It’s typically a mix of everything, but predictive maintenance, anomaly detection and failure analysis are the most common. For example, there is one process that does trend analysis and tries to “predict” acceptable boundaries of a certain sensor’s measurements, and this is then compared in real-time with the actual sensor readings. If things fail for some reason, a technical engineer will dive into the data with dashboards (think: Grafana), zoom in, compare the readings with other sensors, etc.
The sheer volume of the data makes it fairly painful. Downsampling does happen, but only after a few weeks. This means that you still need enough storage capacity to deal with the full stream of data in real-time.
Most of the analysis that is done usually falls under one of two categories. First, inferring (you can rarely measure it directly) when something has changed in the real world that is relevant to your business so that you can adapt to it immediately -- the applies to everything from autonomous driving to agricultural supply chains. Second, detecting anomalies -- the unknown unknowns -- so that risks can be managed when the real world appears to not conform to the models upon which you base decisions. A third category is support of industrial automation, which benefits immensely from high-resolution multimodal sensor data models, though this is largely a cost reduction measure. These categories are hand-wavy but in practice, boring industrial companies have concrete metrics they are trying to achieve or risks they are trying to manage in the most efficient way possible.