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?
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?
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.
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.
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?
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?
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.
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:
It is a chilly morning. I stand in the middle of the kitchen and look at my lovely daughter after our regular morning battle to get her ready for school. Apart from all rage that she carries right now inside, she is like a delicate flower torn by the wind. I ask myself where is the point to force her to get up so early and expose her to all these frustrations that will come for sure today when she tries to remember all useless knowledge. The Polish education system sucks.
My daughter, as the next generation of humans, will face many new challenges in the near future. Climate crises, energy crises, increasing inequality, overpopulation, the collapse of democratic rules … just to name a few. The current education system does not prepare our children for any of the challenges of the 21st century.
Experts agree that for our kids to be able to adapt to the new environment and face what the future will bring, they must master four basic human skills. They are called 4C’s for the 21st century: Critical Thinking, Collaboration, Communication, Creativity. And what is more! According to the experts’, 4C’s are the cornerstone skills learners of all ages need to be successful in life.
What the hell, do these 4C’s have in common with data storytelling?! You would ask. Well, I got an idea for this post asking myself how can I support my daughter in developing 4C’s. Then I asked myself if I was using 4C’s and how beneficial it would be.
4C’s for Future, 4C’s for Today
If you’re wondering where the future starts, the simple answer is today. It doesn’t matter how old are you and what challenges you face in your daily life; these four skills definitely help you achieve more in less time.
Critical Thinking – foremostly
In the past century, people have struggled with collecting and obtaining data for their studies. We are now reaching the point where anyone with access to the web has access to a large amount of data and can do their own analysis. Data democratization, like everything else, has two sides of a coin. Unfortunately, the dark side of the common usage of data is to mislead people and create fake insights.
I love the TV series “Ancient Aliens” but the level at which they treat and interpret scientific facts is very innovative – gently speaking. For me, it is a piece of good entertainment, but we can imagine how that trivial approach to science and what is worse mass-broadcasting this approach, can implicate damage in some people understanding of ancient history without questioning that “revealed truth”.
Critical thinking has its roots in curiosity. Before you judge or draw a conclusion based on information, you should dig deeper to make sure that your conclusion is not skewed by shallow analysis or dubious data. Similarly, to “Ancient Aliens” you can create the most breath-taking story about your discoveries, but where is a meaningful value from this fairy tale?
Critical thinking is a habit of questioning others and yourself and the good news is that everyone can learn it. To develop this habit:
1.Ask the right questions and validate your own logic.
“There are no stupid questions!”. I hope that you’ve heard that many times. If you haven’t – change organization! Asking questions is the simplest and the best way to verify your or others reasoning. Use the below questions to warm up your critical thinking: “Where data came from? Do I trust data sources?” “What is data quality? Are there any missing entries?” “Does the data sample is big enough? Does it present only a small part of the bigger picture?” “Do all factors are included in the analysis?” “What business questions does this analysis cover?” “Do I not overcomplicate things?”
2. Deal with your (or others) biases. Remember we too often look for evidence that supports our prior beliefs.
All of us have some kind of the burden of biases. It strongly affects how our brain interprets information and draws conclusions. Studies show that we have a natural tendency for ensuring that we already believe. That tendency can be very harmful to the recommendations which we provide. To understand better how our biases play tricks on us read the book “Mindware. Tools for smart thinking” Richard E. Nisbett.
3. Take time to evaluate the topic from different sides and seek diversity.
Most of the time we are in rush and that hurt our reasoning and the quality of work we deliver. So, hold your horses and invest time in finding out other people opinions. One question about “What causes revenue decline” can have multiple answers depending on the point of view. These points of view can be very valuable and let you create a story with a wider spectrum.
The self-made man is a myth. No one is one hundred percent accountable for his/her success or failure. We are the result of many factors like genetics heritage, family relations, culture constraints, environmental influences, and life experiences. All together constantly have a huge impact on how we perceive ourselves and make sense of what surrounds us.
Have you read a biography of Bill Gates? Bill Gates maybe wouldn’t be so successful in his field without a few coincidences like exposure to the computers in the earlies ’70s as a teenager (what kid had that opportunity!) and mother who served in IBM board and helped in securing his first big deal with IBM. Of course, he used those opportunities very well, but would he have been the same Bill Gates without those chances?
We as humans operate in tribes. Without other members, we wouldn’t survive. If you want to be successful in your life collaborate with other people and leverage their skills and knowledge, especially because domain specialization is so deep that it is hard to be a Leonardo da Vinci in the 21st century.
Some people find it easier to collaborate with others, others find it harder. And again, self-discipline and practice can help you develop habits:
1. Invite subject matter experts to discuss and review your data, analysis outcomes, recommendations. They can bring a new fresh outlook to the table and create together with you more valuable insights.
2. Ask other analysts how they would approach the analysis of particular datasets. Maybe they did something similar in the past and you can save plenty of time.
3. Gather as much information as possible from stakeholders to focus on what matters for them instead of waist time on general questions and findings.
No other animal has developed communication skills like humans. We wouldn’t be able to conquest the whole planet without that one unique skill. Due to that skill, we can build strong relationships inside our tribe and with other tribes, convey abstract ideas and pass on incredible stories about faraway lands.
Good communication starts with a good strategy. How many times have you failed to convince others even though you have done an excellent analysis and prepared actionable recommendations? Your message didn’t get through because it wasn’t appealing to them. Consider the below points and tailor your message to be more impactful with your audience:
Ask yourself what are the main pain points for your audience?
Are they data literal and how advanced?
Are they subject matter experts or do they need more introduction?
What can they expect from you? Raw analysis with insights or clear guidelines and scenarios with recommendations?
I’m not a fan of getting too creative in the visual representation of data. Data visualization is already an abstract form and making it more complicated by adding non-intuitive graphic shapes does not make it better. However, using creativity to look at a problem from a new perspective and consider new possibilities is a direction every data storyteller should take. Most of the time we stay within our standard thoughts or typical suspects. This leads us in the long run to
However, using creativity to look at a problem from a new perspective and consider new possibilities is a direction every data storyteller should take. Most of the time we stay within our standard thoughts or typical suspects. This leads us in the long run to intellectual castration, which has several serious consequences, such as missed opportunities for the organization, unrecognized in time threats, and a retreat in development. Creativity is again a skill that can be acquired and mastered. Experts recommend the following exercises to strengthen it:
1. Learn from others and surround with inspiration The more you collaborate with others, read a lot, and learn new things, the more creative you are. You need to have enough information gathered to connect the dots and then new ideas start appearing.
2. Enjoy what you do Doing things with passion produces unexpected outcomes. You need to be truly dedicated to your work to be able to find new solutions or patterns. If you do not like what you do, you are not involved and interested, do not expect from yourself outstanding performance. Maybe it is high time to change profession?
3. Findtime to do nothing Give your brain a break. My best ideas show up mostly when I do something different like taking shower, doing exercises, drawing, or reading. When you feel overwhelmed, simply switch your activity, and focus on something else. Your brain anyway still processing that idea in the background and doing the magic.
4. Walk Stanford study has shown that walking improves creativity. So, when you have a problem, simply take a dog for a walk. Many CEOs already have introduced walking meetings within their organizations to increase people ability to think out of the box.
5. Hypothesize One of my co-workers taught me a great technique. It is a simple question to ask, “What would have to happen to achieve XYZ”. That simple technique removes any barriers from our brains and shifts from concentrating on constraints, what we naturally do, to focusing on possibilities.
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.
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.
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.
The research was carried out for two commonly known visualization: a line chart and a bar chart. Both graphical choices gave similar outcomes.
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.
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.
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.
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.
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.
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.
I promised to prepare a post about strategies of BI product successful adoption. It is a really hard work to achieve this. Not because your company CEO is a miser, and he or she doesn’t want to give any penny more for technology or on hiring a new workforce or outside company that works for you. The true challenge is to change the way people think … and behave.
Is Excel still the main data processing tool in your company? Do people still value working with this tool, because of its simplicity? If your answers are yes, you should already feel that changing their work habits is not a piece of cake.
BI solutions are new tools that need to be adapted in your organizational structures with proper care. Introducing a new tool goes hand in hand with introducing a new process. Introducing a new process involves managing change. And that is exactly what adoption is – the change management case.
Many organizations have in their structures Change Management department that can support BI projects in better and faster implementation by leverage knowledge of change management processes and techniques. Human Resources department can be very useful as well when it comes to redesigning some people habits and behaviours. I highly recommend asking them for support in any initiatives involving the introducing any new solutions.
Before we delve into the subject, let me briefly explain what change management is. It is a structured approach to prepare and support the entire organization and individuals in making organizational change.
For me, the term “change journey” is more appealing than “change management”. I associate change with the human factor more than with processes because without people’s willingness any change will take place. There are several methods or frameworks to lead successful change, however, for BI products adoption I found ADKAR modelappropriate.
A like AWARNESS of the need for change
From my experience, it is very important to start communicating about the change a long time before it happens. There is a psychological explanation behind this: people don’t like changes. They must get familiar with it, so preparation is key.
There are many channels that can be used for that purpose such as intranet, emails, workshops, and face to face meetings. The message should focus on answering why change is needed and on the benefits for each individual and the entire organization. It is important is to address any concerns or biases related to the change. (I wrote more about it here).
Ask HR department for support in this sensitive case. Involve top management as the voice of change.
D like DESIRE to participate and support the change
Although all efforts go into Awareness phase, it doesn’t mean that the results will be spectacular. The reason is that each person must make their own inner decision whether to support the change or not. Many practitioners point out that win hearts and minds is the most difficult part.
The main challenge here is how to get people to care about something they don’t care right now?
As unfaithful Tomas, most of us have to see to believe. Data platform projects are relatively long-term and for most of the time, end-users do not see results. Fortunately, we often create PoCs (proof of concepts) or prototypes to test certain assumptions. These small pieces of work can be shared to prove major concepts of a new approach. If this prototype is prepared to address one of the main company’s pain points, it would be easier to promote the new approach in the organization because of its undoubted value, which shows how this change can work for them.
K as KNOWLEDGE on how to change
This phase is associated with learning new tools and new skills. Many organizations use Excel to communicate data. Most of the time they prepare reports and send pdf files by email. Introducing a new tool like Power BI or Tableau forcing breaking old habits and behaviors and building a new one. This transition must be supported by delivering inhouse training that will bridge the gap between current knowledge and skills and desired one. In addition, all training must follow with creating an internal space where people have access to information about this new tool and have a place where they can share their experience and find answers to their questions.
Too often I observe a common scenario, that a new tool is introduced, however, staff training is not budgeted. This gives rise to a lot of frustration when people are required to provide valuable analysis, but they lack skills.
A as ABILITY to implement desired skills and behaviors
Having knowledge doesn’t mean that you know how to put it in practice. It takes time for people to develop a strong conviction that they are capable to use new tools for expected results. They won’t do it without support from the company side. Bringing in trainers or field experts who will work with them for a while can speed up learning process and smooth transition from the old to new approach. The main slogan here is practice, practice and even more practice.
R as Reinforcement to sustain the change
Have you heard about the “JoJo effect” when it comes to weight loss? It often happens that people who put a great effort into losing a few kilograms and spent several weeks or months on exhausting diet and psychical activities, very quickly regain their original weight. The reason is that they didn’t change their habits but only suspended for a while. There is even scientific proof that our brain reverts to safe, comfort and well-known practices. Therefore, maintaining the change is very demanding.
Before we are going to introduce a new approach, we must find out how the current processes are like and what people think and feel about it. Most of cases in organizations there are two or even more ways people do certain things. The first one is official procedure which can be found in organizational documents or regulations. The second one is the informal way people really work. This informal approach manifests their habits, behaviors and beliefs and is significant for us. Without revealing true processes, the new change won’t be successfully implemented due to lack of knowledge of how to implement it in such a way that people would be open to accept it.
You don’t have to start big. Start small.
When working with the client, we usually choose only one business area to improve. This could be sales performance, for example. Then we makeover reports, or we design them from scratch, develop and make them available as the reporting platform. This short cycle has many benefits. First of all, we can quickly verify technical aspects of the proposed solution, check with the stakeholders whether the product meets all the requirements, and what is most valuable if the product can be release to wider audience and prove its usefulness to them.
Leverage old tools
Instead of introducing rapid change as revolution, sometimes we can achieve better results by doing it in slower pace like evolution. If your employees are used to using Excel, don’t take it away from them. Most of the BI products have possibility to extract data into an Excel file. Focus in the first phase on process automation and ensuring a single source of truth. Anyway, they have to use the BI product to retrieve some data. Over time, as they trust and become familiar with the tool, they will start using it instead of extracting data from it.
Top management involvement
Recognition and a pat on the shoulder is not enough. Every change (as well as every initiative) requires fully committed top-level managers.
Several years ago, at one of my previous employers, I was involved in designing and implementing a new business intelligence tool. The goal was to provide a large number of reports covering all business aspects. The task wasn’t easy due to its complexity and data accesses challenges. Most of data were stored with IT department which didn’t want to share accesses. The first release took us almost a year (it was long before I heard about Scrum 😊). As you can imagine tremendous effort and time has been invested in delivering this tool.
This project was under company digitalization umbrella and aiming to improve the availability of information at every level of organizational hierarchy. However, most senior managers didn’t use this new platform, where they had all important information at their fingertips. They preferred the old-fashion style to send tones of emails asking for these essentials.
As you get the impression the adoption wasn’t spectacular, I would say that we missed the momentum.
There is a proverb that “the example comes from above”. I believe that if senior managers presented themselves as hard users of the platform, it would have enormous impact on the platform usage.
Ambassadors of the new approach on each level of organizational hierarchy
Apart from Top management, you need army of true believers, who will be a voice of change. These people should come from different departments and from different levels of company’s hierarchy. They should be a role model for their colleagues.
There is no better option to involve people by giving them the chance to become fathers and mothers of the initiative. Parents love their children selflessly.
You can follow the tactic of one of my clients. They formed working teams with people from different departments, who were involved in the design of a brand-new reporting platform. These people talk about their new project in the halls, canteens, and during cigarette breaks. This is a perfect example of viral marketing!
Support, support and once again support
How would you perform driving a car without hours of training and a good teacher? Likewise, your people need teachers and resources to learn and master their skills. You can leverage whatever works: on-demand or instructor-led training, online resources, community groups or newsletters with examples how to use and read data from the new BI product.
One of my clients constantly uses emails to send out extensive examples presenting usefulness of the BI product. They provide screenshots and guide others on how to use a tool, but more importantly how to analyze with the tool and create insights.
Start with day one
The last good practice that I want to present is to combine BI products into internal processes. This tactic forces people to use this tool and cut any discussion, whether they deem it relevant or not.
That tactic is for companies that really have ambitions to become a data-driven companies quickly. In such case all teams have to start workday by checking the latest data and on that basis and making decisions what they will do today to improve the performance, for example.
The great example is Daily Scrum – meeting (one of Scrum time boxes). During this event, a team relies on yesterday’s activities planning today’s activities. They use Kanban board to track data about the progress of current work.
Likewise, dashboards or reports should be used as a mandatory tool for daily stand-ups to discuss ongoing performance and set the next directions.
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:
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 somecolour 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.
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.
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).
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.
Do you need to know how to tell stories with data?
Ask yourself how often do you use data in your daily job? Or maybe how many times do you use data to convince others to your ideas? If your answers range from rarely to often, then this post is for you.
One scene from the movie “Silver linings playbook” stuck in my memory. The main character after having an explosion caused by hearing his wedding song, is sitting in the therapist’s office, and complaining that it would not have happened if that song had not been played in the therapist’s office. The response of the therapist was clear and brief “You need to build your own strategy how not to be afraid of that song”.
Building strategies helps us to be more productive and perform better, whether it is in our work environment or our private life. Our brain just loves mental shortcuts, and strategies are those shortcuts. Especially when we are in a hurry and need simple solutions which always work.
Let’s see what strategies we can prepare to make data communication more effective and efficient.
Comparisons are always a good choice when we want to present the progress of initiatives, outcomes of introducing new processes, or showcase sales performance in different markets. People compare things in their brains all the time, so any story based on comparison will be easy to understand. But it needs to be well-crafted.
Before and After
This strategy works well when delivering outcomes of recently introduced new initiatives or processes. Old state data is the best background to emphasize big change or the success of a new approach. You can present benefits or results in several dimensions: process, employees’ satisfaction, increase in a number of clients etc. Anything you deem valuable for your business.
As an example, we can put together two dimensions: employee’s satisfaction and a number of human errors. In Picture 1, it is easy to see that changes have improved the employees’ satisfaction and resulted in a decrease in human errors. Simple column charts displayed side by side will suffice to represent this data. Adding lines connecting columns makes visualizations more suggestive.
Us vs. All
Every good manager should brag about her or his team and highlight what a great job they do for the organization. If your team, the product, sales, or converted leads are the best, show how they stand out from the rest of the company.
To draw attention to your data, you can change its colour. This simple trick will distinguish your data from the others and push it to the foreground. See Picture 2.
Where are my stars?
When analysing revenue growth, we consider what is pushing it forward and what is holding it back. A very popular concept is to present leaders and laggers. The popularity of this concept stems from human nature. We admire and envy the best, but love the worst because they are worse off than we are!
C-Levels managers like to see contributors of the growth on the waterfall chart because this visualization shows at a glance which contributors have made money for the company and which have lost. For our revenue growth example, we can use two different colours to indicate leaders and laggers.
Changes over time
Changes over time are the next group of strategies which use the familiar comparative idea with a whole story set in time. We can present how something develops over time and what is more appealing for our audience how it might be in the future. For such stories, we use line charts.
Show me the bright future!
Who would not want to know the future? Well, I do not… But, when it comes to the business environment the answer is always: everyone. When I work with clients, the trend of any phenomenon is a must. Many decisions within an organization are based on current trends and an estimation of future outcomes.
However, every data scientist will warn against relying too much on historic data. There is a strong tendency to predict future business performance behaviour based on past results. To temper expectations, we can provide several scenarios based on the same dataset. This approach will add value to our analysis if we introduce factor parameters to each scenario. Typically, three scenarios are provided: optimistic, realistic, and pessimistic.
To illustrate the technique, I will use an example with revenue growth (every CEO cares about revenue growth). The main factor in the example could be the launch of product A in a new market. As we all know, launching a product on a new market can be a huge success, but on the other hand, it can also be a spectacular failure. Using sales of product A as a parameter, we can create three separate revenue scenarios for the upcoming fiscal year.
Factors of success or failure.
Another story which is attractive for the audience is about factors which influenced the results of the phenomenon. This narration is based on our natural tendency to look for cause and effect relationships. Maybe if we knew what had triggered results in the past, we could use it in the future to prevent bad impact or use identified factors to achieve better outcomes? This strategy is great when you want to convince senior managers to spend money on the next marketing campaign. Simply show them the periods with and without marketing campaigns on the line chart, where they can easily observe the ups and downs of the line representing sales. Do not forget to add some call outs to strengthen a message. See picture 5.
The last strategy which I want to bring closer to you is about presenting the most crucial business metrics on the one-pager. This strategy is a master level, because whoever prepares it must be aware of connections between separate metrics and the overall influence which they have on the business health. This is very practical when trying to understand which processes drive others. The one-pager can show usual suspects, threats, and opportunities. For instance, if your core business as a company partner is selling services to the specific hardware, you can expect a drop in sales if hardware sales fall down.
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.
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). 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. 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.
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).
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.
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).
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.
More practical colour ranges you can find here, and if you would like to test your composition on specific charts use this website.
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).
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).
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.
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.
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.
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).
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.
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. 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: