Why Your IoT Data Isn't Fit for ML—And How to Fix It

When you’re dealing with IoT deployments, especially in places like Kenya where connectivity issues and budget constraints are common, you quickly learn that IoT data quality can fail in unexpected ways. Before it even reaches your ML model, numerous problems can arise. I've managed over 2,500 IoT devices under these conditions, and it can be quite a journey. The data collection chaos Initially, I assumed that gathering data from devices would be simple. The first signs of trouble appeared when we installed a new batch of sensors in a remote area with unreliable internet. Instead of a clean stream of telemetry data, I received an erratic mess. There were nonsensical data spikes, inconsistent timestamps, and sometimes data packets arrived out of order. I learned that poor connectivity can wreak havoc on data integrity. The issue isn’t just about data loss; it’s about receiving corrupted or incomplete information. Reliability isn't guaranteed. To address this, implementing a simple retry