The biggest mistakes made over bad data throughout history
We all know that bad data can lead to costly decisions. IBM estimates that bad data costs the US economy around $3.1 trillion each year. But have you ever thought about the decisions throughout history that came about as a result of bad data? Utopia Inc, has compiled a list of famous examples throughout history of how bad data changed the world. Bad data causes more than just a tiny ripple effect in our society as the ramifications of bad data can be far reaching.
Let’s explore some of the examples of bad data throughout history:
Christopher Columbus and the discovery of the Americas
Christopher Columbus made several miscalculations when charting the distance between Europe and Asia. His first mistake was using navigation given by a Persian geographer over the more accurate and accepted calculations of a Greek geographer. Columbus could have verified that the units of measurement were correct himself but instead relied on bad data. Instead of going to Asia like he had planned he wound up discovering the Americas.
In 1985 Coca-Cola launched “New Coke” to compete with Pepsi’s growing market share during the 1980s. “New Coke” was a classic example of bad market research data. Coke relied on testing 200,000 subjects who preferred “New Coke” over both Pepsi and the traditional flavor of Coca-Cola Classic. It turns out that there were several factors that market research didn’t account for. It turns out customers are motivated by more than just taste. Replacing the product over the original formula turned out to be a horrible decision. Coke was quick to kill “New Coke” and bring back the traditional flavor that their consumers demanded.
During the early 2000s Enron grew to be the sixth-largest energy company in the world with soaring stock prices throughout the late 1990s and early 2000s. However, a host of fraudulent financial data led to their eventual collapse in 2001. It turns out that the data being provided to shareholders in annual reports and financial statements were largely fictionalized. Dozens of indictments resulted in many Enron executives being incarcerated.
The 2016 US Presidential Election
The 2016 Presidential election was mired was bad data. National polling data used to predict state-by-state Electoral College votes led to the prediction of a Hillary Clinton landslide victory. This forecast may have lead many Democrats and other voters to stay home on Election Day. This bad polling data was delivered across a host of new publications worldwide. This could have been prevented by using advanced statistics to analyze previous elections using machine-learning and created different models based on voter rolls. These methods are ultimately costly and very time-intensive.
Better data ultimately leads to better decisions. We can all learn from these examples of bad data changing history and do our part to slow the spread of misinformation throughout our society.