In skilled hands, data visualization can convince a client to buy your product, an investor to give money, a person to sit at home during quarantine. But behind the successful graphs and charts there are dozen of experiments. Poor visualization can be misleading and lead to erroneous conclusions.
History knows several cases where important decisions depended on visualization. Let's look at 4 stories:
In the nineteenth century, doctors believed that cholera and plague were transmitted by air. When in 1854 there was a cholera outbreak on Broad Street in London, the cause was also sought in the air.
John Snow, a doctor from London, was skeptical of this theory and decided to study the spread of cholera in the Soho quarter. He suggested that the cholera carrier could be water. To prove his theory, the doctor began to gather evidence.
John Snow decided to show mortality not through graphs or charts, but by using a map. He took all the data on the number of deaths from official sources and indicated them on the map. Also, on the map he marked the pumping stations.
The doctor noted all the deaths and discovered a higher number of deaths near the pump on Broad Street than near the other pumping stations.
Hatching represents number of deaths
There were two exceptions. Near the pump on Broad Street housed a brewery and workhouse. The people who worked there were not infected with cholera. John Snow began to search for the cause and found out that the brewery and the workhouse had their own wells. The brewery owner also allowed workers to drink beer during the shift. This prompted John Snow to conclude that the workers did not drink water from the pump station on Broad Street.
A map of John Snow shows an outbreak of cholera in the aggregate data: location of houses and pumping stations, number of deaths and place of mortality. Such visualization allows the reader to see the causal relationship between water and the number of diseases. We see the cause - the pumping station, and the consequence - death around it. If the doctor simply showed a list of dead people in the context of time, this would not give such a visual effect.
John Snow saw the connection between indicators and proved his theory, focusing on causal relationships.
The John Snow map convinced the government to remove the pump handle on Broad Street so people could not use it. Immediately after this, the number of deaths decreased and the outbreak of cholera stopped.
In March 1854, a war broke out between France, Great Britain, Sardinia and the Ottoman Empire on the one hand, and Russia on the other. Most of the fighting took place on the Crimean peninsula, but the wounded British troops were sent to Turkish hospitals with terrible sanitary conditions. As a nurse in Turkish hospitals, Florence Nightingale understood the importance of sanitation.
She wanted to find the true cause of the high mortality rates among soldiers. For this, Florence Nightingale compared the number of soldiers who died from various causes: age, injuries, conditions in the hospital. This showed that most died precisely from poor sanitation.
Florence Nightingale made the right comparisons and showed that hospital conditions are the key cause of death.
Florence Nightingale began to implement sanitation in hospitals and the rules for caring for the wounded. Thanks to her, mortality decreased from 42 to 2%.
After the war, the nurse returned to the UK and created a chart. She demonstrated that more soldiers died from conditions in the hospital than from wounds in the Crimean War. The visualization of Florence Nightingale proved to the Queen of England that most soldiers are dying from hospital conditions and they need to be improved.
Each of the sectors corresponds to one month (from April 1854 to March 1856). The blue layer shows mortality from diseases (which could have been avoided by improving conditions in hospitals). The red layer indicates mortality from wounds, and the brown layer indicates mortality from other causes. The area of each sector is proportional to mortality.
Two charts depict the situation before and after a commission from London was sent to improve the hygiene (in March 1855). From these diagrams it is immediately clear that the main cause of deaths was illnesses in the hospitals. After March 1855 (the smaller diagram on the left) the number of deaths decreased significantly.
In 2003, 82 seconds after the launch of the Columbia shuttle, a piece of thermal insulation foam fell out of the mount and hit the shuttle's left wing, damaging the thermal protection system. This did not stop the mission from continuing, but the question arose whether the crew would return safely to Earth with such damage.
Boeing Corp. has provided NASA with three reports that proved that a foam blow wasn't dangerous for the ship. But they did not notice that during the tests the impact force was 600 times weaker. The presentation convinced NASA and it was decided that the crew would return to Earth on their ship. The Columbia shuttle collapsed upon entering the Earth's atmosphere before landing.
Let's look at the slide and its mistakes:
In 1986, at the 73 second of its flight, the Challenger space shuttle exploded. The launch was broadcast live and the whole country watched the explosion. The catastrophe led to the death of all crew members. The official cause of the disaster was the explosion of the fuel tank due to a malfunction of the sealing ring.
On the launch day, a very low air temperature was expected. Engineers speculated that this could be dangerous. The evidence was presented in 13 tables and faxed to NASA.
The report had several visualization errors:
Let's look at the most important mistake - sorting data by the wrong parameter. The launch data had two main parameters: temperature and launch date. The engineers wanted to show that temperature affects the deformation of the ring. To prove this, it was necessary to show the success of the launch depending on the temperature. This would demonstrate that three failed starts were at temperatures below 64 degrees.
In a NASA report, engineers sorted data by launch date. This did not show a causal relationship - the dependence of failed starts on temperature. The tables of the designers were not convincing, the arguments against the launch failed and the Challenger exploded.
If you want to convince your stakeholders it is not enough to simply show the raw data. It is important to demonstrate the findings in a logical and transparent way. Data visualization will help you with achieving your goal. As they say, a picture is worth a thousand words.
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