There’s money to be made in IoT data

With the explosion in IoT sensors, companies across virtually every sector have been encouraged to collect and store vast quantities of IoT data. And while some are effectively harnessing that data to improve business operations, there is a growing recognition that this data could have a broader value. Indeed, as companies scrabble to realise a return on the investment in IoT sensors and vast data storage resources, there is a growing push towards leveraging these data resources by selling them to the highest bidder.

But let’s be realistic here – is there any actual value in that data set? Simply loading every piece of IoT sensor data into a vast cloud-based database and mashing it up is not enough: data without context inherently has no value. So where is the context? Where is the additional data source, or sources, that when combined unlock real insight?

Data potential

For example, with edge computing, refrigeration unit sensors are increasingly used to avoid food wastage by continuously monitoring temperatures and taking action should a fault occur; while predictive analytics of historical performance data is identifying potential points of failure, enabling remedial action. Clearly, this performance data is also incredibly valuable to the refrigeration unit manufacturer, especially when combined with contextual information about different locations and operations. Manufacturers could use this insight during the design process to improve efficiency and address problems within specific operational areas.

Similar additional value could be gained from consolidating information from sensors that monitor exposure to gas or particulates within hazardous operating environments. While this insight is increasingly critical to employee safeguarding and regulatory compliance, this data could also be shared with the insurance company to demonstrate high levels of safety and hence drive down premiums. Furthermore, the insurance company could gain huge additional value by consolidating diverse sources of sensor data to gain real depth of understanding across the market as a whole.

Offering context

Think laterally and the opportunities are incredibly exciting. But there are a number of issues to consider, not least: who owns the data? While clearly the company that has deployed a sensor has ownership of its own operational data, can the product manufacturer, the vendor of the gas sensor or refrigeration unit, also lay claim to the product’s performance data?

Furthermore, how is this data to be packaged up in a way that is both secure and provides value to a specific third-party sector? How is the additional data context to be added – and by whom?

Layering data sources over the IoT delivered insight is fundamental in being able to truly monetise IoT - the whole is definitely greater than the sum of the parts when it comes to data – but achieving that requires a clearly defined business model and full understanding of issues, from ownership to security and data delivery.

Conclusion

It is true that monetising IoT data could be the secret to achieving a return on IoT that many companies have yet to realise. But is it not just a case of bundling up all the data stored and offering it up to the highest bidder?
Without effective data models, a clear vision of how to add context and the ability to securely present that data to the right people to enable effective decision making, plans to monetise IoT data will fail. With the right approach, however, IoT really can become an incredibly compelling new value stream.