Seen your share of data disasters? Post them on the community!

[#1]

As someone who works with data, you know the devil’s in the details. When data is used correctly, it’s a powerful tool for building customer loyalty, preventing fraud and reducing costs—just to name a few applications. But, when data is misused or misapplied the results can be catastrophic. These mistakes matter. That’s why we want to feature your “data disaster” stories. From your many years as data experts, tell us about your experiences!

Not sure what we’re looking for?

We recently spotted this error on a major home improvement store’s website. Can you spot the data management mistake?

Find it? Yeah, we didn’t think this fence post should be in the bathroom section either! This is a classic case of poor data management with real impact for this retailer: the right customers won’t reach this product if it’s placed in the wrong category. Not to mention, it’ll confuse customers looking for bathroom accessories.

We also welcome your more positive stories—maybe someone else’s data error was in your favor. One of my former classmates had the same first and last name as a popular celebrity. He said that he often got free perks, extra products and VIP attention from companies thinking he was the real Mr. Big Shot. Companies made this mistake over and over again, even though the two men had different middle names and the classmate’s zip code wasn’t 90210, among other dead giveaways.

Post your experiences (by commenting below, writing another article, or posting a discussion) and we’ll highlight the data errors. Victim or instigator, active participant or observer—we want to hear your stories. No judgments, we promise!

[ballardw]

The biggest data disaster I ran into was related to a basic regression line issue. An electic utility had looked at a regression line of total useage over a period of years that included a 20 year estimate trend line past the last year of data. Unfortunately no one had paid attention to the fact that most of the change was industry transitioning from their own "boiler" power to the grid and was much larger than the population growth. The regression was prepared at about the same time as 95% of the customers likely to make such a change was completed. So the company spent lots of $$$ to expand power but the actual growth changed from a nearly 10% increase projected per year to about 2.5%.

Moral of the story: Look into the drivers of your trend lines, not just the lines themselves.

[AnnaBrown]

Excellent point, ballardw, considering external drivers is very prudent - in any industry. Thanks for sharing!

Anyone else have data disasters that come to mind?