Bad data can have disastrous — even fatal — consequences, but some people still struggle to understand the potential gravity of data quality problems. The longer a business survives without a data quality disaster, the more complacent they tend to become with assessing and managing their data.

Check and recheck your math — then check it again

Something as simple as a misplaced decimal point can cost you thousands, millions, or even billions of dollars. Just look at the $2.7 billion Spanish S-80 submarine program. One misplaced decimal point led to submarines 70 tons heavier than planned, and engineers feared they would be incapable of resurfacing once submerged. Spain had to spend $14 million to have their weight reduced.

Misplaced decimals are bad enough, but what about calculating with the wrong units? In 2013, the Amsterdam city council used software to calculate housing benefits, and accidentally used cents instead of euros. The mistake resulted in the distribution of 188 million euros in benefits rather than the expected 1.8 million euros, and the city council was forced to ask for the money back. While most of it was returned, many already struggling citizens ended up in extreme debt because of the error.

NASA isn’t immune to unit errors, either. In 1999, a $125 million Mars orbiter was lost because NASA used the metric system, and the engineering team at Lockheed Martin used the imperial system.

Finally, something as simple as a conversion error can be life-threatening. While converting between metric and imperial systems is routine, a small error in the conversion process forced the crash landing of an Air Canada flight in 1983. The math error left the plane with only half the fuel it needed to complete the flight. The plane crash-landed on a racetrack, and its nose was destroyed, but thankfully, no one was killed. Of the 69 people aboard, two suffered minor injuries.

Outdated data

Making business decisions based on consumer data is a good idea. Relying on outdated consumer data? Not so much. Take, for example, the spectacular disaster that was Crystal Pepsi. At the time of its development, PepsiCo was chasing a health food craze, and artificial colors were increasingly unpopular with people pursuing healthier diets. So, Pepsi created a clear soda. Unfortunately, new data indicated the taste of the drink was too far from that of the original formula, and consumers wouldn’t like it. The company chose to ignore the new information, and Crystal Pepsi flopped.

Even game shows have issues with outdated data. Just last year, Jeopardy! relayed inaccurate data about a medical condition known as postural orthostatic tachycardia syndrome (POTS). The prompt used an outdated, offensive nickname for the condition — Grinch syndrome — based on a disproven theory that the hearts of POTS sufferers are smaller than average. The show was forced to make a public apology.

Human error

Humans are imperfect creatures. That’s why we check, double-check, and have someone else triple-check our work on important projects. Sometimes even that isn’t enough — especially if pride gets in the way. Human error is one of the most common causes of data quality disasters. Some examples include:

  • Challenger. While the dangers of O-ring failure in the space shuttle were known, they weren’t considered a serious issue. Prelaunch safety checks missed that the ring had cracked due to cold temperatures. We don’t need to tell you what happened next.
  • Enron. Enron Corp. was an energy giant until it went out of business in 2001 because of spreadsheet errors. The accidental nature of these “mistakes” remains unclear, but 24% of the company’s spreadsheets contained serious errors. Add untrue claims of profit, and several other ethical issues, and the company didn’t stand a chance.
  • Olympic synchronized swimming. The 2012 London Olympics accidentally sold twice as many tickets as there were seats available for the synchronized swimming event. Why? A typo. Someone entered a “2” instead of a “1,” and the Olympic committee was forced to issue a public apology and convince 10,000 fans to accept tickets to other events.

Suffice it to say, human error can lead to serious losses when left unchecked.

Data quality is an essential consideration in any field. This is just a small selection of the significant disasters created by data quality issues. From Mars rovers to game shows, bad data leads to loss of revenue, public embarrassment, or even death—proving the necessity for comprehensive data quality management.

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