In streaming data architectures, data lag is particularly critical because these systems are designed to process and analyze data in real-time or near-real-time. The presence of data lag can lead to outdated information being used for decision-making, affecting the accuracy and effectiveness of those decisions. For instance, in financial trading, a lag in processing market data can result in missed opportunities or potential financial losses.