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:
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.
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.
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.
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.
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
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.
DOS & DONTS
Use colours to catch the attention but remember to choose them in accordance with best practices for colour blindness disabilities. Studies show that around 10% of people population have some disabilities in colours distinguish.
Always provide the legend. The legend should explain the meaning of colours, shape, sizes and even positions of objects on your visualization.
Add short text on visualisation. If there are points that should be emphasis place additional text with an explanation nearby them. The well-balanced text provides context for a particular point.
Plan the objects’ size with available space in mind and readability aspect.
Do not use too small fonts.
Do not use decorative fonts as they are not readable.
Remember about the title and short description of the data visualization.
Leave whitespace around the visualization to not clutter the page.
How 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
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.
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.
“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.
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.
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.
When I say “multicategories”, I mean more than 4 categories. Sometimes, a challenge of visualizing multicategories is like an old polish proverb “eat a cookie and have a cookie”, which is hard to put into practice. I often observe how data analysts try to approach this challenge. Common scenarios are for products, countries, businesses, departments, teams, agents or cost centres. For all these data, they try to find out meaningful insights by depict patterns and highlight interesting points… mostly on one chart. That visual decision creates a beautiful piece of abstract art riched in colours, shapes, different sizes of objects, patterns or crossing lines.
Could you imagine how someone must be determined and persistent to look at and try to understand the column bar chart with 15 categories presented on 5 years horizon? Which category has upward and downward trend? Which is a leading one? And foremost which one is which?
When I think about “multicategories”, my first association popping into my head is “clutter”. The clutter is one of the greatest factors of cognitive overload. To understand clutter impact imagine that, you try to talk to your friend in a crowded space like a bar. You are all ears to follow her or his, and even then, you are not successful. The same effort your brain does, when it is exposed to the flashy visualization.
So how to overcome this challenge?
Both visualisation present the same information:
trends over time,
the best and the worst-selling products.
Doesn’t it look like Spaghetti Monster? You rummage with a fork to find a juicy bit of meat. Similar is with decoding some information from this visualisation (line chart), it costs a lot of effort and time. Our brain decoding one information eg. line colours, then stores it in memory, then compares lines position on the chart, then look for trends for each of lines goes back and forward through the chart to make a sense of it.
On the second approach, I’m showing the different strategy to present the same data set. Splitting information into two visualisations gives clarity and ability to draw a conclusion. The left chart enables the receiver to compare sales amount between products and memorise them easily. The right chart provides information about particular product trend in comparison to average sales. In this approach, we guide the audience through data. We pay their attention to important points. We don’t leave them alone having hope that they draw a conclusion on their own.
I came up with the idea for this article on the last webinar, which I had the pleasure to conduct with my coworkers. One of the participants paid attention to the starting point of the line chart, which I presented. He noticed that the starting point of the axis wasn’t in “0”. He addressed it with the famous book by Alberto Cairo “How Charts Lie” and commented that the line chart should have started at 0.
There is no doubt when it comes to the bar chart that it should ALWAYS start at 0. Bar charts encode data by length. People have developed the ability to compare objects in terms of length for thousands of years of evolution. Thanks to that they were able to estimate how high the food hangs on a tree branch or compare themselves with the enemy to fight or escape. Placing starting point in non-zero skews data and misleads our audience, because in the first place, unconsciously, they will start comparing bars length.
Of course, we can label bars and axes properly. The crime would be to switch off the Y-axis (in such case), what I observe from time to time. But even then in our brain, there is cognitive dissonance. Numbers don’t reflect lengths and proportions. Lengths and proportions are what our brain will remember because numbers are quite fresh phenomenon for our brain.
Let’s compare below examples for the bar and line chart with zero and the non-zero starting point and check what consequences it might have in the interpretation of data.
Skewed Y-axis & Bar Chart
To have no heart attack in the near future and be still in fit, WHO (World Health Organization) recommends taking a 10 000 steps per day. There are plenty of apps which can track your daily physical activities. Above charts presenting my recent results from the same range of dates. On the left side chart, a proper baseline is applied in 0. All daily results fluctuating nearby the daily goal. In one second, the level of dopamine in my blood pomps up looking at bars achieving the daily target.
The right chart doesn’t give me a reason to be proud of my self at first glance. Firstly, my brain notices gaps between bars and target line. And OMG, twice I almost took no steps! If you don’t notice Y-axis label, you can interpret this chart so dramatically. Worse, if you just had a chance to see it for a few second, you would probably make such a conclusion. Your brain wouldn’t have time to notice Y-axis labelling. But two times I exceeded the target more than twice. Awesome! Everything WRONG.
Skewed Y-axis & Line Chart
A different situation is with line charts. There is no length to compare. There are only slops and positions. In this case, context and narration play first fiddles.
On these line charts, the same data set is presented. From the chart on the left side, we can take out a similar story. The performance is almost aligned with the target. However, looking on the right chart, our brain doesn’t make automated assumptions on lengths because there are no lengths. We see connected dots.
And now is a question. Does the non-zero axis skews data at line chart or not?
There is a discussion around it. Still, non-zero baseline, even though there are no bars to compare, can mislead the audience, presenting steep slope of tiny mountains. However, in some cases, having a particular purpose in mind, it can be the best option to choose. Non-zero axis at line charts is good for presenting minor fluctuations or changes of phenomenons like stock exchange rates, products quality tracking (production series) etc. Especially, tracking performance within companies. Even small changes can have a huge impact.
In our scenarios. Well, to pat on the back myself, I would choose the bar chart with “0″ baseline, but to be able to control my daily results in details, I would definitely choose the line chart with non-zero baseline.
Seneca said, “We are more often frightened than hurt, and we suffer more from imagination than from reality.” Imagination is a powerful weapon. Designing compelling data visualizations to sell stories might get a human imagination down to work.
To make it happen, the context is a key player. Without the context, it is hard to understand presented numbers or outcomes. The human brain always seeks for comparisons to create a meaningful picture of the world. In this article, I would like to talk about how we can add context to presenting the behaviour of the phenomenon over time.
From my experience, I often see a single line of eg. revenue, sales, costs or number of claims presented on a line chart. However, without the proper highlighted background, it’s hard to say if what we see is positive or negative. Is this change is for better or worse. Using additional information, the message is strengthened and helps tell a thoughtfully crafted story.
This approach is especially important when the report supports the decision-making process. Quick business insights can be easier revealed when decision-maker can benchmark presented data to thresholds.
Let’s check how different stories can be told. On this chart, we can see a single line represents revenue of company X. Analytical eyes will see the downward trend over time. However, maybe this observation is not so clear for people who have other skills then analytical.
The first story can be about a decline in revenue over two last years. The declines in revenue can be depicted with an added trend line. In real scenario would be good to highlight specific points in time which caused this change.
The second one can focus on now and then. Comparing the two times period, current and last year helps see the magnitude of change. However, it’s good to remember that on such visualization trend over the longer period is lost.
The last one doesn’t emphasize changes over a longer time at all. It just presents performance vs. budget and directs the audience attention to “here and now”.
In conclusion, there are three different contexts for the same dataset, which changing the data perspective. Frankly speaking, combining thesethree perspectives gives an insightful story of revenue condition.