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Say you are a vertical manager at a logistics company. Implementing a real-time IoT system that generates streaming data, you now have access to aggregated analytics data in real time. However, can you trust this data?

In IoT systems, out-of-order data is one of the most common and vexing issues. Out-of-order data can result in inaccurate business insights, impairing your ability to optimize asset utilization or identify anomalies. Therefore, it is crucial to understand why streaming IoT data tends to show up out of order and how to improve data quality.

Out-of-order data in IoT platforms is primarily caused by issues in the first two links of the IoT chain: the devices and their connectivity. IoT devices may send out-of-order data if they operate in battery-save mode or have poor-quality design. They may also experience connectivity loss, such as when they travel outside of a cellular network’s coverage area. Additionally, devices may crash and reboot, leading to data being sent hours or more after the actual event occurred.

Connectivity lapses are not the only cause of out-of-order data. Many devices are programmed to extrapolate readings when they fail to capture real-world data accurately. This practice can lead to inaccurate entries in databases, making it challenging to differentiate actual measurements from the device’s best guess.

While it may not be possible to prevent data-flow interruptions completely, there are methods to process streaming data that mitigate the impact of out-of-order data. One solution is to use a real-time data processing engine with specific features designed to handle out-of-order data.

Look for a data processing framework that offers bitemporal modeling, allowing you to track event readings along two timelines simultaneously. This feature helps identify any lapses between when a measurement is recorded and when it reaches the database. The framework should also support data backfilling, enabling later corrections to data entries and accepting multiple sources of data, including streams and static data. Additionally, smart data processing logic, which incorporates machine learning capabilities, can help debug and process data in real time.

By leveraging these capabilities, you can build an IoT system that detects and corrects out-of-order data, preventing it from causing issues. Choose a unified real-time data processing engine with a rich machine learning library suitable for your specific data processing needs. With the right tools, you can ensure the quality and reliability of your IoT data.

The post How to Improve IoT Data Quality: Dealing with Out-of-Order Data appeared first on satProviders.