Artificial Intelligence (AI) is transforming industries globally with its ability to enhance efficiency, optimise operations and unlock new insights. Central to leveraging these capabilities in AI is data integrity, which ensures that the data used by AI systems is accurate and reliable, enabling more informed decision-making.
Now fleet managers and telematics users are exploring the integration of AI to increase reliability and security. Because telematics systems collect vast amounts of information, AI has the potential to become an invaluable asset for interpreting this data, offering insights that were previously unattainable.
This article examines the role of AI and data integrity, and the need to maintain high standards of data integrity to leverage telematics and AI effectively.
What is data integrity in telematics?
Data integrity in telematics ensures that the information being analysed is accurate and reliable, which is crucial for systems that depend on high-quality data to make predictions and provide insights. Poor integrity in data can lead to inaccurate outputs, which can result in costly errors and operational inefficiencies.
Therefore, as AI continues to be explored for telematics systems, maintaining stringent standards for integrity in data becomes increasingly important.
Device calibration for reliable data collection
The first step in effective data management involves proper device calibration, appropriate wiring and ensuring the device is suitable for the asset it monitors. Correct installation is essential for reliable data collection.
“It’s important to remember the device you’re putting in has no prior knowledge of what’s happened to the asset. So it needs to be appropriately calibrated,” explains Max Girault, Chief Commercial Officer of Inauro.
The role of communication in data integrity
Effective communication is equally vital for data integrity and management in telematics. Often, the individuals performing field tasks may not be adequately briefed. This can lead to errors in data collection.
These inaccuracies can undermine decision-makers’ confidence in the telemetry system. If the data is perceived as unreliable, the system may be dismissed altogether.
AI’s potential in predictive maintenance settings is being explored
Increasing potential use of AI in telematics
AI’s role in telematics has the potential to enhance predictive maintenance. This is a process where data from operational assets is compared to manufacturers’ benchmarks to anticipate failures.
The scope of AI in telematics to offer predictive maintenance capabilities across entire fleets is exciting. However, it might be too early to label these advanced analytics as AI in the full sense (often they are more accurately described as complex algorithms).
Telematics systems produce massive amounts of data and the sheer volume can make it difficult to extract meaningful insights. AI has the potential to transform raw data into actionable insights, allowing it to address specific queries and identify patterns. For example, in a fleet with different truck models, AI could identify which models are most reliable or depreciate the least, informing decision-making.
However, data integrity underpins these capabilities. “You can use AI whenever you want, but it all comes back to the data,” says Max. “If the AI is based on data that’s inaccurate or irrelevant, then it’s not going to be providing anything of value.”
Data integrity is at the core of robust, appropriate and useable telematics systems
Inauro’s approach to ensuring data integrity
Leveraging the Perspio™ platform, the team at Inauro integrates data from different sources, providing real-time analytics to enhance operational efficiency. Data integrity is at the core of what we do.
“We generally start with the data of the existing system and soon notice if the data is incorrect,” Max explains. “For example, for one business, we identified a miswired refrigeration unit through incorrect temperature readings, signalling a disconnected sensor.”
Inauro also focuses on maintaining device health across fleets. “If anything goes on the road, the device needs to be 100% operational,” says Max. This approach enables quick reporting and immediate action on data anomalies, such as generating work orders for offline sensors.
Looking forward, Inauro is exploring AI to further enhance its capabilities and services. “Data integrity is important now, but it’s going to be even more important if, in the future, you start looking at AI-driven telematics,” Max says. “You want to make your data management really robust before you do that.”
Integrity at the core
As AI continues to evolve in telematics, the emphasis on maintaining high standards of data integrity grows. “You always want to ensure that the data that’s being generated is accurate,” Max says. “It’s key for a lot of things because it touches so many different parts of the business.”
Inauro offers tailored data solutions that are grounded in robust data integrity. Get in touch with our experts today to discover more about the potential of AI and other technological trends in telematics to drive your business forward.
