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.


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.


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

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.


  • 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.


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:

Storytelling structures that support presenting data.

Key points:

  1. How to structure your presentation to keep the audience attention?
  2. What to avoid to not overwhelm people?

Many professionals struggle with conveying their messages in an effective way. They often fail to convince others with their ideas or perspective. Most of the time the reason why it happens is poor delivery of the presentation. It is a hard job to present a topic in a logical way that does not confuse the audience. The second challenge is to create an engaging and exciting experience for the audience. Things even get more complicated when you use numbers and figures in your presentations.

Why do you need storytelling to present data?

The simple answer is because numbers are too abstract for the human’s brain. Presenting or talking about numbers is not such a piece of cake. Thousand years ago, nobody had to analyse sales trends or try to understand what influences shipments delivery.  Data analysis and data visualization is relatively a new demand, and a lot of people struggle to gain this new skill.

If you do not weave your facts and charts into a story framework, you overwhelm your audience and lose their attention, and in the end, you fail with your ideas.

Since the beginning of humanity, people are storytellers. For ages, they have been passing on the information by narration about incredible actions of heroes, distanced journeys, and the most important, gained wisdom and knowledge through those stories. For humans, the narration is the most appealing and easy way to consume and remember information. The message “Do not eat berries” sounds flat compared to “Do not eat berries because half of the neighbourhood tribe died after ate them.”  And what are the most important, stories ignite emotions which drive people to take decisions and actions more often than logical facts and data (if you doubt this, check the latest studies of behavioral economists)

The Basics

I bet that 100% of corporate presentations have the purpose of influencing people to take action or decisions. Most of the time we:

  1. solve some problems, and we need allies and sponsors to support our ideas,
  2. break a status quo and introduce a change in the organisation’s business model to be ready for future challenges, and we must convince and inspire others,
  3. pass information needed to make strategic decisions

So, first, you must decide the purpose of the presentation. However, sometimes I have an impression that some speakers present data analysis for themselves. They just want to boost what they found, but they lack the audience perspective.  There is the truth worth remembering: people are interested only in themselves.

“You can make more friends in two months by becoming interested in other people than you can in two years by trying to get other people interested in you.”

Dale Carnegie

When you prepare your presentation, focus on benefits for the audience. The simply Five Ws + How technique can support you in this task.

  1. Who – think about people to whom you are going to present.
  2. Why – think what kind of questions they can have or ask them directly, and try to give them answers or feed them with insights that help to find them, on the other hand, do not forget about what you want to achieve.
  3. What – now when you know their perspective, you can start analysing data, and create a storyline.
  4. How – this is a time to start thinking about the structure of the presentation and the result which you want to achieve.
  5. When – at which time you will deliver a presentation is important as well, check out a corporate schedule, if the important event is coming shortly, it would be better to hurry up.
  6. Where – make sure that place is convenient for your audience, there is enough room for everyone, and the place has required equipment.

Let us move further to HOW to design a presentation as a true storyteller.

Storytelling structures

Every good story has three points to cover. Every book, drama or movie follow that simple structure. Nevertheless, how and when you cover will change a narration.

  • Conflict – it is a background for the story: a current situation or state, past actions and discomfort it makes.
  • Climax – it is an essence of the story, critical point, the whole story is told to convey this one message.
  • Resolution – a new desired state or actions need to be taken.

Let us check how we can juggle these points to get different narrations.

Storytelling techniques good for presenting data.

When you present something to the audience, you want to make them listen to you. Several techniques help you achieve this experience.

The Narrative ARC

One of the most common structures is the arc. It is a very logical structure with straightforward, easy to follow stages for the presenter and the audience. It is the extension of the conflict, climax and resolution. It follows:

  1. Exposition – this is a background, a current state, a time snapshot, circumstances. All of these establish the context of your story. It is an excellent place to reveal all possible questions which your audience can have.
  2. Rising action – in this point, a conflict is presented. It can be an unsatisfied situation or result. At this stage, to add some tensions, describe some risks and threats to the audience if the status quo remains, and future possibilities which you will cover further.
  3. Climax – the critical or turning point, the undoubted evidence that some decision must be made. It can be your main findings.
  4. Falling action – at this stage, different conflict solutions can be presented with pros and cons.
  5. Resolution – final recommendation, needed decisions, actions or solutions.

In media res

This structure immediately moves the audience to the essence of your message. This strategy has on purpose to catch the audience attention and engagement. The structure follows:

  1. Climax – the critical or turning point, the undoubted evidence that some decision must be made. It can be your main findings. At this stage, to add some tensions, describe some risks and threats to the audience if the status quo remains, and future possibilities which you will cover further.
  2. Conflict – this is a background, a current state, a time snapshot, circumstances. All of these establish the context of your story. Reveal at this stage all possible questions which your audience can have.
  3. Resolution – final recommendation, needed decisions, actions or solutions.

This structure can be highly effective when presenting to senior managers or executives who are always in a hurry and like to go straight to the point, and you need some decisions or actions from their side.

Dos and Don’ts

Last but not least. You can tell the best story, but numbers need to be shown. What is more, people are visual creatures. For most of us, to understand means to see. Designing the presentation, consider the below tips to avoid overwhelming the audience.

  • Too many charts on one slide – it is better to unfold visuals to more slides instead of clutter one slide with too many elements. A thoughtfully adjusted number of slides will support your story and lead the audience step by step.
  • Too much text – the same situation is with text. Good presentation is economical in text. Just a few of the most important words, insights or phrases. So do not expose your audience to the wall of the text.
  • Too small fonts – this one relates to the previous. If you do not have the wall of text on your slide, there is room for readable size fonts. To adjust fonts size, consider the conference room size. The fonts for the axis should be at least 12.
  • Too small visuals – similar with fonts size. When using visuals, make sure that these are big enough to be visible to the audience
  • Unreadable fonts – some font types are hard to read. They look exciting, but in the end, they are slowing down the decoding information process. Stick to simply fonts like Calibri, Arial, Verdana.
  • Keep a short harmonic colour palette – colours evoke emotions (but this is a topic for another post). Build a colour palette around five to seven colours and stick to it in your presentation. Decide which colour would be the main one and cover 60% of the presentation deck. The following 30% leave for secondary colour, and the rest 10% for colour, which will be used for highlighting the most critical information.
  • Keep agenda visible – save a place on the slide for displaying agenda. It can be at the bottom of the slide, on the side or on the top. The audience is provided with information in which part of the presentation they are right now.
  • Do not add page number – if you are going to display 50 slides, it is better to keep it secret 😊
  • Use grid – the human eye does not like asymmetry. The grid can help you align all objects with themselves and keep a clean and orderly layout of slides.

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.


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.


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.


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.


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.

The seven common biases connected to report adoption

In this article, I will summary seven common people biases that I observe when dealing with introducing a new reporting system.

Firstly, we need to understand why there is so much resistance in embracing a new? Most people are afraid that they don’t have adequate skills to use new tools, or they won’t easily understand the content of those tools. In some cases, the root of incomprehension is a lack of comprehensive information on why a new process is introduced.

Suppose we want to carry out a successful transition from the old process to a new one. In that case, it’s crucial to understand and address the specific fears and biases of our employees and manage those emotions. Change management strategies should be built around addressing people fears, untrust and incomprehension. Not without significance is a group of employees. Those strategies must differ when you approach data people and non-data people.

But before building strategies that help overcome those challenges, let’s name them.

The issue with data literacy

Data democratisation has enormous potential to change how we work and how we think. A great example is the work of Hans Rosling, a Swedish professor, who, thanks to exposing an audience to data, was able to influence their perspective about how positively the world change during the XX century. However, he didn’t leave the audience without assistance to understand and consume the data. When we give access to data for a wide range of people, we have to remember that among the employees’ population are individuals with various disabilities like dyslexia, dyscalculia, colour blindness or any other cognitive deficits. If we do not consider and address their limitation, our ambitious plan to empower each employee with data will fall.

The technology barriers

The biggest mistake is to assume that for other people, new technology is as intuitive and easy to assimilate as for us (it is a shared conviction among some people who already use some technology). However, the reality is quite different. People do not assimilate technology at the same pace. Some of them need more time and more assistance to become familiar and feel comfortable with new tools. Employees in our organisation have different educational background, age and skills that influence the understanding of any new technology.

The fear of an incomprehension

The result of neglecting issues with data literacy and technology barriers is that people will not use the new tool. They will not build a firm conviction that they can analyse data in a meaningful way and create business insights. For those who already work with data, BI platforms can be seen as data Eldorado. However, for non-data people, the same tool can turn into a nightmare. For data people, It is obvious that we live in the data ocean, and well-prepared data can enrich any process within an organisation or in private life. But not everyone has an analytical mind, and interpretation of data can be a challenge.

The fear of being redundant

Data people often see technology as a threat to current jobs. And this bias has strong evidence in factories and back-offices. If we automated the work of three people, who had done it for 80% of their time, they can feel anxious about their role in the organisation and be reluctant to use new tools. They can even present a negative attitude to a new device or process, explaining its unreliability. However, we need to remember that automation in the BI field is a blessing because it gives employees space to release their potential. In analytics, about 80% of the time is taken on data transformation jobs. By automating this part, people can have more time to explore data and create thoughtful insights and recommendations.

In here, we have reached another bias connected with someone’s skills. Even for people who have the potential, it can be hard to switch from one role to another in a short period. The organisations that appreciate human resources’ value are willing to offer upskilling training, which prepares for new positions, often more demanding. If well-designed and well-performed, this transition can be an excellent opportunity to grow, both for organisations and individual employees. More and more data requires more and more professionals who know how to take out the best insights and communicate them. In the future will be lower demand for data analyst but higher for data storytellers. That shift will be towards a better understanding of the business environment, business constraints, and connecting all those dots into one thoughtful piece of information.

The fear of being seen

BI platforms with updated on daily basis reports give enormous performance transparency in all fields. It enables monitoring people performance more outstanding than ever before. No one will hide. The dark side of this transparency can manifest itself in increasing stress experienced in the workplace by employees. People performance reports should be carefully designed to underpin organisational culture. Depending on the competitive culture or cooperative, the approach to data narration would differ. If you care to have a solid and effective team, your latest goal is to emphasise individual performance. Numbers in such case should reflect the team capabilities and contribution of individual members but not a comparison that can lead to competition within the team. Even in a competitive culture, data narration can have a positive or negative impact. When our objective is to attain goals in the longer time lag, we will desire to evoke positive employee motivation. Positive motivation is a reward for good performance, negative – pain avoidance. A good practice is to research before developing any reports to check what data visualisations bring what emotions and how they resonate with the audience. The red colour is a good example. For instance, at one company where I worked as a consultant, employees decided that red colour on reports negatively impacted their performance and well-being. The red colour was used to emphasise the sales budget realisation, which was under the threshold and shinned in red most of the time during the month. In such a case, another option is to highlight budget realisation and compare it with timeline, or simply change perspective and communicate current goal attainment (in green or blue).

The fear of responsibility

If you are thinking about creating an organisational culture based on individual employees responsibility and engagement, there is no better way than democratise data. The idea behind data democratisation is to democratise the decision-making process as well. However, on the other hand, as a side effect, we delegate more responsibility to the employees in lower positions. Some of them are already capable and just looking forward to that chance; some would be more reluctant. Nevertheless, the essential purpose of data democratisation is to equip employees with a tool that gives them authority to impact their performance significantly and, through this, on the entire company. Another aspect worth mention is reducing micromanagement that negatively influences employees’ efficiency, self-esteem and increases frustration. Having BI tools in place, employees are welcome to use them more frequently in self-management and drive their actions.

The issue with trust

Before we release a new BI platform, we need to be 100% sure of data accuracy. As an old proverb says, it is easy to lose credibility but extremely hard to regain. Data platforms are no exception in this case. They need to have a status of a single source of truth and be irrefutable. Otherwise, the audience won’t rely on provided data and go back to old, common practice. Establish a single source of truth and one dictionary for all measures is crucial. After the release of the BI platform, all other data resources need to be withdrawn for everyday use not to mislead users and not create a parallel reporting system with alternative truth. The massive challenge in this field is to convince people. In addition, shared access to guides and other materials which easily explain how the solution works, data is prepared, and measures are calculated can help people trust the new tool.

In the next article, I will go deeper into practical strategies that are available and easy to introduce in all types of organisations. So stay tuned.

Time orientation

Time orientation is crucial for the modern world to understand events and draw the correct conclusions.

The pre-industrial culture had not been so tided to time, and most often people perceived time in cycles as day-cycle or season-cycle. However, industrialization forced on us to create precise time systems and changed circularity to the linear phenomenon.

Currently, the majority of people live within time, and this time has for most of us one orientation from left to right and can not be reversible. It is one of human heuristics – mental shortcut, which helps us understand the world.

The example

Data visualizations best practices tell us to display time on the x-axis with left-right orientation (most of the culture except, e.g. Middle Eastern) and do not play with it especially when charts are going to be short displayed. In the end of August in Polish Public TV, a chart for unemployment rates was presented (see image below) with all possible misleading characteristics. I can not tell if it was intentional or not and politics are not the topic of this post, but let’s have a closer look at how this chart is designed and why it is designed wrong.

I have mentioned above that the human mind craves for mental shortcuts.  A quite possible scenario, in this case, can be that receiver reads only the first label for first bar from the left side on the x-axis and understands and remembers that on x-axis there are months of 2020 start from July (Lipiec 2020). The automated interpretation would be that two next bars represent data for two upcoming months, so August 2020 and September 2020. Of course, someone can raise a question in here “We don’t have data for September yet”, but my question is what a level of general data literacy and competency within society is? I am going even further and asking is it ethical to show data visualization for short time without a proper explanation of the graph? But it is a topic for another post. Going back to our example, the conclusion which can be seen is that the unemployment rate has decreased. Where is totally opposite.

However, let’s put ourselves in devil’s advocate shoes and consider, can we approach creatively presenting timeline or not? As I mentioned above, human eyes are used to interpret the timeline from left to right side. Due to that, it is good to keep that order. Sometimes we have a temptation to change it because for example, we would like to compare year over year change and we use last year data as a benchmark. However, that way of presenting data will not be intuitive for receivers. We must be very careful, when we are dealing with data associated with time.

How to fix it?

So how we can fix this visualisation?

First of all, let’s break years into two separate columns and give the time a proper order. Adding columns with years, we clearly indicate that we are dealing in here with two different time stamps. A title or a subtitle itself can help us emphasise that we are presenting a comparison between time points(July 2019 to July – June 2020), so don’t hesitate to include it. Also, I decluttered visualisation by removing background colour and 3d effects, which helps receivers focus only on data. To highlight the most current bar, I changed colour to orange.

All those changes enabled to present data story professionally and properly. Apart from all aesthetic aspects, data visualisation designers need to remember about ethics. The same as in other professions, data visualisation designers have their code of conduct.

“Ghost in the Project”. Chasing the Product Owner.

Sometimes it is tough to keep calm and be professional. Especially when your project stuck in the middle and what is worse, you know how to get out of this situation, but you don’t have any authority to move further. 

Early afternoon. The call started. You are the only person on the call; however, several people should be already in. You are waiting and wondering if they will show up or not like on several latest meetings. They accepted the invitation, but it doesn’t make any difference any longer. Minutes are passing by, I’m still waiting, and I’m trying to find some positives not to have a feeling that I’m totally wasting my time. Recently, I’m working on naming my emotions, so I’m taking that advantage and start. The frustration wouldn’t be the best description. There are much more picturesque feelings to describe my state like: disappointed, disrespected and unimportance. All of this makes me annoyed.

To achieve success in BI project, the commitment from all parties is needed. Especially from the Product Owner side. The Product Owner is a person who is the mind and the heart of the end-vision of the product. He or she knows exactly what features and characteristics the product must and must not have. In other words, you can not run the project without this Very Important Person. 

There are many things which can be frustrating as lack of data availability, weak engagement of IT department or infrastructure with huge technological debt. But those are issues, which proper approached, can be solved. Missing and uncommitted Product Owner is a real threat to the project succeed. 

So the question is how to influence Product Owner commitment? I won’t deal with that question. I’m not a psychologist. However, there are several strategies which can be used to find a solution from that impasse.

Change the Product Owner

That is the best strategy. In internal projects, people who are the Product Owners in major cases are the same who have “business ownership”. In other words, they are already busy. They don’t have room to take another responsibility on them. Simply as it is. Frankly speaking, within an organization, there are other people who can be a successful Project Owner for your project. If you have the opportunity to switch the Product Owner, do it. From different reasons, you don’t have to remove the former Product Owner from the project. That person will be a precious stakeholder. 

Treat the Product Owner as a Stakeholder

By the way, talking about stakeholders. If there is no chance to change the Product Owner, it is good to set boundaries regarding what kind of decisions she or he has to make. Leave for them only the most important, the most critical decisions. The rest of decisiveness makes within the development team. This approach will reduce the waiting time for approvals and other blocks. Of course, don’t forget to establish those boundaries with the Product Owner.

Find a Subject Matter Expert

If the Product Owner doesn’t have time to help you in designing a product, who else can help? Who can help you prioritize backlog items, answer all development team questions, make those numerous decisions and always support you? It can be someone who works closely with the Product Owner and well-knows the business field in details. And what is more, has time to be present within a project. Another advantage is that in the majority of cases, those people are the end-users of the product, so they really know what they need.

Last but not least. Don’t forget that it is only a job. Take a few deep breaths and after working day, engage yourself in more important things ;).

“Know Your Customer” (Part II)

Purpose of my work is oriented on preparing the best BI (business intelligence) tools to support achieving goals within the organization. In the last post, I described four initial points to start the KYC process. Today I’m going to focus on next steps. In this post, I’m going to share my experience and my best practices on how to gather requirements effectively.

I have seen many guides about what kind of questions is good to ask during business or data analysis. Most of them are useful and relative to the topic. However, my role doesn’t focus only on business or data analysis. The subject is much wider. Daily, I’m using techniques and methods from UX & UI design (user experience and user interface) and data visualisation to create the state-of-the-art BI product. In here I would like to clarify one thing I’m not a BI Architect, who is responsible for the back-end. My research and tasks focus on overall business needs and front-end outcomes. I create content and the “look and feel” experience.

So, you already gathered as much as possible information about the customer company, industry challenges and trends. You are ready to kick off the project and meet with the customer. And now what should happen? What a plan is? How to find out what needs to be delivered? Where to start?

There is no better way to gather requirements than directly ask your customer. But customer can be a broader audience. In fact, there are plenty of people with different needs and different objectives. It’s necessary to identify those separate groups of users. And at this point, we have reached a first question which should be addressed.

Who is going to use these reports?

The common approach which I encounter is to design One Report for All. In a nutshell, the main idea is to design one report, from one data set and try to bring together all expectations. How does sound for you? Do you think it can be achievable?
Do you know what is told about a compromise? That it is not satisfactory for any of the parties. The same result is when we offer one data view for different viewers. Still, we can have the same data set, but, e.g. Marketing Director will look for other information than Controller. Their fields serve for other purposes in the organization; that’s why they need to have tailored alerts and KPIs (key performance indicators).
Having in mind those varieties my first question to the customer is about how many different end-user groups we are going to have.

What kind of actions/decisions these people are going to make?

This question is my second crucial one to understand how these groups use and consume data. Understanding of actions and decisions giving me the most valuable information about the true purpose of having reports/dashboards in place. Thanks to that, I can design dashboards with higher adoption, because they respond to daily challenges.
Another benefit of this approach is to establish a better and stronger relationship with the customers, who feel that they are heard. What is more, sharing such an attitude we can become a partner, not just the provider.

Of course, those are only opening questions, because just right around the corner, the following questions are waiting:
” What business needs do those groups have? “
“How often they use data?”
“Which channels they use to communicate/consume data?”
“Which devices they would like to consume data on?”
“What is the data literacy level?”
“Is the data culture established within the organization”
“What are the habits and behaviour patterns regarding data usage?”

To collect answers to these questions, the best way is to hold interviews or workshops with representants of each group to learn their perspective.

Tangible outcomes of KYC.

The success of all projects lies in the preparation. KYC process in BI world is nothing more than a sequence of steps leading to create the vision of the final product. At the end of this stage we should have some tangible outcomes:
– well-defined groups of end-users. Those groups are called PERSONAS – fictional characters who represent types of users.
– description of product vision: scope, KPIs and alerts list, UX & UI assumptions and foundations.


KYC – “Know your customer”. How many times have you heard that phrase?

It already became one of the must-have tools if you want to deliver value for your customers successfully. In each situation, the goal is the same, to get as much information about your customer as possible.

But how to achieve it? What is more important, how to achieve it effectively?

Regarding my own experience, my major goal is to design with a customer an analytics platform, reporting application, one single go-to point for key metrics which will be relevant, appealing and informative for end users. The better we can design it, the higher adoption it will have, and more data-driven the organization will be.


“Know your customer” for me is a process, and like any other process, it has some steps. Those steps we can arrange into two, parent groups. The first group refers to information, which is gathered externally without contact with a customer and a second internally – gathered during workshops and interviews with a customer. However, before even putting a foot on a customer’s office floor is good to know some key facts about customer business.

You can ask why to take time? You are not coming to building a new strategy or develop new products. Aren’t you? From my experience, starting works on designing and implementing a new analytics platform within an organization boosts other positive actions like changing approach to manage some processes within an organization, optimize them, allow people make better decisions thanks to having proper metrics which describe and monitor business performance. So, in the end, I can say that work has a direct impact on creating a new strategy and developing new products. Another motivation to take time and do some pre-research is to be a partner in discussion with a customer in upcoming workshops and interviews.


In this post, I’m going to cover my best practices regarding the first group of steps – externally gathering information about a new customer. I’m using simple methods which are easy to remember and easy to apply. I would say that they can be arranged into check-list. To illustrate an example, let’s imagine that there is a new project on the horizon.

There are a few areas which I’m willing to research myself as a starting point:

1. Customer company. The first research is about customer company to get familiar with basic facts like industry, size, products, vision, values, markets—everything possible to read on the company webpage and beyond.

2. Customer industry key characteristics. This step gives you an overview of customer industry opportunities, threats and challenges. That information helps you understand the business environment and customer market. For instance, a company from logistic industries has different objectives on their radar than finance institution.

3. Business function top KPIs. Mostly, when I start my work, I already know the initial assumptions like the area of business for which the project will be run. It can be finance, controlling, logistic, sales, production or other. Each of those functions has specific, crucial measures for monitoring performance and their health. The internet is a great source of knowledge, so even then you are not an expert in finance or production, you can easily find top KPIs. Those top KPIs will help you understand better particular department challenges.

4. Customer industry business news. Besides the above information, I’m looking for some success stories for industry leaders, especially regarding technology or business optimization activities. That is a great opportunity to learn about the latest trends and growth directions.

The better you are prepared, the more professional you look and the more successful you can be in your role. So spare no time and get to know your customer.

Do not forget to get more hints in part II.

Use the force of tables, but choose wisely.

“You must unlearn what you have learned”, said Master Yoda. Tables are not visuals! Truth? Have you ever heard that?

Nothing more wrong. Tables are a very powerful tool for visualizing data if you use them wisely. The main advantage of tables is the ability to present several measures for the same category in one row. This allows your audience to make quicker decisions because all important information is “on the table”.

However, the human brain READ the table. There are plenty back and forward iterations which it does to understand table content. So to make understanding easier, some additional elements should be introduced into tables. In the end, we don’t want to overload the lazy brains of our audience. Let’s see how we can improve tables to make them more accessible for people.

What makes the bottom table better than this at the top? There are several bullet points, which I’m going to address. You should have already noticed titles. Titles, itself, are introducing a huge difference.

Flat table

This table is simply flat. All information is at the same level, which means that they equally attract your attention. Nothing is highlighted, except for the second rows… which is unnecessary. Well, it’s hard to read, right? There are more sins: small fonts, cluttering elements such as lines, grey backgrounds, no formats of values.

Meaningful table

In the table, I’ve introduced information hierarchy by using different font colour. Rows and columns headers are in the background. Values have the darker, bold font. What is more, visual elements are added. Bars differentiate revenue volume, RAG icons simply convey the message about target realization, arrows indicate the direction of the year over year change. Columns headers well describe a column content and columns order leads through information importance.