Name of "Broad Street Analytics" was inspired from Dr. John Snow and his study during 1850's about cholera outbreak in London. One of the most powerful examples of astute data visualization eventually leading to the establishment of causality dates back more than 150 years. It was not yet known that germs cause disease and cholera was among the most feared. The disease arrived suddenly and was almost immediately deadly: people died within a day or two of contracting it, hundreds could die in a week, and the total death toll in a single wave could reach tens of thousands. The leading theory was that "miasmas" were the main culprit. Miasmas manifested themselves as bad smells, and were thought to be invisible poisonous particles arising out of decaying matter.

Dr. Snow had noticed that the onset of the disease almost always involved vomiting and diarrhea. He therefore believed that the infection was carried by something people ate or drank, not by the air that they breathed.

At the end of August 1854, cholera struck in the overcrowded Soho district of London. As the deaths mounted, Snow recorded them diligently, using a method that went on to become standard in the study of how diseases spread: he drew a map. On a street map of the district, he recorded the location of each death.

You can see Snow's original map above and click to expand. Each black bar represents one death. When there are multiple deaths at the same address, the bars corresponding to those deaths are stacked on top of each other. The black discs mark the locations of water pumps. The map displays a striking revelation—the deaths are roughly clustered around the Broad Street and its water pump. Although association and causality are two different things in science, noticing this association was the first hint. He later discovered sewage water had been contaminating some parts of River Thames, hence, drinking water which had been supplied by Broad Street pump.

Reference and more details: Computational and Inferential Thinking