Common use cases for Data Stream Analyzer
Fuel calibration and monitoring for fleet vehicles
Managing fuel costs is a critical challenge for fleet operators, and even minor discrepancies in fuel sensor data can lead to inefficiencies and financial losses. The Data Stream Analyzer addresses these issues in several ways.
Fleet managers can create and fine-tune calibration settings for fuel sensors, ensuring accurate readings regardless of sensor quality or external factors.
A deep dive into past data enables precise calibration over time, helping to improve long-term fuel monitoring accuracy and uncover recurring issues. This leads to better cost control and fleet efficiency.
For example, if a fuel sensor reports irregular values, the Data Stream Analyzer can highlight the discrepancies, allowing managers to recalibrate settings quickly and ensure that no litre of fuel goes unaccounted for.
Ensuring complete data from GPS trackers and vehicle sensors
IoT system integrators often encounter situations where some crucial data attributes (e.g., speed, location, or fuel level) are missing or delayed, compromising system performance. Let’s say, a GPS tracker stops reporting fuel level data intermittently At this point, the tool can help figure out whether the issue is related to the tracker’s configuration or connectivity with the server.
Fleet operators and integrators can monitor incoming data streams for completeness, ensuring that all attributes are received and processed correctly.
When irregularities occur, users can pause the data stream to inspect historical and real-time data, pinpointing exactly when and where transmission issues began.
Whether the issue lies in hardware, firmware, or network latency, the Data Stream Analyzer helps identify the root cause, enabling quick resolutions.
Troubleshooting missing geolocation data
For logistics companies, consistent geolocation data is critical for efficient route planning, real-time tracking, and fleet management. Gaps in GPS updates can lead to delays and poorer operation. For example, certain vehicles in the fleet are updating their location every 30 seconds instead of the expected 10 seconds, and you want to know what’s the cause—tracker configuration or server-side processing issues. Data Stream Analyzer can help identify it.
Users can inspect the raw data to identify gaps in attributes such as coordinates, speed, or timestamps, enabling quick detection of inconsistencies.
Using null value filters, users can remove noise or incomplete data, focusing only on what matters to isolate the problem.