesting output with the infographics team.

Adolfo Arranz: I’ve been in Hong Kong since 2011, before that I was working for a long time with El Mundo newspaper in Spain, first with regional issues then national editions. Here in Hong Kong, I learned a lot and discovered new ways to make stories visual. I had the opportunity to develop standalone graphics, which we refer to as the “back page.” These past years have been a big evolution, very similar to what Marcelo said, since I also came from print. For me, the jump from print to developing online projects was very challenging.

Darren Long: I’m the creative director at SCMP. This is my second stint in Hong Kong and also my second time at SCMP. I first came to Hong Kong in 1990 before moving to Malaysia in 1997 just before the UK transferred the sovereignty of Hong Kong back to China. Back then, everyone was worried about political and social changes that didn’t happen, followed by an almost 20-30 year hiatus where things sort of stood still. But recently, the changes that we thought would take place in ‘97 are happening rapidly now instead. 

In terms of my career, I work mostly as an art director, bringing together the visuals and text and making sure they all work together. I have experience in a variety of spaces: news, luxury, lifestyle, even branding, consultancies, and airlines. But my first love has always been news. I’m a news junkie, so coming back to SCMP and working with the likes of Adolfo and Marcelo is like a dream come true.

Raeedah Wahid: Before diving into the work, what is it like being in Hong Kong right now? I know that there have been a few things involving economic downfalls due to COVID-19 and, in addition, the ongoing protests. How does all of this affect your ability to work, your outlook, and the purpose of the work that you do?

Darren: The diversity is really useful for our team. On this call, you’ve got a Brit, Spaniard, and Chilean. In the rest of the graphics department, we have mainland Chinese, Hongkongers, an Egyptian and we used to have two designers from Costa Rica. The fact that we all have unique viewpoints of what’s going on in Hong Kong and elsewhere makes for an interesting environment. We have, sometimes, quite heated debates — never in anger, but robust. 

Marcelo: Being here for four years, I have experienced many changes and have been witness to a big departmental evolution.

When I arrived, the prestige of the infographics department was well established because of Alberto Lucas Lopez [currently Senior Artist, National Geographic] and Adolfo,  who started with full-page infographics in print. We always had a lot of respect from within the newsroom, but the big challenge at that time was turning our visual pieces into digital experiences.

When I started here, the nature of our topics was not always hard news. Many subjects were more general-interest and not always directly related to Hong Kong or China. But since readers are demanding more refreshing and original news stories from a China perspective using different platforms, we have developed into a visual journalism department closely following the news. 

Hong Kong has been through many different events: the protests, the coronavirus, and other things. This city is in continuous evolution and change is omnipresent. We are a reflection of that. We have to follow that pace. Hong Kong is an amazing city and this is my favorite place. It’s always a pleasure to deliver stories that people here appreciate.

Adolfo: Well, what can I say after Darren and Marcelo — I agree with them!

We have a lucky graphics department. The most important thing, for example, is that almost 90 percent of our projects are our own. They’re not requests from the newsroom. The projects we offer are mainly our own stories based on hard work and research from our team. We’ve seen that SCMP Infographics are appreciated worldwide. We feel a certain recognition from the industry — from the SND, Malofiej… That is a wonderful experience.

We focused on the protests for the majority of last year. That was the news. We had many projects scheduled for 2019, but the news was the protests and we were absolutely focused on protest topics and stories. In some ways, it was easy because we were witnesses to these protests. When you leave the office, you might find yourself in the middle of a protest. On your way home from work, you were in the middle of some protest or you could not reach the subway because of the protests. We were in the middle of the storm. So, it was easy to understand how to develop and deliver the stories based on our own feelings.

Marcelo: We are really enjoying this moment. Our group is only six people. We enjoy each other’s company. We are having a great time doing these long-term infographics projects. For most of us, it’s not like a regular job. We enjoy producing infographics. We’re kind of infographic nerds. We’re always talking about how to visualize things. That shared mindset is a kind of ingredient that we are lucky to experience.

Raeedah: I know that the SCMP follows a clear mission to “lead the global conversation about China.” Are you feeling any sort of pressure or anxiety related to China’s increasing attention towards your coverage then?

Darren: No. I can honestly — hand-on-heart — say there is no pressure from outside sources as to how and what we should report.

The only thing I have taken slight umbrage to is if a sub-editor inserts a word like “radical” in front of “protesters,” in certain contexts. For instance, when some arrests during the protests were made, we said they were radical protesters, but some were just regular protesters and bystanders caught up in the melee. But you know, that’s a natural process. There are always going to be disagreements between the writer and the sub, and to the exact nuance of each word. But in terms of pressure from Jack Ma or Alibaba or Beijing, we’re completely unaware of any pressure whatsoever.

I mean, it might be different for the editor-in-chief, I don’t know. She never puts any pressure on us. I think it’s very well-established that SCMP believes in and agrees with the “one country, two systems” policy. I think as long as we report in the most unbiased manner, we’re able to say whatever we want — as long as we offer both sides of an argument.

Raeedah: Darren, you said at your DataFest Tbilisi 2020 talk (“Translating data into journalism”), that visuals are also data. It’s interesting to think about how illustrations and photography can also be considered information. So maybe, Marcelo and Adolfo, you can speak to this, but how do you bridge the gap between these mediums and data visualization?

[long pause…]

Darren: Good question — you stumped them both!

Adolfo: When I came to the SCMP in 2011, Simon Scarr [currently deputy head of Reuters Graphics] was here and we started to develop the standalone back page. The idea was to produce big infographics, honest stories, and mix visual data and powerful illustrations, or dramatic graphics with big explanations and text. I came for that because my best skill is mixing illustrations with the story and graphics.

From that point on, we evolved enormously. Right now—because it’s not only print — we are focusing on online projects, we are exploring animation. I think our identity flag from SCMP is the use of illustrations. We want to use many illustrations because we see that not many newsrooms do that. For me, huge visual data is nice, but if you combine that with illustrations, photos, or videos, you’re adding valuable visual interest. It can be less boring for the reader. The best example is Marcelo’s work.

Marcelo: For me, the move from print has been difficult. These kinds of languages, coding Javascript for example, stuff like that — the tools used to make an interactive project — are always evolving. I’m 50 already, if I want to go into code and learn something new about code, when I finish learning, the rules have totally changed and I have to learn again. 

So for me, the main skill that I have developed is telling visual stories. Someone will always support your ideas, coding, or developing your ideas into art — interactive or standalone — but the most important thing is to recognize and manage stories and the resources to tell those stories. That’s really important if you choose animation or illustration or data visualization. It’s more important to be accurate in the data, in terms of deep research, and to be responsible with well-supported information, and then transform that data through compelling and interesting storytelling. Data visualization has been something interesting to discover.

I’m not an expert. I’m just trying to experiment, sometimes with data and portraying it in some visual way, but there are many trends . I’ve seen many graphics trends, working for 25 years in infographics. Data visualization is one of those things that came into the newsroom and suddenly many places were producing these kinds of pieces. It’s useful when the audience feels that the story can be told in a smart way — like artists made these artistic and super attractive visuals and geometric shapes. But, we always try to be careful with data visualization because when you manage the data in that way, you have limits.

When you go deep into data visualization, it can be understood. When data visualization is hard for us to understand, it means for the audience, it will be impossible to understand. Sometimes a complex data visualization is less understandable than a simple table or a simple bar chart. We have to stay focused. We’re delivering information, rather than trying to show our skills to the world, so we have to be careful.

Darren: For me, the beauty of what Marcelo and Adolfo do is, they’re able to get to the essence of whatever story they’re telling. They’re able to really cut it right back to the very, very single most important ingredient.

I think that’s partly because they’re not data experts, so they have to understand it themselves first and really analyze it, and then communicate it. That’s what we do with the hard data, but I think other aspects that are unique to SCMP are the visualizations and the illustrations that you alluded to.

Because while the data is hard facts, we can balance that with the warmth and humanity of illustrations and rely on the ambiance and mood. Those elements can communicate in a more nuanced way than the facts. So with the data and storytelling, you’ve got the best of both worlds. You’ve got the one that’s ephemeral and open to interpretation and you’ve got the one that’s facts-based, which you can’t debate or argue over. That helps us avoid any sort of censorship within the newsroom as well. You can’t argue with the facts and the illustrations are left open to interpretation. You can’t police the way people think. Well, hopefully, you can’t.

Raeedah: How do you go about conceptualizing these illustrated pieces and then deciding how to visually and creatively package such data-heavy content. Where do you draw your design principles from and the artistic vision ? How has that evolved over the years?

Adolfo: It varies depending on the subject and project. I start developing my projects by doing a rough sketch using sketch paper, then preparing a final draft. I used this often for the back page and because it was effective in selling the story to editors.

Sometimes time constraints require that you abandon the complicated ideas and use only charts or simpler graphics. You have to try to be smart and do something quick, but still powerful. Sometimes it’s like a lottery. You have some idea in your mind, but the result is absolutely different from what you had in mind.

Marcelo: The ideas come from our group. We always discuss ideas with Darren, what is the relevance of the story that we’re about to tell? But we don’t have unlimited resources. We don’t have satellite images. We don’t have a full network of photographers around the world. So, we need to be smart about what we want to deliver. The other thing is, we try not to follow all the other newsrooms. We’re always trying to go a different way or from a different angle with our subjects.

For example, we are not in a good position to cover the U.S. elections compared to an American newsroom. So we prefer to find a new angle, a counter-intuitive approach. The way that we start to develop a project is always conceptualized through teamwork. When we’re trying to cover a big event like the coronavirus. We always act as a team so we can cover more ground efficiently.

The style that we choose for the illustrations or graphics and the way that the story develops is something that we have to figure out in the moment. We test and make mistakes and try different solutions. The combination of our ingredients — the people in the department—is so diverse that every one of us has different kinds of visual memory, different resources to have an influence. We are always open to new ideas and new expressions. We’re always trying to put the most innovative ways to tell stories at the front. That’s why, in some projects that have been developed with the group, the illustrations or the diagrams to explain something scientific are not in the same style. Sometimes our solutions are more expressive or more consistent with the style of the entire piece that we are developing.

We are trying to put that sort of difference in our assets and in the standalones because there are many hands working. Of course, we try to follow a style. We have a sort of style to apply in all our projects. But, project to project, you can find very different expression colors and ways to explain data.

Darren: As someone who’s sort of in an almost observational position, the way I see the visuals come about, is through the research. I don’t know how unique it is, but basically, we build the graphics and the story from the beginning right through to the very end.

So we will do our own research, our own writing, our own reporting, and our own editing. We’re part of that whole process. Whenever we have a new topic, I see the guys immersing themselves in each topic, just headlong diving in, finding loads of stuff. It’s almost like they’re sort of grabbing things from all over the place and putting it all on the table, then sifting it out. And then gradually removing things too, until they get to the essence. Without wanting to simplify the process too much, it’s almost as if the visuals design themselves through the research. 

To reiterate what Marcelo was saying with the different styles: there’s a strong visual identity with SCMP infographics. But that has more to do with the philosophy behind the graphics, rather than the visual—the handwriting. We have a singular way of thinking about these things and that’s about research and simplifying it so that anyone without expert knowledge can understand what the story is and come away very well-informed.

Raeedah: You’ve spoken about collaboration across your team. How many people are usually involved in each story? What are their roles and how long does it usually take for the team to craft the work?

Adolfo: When we released our COVID project, we finished the standalone in one day. That was because we worked together and we were all focused. It was an incredibly intense day, but we delivered on time. It felt amazing when we finished.

I think there are two kinds of different projects: 1) Those where we all work together, like on COVID, the protests, or when it’s something like breaking news or something for which we need more resources. 2) Other projects that are very personal. Those projects tend to be long-term. You need to do the research, but it’s your own project. Of course, you’ll need help from all the other members of the team, but that’s your project to finish. It’s quite different from working at other places. There’s a more personal credit system. And they can be opinion projects.

Marcelo: Yeah, true. It depends on the subject. As Adolfo said, I remember some months ago, we developed a product about Shenzhen 40 years made by Dennis Wong and myself.

We spent one week on the entire process — from the research to delivery in creating all the assets, and putting them all in the standalone. It took us only one week because the data was available. It was easy to research and the solutions, in terms of interactivity or the assets, were easy to produce. 

 Sketches for “Cantonese performing art” by Marcelo Duhalde, Yan Jing Tian, and Dennis Wong

Some time ago, we had to cover the Thailand cave when the kids were trapped. There, we worked incredibly fast. We organized quickly to deliver in two days, something interesting because in most cases, our standalones can and should have a print version too, for the newspaper. We always try to combine skills to deliver something online and something distinct for print because sometimes the research allows us to have many different pieces and graphics. The processes sometimes depend on the subject and the types of projects. For especially personal projects, the way that we develop them can also occur in between our other daily duties.

Sketches for “How the Thai cave rescue mission unfolded” by SCMP Graphics team, July 8, 2018

Darren: One of the strengths of this team, is its flexibility and its ability to learn on the run .  I’m thinking specifically about how Adolfo just referred to the COVID or coronavirus story, which we put out extremely quickly.

Part of the reason we did that was that we’d learned from the protests. One of the most popular of our protest stories was supposed to be ready in two or three days and ended up taking two or three months because the news kept evolving and we kept playing catch up. There was a new protest and a new news peg every day so we had to keep going on and on.

When COVID first broke out in China we realized “Hey, look, this is going to be the biggest story in the world. We’re living right on top of it and it’s going to keep developing rapidly.” If you remember when it broke in Wuhan, the rest of the world assumed it would be a China issue, at most, an Asia issue. But having lived through SARS, we kind of predicted that it was going to go global. We realized we really needed to get that story out fast, to be the first newsroom to do that.

So learning from the protest story, we focused on one specific angle and got that story up and running so we could then keep re-nosing it over the next two or three weeks. The first iteration was purely about Wuhan, where the virus was discovered and why and how it could spread so quickly. Wuhan being a hub city is connected to the whole of China but also has many international connections with the likes of Hong Kong, Thailand, etc. And then we updated the story over the next few days, adding how the virus spread physically through sneezing or through the air.

So we had to respond and react constantly and keep changing the narrative after it was initially published, which, I think, is something of a breakthrough for us. We know we can do that now. So that adds yet another weapon to our armory.

Raeedah: I know that you talked about collaboration within your six-person team. Do you ever collaborate with other desks outside of Infographics using similar approaches?

Darren: Yes, we do. We do to varying degrees — the issue with that is, other desks tend to come to us with a conclusion and no data to back it up saying, “Okay, we want a visual to explain this.”

Our way of working is, “Well, no, actually we need to get all the information, analyze that, and then come to a conclusion.” So, we’ve had some very successful collaborations, but each time it’s sort of like an educational process with the other desk to explain, “Hey yeah, great idea, but we’re not sure if that’s the correct conclusion. So you need to go back with us through the data.” That can be a little… How to say it diplomatically? It can get a little frustrating for both sides. But as we’re learning and progressing, we hope to do more and more of those types of stories.

I’m thinking about one in particular. Adolfo did a lovely piece about the number of arrests that were made in Hong Kong during the protests. That was done with the City desk. Another one recently with the City desk, was the Hong Kong COVID-19 testing that Kaliz Lee—a newish member of the infographics team—[worked on].

She worked directly with the City desk, who got her the data, and then she interpreted it. We’re collaborating more and more across the newsroom, and I hope it’s something we can continue.

Adolfo: As Darren mentioned, now there are different stories. I don’t think it’s related to SCMP though. All my life I’ve had the same issues that sometimes, a writer or editor comes to you with some idea, some data, or some information and they think that this information or this data is enough to do some important graphic, some important project, but finally, you realize that there is nothing to it. Sometimes there are frustrations, but I think this is more related to the newsrooms, and was more typical years ago. Right now, the editors know the data and the visualizations and the infographics well. They have learned.

And it’s not the same as it was 10 years ago, where sometimes the editors came in with confused ideas that they wanted to do. It’s easier right now. For example, the recent project about COVID with Kaliz or another about the arrested development that I did with the Hong Kong local desk — it was easy and they were useful and cooperated with us. The project was finished in a couple of days, in an easy way, and was wonderfully successful.

Marcelo: For me, it’s easier when the editors announce that they will need a project about something in particular, rather than providing us the data and saying, “Okay, this is the data. You will do something with that.” It’s better when they can say; for example, some years ago, the entire group got into a project called China, 2025 and we split different subjects. In my case, it was 5G and semiconductors. But at that time, I preferred to understand what 5G was first. And they offered help if we needed some other information. So my process was to first read and understand a little how to explain 5G. Then I was able to collaborate with a person from the China desk who provided me with solid information. I was able to ask her precise questions about what I wanted to include in the project. That was a smooth and easy process because we were able to finally show what we wanted to explain.

I think the newsroom now trusts us when they say, “We want something about this and you will be the one who will lead the development and production of this piece,” rather than giving us a summary of what that project should include. In that way, we can feel ownership over the subject, even when the suggestion is coming from other desks.

Raeedah: Yeah, a lot more agency. The last question I have, and hopefully, since our hour is almost up, it’s a lighthearted and fun one! For each of you, what has been your favorite piece to work on so far, and why?

Marcelo: Wow. Well, sometimes my favorite pieces are not the most successful pieces. They’re about feelings, about your own story, or about your life. My favorite pieces are more related to problems that people have to face all the time, every day, or the people who are forgotten. Sometimes, those have not been successful in terms of clicks or visits. One of my favorites, something that I did some time ago, was about refugees during coronavirus—about the asylum seekers and refugees during the pandemic — what is happening to them?

I really enjoyed doing this. I found very interesting data. I prefer complex situations that you can explain visually. I developed an online standalone and double page spread in the newspaper, and I was really happy delivering this kind of subject. Maybe it was not really successful in terms of clicks, but I had a good time developing it.

Adolfo: My favorite piece — I think there are a few—the Kowloon Walled City, but it’s not an online project. It’s an old project, but it was enjoyable. After all these years, some pieces start as my favorite, but then after the years, you change your mind. You notice there is something that is not okay and if you could do it again, maybe you would do it another way. My favorites are Kowloon Walled City and also Safe Skies. That was another back page that had an online version about safety in aviation. I used a full page with a thousand dots to represent the number of flights and the number of accidents. It was prepared like breaking news—in a couple of days.

My favorite pieces are all those projects where I can enjoy drawing. For example, I like a project that I did a few years ago about the bad behavior of football players. This was not related to visual data nor is it considered an infographic. It’s more like storytelling. It’s a companion to a piece about the bad behavior of soccer players and it was done with animation. It’s like a comic. I enjoyed this project because it was my first animation project.

Darren: Yeah, just hearing Adolfo reminded me of a couple of things. The safe skies story, the safest year on record, and Adolfo visualized how few flights ended up in fatal accidents.

There are two things that stick in my memory about this project. When he first thought of his visual solution, it was like that eureka moment. I remember him jumping up out of his desk, “I’ve got it. I’ve got it!” And running around and then doing all these dots, which was quite a magical moment.

And then with the same project, it was really funny. I handed it over to the news editor for the print version to say, “Hey, yep. This is what we’re going with on the back page tomorrow.” And he looked up to me and said, “You’re completely mad. This is crazy. It makes no sense whatsoever. It’s a page of static.”

Then we explained to him what it was and he was like, “Oh, okay. Yeah, yeah, that works.” Then, of course, Adolfo won every award going with that. So, I was able to email that particular editor back and say, “Hey, who’s crazy now?” So that’s one of my favorites, but the stuff I’m proudest of is the protest stuff that we did recently. The reason for that is because it was recognized for its journalism, inasmuch as for the visuals. That makes me extraordinarily proud that we’re now taken seriously as visual journalists and not just designers.

The post Interview with the South China Morning Post Infographics Team appeared first on Nightingale.

Cognitive Load + Data Visualization

Let’s start with a quick primer on cognitive load. The deeper you venture into the dataviz world, the more you’ll hear this term pop up, and for good reason. Cognitive load describes the amount of working memory someone uses when they take in new information and transform it into their long-term memory. In simpler terms, cognitive load helps us consider how easy or difficult it is for someone to make sense of something new. 

Cognitive load has its origins in the psychology of instructional design: think teachers designing lesson plans for students. The concept comprises three different types: intrinsic, germane, and extraneous loads. Yep, we’re throwing around some technical jargon here. But stick with us, because these three types of cognitive load align beautifully with the three components underpinning every data visualization. 

Intrinsic Load  |  The Data

Intrinsic load describes the new information’s inherent complexity. No matter how you slice it, learning calculus is more complex than learning basic addition. As instructors or analysts, intrinsic load is where we tend to have the least amount of control. The information or dataset we’re tasked with explaining comes with all its gnarly complexity (or lack thereof) baked right in. 

Germane Load  |  The Audience

Germane load has to do with how familiar the audience already is with the new information they’re processing. Can they tether that new information to some preexisting framework, or are they starting with a blank slate? The same recipe will look different to a curious novice than to a professional chef. Likewise, the same visualization will look different to an average citizen than to a trained statistician. Here again our control can be relatively limited. Sometimes we pick our audience, but oftentimes we don’t. 

Extraneous Load  |  The Visualization 

Last but not least, extraneous load has to do with how the new information is actually presented. The goal is usually to match the content to the most intuitive presentation possible and therefore minimize the amount of extraneous load. Sure, you could verbally describe an equilateral triangle or the rhythm of a waltz, but it would be faster to just draw the shape or clap the beat. As a teacher or analyst, extraneous load is where we have the most control, and therefore, we should consider it last. That enables us to ask ourselves: “Given the existing intrinsic and germane loads, how much more cognitive load are we comfortable adding to the mix?”

When we add up these three components, the resulting cognitive load might be light, heavy, or somewhere in between. 

simpler information + knowledgeable audience + pared-down delivery = lighter cognitive load

complex information + novice audience + detailed delivery = heavier cognitive load

(See how we just dialed back the extraneous load?)

12 Spectrums: It’s all about creating a good fit 

So with that foundation, our goal becomes accurately assessing and calibrating the cognitive load in our own data visualizations. The following 12 spectrums are designed to help you do just that. Each spectrum below takes a different data visualization component and breaks it down, with lighter cognitive load on the left and heavier cognitive load on the right. The idea here is to take your visualizations and work through each spectrum to tally up its corresponding cognitive load. The answer may surprise you.

Just a quick note before we dive into the details of each spectrum. Remember that there’s no value judgement associated with either side of these spectrums. It’s not inherently right or wrong to strive for a low or high cognitive load. Both simplicity and complexity have an important place in the world. Once we’ve accounted for sound data collection, truth analysis, and accessible design, it all comes down to fit. You could have the most beautiful, information-rich, exploratory visualization, but if your target audience is a C-Suite commuter with 10 seconds and minimal context, you’re out of luck. Conversely, an aggregated dataset made into a succinct bar chart is wasted on a roomful of domain experts eager to dive into the details and gather their own insights. Visualizations are like a puzzle piece that connects your data and your audience: to be successful, your creation must respect the contours that already exist on either side. 

Data Spectrums: How simple or complex is your data?

First up: the dataset. These four spectrums will help you evaluate whether your visualization’s intrinsic cognitive load is light or heavy. While this is typically where we have the least amount of control, a little data cleaning can tip the scales. As you tease apart the characteristics of your data, take note of potential opportunities to lighten some cognitive load by aggregating noisy categories or creating more intuitive calculations, like percentages or indexes. 

1. Measurement (quantitative → qualitative)

Let’s start with that age-old question of making data: can it be counted? Is your data’s measurement quantitative, qualitative, or somewhere in between? Put another way, does the measurement have a predetermined or obvious unit like dollars, points, miles, or milligrams? Or is it squishier, like a rating scale from 1-5 or from “very dissatisfied” to “very satisfied”? Or is it a concept quantifiable only by proxy, like “happiness” or “democracy”?

2. Knowability (certain → uncertain)

Knowability has to do with our level of confidence that our data is true. Some things are easy for humans to know. Some things are downright impossible. Is your data easy or difficult to collect? Could the methodology skew the data? Does the data directly answer the question being asked? Can you reduce the level of uncertainty with more data? Does it come from a contained universe, like the teams in a sports league, or does the data come from an extrapolated sample, like the number of estimated swing voters in a given state? 

3. Specificity (precise → ambiguous)

As humans create data, we inevitably create categories, the specificity of which varies widely. Are your categories clean cut, like the scientific classifications of blood type or species? Or are your categories blurrier, like the socially determined concepts of class, gender, or race? Are the categories fixed or in flux? Would most folks produce the same categories, like political parties, or would the groupings depend on who you asked, like categorizing wealth? Aggregation can introduce ambiguity as well, sometimes as an important tool to anonymize data. Does your data have a high-resolution granularity or a low-resolution aggregation?

4. Relatability (concrete → abstract)

Finally, let’s look at how relatable your data is. Does your data describe something super concrete or does it capture a more abstract concept? Do folks interact with these items or ideas in daily life? Are you dealing with quantities in common amounts, like coffee or rent, or in astronomical sums, like a billionaire’s wealth or GDP? Is your data easy to visualize, like a family of five, or impossible to bring to mind, like the entire population on earth? If your data is leaning toward the abstract, are there analogies you can offer to anchor it to something more familiar? 

Audience Spectrums: How much bandwidth does your audience have?

Next up: the audience. Picture the folks who will actually consume your visualization, whether they’re a group of three stakeholders or a conference session of 3,000. As you work through the next four spectrums, remember: you are not your audience. That’s the biggest blunder we make as analysts, and we make it all the time. You and your audience will—and should—land in different places on these spectrums. Return to the cognitive load framework: your audience is always lugging around way more germane load than you are. After all, you’re the one knee-deep in the data, slogging through the details, actually building the visualization. That process matters. It’s why consuming your own work is infinitely easier than consuming anyone else’s.

5. Connection (intentional → coincidental)

Let’s start with first impressions. How will your audience end up looking at your visualization? Did they seek it out? Did they subscribe to your newsletter, get a ticket for your talk, search for your work by name? Or did they stumble across your piece? Did your visualization pop up in their social media feed or happen to be in the waiting room’s magazine? Keep in mind that the circumstance of the connection will often affect the audience’s headspace. Are they feeling interested, engaged, supportive? Or are they feeling ambivalent, frustrated, or skeptical? 

6. Pace (slow → fast)

Ok. Now you’ve captured your audience’s attention. How long do you have it? Three seconds as they scroll through a morning news brief? Five minutes on the board meeting’s agenda? Or is your intended audience settling in for the next hour with a fresh cup of coffee and your annual report? Are you personally dictating the pace by leading a live presentation? Or will the audience skim through your work independently and close the browser the moment their interest wanes? Get honest here. We all want folks to take more time to engage with our work than they typically do.

7. Knowledge (expert → novice)

Now let’s evaluate your audience’s content knowledge. Are folks super familiar with the data? Did they help generate the data themselves, like sales reps examining their own progress? Or should you assume your audience has no familiarity with the data whatsoever, like a general audience walking in for a TED Talk or opening the morning paper? Or perhaps your audience’s knowledge base lies somewhere in between. They’ve got a basic foundation to build upon, but will need you to provide some additional scaffolding if you want them to follow you to that “ah-ha” finale. 

8. Confidence (confident → anxious) 

Finally, set aside the data and consider your visualization’s format. How much experience, if any, does your audience have consuming new information through this medium? Has your audience been primed on how to get the most out of this type of interactive dashboard, longform essay, or video format? Or will they need to learn how to engage from scratch? If you’ve got an audience full of format newcomers—maybe even visualization newcomers—take the time to show them how to learn, not just what to learn. Otherwise frustration or embarrassment will take root and your audience will tune out before you’ve communicated a thing.

Visualization Spectrums: What visualization best connects your audience to your data?

At long last, it’s (finally!) time to create some visualizations. You’ve taken stock of the data’s inherent complexity and your audience’s level of preparedness. With a sense of how light or heavy the combined intrinsic and germane cognitive loads are, you can start making some informed design decisions. Do you have the breathing room to lean into a more complex visualization and dial up the extraneous cognitive load? Or is your audience already facing an uphill battle with a complex dataset, and your visualization needs to keep any extraneous load to a minimum? These final four spectrums will guide you towards a presentation that enables your audience to best understand your data.

9. Chart Type (common → rare)

Let’s start with the chart type itself. Is your audience used to reading this kind of chart? Does it have standard x- and y-axes, like a bar chart or a line chart? Is it a map of a familiar place at a familiar scale? Or is the chart type more obscure? If you handed the chart to a colleague, would you feel the need to preface it with a quick verbal explanation? Are the axes unusual or missing altogether? Count up how many variables are encoded in your visualization. Will the audience understand the chart immediately, or will they have to slow down and build the chart’s logic from scratch? 

10. Interpretation (accurate → approximate)

Humans are adept at reading some types of visualizations, and lousy at interpreting others. Is it important for your audience to take away exact values, or will a general understanding of basic ratios or relationships suffice? Are you asking your audience to compare highly legible components like length or position? Or are you asking your audience to draw an approximate interpretation like angle or area comparisons? Accurate color differentiation tends to be more limited than we often assume. Does your visualization leverage just two or three colors or will your audience need to reference an extensive color legend?

11. Composition (concise → detailed)

Next evaluate the overarching composition of your visualization. (We’re already assuming good foundational design here. The chart junk is gone, the data to ink ratio is up, etc.). How much information is on the page? Is your audience looking at a few aggregated bars with the key takeaways baked into the title? Or are they considering dozens of individual points in a scatterplot, hovering over each one for additional information? Are you providing the context for the visualization outside of the graph itself, through a presentation’s previous slides or an article’s surrounding paragraphs? Or is the visualization packaged up with its own contextual annotations that will need to be read?

12. Delivery (explanatory → exploratory)

Lastly, consider the final delivery of your visualization. Are you taking an explanatory or exploratory approach? Put differently, who’s in the driver’s seat: you or your audience? Will you walk the audience through your argument on a curated path? Or have you built an interactive environment in which the audience is expected to explore and make discoveries for themselves? Are you a domain expert who’s already analyzed the data and now wants to present your findings? Or are you a visualization expert democratizing a previously inaccessible dataset with a new approachable format?

Cognitive Load as a Guide: Immediacy Isn’t Everything

Our conversations around data visualization are heavily influenced by society’s broader pendulum swing towards immediacy. And given today’s onslaught of information and pervasive sense of overwhelm, who could really blame us? In fact, data visualization’s unique ability to expedite insights is one of its greatest strengths. But when we talk about cognitive load in data visualizations, the message is often a one-note insistence that cognitive load is fundamentally bad. We seem to have imported the term from psychology, but flattened its complexity along the way. By presenting these twelve spectrums, our hope is to offer a more nuanced perspective. We see cognitive load as an excellent guide: it can lead you to the visualization that will enable this particular audience to understand that particular dataset. 

No visualization should have a heavy cognitive load because it’s poorly made. But plenty of well-made visualizations carry a heavier cognitive load. Just take a look at the visual essays by The Pudding or the portfolio of information designer Giorgia Lupi. These works aren’t instantaneously absorbed, nor are they intended to be. They remind us that many things worth knowing take time to unpack and a bit of elbow grease to understand. That’s not inherently a bad thing. In fact, if we slow down long enough to engage, we’re often reminded how much this human brain of ours enjoys untangling the world’s complexity. 

We’ve created a summary print out you can download here.

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After discovering there were over 500 designer breeds (all wonderful-sounding tongue twisters like Shelestie, Bolosilk, Havamalt, or Pookimo), our intrepid author and researcher Emily Barone wrestled with the combinations and organized them beautifully into a spreadsheet that I could work with and visualize, listed in columns by Mate 1, Mate 2, and New Breed. I wanted to create density so I silhouetted the pups, arranged in a circle by breed group, and plotted 90.

Drawing the lines from mate to new breed was challenging, but I wanted them to be organic and flowing, not rigid or clinical with right-angled pointers.

Later we added a few celebrity hits and factoids to fill it out more, and we referred to it as the big “fur ball!”

Different treatments:

We developed several treatments. The most successful version was the big poster-like one (the lead image) as it has multiple entry points, holds up as a single visualization, and shows how varied and broad the breed combinations can get. 

On the phone users tapped a group of dogs, then scrolled through the breeds as the variations lit up.

I didn’t produce this animation, but this was another way to show the visualization:

This work also appeared in the volume ‘Visual Storytelling: Infographic Design in News’, edited by Liu Yikun and Dong Zhao, published in Australia in 2015 by The Images Publishing Group Pty Ltd.

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Translation by Vivan Andreozzi

É fácil apontar o dedo para os clientes quando falhamos como designers. Preferimos pensar que a culpa é deles se nossas ideias brilhantes nunca chegam a ver a luz do dia devido à sua falta de sensibilidade e visão. Mas fazer com que o design seja aprovado pelo cliente também é uma habilidade essencial que nós, profissionais do design, devemos ter. A responsabilidade também é nossa se somos incapazes de articular nossas ideias de maneira convincente. Aqui estão algumas sugestões que utilizamos (ou pretendemos utilizar) na Voilà: para aumentar as chances das nossas ideias serem aprovadas pelos nossos clientes.

Tenha boas ideias

Comece sendo bom no que você faz. O design é tanto uma arte como uma ciência. Aprenda a ciência, e desenvolva a arte. Se você quer culpar os clientes por terem preferências arbitrárias, então você tem que desenvolver as suas próprias preferências. Suas ideias devem ser autônomas e ter uma certa qualidade objetiva, independente do seu estilo.

Aprenda os termos

Saber quais são os termos utilizados na sua área de atuação e se expressar com clareza são sinais de profissionalismo que ajudam a construir credibilidade. Isto mostra aos seus clientes que você sabe do que está falando, o que os torna mais propensos a respeitar a sua expertise. Este cuidado é especialmente importante se, assim como eu, você é um designer que veio de uma outra disciplina, o que é bem comum na área do design de informação.

Desenvolva o seu estilo

A grande realização do esforço de um designer é ser procurado pelo seu estilo. Pense num ilustrador. Se o cliente embarcar no seu estilo antes mesmo de te contratar, será muito mais fácil convencê-lo a aprovar a sua proposta. Mas não é fácil definir um estilo próprio, ater-se a ele, e ser reconhecido por ele.

Apareça quando ele mais precisar

O contexto do cliente irá influenciar o quão aberto ele estará a novas ideias. Será mais fácil convencê-lo se ele estiver insatisfeito com a situação atual e estiver aberto a mudanças. A imagem que ele fará de você pode ser afetada por experiências recentes com outros designers, quer tenham sido positivas ou negativas. Preste atenção a estes sinais nas trocas que tiver com ele e ajuste a proposta e as interações de acordo com isso

Clarifique as expectativas do cliente

O design também é uma questão de opinião. É importante entender a perspectiva do seu cliente antes de começar a trabalhar no design. Designs diferentes podem ser igualmente bons, mas podem não agradar ao mesmo público. Na Voilà:, usamos moodboards para que o cliente indique aquilo que mais gosta. Também pedimos que ele traga exemplos de outros projetos que goste.

Apresente a sua proposta

Quando a proposta estiver pronta, agende uma reunião com o cliente e chegue preparado com a sua apresentação. Apresente um a um os seus argumentos e só depois mostre o design, que deve estar alinhado com a sua visão. Use o tom e ritmo para demonstrar conhecimento e convicção. Para apresentar uma proposta de design para um relatório, normalmente utilizamos de 20 a 30 slides. Apresente tudo antes de abrir para comentários/feedback. Dica: dê alguns segundos para que o seu cliente possa absorver todo o conceito depois de mostrar o design.

Liste as restrições

Todo projeto de design tem as suas restrições, e ainda bem. Antes de desenvolver a sua proposta, faça perguntas para esclarecer qual a mensagem desejada pelo cliente, diretrizes visuais, temas a evitar, imagens existentes, etc. Quando for apresentar a proposta, lembre o seu cliente destas restrições antes da grande revelação. Este lembrete vai ajudar a restringir o âmbito das possibilidades, assim como as sugestões que o cliente possa fazer para levar o projeto para um caminho diferente. Também divide a responsabilidade das decisões criativas com o cliente.

Faça o seu design brilhar

A forma como você apresenta as suas ideias é muito importante. Apresente os seus designs como você o faria em um portfólio. Pense em roupas expostas numa loja chique, em peças de arte exibidas num museu, na apresentação de pratos num restaurante de luxo. Nós, por exemplo, usamos mockups que dão vida ao design e os fazem parecer bem acabados e desenvolvidos. Estes mockups também dão uma ideia melhor de como será o produto para o usuário final.

Escolha as suas batalhas

O seu design pode ter aspectos básicos que o fazem funcionar, que serão claros para você como profissional de design. Mantenha-se fiel a estes aspectos, e não se apegue a aspectos menores e de menor importância. Isto demonstrará flexibilidade e convencerá o seu cliente de que você não é teimoso e sensível à críticas, mas consciente e de cabeça aberta. Chegue no momento da apresentação sabendo o que é importante e o que não é.

Aceite (algum dos) comentários do seu cliente

O cliente pode nem sempre estar certo, mas às vezes pode estar sim. Sua primeira reação a um feedback pode ser defensiva, dado o cuidado que você investiu na proposta e o tempo e energia despendidos. Mas se você refletir um pouco, pode encontrar algum valor no feedback do seu cliente. Pode até se inspirar a fazer ainda melhor. Se você não puder ajustar na hora, dedique algum tempo para ponderar sobre como atender àquela solicitação e ainda assim entregar um trabalho de qualidade.

Como designers, devemos levar em conta a experiência do público. Isto deve se estender às nossas próprias interações com os clientes, que devem estar pensadas de forma a atingir os objetivos deles da melhor maneira possível. Afinal de contas, os clientes também são um uma espécie de público.

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Les clients sont un bouc émissaire facile pour nos échecs en tant que designers. Nous aimons penser que c’est leur manque de vision et de goût qui fait que nos idées de génie ne se rendent pas en production. Mais faire accepter un bon design par un client est une compétence importante pour les designers. Nous partageons le blâme en ne sachant pas articuler nos idées de manière convaincante. Voici donc quelques moyens que nous utilisons (ou auxquels nous aspirons) chez Voilà: pour augmenter les chances que nos clients acceptent nos suggestions.

Ayez de bonnes idées

Commencez par devenir bon dans votre travail. Le design est à la fois un art et une science. Apprenez à connaître la science et développez l’art. Pour pouvoir blâmer les goûts douteux des clients, il faut avoir développé les vôtres. Vos idées doivent également être autoportantes et avoir une certaine qualité objective, quel que soit leur style.

Apprenez les mots

Connaître le vocabulaire de son métier et s’exprimer avec précision sont des signes de professionnalisme qui contribuent à renforcer sa crédibilité. Ils montrent aux clients que vous savez de quoi vous parlez, les rendant d’autant plus susceptibles de respecter votre expertise. Cet effort est particulièrement important si, comme moi, vous êtes venu au design à partir d’une autre discipline, ce qui est assez courant dans le design d’information.

Développez votre style

Le couronnement pour un.e designer est d’être recherché.e pour son style. Pensez à un.e portraitiste. Si le client adhère à votre style avant même de vous donner du travail, il sera beaucoup plus facile de le convaincre d’accepter votre proposition. Mais il n’est pas facile de choisir un style, de s’y tenir et de le faire reconnaître.

Arrivez au bon moment

Le contexte du client influencera son ouverture aux idées nouvelles. Ce sera plus facile pour vous si le client est insatisfait de sa situation actuelle et prêt pour un changement. Leur perception de vous peut aussi être entachée par leurs expériences récentes avec d’autres designers, positives ou négatives. Portez attention aux signaux dans vos échanges avec eux et ajustez votre proposition et vos interactions en conséquence.

Clarifiez les attentes du client

Le design est aussi une question d’opinions. Il est important de comprendre les goûts de votre client avant de commencer à concevoir. Des designs complètement différents peuvent être tout aussi bons, mais ils ne plairont pas aux mêmes publics. Chez Voilà:, nous faisons choisir aux clients ce qu’ils aiment sur des panneaux d’ambiance. Nous leur demandons également des exemples de projets qui leur plaisent.

Présentez vos concepts

Une fois votre proposition prête, organisez une rencontre avec le client et arrivez avec votre présentation. Construisez votre argumentaire pièce par pièce puis révélez votre design, en accord avec votre vision. Utilisez le ton et le rythme pour montrer votre expertise et votre confiance en votre proposition. Pour présenter un design pour un rapport, nous pouvons utiliser de 20 à 30 diapositives. Présentez tout avant de recevoir des commentaires. Petit truc : après avoir révélé votre concept, accordez quelques secondes de silence au client pour qu’il l’assimile.

Lister vos contraintes

Chaque mandat de design vient avec des contraintes, Dieu merci. Avant de développer votre proposition, vous devez poser des questions pour clarifier le message du client, les normes visuelles, les thèmes à éviter, l’imagerie existante, etc. Au moment de présenter votre concept, rappelez ces contraintes à votre client avant la grande révélation. Ce rappel aidera à réduire le champ des possibilités, ainsi que les suggestions que le client pourrait faire pour amener le projet dans une direction différente. Cette approche partage également la responsabilité des décisions créatives avec le client.

Pleins feux sur votre design

La façon dont vous présentez vos idées est importante. Présentez vos designs comme vous le feriez dans un portfolio. Pensez à des expositions de vêtements dans une boutique chic, à des œuvres d’art dans un musée, à des repas dans un restaurant chic. Par exemple, nous utilisons des maquettes qui donnent vie au design et lui donnent un aspect poli, développé. Ces maquettes donnent également une meilleure idée de ce à quoi ressemblera le produit final.

Choisissez vos combats

Votre design peut avoir des aspects clés qui le font cliquer et vous les reconnaîtrez en tant que designer. Défendez-les becs et ongles mais acceptez de changer des aspects mineurs et moins importants. Cela démontrera de la flexibilité et convaincra votre client que vous n’êtes pas têtu.e et susceptible, mais bien informé.e et ouvert.e d’esprit. Arrivez à la rencontre sachant ce qui est important et ce qui ne l’est pas.

Acceptez certains commentaires de vos clients

Le client n’a peut-être pas toujours raison, mais parfois oui. Votre première réaction aux commentaires peut être défensive, comme vous avez réfléchi à votre proposition et y avez consacré temps et énergie. Mais avec un temps de réflexion, vous verrez peut-être la valeur des commentaires de votre client. Cela peut vous inciter à faire encore mieux. Si vous ne pouvez pas vous adapter en direct, prenez le temps par la suite de réfléchir à la manière dont vous pouvez répondre à leurs demandes tout en fournissant un travail de haute qualité.

En tant que designers, nous sommes censés prendre en considération l’expérience du public. Cela s’applique à nos propres interactions avec les clients, qui devraient être conçues de manière à atteindre au mieux leurs objectifs. Les clients sont un public, après tout.

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Clients are an easy target to blame for our failings as designers. We like to think that it is their fault if our brilliant ideas never make it to production due to their lack of taste and vision. But getting a good design through a client is also an important skill for designers. We share the blame if we are unable to articulate our ideas convincingly. Here are a few ways that we use (or aspire to) at Voilà: to increase the chances that our clients accept our ideas.

Have good ideas

Start by getting good at your job. Design is both an art and a science. Get to know the science, and develop the art. If you want to blame clients for having random tastes, then you have to develop your own. Your ideas should also stand on their own and have a certain objective quality, regardless of their style.

Learn the words

Knowing the vocabulary of your trade and expressing yourself with precision are signs of professionalism that help build credibility. They show clients that you know what you are talking about, and make clients more likely to respect your expertise. This effort is especially important if, like me, you came to design from another discipline, something that is fairly common in information design.

Develop your style

The crowning achievement of a designer is to be sought after for their style. Think of a portraitist. If the client buys into your style before they even commission your work, it will be much easier to convince them to accept your proposal. But it is not easy to pick a style, stick to it, and find recognition for it.  

Come at a moment of need

The client’s context will influence how open they are to external ideas. It will help if they are at a point where they are dissatisfied with their current situation and ready for a change. Their perception of you might be tainted by other recent experiences with designers, positive or negative. Pick up on those cues in your exchanges with them and adjust your proposal and interactions accordingly.

Clarify the expectations of the client

Design is also a matter of opinions. It is important to understand the perspective of your clients before you start designing. Opposite designs can be equally good, but they may not please the same audience. At Voilà:, we use mood boards where clients have to choose what they like. We also ask them for examples of projects that please them.

Pitch your concepts

Once your proposal is ready, organize a meeting with the client and come prepared with your pitch deck. Build your argument piece by piece and then reveal your design, in line with your vision. Use tone and pace to show expertise and confidence. To present a design proposal for a report, we may take 20 to 30 slides. Present everything before you take feedback. Tip: give a few seconds of silence to the client to take in your concept after you reveal it.

List your constraints

Every design job comes with constraints, thank God. Before developing your proposal, you should ask questions to clarify the client’s desired message, visual guidelines, themes to avoid, existing imagery, etc. When you present your concept, remind your client of these constraints before the great reveal. That reminder will help narrow the realm of possibilities, as well as suggestions that might take the project in a different direction. It also shares the responsibility of the creative decisions with the client.

Make it shine

The way you present your ideas matters. Show your designs like you would in a portfolio. Think of clothing displays in a fancy boutique, of art pieces in a museum, of meals in an upscale restaurant. For instance, we use mockups that bring the design to life and make them look polished, developed. These mockups also give a better idea of what a final product will look like for the end user.

Pick your battles

Your design may have key aspects that make it click, and they will be clear to you as a design professional. Stick to your guns on these, and let go of minor, less important, aspects. This will show flexibility and convince your client that you are not stubborn and thin-skinned, but knowledgeable and open-minded. Come to the pitch with the knowledge of what is important and what is not.

Accept (some of) your client’s feedback

The client may not always be right, but sometimes they may well be. Your first reaction to feedback may be defensive, given how thoughtful you have been with your proposal and how much time and energy you have put into it. But given some time to reflect on it, you may see the value in your client’s feedback. It may inspire you to do even better. If you cannot adjust on the spot, take time afterwards to consider how you can meet their requests and still deliver high quality work.

As designers, we are meant to take into account the experience of the audience. This should extend to our own interactions with clients, which should be designed in a way that best achieves their goals. Clients are an audience, after all.

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