Who Owns What Your Sensors Know?

Sensors are generating valuable data across factories and digital platforms. As AI turns that information into business intelligence, questions around ownership are hard to ignore.

Every sensor tells a story. A machine vibration can signal an impending failure. A camera can identify movement on a production line. A connected device can reveal usage patterns, while a smartphone can track location, preferences and behaviour.

Individually, these signals may appear insignificant. Combined with artificial intelligence (AI), they become valuable intelligence. This can improve operations and influence decisions.

But as businesses expand their use of connected technologies, a critical question remains: who owns the data sensors collect, and who has the right to use it? Dig down to know more about this. 

From data collection to commercial value

Sensors are more central to industrial automation and consumer technology  than ever before . They continuously generate information. This information is analysed by AI systems to identify patterns. It also helps to make recommendations.

In manufacturing, for example, sensors can provide the necessary data for predicting equipment failure. This will help in reducing downtime. Ultimately, improving production efficiency. The same idea can work in the case of connected consumer devices. This can be done by allowing for creating personalised services and automatic responses.

However, the value of the collected data exceeds the original goal. The data that was collected in connection with the monitoring of the machinery can become a tool for training predictive models. The information collected via a consumer application can help improve recommendation systems. Eventually, companies that are gathering the data can use it as the basis for competitive advantage.

That raises the question of ownership of the data. As the same data can bring together the interests of the device operator, equipment provider and information aggregator and the platform running analytics on it all with their set of agendas with it ..

Data collection does not imply ownership over this data per se. However, the entity managing the infrastructure usually has the highest practical opportunity for the data storage, processing and monetising.

The ownership question gets complicated

Traditional ideas of ownership become difficult to apply when data moves between devices and through platforms some on premise some on the cloud. A factory may own its equipment but rely on a third-party supplier to process machine data. One company can provide sensors, and another company can offer the e-analyzing system. An artificial intelligence firm can take advantage of the data for training their models which in turn helps the manufacturer run sharper analysis..

Who is the owner of raw measurement data? Who has access to processed data? Can the supplier take advantage of operational data to create products for other clients? What happens at the contract’s termination?

These questions are not only technical. They have implications for intellectual property, commercial confidentiality, cybersecurity, and power in negotiations.

Consumer information raises similar issues. Voice assistants, connected devices, or online platforms acquire the data about consumer usage of various products and services. Depending on the technology and its setting, the data may be stored, processed, or utilized to enhance AI systems.

Users may sign lengthy contracts without fully understanding what is going to be done with their data. In industrial terms, the opposing side runs the risk of accepting data-sharing agreements without a clear understanding of their rights in terms of utilization and reuse of data.

AI makes transparency more urgent

AI increases the value of data because it can extract patterns that were not obvious when the information was first collected. It can also combine datasets to generate insights about equipment performance, business operations or individual behaviour.

This leads to a challenge in transparency. Companies may explain the reasons they collect data, but they do not adequately explain the length of time they will keep the data, which third parties can get it, or if it is used for AI training.

After the data has been used to build a model, it can also be difficult to remove it from the model. The deletion of the original file does not surely correspond to the removal of the acquired knowledge.

However, this does not imply that every artificial intelligence system uses every piece of collected data. The use of data will depend on the system, the terms of contracts, the settings of confidentiality, and the law. Nevertheless, there is a need to address the difference between data collection and usage for AI creation. Regulations have started addressing the issue of data usage. 

The next battle is over control

The debate around AI often focuses on computing power, model performance and automation. Yet data remains one of the industry’s most valuable resources, and sensors are increasingly the point at which that resource is created.

Companies should not expect their customers, workforce, or business associates to trust electronic intelligence if explanations about data processing are absent. Data access does not give companies the right to exploit data at will. The problem is how to create robust rules regarding data use before data flows down the previously unthought-of chain of connected devices and providers of software and intelligence.

It is true that sensors make people aware of something, though does that awareness come with unlimited rights for a dataset owner in relation to how that knowledge will be used. The more sophisticated AI use becomes, the better position organizations that develop understandable and transparent data rules will get in terms of trust.

The issue here is not really related to what sensors can know but about who will have access to that information, who all can take advantage of it, and who will be responsible if something goes wrong.