Tag Archives: data visuailizations

How to present numbers involved in people tragedies?

Every day, we have been bombarded with news about people cruelty toward other people or animals and natural and unnatural disasters that result in many deaths. It is now even doubled because of COVID-19 and its death toll. You could say that there is nothing spectacular in it. From the first time man set foot on Earth: Famine, Plague and War are our inseparable companions, and in the era when we plan to conquer the Universe, they are still not defeated.

However, most of these terrifying scenes are somewhere long distance from our safe and cosy homes. In addition, we are overwhelmed by violence presented in mass media. That gives us the impression that those situations are unrealistic and abstract. We hardly attach them to real people, victims and it is going to be even worse as we learnt from the latest studies about decreasing empathy.

For instance, I experience the same feeling of indifference when looking at COVID-19 statistics. These are ONLY numbers. Dehumanized numbers like production series or kilometres run in your tracking app. And that scares me a lot.

A situation when people (or any other living creature) are presented as a sequence of numbers scares me. When we use some abstract forms to identify persons, there is a danger that we will perceive them as objects and not as subjects. I witnessed behaviours involving the use of employee numbers in internal communication and it was a part of the culture. For me, this approach detached living people from their formal functions and roles. Roles become impersonal. There are no people, there are only cogs in the machine or resources to use and to get rid of when used.

So how to present numbers and communicate real people tragedies?

Language

Another thing is the language used to describe victims. Many times, the word “case”, “deaths” or “fatal accident” replaces words “wounded people”, “died people”, “victims”. Especially in medical statistics like presented in Figure 1 number of people who died and recovered from COVID-19 (statistics for a particular point in time).

Using abstract forms do not help in building vivid pictures in the mind of our audience of happy people, who recovered from the awful disease and went back to their families or plunged in grief over the loss of their loved ones. And this is what we would like to achieve – move their imagination to evoke their feelings.

Which of those subtitles in Figure 1 are more dramatic?

Figure 1

Numbers

People, in general, have problems with understanding big numbers, statistics and abstract visual forms presenting the information. The numbers in Figure 1 are so enormous that is hard to imagine them. To convey information effectively we must downsize it and chunk to the well-known, familiar, and easy to interpret elements.

In Figure 2 we can see the percentage of how many people died vs recovered from COVID -19. I used the abstract visual form to present information – pie chart and impersonal, medical description – death rate, closed cases. Nothing about victims.

Figure 2

How can we interpret this picture? If we are good at maths and understand the concept behind percentages, we can have the impression that 2% is a quite low chance to die of COVID-19 and there is no big deal (I won’t vaccinate myself! It’s a mystification to implant a chip on me!). And again, using the word doesn’t help us understand a real, current threat. “Death” for most of us is a metaphysical conception that lies somewhere in the far distant future.

Iconographic

To downsize information and present it in a more readable format, we can use graphical representation, small objects that symbolize humans. This approach lets an audience understand the range of coronavirus death toll because the big number was chunked into small pieces (1 out of 50). Number 50 is much closer to our imagination than 5 613 594. Using human symbols I emphasized that numbers are related to people.

Do you feel now more or less certain that COVID-19 is not a big deal?

Figure 3

Time

We can use the time to strengthen our message significantly when we embedded our audience into the present moment and convert statistics into occurrences. With this tactic, we can easily emphasise how human life is fragile because when you are reading this text every nine-second someone passes away because of the corona virus (again I used a 2% death rate). You can use animated gifs to be more dramatic.

How do you feel now with this knowledge?

Figure 4

I do not say that standard data visualisations are bad, and we should not use numbers or statistics. I just want to challenge anyone who communicates information to a wide audience to tailor better channels to make sure that a message gets properly understood, and people will start looking again at those who suffer… with appropriate respect.

Map your maps.

During the holidays season, I’m having more time to catch up watching movies. On that long list a film “Another round” can be found. In a nutshell, the plot is about four friends and their unexpected alcohol experiment. Everything is done in the spirit of science, of course. In truth, this dark comedy-drama touches on a very sensitive social problem that affects many people around the world.

I’m wondering how Poland looks compared to other European countries and if Poles on average drink more or less in comparison to Danes? According to WHO (World Health Organization) data from 2018 average Pole drinks 11.71 pure alcohol and Dane 10.26 (15+ years). The difference is 1.45. Is Poland near or far from Denmark? Depending on the colour palette and applied scale we can perceive it differently, and consequently, convey different stories or draw misleading conclusions.

5 stepped colour

I used Tableau Public to visualize data. This visualization is automatically chosen by Tableau. According to the visualization, Poles are not in the lead for European countries and Danes are somewhere in the middle of the scale.

3 stepped colour

But wait a minute. What a shame! Poles are heavy drinkers. Now I can see it clearly.

7 stepped colour

OMG… how much beer average Czech had to drink to win this competition? When it comes to Poland, it is not so bad. Poland is near the middle of the range.

Reversed 3 stepped colour

Hm… I’m a little bit confused. I have the impression that Poles don’t avoid occasions to celebrate the fragility of life, but now I can see is opposite. (Who would check legend description? Waist of time, data visualizations are intuitive!)

Attention: Remember in our culture stronger colour saturation means increased occurrence of the phenomenon.

As we can see, each of the four above examples depicts the same information differently, and that difference can be significant.

Maps are commonly used in public media and people like them. The same is in the business world. However, knowing it from experience, it is very easy to manipulate information presented on maps. Before you publish or share your map ask yourself:

  • Does scale represent the statistic bins,
  • Are colours adjusted to the topic,
  • Is reverse scale justified?

Data source: https://data.worldbank.org/indicator/SH.ALC.PCAP.MA.LI?view=map

Mind the Gap! – How visualizing missing data influences people’s trust in data quality and affects decision-making processes.

The ethical approach to data visualization has many faces. One of them is dealing with missing data and the way of communicating them to the audience. In the real world, we face situations that our databases are incomplete.  This is a common case of many reasons. Some are technical errors that can occur during ETL processes, others appear when data is collected manually, especially as a result of surveys, as people often fail to answer all questions.

Statistical procedures often eliminate entire records when only one variable is missing. This leads to a dramatic shortage of statistical samples. However, many times, even though our data is leaky like Swiss cheese, we have to present them and what is even worse, draw conclusions, because having 100% of data is in many cases ineffective and unrealistic in terms of costs and time.

Statistical approach

To stay honest with our audience and to present the observations or phenomenon to them in the most transparent way, we have only two options: to present gaps in the data or imputed data in place of missing data. There are several imputation methods widely used in statistics and statistic data modelling. The most common ones are:

  • Case deletion – omitting cases with incomplete data and not take them to analysis.
  • Zero-filling – imputation of value 0 for all missing data.
  • Linear interpolation – replacing missing data with estimated values.
  • Marginal means – the mean value of variable is used instead of missing one.

More explanations of the specific methods you can find here.

Nevertheless, what method we are going to use, we need to communicate to the audience about which data comes from observations and which ones are imputed. This communication should be given in voice and visual form to strengthen the message leave no room for presumptions.

Dilemma – show gaps or imputed data?

Many strategic decisions are data-driven and missing data impacts the overall understanding, interpretation and reasoning of a phenomenon if not properly addressed.

Recently I found interesting research by Hayeong Song and Danielle Albers Szafir that shed some light on how we visually communicate missing data, which has a significant influence on data quality perception and on confidence in drawing conclusions. Research emphasizes that visualizations that highlight missing data but do not break visual continuity are perceived by responders as those with higher data quality. The general conclusion is that imputation methods are better graphical choices than simply removal of information as they do not decrease perceived data quality as much that have consequences in the decision-making process. However, the very important aspect is to highlight imputed data by different shapes or colours. Another interesting graphical decision is to present imputed data as error bars. It gives our audience additional information about the likely range of values.

source

The research results in Figure 5 (b) clearly show that linear interpolation has the greatest positive impact on the perceived quality and accuracy of the data, and the visualization with data absent (Figure 4 (a)) is the lowest.

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The research was carried out for two commonly known visualization: a line chart and a bar chart. Both graphical choices gave similar outcomes.

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Conclusion

I have several books which are like a shining star that guides me through the darkness. One of them is “The Little Prince” Antoine de Saint-Exupery and quote from that book: “You become responsible, forever, for what you have tamed”. I believe we should have exactly the same approach to our analyses and their graphical representations as data analysts or data storytellers.

My personal attitude towards data – ethics in data storytelling.

On September 26, 1983, in the middle of the Cold war, Russian lieutenant Stanislav Petrov was on duty at the command centre of the nuclear early-warning system. The system reported that six missiles were fired from the US toward the ZSSR. Petrov based on provided information had to decide whether the alarm was true or false and to obey or not obey orders. After countless minutes that seemed to be an eternity, Petrov judged that it was a false alarm and saved the world against third war – the nuclear for sure. Later, the investigation revealed that the system malfunctioned.

But what kind of the world could we live in now if Petrov had not considered other options of the system’s response? Having that historical event in mind, can we trust any information without a doubt?

As data analysts or data storytellers, we are like a nuclear early-warning system. We provide people with the information they need to make critical decisions and shape the future. It is a very responsible role.

Why is so hard not to lie with data?

Does it sound controversial?  I believe so. Does it sound realistic? For sure. Why do I think so? Are you confident that you know all aspects of a subject that you want to present to others? Have you considered all possible options and looked at them from all involved stakeholders’ perspectives? Are you sure that the data set time period is long enough, and data quality is high? There are more questions than answers. So, tell me which version of the truth you are holding in your visualizations?

I do not accuse anybody to mislead people on purpose. Most of the time when we prepare data analysis and data visualizations to communicate information, we have pure intentions. The case is that we hold some biases and believes, and our brain uses previous experiences, and constantly makes unconscious assumptions. All that influences our thoughts and perception.

Harmful data visualization

Let’s do the mental exercise and think together about how harmful data visualization can be. Currently, I’m reading an exciting book by one of the most recognizable authors of the information visualization domain Alberto Cairo “How charts lie”. In one of the chapters, there is a story about nationalist Dylann Roof, who killed several Afro-Americans by being influenced by some charts that presented a number of crimes vs ethnic roots. That shocked me and opened my eyes to the potential consequences of distributing misleading visual representations of data.

That warning is more for data journalists and other people who juggle with data publicly. Often to get more votes or support or to influence some kind of the audience line of thinking. However, even in the business environment, we must be cautious not to make the same mistakes, because results can be catastrophic and have a real impact on people. Nevertheless, all of us should remember that when we share any data on social media or on other web pages.  

The potential negative impact of wrongly done analysis and poor data visualizations:

  • Hundreds of people can lose their job,
  • Profitable business sector can be shut down,
  • Launch of a new product can miss the target,
  • Thousands or billions of people can be at threat because of the release of the new drug.

This vulnerability is real because people who make decisions make history. There is always a human factor in any success or failure.

Do you feel like an influencer?

Some time ago I had a lot of fun preparing and sharing data visualization. But currently, I’m not so eager to do that. I didn’t have enough confidence in the data that are available, and I don’t have enough time to dive into and understand the specific subject, make analyses and investigations.

In upcoming posts, I’ll focus on ethics from a data visualizations point of view. The first one is data range.

Data range

Insights could differ very much in case of changing data scope. Anyone who has some shares on the stock market knows that depending on the selected time range he or she can observe positive or negative trends. The same cognitive dissonance we can have presenting data within our organization. Maybe in the last two years, we achieved tremendous revenue growth, but looking at revenue from a longer perspective, it can turn out that we even got closer to the results from the financial crisis (pick your favourite one as an example, they come and go periodically).

Figure 1 depicts what kind of understanding and feeling the investor can have to look at the same data but from different ranges. The left chart can indicate that results are declining, but when we look at the right one, we can see that in the longer perspective trend is positive.

Figure 1

Of course, our narration can be built around the latest two years of growth, but we shouldn’t hide information from the bigger picture. The approach in such a case should be to display the bigger picture first – a longer period of data is displayed and then zoom in on the last two years to present factors of recent revenue growth.

Another example, which is notoriously used to present voting results, is presenting people support for particular parties but having only people who voted as the full population. When I listen to the news in the mass media, often people refer to the election results without considering the voter turnout. That narration skews reality. Let’s see the below example. Figure 2 shows the result of the latest presidential elections in Poland. What will most people remember from the chart? That Duda won and had more than 50% of public support.

Figure 2

But this is not true! The real public support for Duda was 34.49% if we consider the voter turnout. The voter turnout in this election was 68.18%. It means that 31.82% of Poles didn’t go on the election. I would love to see in the mass media charts which present the entire election results, including those who didn’t vote. Then we would have the complete picture of people’s political preferences. However, I still see truncated data scope.

Figure 3

By manipulating data range as a timeline or included/excluded categories, we tell different stories about data and evoke different understandings and feelings in our audience about the subject. Let’s remember that to not lose in translation the most objective view possible.

How to better design dashboards and reports. Data Storytelling in BI products design.

Not everyone has an opportunity to be on the first line and present data in front of the audience. Many are silent data heroes at the back of the stage. They constantly work with data to make sense of them and pass it on to others.

I know from my experience that in many organizations people work in silos, and it can be a tangible barrier in delivering well-designed, actionable dashboards. The best option to overcome this phenomenon is to make an effort and find end-users to gather their requirements and tailor reports for their specific needs. Only in this way you can find out what the true story should be built around a particular data set. The rest is a piece of cake.

Nevertheless, if you are one of that data heroes, to be honest, you are the true master here. You decide which data sets will be distributed within your organization and to what extent.  So, you may not be presenting the results in front of the audience, but they are likely seeing them with your eyes.

However, it is a double-edged sword. Having great influence results in having huge responsibility. It is a challenge for every communicator, and you are a kind of communicator because you prepare and hand down information.

I will just present only a few which I find very useful, and I often use them in my work. These technics are easy to remember and easy to implement, so everyone can benefit from them. They have similar usage as linguistic construction which can influence you to buy or do something.

We will go through:

  • Colour
  • Size
  • Shape
  • Common region
  • Position

COLOR

Humans see colours, maybe not in such spectacular range like other animals (check this article about hummingbirds), but still it is one of the most important senses that helps us understand the world and allows us to run away from wild animals in the jungle.

When it comes to designing dashboards, use colours to lead the audience from point to point. It is important to use just several ones. There is a good rule of five. Take five colours, assign to them meaning as for example white – the main colour for background, grey – major of data in data visualization, dark blue – numbers, black – text and icons, and orange – focal points. You can extend orange to orange and green if you want to differentiate positive and negative results.

In such way, you use colours on purpose and teach the audience their role in conveying the message.

To illustrate that we can compare these two pictures. Both charts present the same information – sales of regions. But the chart on the left side doesn’t promote any region. We can see all of them equally. It just aggregates information and presents them on the graph. However, the chart on the right side emphasises one of the regions (yes, that chart is created for the north region manager) by making it orange ( the darkest colour) and the rest regions greyish and tells a story about this specific region performance. The rest of the regions give context to the story.

Due to that simple change, you draw attention to one region and force others to look at it closely with avoiding special interest in other regions.

SHAPE / SIZE

What else you can use to push some information in front of another? Humans can see easily changes in sizes or shapes, so why not to use it for our purpose? Especially when we remember about people who have some colour seeing difficulties. Size and shape are another visual channel which can be used to spotlight some data. Make it bigger, make it stronger.

When we change solid line of North to dashed one and thicken it, our brain processes information even faster than before, because we use three visual channels to code this information: colour, shape, and size.

Even when we take out colour and leave visualization black and white (which sometimes serves the best for better contrast), we can still achieve the same result.

Size cannot be introduced in all visualizations. Would be hard to do it with bar chart. But regarding shape it is much easier. You can use pattern to fill in North bar.

Size is essential for presenting numbers. Differing numbers sizes, we control which of them play the first fiddle and which ones are providing additional information. Shape can be manifested in font type or its boldness. But we must remember here about the parent rule of readability. There is a general rule that on dashboards we use sans serif fonts because they are without any additional decorations and work better for displaying on screens.

Unexpectedly, font types can evoke some emotions or can reflect word meaning in their look. It is especially handy when you are about to design infographics.  See examples.

COMMON REGION

Do you know that people tend to group and interpret objects which are in the close or shared areas? This principle has even its own name as the Law of Common Region and was devised by Gestalt group in 1920s.

I’m a hard user of that techniques when it comes to design dashboards. A single piece of information itself has no impact, however, when you connect a few dots together, the message can be powerful. To make it happen, it is important to create a common area for these elements. We can do this by adding background or border and create visual boundaries.

POSITION

Studies regarding how people view websites, commonly known as Eyetracking, are consistent in results. The area with the greatest attention is the top-left corner of the page follows by the top-right corner, then the down-left and the last one is the down-right corner (see image below).

source

Of course, that we can use it to support data storytelling! Just divide a dashboard area into four quadrants and follow these two simple rules:

  • In 1&2 place information which you want to highlight as KPIs, the crucial changes in trends, threats and opportunities, and components which are essential to navigate on the dashboard.  Do not forget about the title. Use the best practices of designing UX (check this link about best practices in UX and find out what we have in common with goldfish).
  • In 3 & 4 are additional information that broadens perspectives or sheds another light on the already presenting data. At the bottom is the great place to place information about last data refresh, or report confidentiality.

Data storytelling is a mix of knowledge about data visual presentation, design and people perception. Having these components in place you are armed with a very powerful tool, which makes the audience listening to your voice…, even when this voice is behind dashboards that you deliver.

Embrace diversity – how to design data visualizations for people with visual impairments.

Have you ever thought that it is possible to discriminate people through data visualization design? Several years ago, it sounded strange to me too, but indeed, it can be done unconsciously if you are not aware of the topic.

Discrimination is most often associated with skin colour, gender, age, religious beliefs, or nationality. However, this negative social phenomenon can have much broader spectrum. One of them, not at all intuitive, is data visualizations practices. The topic is gaining importance as more and more data is used to explain global processes, and those with difficulties in that area are being left behind. It may not be simple, but the onus is on data community and data visualization practitioners to develop new best practices to communicate data in more democratic way with those with difficulties in this area in mind.

To make data visualization more accessible to a wider audience, three dimensions can be improved: vision, cognitive and learning difficulties, and motor capabilities. The basic, obvious difficulty is related with vision impairments; but the degree of impairment is key. I will not discuss the most severe degree, which is blindness (this is a topic for different post), but I will bring closer the subject of colour-blindness and low vision impairments.

COLOUR BLINDNESS

In data visualization, colour is the most important communication channel. The ability to see and understand the meaning of colours helped our ancestors to survive in deep jungles or on savannas. Colour informed them about non-toxic food or allowed them to spot predators in the forest.

Today, we are still sensitive to colours and these naturals reactions are used in many ways. For instance, most warning signals use red colour, because we naturally associate it with danger or action (red is a colour of the blood)[1]. Studies show that prolonged exposure to the red colour can cause the heart rate to accelerate as a result of activating the “fight or flight” instinct[2]. In opposite, blue colour has a calming effect.

However, not everyone can see colours. Approximately about 10% of human population has trouble seeing colours correctly. If you would like to deepen your knowledge about types of colour-blindness, please check the website. There you can learn about causes of colour-blindness, test yourself, and find a tool to check if prepared visualization is in line with best practices.

There are several basic principles that improve your colour palette and enable visualization for broader audience. To understand them we need to understand two important colour properties:  hue, and saturation. Hue defines colour in terms of pink, blue, yellow, or magenta. Saturation is nothing more than volume of the colour. By juggling these main properties we can improve or worsen results of our work.

RED-GREEN

First of all, stop using red-green palette which is confusing or even unrecognizable to colour-blinded people. This is my humble recommendation. For most people with colour difficulties this red and green colour look the same (see Picture 1).

Picture 1

Most modern data visualization tools, such as Tableau or Power BI already have available colour palettes that handle with the topic. Both mentioned tools have also option to create custom compositions and upload them to the application (custom colour palettes for Tableau and Power BI).

If you are wondering about the right colour palettes, check out the ones presented on Picture 2 and Picture 3. They are nice, clean, and fancy and will work for any reports.

Picture 2 – Vivid & Energetic
Picture 3 – Elegant & Sophisticated

CONFUSING COLOUR PAIRS

Even though we try to avoid the red-green colour range there are still other pairs that resulted in similar way. In recent years I have been observing the dizzying career of the grey-blue duet. I like this combination as well, however, it is essential to match them wisely (see Picture 4).

Picture 4

MONOCHROMATIC SCALE

Sometimes the best option is to simply stick with one colour and play with its saturation to differentiate specific categories or data ranges (see Picture 5). This approach can be used in most visualizations.

Picture 5

More practical colour ranges you can find here, and if you would like to test your composition on specific charts use this website.

SHAPE

Another interesting channel we can use to help visually impaired people easily distinguish between coded data is to assign shapes to different data categories. A good example of how the introduction of shapes can make difference is the well-known RAG.

RAG stands for RED-AMBER-GREEN and is widely used in business environment to communicate performances, risks or statuses of activities. It is most commonly used in project management to report status of tasks, but due to its simplicity, it is also used in data visualization to highlight for instance KPIs (key performance indicators) performance. Red indicates about underperforming, amber that something is an issue and needs to be monitored, and green that is fine.

But as you already know RED-GREEN can be very confusing for colour-blind people. So, my suggestion is to use a shape as another visual communication channel to make sure everyone is on the same page. Instead of format with coloured background, it would be better to introduce icons that have different shape and are coloured in red, amber, or green (see Picture 6).

Picture 6

But what about charts like line chart or bar chart? How can we improve distinction between specific lines or bars? We can use different patterns to distinguish one bar from the rest one or to present several lines on one chart (see Picture 7).

Picture 7

WRITTEN INSIGHTS

Written descriptions, recommendations or insights can be tricky. Especially when you want to use colour names to emphasise certain points, data categories or issues. How someone, who does not see green colour (see Picture 1) can understand a message “All departments represented by green bars have exceeded their sales targets this year”? This message must be rewritten to “Departments A,B, and C have exceeded their sales targets this year” to ensure that all stakeholders understand it.

LOW VISION

In addition to the most recognizable challenge, which is colour blindness in data visualizations design, there is another related to vision loss due to age, accidents, or genetics. For those who suffers from low vision, we must remember that size and contrast of displayed text matters. Especially when we display some materials on screens in conference rooms, but even when you present something via communicators as Teams, or Zoom, size matters. You can read more about the topic here.

SIZE

When it comes to the font size, there is no one good recommendation. It depends on the purpose. If you are going to display materials at a conference in a large conference room, it is better not to use smaller fonts than 18 when describing axes or legend and have less information on the slides. There is nothing wrong in having more readable slides rather than fewer but cluttered.

A different approach can be taken when creating reports. I would say use a font size 9 or 10 for axis or legend description, but in no other case should you go lower than 12. In reports crucial thing is to group information together or to display them in close proximity to make it easier to interpret or make decisions. That is why optimizing space is so important. These screens can always be enlarged, and anyone can take advantage of them.

Picture 8

CONTRAST

The general rule is to maintain high contrast between background and foreground (e.g. white – black, black – white). A typical accessible barrier for people with low contrast sensitivity is grey text or figures on a light background. However, for some people better combination is with lower contrast, because they suffer from the bright background (e.g. they have to change a screen background to the darker to be able read what is on the screen).

As you can see there is no single best answer how to approach this challenge. A good practice is to give people the option to change the display mode from bright (light background and dark foreground) to dark one (dark background and light foreground).

Picture 9

By these small changes, we are bringing better user experience in our organizations or widely, if we prepare data visualizations for the media or other public usage.

[1]https://rochester.edu/news/show.php?id=3856

[2] https://journals.sagepub.com/doi/full/10.1177/2158244014525423

A confusing tram trip in Cracow – How humans read information.

It was several years ago. I was in Cracow and, I made an appointment with my friends in a nice vegetarian restaurant. I took with me my nine-year-old daughter. To get there we took a tram. My daughter was very excited about the event and, as a typical child her age, the minute we entered the tram, she started asking where we were getting off.

Fortunately, we sat down just next to an information board, where all the tram stops were displayed. So, I told her stop’s name, pointed to the board, explained to her how it works and proposed counting the stops on her own. I didn’t pay too much attention to it because Cracow is my hometown, and I knew it wasn’t far.

What a surprise it was to hear: “Mom, we’re on the 12th stop”. Knowing it cannot be right (the right number was 3rd), I asked her to count them again, but the response was the same. This situation repeated a few times till eventually she exploded and yelled at me. I swiftly considered the hypothesis that she must have been swapped in the hospital (obviously my own child would be smart enough to correctly count to 5!) and rejected it. Finally, I looked at the information board, and everything became clear. 

The culprit of this confusion was interpretation of the information board with tram stops. You see, western civilization uses the left-to-right reading pattern, so this reading order seems natural to us[1]. Linguistic and reading patterns affect reasoning of time and space, as well as relations between these two dimensions. My daughter made a subconscious assumption that the tram stops on the board were displayed in the “normal” order. Her assumption was totally right.

However, it displayed stops according to tram moving direction (right to left) but not with alignment of left to right perception of time (unexpecting design choice). Even though it consisted of a direction arrow, names of stops, and a moving ball pointing to the current stop, my daughter’s brain was still searching for the familiar left-to-right pattern.

And that is why her answer was 12th (count from the left side the stop marked with yellow circle!, but the start of the trip is on the right side)


This story is a great example how people consume information embedded in the time and how they expect it to be displayed. It’s worth remembering that, in our culture, information is read from left to right and from top to bottom. When we work on reports, dashboards or any data visuals, the human brain uses built-in patterns, helping to store information and save energy. Following this simple rule significantly improves the user experience. 

Check out my other posts about importance of the time orientation in data visualization:

[1]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3322406/

Circle Charts – when design meets data

  • Circle charts are better to use for entertainment or information purpose. They are not the best choice for a business environment.
  • Circle charts are attractive for receivers and can pull them into your story.
  • Using multilayers demands providing a well-defined legend.

Humans always have had a special attitude to the sun. In prehistoric and ancient ages, in some cultures sun had the status of God. Without any scientific theories, they just knew that the sun is unique and has a crucial role for our planet and any living creature. Even in cultures where ancient humans did not worship the sun, the sun motif was commonly used to decorating buildings, everyday items, or apparel.

Nowadays, we still willingly use the image of the sun, especially in art and architecture. Something is appealing in this figure. Centric circle shape with rays around them somehow reminds me of the wheel of life with rays as special moments.

Maybe that is why the pie chart and all variations of pie charts are so popular and like among people. The father of the most known data visualizations is William Playfair. He invented a pie chart in 1801, and it is still commonly used to depict data.

My personal relationship with a pie chart is …. complicated. I do not use them often in a business environment. It is hard to present accurate data on a pie chart, especially with a good number of categories. When it comes to present information for making decisions it is better to go for more readable visualizations like bar charts (check my post: “PIES ARE FOR EATING NOT FOR DISPLAYING DATA”).

However, a different story is with data journalism, when the purpose is to entertain, or inform the audience. In such case, I would give green light to anybody, who would like to present any complicated data on any variation of a circle chart like a sunburst, radial chart, or spiral chart.

Those charts give you an opportunity to present complex hierarchical information on one chart, so even though there are maybe not idealistically readable, they are still concentrated within one visualization, which is an advantage for the audience. Do not forget that data journalism has a different purpose. The main goal is to pull readers into the story. Surprisingly, more complex visualizations with a huge number of details, colors and shapes can be a better agent than simple one to achieve that mission. It is because readers must spend more time decoding that visualization and retrieve information from it. Another aspect that increases the involvement of readers is chart interactivity. Of course, that case can be applied only on website media.

EXAMPLES

Below infographics are good examples of the complexity vs. the reader engagement. It is hard to understand them at glance. You need to hang your eyes for a longer time and go deeper to acknowledge these images.

The huge advantage is adding other layers or rings to the image. Thanks to that technique additional data are introduced into a chart and we can interpret or read information from different angles or levels. Looking on the same image with several layers of information helps us to find interesting patterns and observation. Would be much harder to achieve that effect when having separate charts.

Global statistics

Our Mother Earth is round at it has a connotation with a round object like a circle. Why not use it to strengthen the message. The chart is combined with several charts placed on circle x-axis: life expectancy and average hours of sunshine is a bar chart. Life satisfaction is a heatmap.

https://www.designboom.com/design/sunshine-and-happiness-infographic/
https://www.visualcapitalist.com/visualizing-all-of-earths-satellites/

Time

The time in western culture is perceived as linear from years perspective. When we present years the line chart or bar chart would be our first choice. However, when it comes to the elements of one year, we perceive them as a cycle. What I definitely admire in circle charts is the possibility to present any periodical phenomenon connected with time:

  • Seasons: Summer, Autumn, Winter, Spring
  • Months
  • Weeks
  • Hours
  • Minutes
https://www.digitalartsonline.co.uk/features/graphic-design/award-winning-infographic-designer-nadieh-bremer-on-how-create-powerful-data-visualisations/

Hierarchical information

Presenting hierarchical data is challenging. However, sunburst charts can handle that. Sunburst charts consist of rings that represent a separate level of hierarchy. This visualization gives us an opportunity to present very complex information in one view.

Note that hierarchical information can be presented as qualitative or quantitative.

The below example presents types of cheese categorize by type of milk and their hardness. This information is qualitative. Another type of visualization that we could use would be a treemap. However, a treemap does not look such good as a circle chart.

https://stackoverflow.com/questions/17069436/hierarchical-multilevel-pie-chart

DOS & DONTS

  • Use colours to catch the attention but remember to choose them in accordance with best practices for colour blindness disabilities. Studies show that around 10% of people population have some disabilities in colours distinguish.
  • Always provide the legend. The legend should explain the meaning of colours, shape, sizes and even positions of objects on your visualization.
  • Add short text on visualisation. If there are points that should be emphasis place additional text with an explanation nearby them. The well-balanced text provides context for a particular point.
  • Plan the objects’ size with available space in mind and readability aspect.
  • Do not use too small fonts.
  • Do not use decorative fonts as they are not readable.
  • Remember about the title and short description of the data visualization.
  • Leave whitespace around the visualization to not clutter the page.

How to speed up information decoding by simple data visualization tricks – the story of one chart.

How many times have you struggled to quickly understand what a chart is presenting? It is something that I often experience in media when reading articles or watch some statistics on TV. Sometimes is extremely hard for me to make sense of what I see, just because I am not the subject matter expert and those data at a glance do not seem familiar. And let face it, I am a data person. What must feel ordinary people, who do not work with data on a daily basis and are not highly data literate?

This post is inspired by data visualisations in the article that I have read recently about the employment situation in the UK. You can find the link to the paper at the bottom of the post.

We are going to focus on three easy to introduce improvements to make any chart more readable, impactful, and thoughtful:

  • Additional Axis labelling
  • Annotation
  • Preattentive Attributes                

As an example, we will improve the below chart that presents changes over years of staff availability index.

Additional Axis Labelling

I am not familiar with the staff availability index. From the title and footer of the chart, I understand that the higher, the better. However, that information could be served on the plate. Based on my experience, I can see an easy fix for such a case that speed up the cognitive work of my brain. Most of the time, when some charts are presented, they present some changes over time or comparisons between two or more phenomena. 

In this case, adding small arrows to the Y-axis and additional words describing axis directions give much more sense to the chart and improve the audience experience. Now the chart presents not only changes over time but informs the expected direction of change.

Annotation

There is a common myth that “Data speaks for itself”. No data can speak because it does not have a tongue. The responsibility of proper understanding of the message lies on the messenger side.  Another quick win is adding more text to the chart itself. Additional description or insight help people to process information more effectively and, thanks to visual presentation, make it easier to remember. 

I have added a sentence from the article next to the point that I have wanted to emphasise. The rich text pays attention to the audience eyes, and the soft grey line directs to the specific point on the chart.

Preattentive attributes

Each object on Earth has properties like shape, colour, size, position. This is what we notice without using conscious effort, and because we do not involve too much conscious effort, we must take advantage of it to decoding information faster. Thanks to them, we can guide the audience eyes through our data visualisation and point them exactly where we want.

Introducing a small red dot is a true game-changer for presenting information on the below chart. We can get this effect by taking off the line chart colour and add to the chart another object with a different shape (circle), size (the circle is significantly thicker than the line), and by adding contrasting colour (the red one). At the final stage, let us analyse our eyes movement. First of all, our eyes start looking at the chart with the title (that is why do not forget about titles! Never!). Then they go straight to the red dot. Just next to the red dot is an insight that explains that point.  Next, they track the line chart and finally look at additional Y-axis labelling. Now, our brain, after collecting all this information, can process them and make sense of those data.

I would recommend those three easy to remember and use tricks to uplift any data visualisation that will improve your audience experience.

The link to the article:

https://www.theguardian.com/business/2021/jul/08/uk-employers-struggle-with-worst-labour-shortage-since-1997?CMP=Share_AndroidApp_Other

Four Levels of Data Storytelling – Where Do You Perform?

In this post, you will find out the four crucial skills to become a data storytelling master and why you should improve them.

Firstly, I have good news for you. Everyone can be a data storyteller and possess the required skills on a “good enough” level. Of course, each of us starts from a different position, and the time necessary to reach a “good enough” level will differ. There is scientific proof that you need about 10 000 hrs to be professional in any picked field, but only 20 hrs to have a basic knowledge of a subject. There are four fundamental skills that made you a data storyteller.:

  1. Analytical skills
  2. Data visualization skills
  3. Communication skills
  4. Subject comprehension

Analytical skills

This skill is a basic of basics. Without understanding numbers and reading them, you cannot adequately prepare a story about them. Even when you are not a “data person” – someone who already has had the skill to transform and interpret massive data sets, you still can learn it.

Our brain is divided into two halves – left and right. The left half is responsible for analytical, logical and sequential thinking. In this part of the brain, the centre of language is located. The right half gives us the ability to perceive in a non-verbal way: see objects in space, compare similarities, have intuition, have a holistic view of something. Most people experience domination of one of the halves. However, it does not determine that you are an artist or an accountant. If all humans have both halves, all of us can analyse and interpret data. Naturally, some of us are more gifted than others, but I would be far away from the opinion that you cannot learn analytics unless you suffer from solid dyscalculia.

But where to start your journey with data analytics if you do not have previous experience?

Foremost, understanding descriptive statistics is a game-changer. Descriptive statistics are methods for organizing and summarizing information. Having those statistics in place, we can start asking the right questions that help us reveal some insights. Mostly, we do analytics to see some trends, picks and falls, a contribution of factors or distribution of one of the characteristics within the population.

Data Visualization skills

Ok. We gathered all required data, organized and summarized them using descriptive statistics. But how make them readable for others?

In lots of companies still, a primary tool for performance reporting is MS Excel. And still, in those companies, the primary manner to present numbers is an excel table. There is nothing wrong with using tables, and sometimes they are even the best way to communicate results. However, we have much more tools to select to communicate numbers effectively. There is quite an impressive range of available data visualizations in any common software like Excel, Power BI or Tableau, just to name a few. Visualizing numbers is a skill like any other. You can learn it and master it.

Nowadays, this skill is more important than ever when we are submerged in the data ocean. Data visualizations are often the only way to make sense of data, find patterns and understand the surrounding world. Data visualization utilize human perception to communicate and receive data. If we do it without proper diligence and mindfulness, we can mislead our recipients, and as consequences, they will draw wrong conclusions. The worst-case scenario would be misleading the audience on purpose. Regardless of designer intentions, it is an ignorance of using and presenting data in an unethical way. There is plenty of sources that provide rules and best practices on how to use data visualizations correctly. So do not miss this opportunity and earn credibility.

Communication skills

So, two first steps in a process have been already done. You found interesting patterns and insights in the data and prepared their visual representation to make it visible to others. However, how to convey the message?

As a species, we are designed to communicate complex ideas and theories because we have speech apparatus, unlike any other animal. And vocal communication is a basic one for us. Thanks to our ability to pass complex ideas and theories, we have built an advanced civilization. But even when we speak the same language, we often cannot efficiently articulate our thoughts, and the receiver can misinterpret our message.

From a data storytelling perspective, there are two crucial components of communication. The first one is to use language adjusted to the audience. It is easy to overwhelm the audience with technical jargon, lose their attention and, in the end, lose their interest in the subject. The second one is the ability to make simple explanations. There is good exercise, at least when you have kids. When you explain your thoughts in a way that a seven-year-old kid can understand, you are the master of communication. To achieve it, try to use as many as possible comparisons, examples and metaphors from your audience experiences.

Subject comprehension

On top of the three essential skills, there is one more. I have already emphasized several times how crucial subject knowledge is. As Steven Covey said, “firstly understand to be understood”. You will not be a convincing storyteller without knowledge of what answers your audience is looking for. There is a simple rule: people always are interested in their business and problems, not yours. So, when you want to persuade them, you must present benefits or threats for them. From my experience data analysts are overloaded with ETL jobs and do not have enough room to talk with business people about business pain points and challenges. Those conversations would significantly enhance provided information. Data without context and understanding what is behind the scenes are useless.

Apart from mastering analytical, data visualisation, and communication skills, try to become a true partner for the business that you support. Build strong relationships with your internal or external customers and listen to them actively. Most people are willing to talk if you are pleased to listen. There is no better source of knowledge than subject matter experts. With those competencies, your ability to have a real impact within the organisation and building your personal brand will grow.

From Beginner to Master of Storytelling

In each discipline, there are levels of mastery. There is no difference with data storytelling. Check out where are you right now and what your aspirations are.

Beginner

This is entry-level. You even do not know that there is something like data storytelling and you can learn it to enhance your data analysis. I often meet beginners as young people who have just started their career. Their heads are plenty of theory, but they lack practice. In the first place, Beginners should improve business knowledge to prepare better and relevant data analysis. In most organizations, there is plenty of internal training and materials that bring closer inner business processes, rules and characteristics.

Recruit

I would say that those are mostly data analysts with excellent analytical skills and data visualization skills who do beautiful data visualizations that often are totally useless. The pitfall here is that when you are experienced in one very narrow specialization, you can have the delusion that you know better than others what they need and how to present it. However, they produce products for their customers and should listen to their voice. Maybe your customer does not know how to analyse data but for sure know what questions are interesting and show you in which direction data analyses should go. Recruits are typical data people, and they put too much focus on technical aspects, and they too often use very technical language when they communicate with non-technical people. They should focus more on the business side of analysis and less on the analysis itself.

Leader

As you can see on the matrix above, I valuate communication skills and business acumen more than data visualization skills in data storytelling. People who already know what the pain points of business are and can draw the audience attention do not need to know advanced data visualisation techniques to impact. However, there is a potential risk to easily mislead the audience if someone uses data without proper knowledge about basic best practices of data visualization. As I have mentioned earlier, data visualization techniques are based on human perception, which is a very fragile cognitive apparatus. Leaders have a special mission in spreading data culture across organizations because they feel comfortable with data, know how to use and present them, and, thanks to their position, can make or influence data-driven decisions.

Master

Masters have proficiency in all four skills. What is more, thanks to the linkage of business knowledge and analytical skills they are true advisors, who can set directions of future growth.