Which aspect of data engineering is most affected by high data latency?

High data latency significantly impacts real-time analytics outcomes because it affects the timeliness and accuracy of the insights that can be derived from the data. In data engineering, real-time analytics relies on the ability to process and analyze data as it streams in. When data latency is high, there is a lag in the availability of the most current data, leading to outdated or inaccurate analytics. This can hinder decision-making, as businesses rely on real-time data to react quickly to changing conditions.

In contrast, while data collection methods, data storage costs, and data visualization performance are important aspects of data engineering, they are not as directly influenced by latency as real-time analytics. Data collection methods may involve different techniques to gather data, but they are not inherently affected by how quickly that data can be analyzed. Similarly, data storage costs pertain more to the infrastructure and resources used to hold the data rather than the speed of processing. Data visualization performance can be impacted by latency to some extent, but it is primarily the analytics domain that suffers the most because timely insights are crucial for effective decision-making in dynamic environments.