Location data can power some of the most valuable insights in your software — from foot traffic trends to real-world behavioral modeling. But what happens when the data itself is flawed?
Many software companies unknowingly build on location data that’s inaccurate, duplicated, or even deceptively manipulated. One of the most harmful examples is replay data — where old signals are re-timestamped to appear new. This creates serious problems for predictive models and real-time analysis. Your app might think users were visiting a store yesterday, when in fact the data is weeks or months old.
Even more quietly destructive are UUID issues. Unique User Identifiers (UUIDs) are meant to distinguish individual devices over time — essential for user-level analysis. But outdated or fabricated UUIDs can contaminate longitudinal models, inflate audience numbers, and degrade personalization features. Most teams don’t even realize they have a UUID problem until their metrics stop making sense.
At Unacast, we’ve found that in some datasets, as much as 50% of U.S. third-party location data is replayed, and the number is even higher internationally. That’s why we created forensic tools to automatically flag suspicious signals and provide UUID metadata so our partners can identify and filter issues before they cause downstream damage.
If your platform relies on location data but doesn’t validate sourcing, timestamps, and UUID consistency — your outputs might be less reliable than you think.
Don’t let broken data break your product. Download the full white paper to learn how to remediate your location data process before it causes damage.
