Part 3/18:

Scaling up data volume introduces frequent issues related to data quality. These include missing information, null values, duplicates, and measurement errors—issues exemplified by experiences with clickstream data. Furthermore, once data leaves a service boundary, its context—like timestamp or origin information—is often lost, complicating interpretation. Semantic noise (disagreements over definitions, like what constitutes a "customer" or an "activation") and temporal noise (differences in data ingestion speeds from multiple sources) further muddy the waters.