What is the purpose of data normalization in telemetry streams, and how does it facilitate cross-subsystem analysis?

What is the purpose of data normalization in telemetry streams, and how does it facilitate cross-subsystem analysis?

• A. Convert data to common units/scale; reduces mismatch; simplifies fusion and analysis.
• B. Randomly changes units for security.
• C. Hides original data values.
• D. Increases data variety to challenge analysts.

Answer: (B)

Data normalization in telemetry streams is about putting measurements from different subsystems onto a common footing—same units and comparable scales—so you can compare, fuse, and analyze them across sources. When every stream uses a consistent unit system and range, the fusion algorithms can combine data reliably, correlations across subsystems become meaningful, and trends are easier to interpret. For example, if one system reports velocity in knots and another in meters per second, converting them to a single unit before analysis lets you accurately merge the readings and detect coordinated behavior or anomalies. The idea is to remove mismatches that would otherwise create false differences or misinterpretations. The other notions—randomly changing units, hiding original values, or increasing data variety for its own sake—would degrade interpretability, traceability, and the ability to analyze data coherently across subsystems.

Data normalization in telemetry streams is about putting measurements from different subsystems onto a common footing—same units and comparable scales—so you can compare, fuse, and analyze them across sources. When every stream uses a consistent unit system and range, the fusion algorithms can combine data reliably, correlations across subsystems become meaningful, and trends are easier to interpret. For example, if one system reports velocity in knots and another in meters per second, converting them to a single unit before analysis lets you accurately merge the readings and detect coordinated behavior or anomalies. The idea is to remove mismatches that would otherwise create false differences or misinterpretations. The other notions—randomly changing units, hiding original values, or increasing data variety for its own sake—would degrade interpretability, traceability, and the ability to analyze data coherently across subsystems.