It’s a subject of ongoing research, but comes down to the robustness of the system.
A robust system needs to be certain of the completeness of its data, and also have redundancy built in.
For mobile network operators, this means relying on more than just the network insights they have internally.
Specifically, they need to find a way to validate internal data with real-time external information that is representative and free from bias.
Bias in mobile network data often comes down to test methodology.
User-instigated speed tests, for example, provide valuable insights for consumers about their mobile performance.
However, if used as a tool for measuring network performance to inform AI decisions, there is the potential for bias to affect the results – people tend to instigate tests either because their network is performing particularly well, or because it is performing badly, and seldom because they’re simply curious.
Problems also exist with field-tested measurements.
Professionally collected, high-quality measurements are the most precise and detailed source of network intelligence.
However, they’re extremely expensive to collect, making it hard to get 24/7 real-time information.
Drive-testing is inherently confined to set locations and times, meaning the depth of information may be extremely good, but the breadth is insufficient for real-time AI applications.
Instead, mobile network operators looking to utilize AI effectively for their network need to look for a data source that is able to collect enough core measurements (such as download speeds, latency, and packet loss), round the clock and without requiring a user to decide when to run the test.
The good news is that, with such a high penetration of smartphones, there is now an army of “citizen sensors” out there, and an SDK embedded into the background of applications can collect these key metrics, without bias and without impacting the user.
Data streams like this provide the broadest picture of network quality regardless of time, location or user.
If a single AI system at Elisa in Finland could lead to a 50% reduction in 4G customer complaints, it’s hard to imagine the kind of efficiency and improvements that more broadly deployed AIs could have on the mobile network industry.
With the right AI, fed the most representative information, mobile network operators have the best chance of being able to understand their whole network and make the right decisions for network planning and optimization.
The result?.Better mobile internet for consumers, reduced churn for operators, and an overall improvement of the quality of mobile networks for everyone.
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