Tag Archives: data analytics

Numbers with Human Face

Recently, I’ve taken part in a discussion about how to present numbers to convey a message about true people stories.

We often forget that these are not only numbers. Each number represents a human being, his/her tragedy and tragedy of her/his relatives.

Statistics often show numbers, % of populations, rises, falls and trends. There is a huge challenge and effort to depict context and tell the story behind datasets. Especially, when we try to depict in numbers the phenomenon such as #COVID-19. We have to remember that “confirmed cases” are real people, who are diagnosed with coronavirus. A number of deaths is a number of people who lost their lives because of this disease.

Daily, we are exposed to numerous statistics in media, workplaces, schools. They describe current situations, accidents, local and global events like car accidents, infants mortality or unemployment. Most of them are expressed as a ratio or percentage. These formats are not intuitive and for most people are hard to interpret. However, there are some methods, which connect numbers with people. Maybe not with individuals, but with countable human beings, with whom we can empathize.

KPI approach

A good example is the unemployment rate, which is one of the most important economic indicators. In the governmental statistics, unemployment is presented as a ratio of employees to all people who can work.

An unemployment rate expressed as a percentage does not cause any emotions among most of us. Most of us understand what see, but … it is nothing personal. Percentages are abstract objects. It is about closer indefinite part of the population throughout the country.

As studies show, we can transform this message in a way to evoke people feelings and make them start to take a more human perspective. Instead of abstract 20%, we can present that 1 out of 5 people is unemployed. Each of us can count to five. Each of us can easily list five people. Behind this number, people’s faces may stand. In such a small group of people, our neighbour or our family member may be out of work. This is no longer an abstraction but a very real threat.

Human approach

The Nature of the Phenomenon. Linear vs. logarithmic scale

The one dataset, two charts, two opposite stories.

The introduced scale has a huge impact on how we digest and interpret the presented data. The linear scale represents natural numbers, which we can easily compare. The logarithmic scale is not intuitive for us. It’s a mathematical concept, which we can use when we want to describe multiplicative factors or when is a huge skewness towards large numbers. We need to use brainpower to understand it. What is more, we are so used to linear one that we can easily overlook that visual is depicted on a logarithmic scale. We should inform our audience that logarithmic is used… and make sure that they understand how to read it.

Because of COVID-19 huge amount of statistic are generated and published across the internet. Those statistics try to tell a story about COVID-19 phenomenon. Most of them focus on a number of confirmed cases and deaths. I notice two data visualisation’s trends regarding presenting data about this virus. The first one concentrates on the growth of a total number of confirmed cases and the second one on the pace of disease spreading.

Let’s feel the difference.

“PANIC chart” — I saw somewhere a good name of such a linear chart. I couldn’t more agree. Tell me, what feelings this chart evokes in you?

This is an exponential chart (another mathematical concept), which depict the growth of the phenomenon. Very rapid growth to be specific.

Below we can see the same data. However, embedded on a different scale. Please, look carefully. Each grid represents 10 to n power. Don’t you think that the below chart isn’t so scary?

What stories these two charts tell us?

Let’s base them on 18th of Mar and 4th of Apr. The Linear chart tells us that till 18th of Mar nothing spectacular happened. Totally opposite to the Logarithmic one, where we can see the fastest growth of confirmed cases. Between 18th and 4th on the Linear, we can see the huge growth. On the second one, the pace of growth decelerates. After 4th of April, the Linear continues to present the same pace of growth (steep hill), but on the Logarithmic, it’s plain to see that the curve flattens.


Recently, I have had an interesting discussion about the pie charts. My interlocutor claimed that the pie charts should be in usage because they are intuitive, and people decoding information from them very quickly. Well, it’s exactly opposite, how research shows. The human brain cannot quickly and accurately compare several angles. What is more, on the pie chart there are not only angles but areas and colours which confuse the human brain as well. As proof, check out the paper of W. Cleveland & R. McGill about the visual decoding of quantitative information. So, how to cope with part-to-whole cases? What instead of the pie charts? How to effectively convey information?

Paulina, what is wrong with you? Why do you hate those pie charts so much? Look. They are based on the ideal shape, and you can use all your most favour colours at once!

Unfortunately, those colours and this ideal shape are a true curse in the clear interpretation of the data. Research shows that the human brain has problems with decoding quantitive information presented by those three attributes:

1. angles,

2. areas,

3. colours.

Let’s see an example. Imagine that parts of the wheel are product categories which you offer in your store. You would like to find out which category is the most profitable one. What kind of question would you ask?

Which category sales the best? Which one is the worst?

Let’s see how visual decisions can affect the process and speed of getting valuable insights.

Note: I’m not adding data labels on purpose. I want to focus only on visual decoding (without text support).


I would never choose a pie chart to compare more than two categories. Are you able quickly and accurately answer questions statement in the example? I’m not. NOT AT ALL.


We can instead of pie chart use 100% stacked bar chart, up to some point. Research shows that people are quite good at comparing lengths. However, colours can distort lengths. The more saturated colours, the larger the object seems to be. The second issue is again with many categories. The more of them, the workload for comparing elements increase.


This is my first choice. There is no field to mislead anybody with a stacked bar chart.

As I mentioned, people are quite good at comparing lengths. When we use one colour of bars, we can be 100% sure that no one will have issues to recognize the longest and the shortest bar. One blink of an eye and you understand what you see. For your lazy brain, it is pure magic.

The Power of Alerts.

Is all information are equally important for running your business smoothly? Or one is more desirable than others? Do you need to see detailed data on the first page of your report and devote precious time to analyze and interpret them? Or maybe it’s better to look at a carefully selected information with additional colour, which guides you through electrifying insights?

Often, when I talk to clients, I find out that most of them still live in the world of long charts and tables. As if a true report should be very detailed, very extensive and covers large areas of the subject.

My approach, which I offer, is slightly different. If we would like to craft an insightful dashboard, it’s good to follow several rules:

1. Select information, which is relevant and depicts the business condition.

2. Design indicators that will change daily and alarming colour coding can be easily applied.

3. Keep it simple. Don’t overload it.

4. The rest of the information is saved for sub-pages.

We aiming in designing a tool, which helps to make a data-driven decision for business decision-makers. Very often, they just have a blink of an eye to see a complex situation.

Let’s check an example. Let’s imagine that you are the Sales Director. What information you would like to check sipping morning coffee?

On below two approaches, we present exactly the same information:

– current sales,

– change over time,

– budget,

– trend.

However, the difference is huge when we take into account the speed of digesting the information, the making sense of it, and perhaps actions, which we are going to take.

Analytical approach

The analytical approach doesn’t provide quick insights. Some time must be involved to understand what a graph presents, then find out the number for current year sales, then to compare bars, then to estimate how large gap is between current and last year sales and budget.

KPI approach

On the KPI approach (Key Performance Indicators) desirable information is presented at glance. KPIs are selected to tell a story and facilitate to understand the situation. No additional effort is needed. You as the Sales Director can easily conclude and act. You just see it.

Time flies! Categorical data embedded in time.

Presenting time-related categorical data can be tricky. Fortunately, there are some good practices which guide us on how to approach the topic. In this article, you can find a summary of DOs and DON’Ts upon a subject.

The first well-documented calendar systems, which portray the linear nature of the time, appeared in the archaeological record around 5’000 BC. Most of us feel the pressure of time. To express it, we use common sayings like “Time is money” or “Time waits for no one”.

The culture shaped human time perception. We think about the time as the arrow shot into space. That has, of course, the impact on data visualization aspect. There are three basic rules regarding presenting data in the context of time. By following them, people easily digest information and form conclusions.

1. Use left-right direction.

2. Keep chronological order.

3. Use typical time units.

Another thing to consider is proper visualization. The decision of using one visualization than another depends on the aspect of continues or categorical data type. Continuous data have only one option — line graph. However, categorical data, which are clustered in periods or bins, can be more tricky.

In today’s scenario, let’s imagine that we present outcomes (level of satisfaction) from the survey grouped by respondents age. What would be the best choice when the data dimension is embedded in time, but not expressed in typical time units?

Stacked bar chart

The general rule is to present categorical data on a stacked bar chart (using Y-axis), with proper descending sorting. Nevertheless, categories which are linked with the time, for instance: age bins, archaeological periods, process’s phases, the human brain decodes much easier on X-axisstarting from left and ending on right.

Stacked column chart

Presenting time-related categories on X-axis is good to remember to keep chronological order. Like in this example, sorting descending categories by the value are not very effective. The audience must use a cognitive power to understand the meaning of X-axis and figure out the order of age bins in the survey population.

Stacked column chart (chronological order)

Then, when we placed categories on X-axis with chronological order, and like in this example the order is from younger to older, we tell the story of distribution of survey outcomes among respondents population in the age group context. Combination of a column chart and X-axis embeds us within the context of population distribution and helps remember the results.