Mar 13, 2018
| 6 min read

Value Vector: The Monetization of IoT Data (Part 1)

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The capabilities of connected technology are expanding rapidly with the trajectory of the Internet of Things moving at a rapid pace bringing monumental benefits to industry. The ability of connected devices to collect, collate, and analyze data has gone to the next level through the ability of devices to sell and trade everything  from storage, computation/analytics to electricity and sensor data. 

This first part of this two-part series takes a look at data platforms and marketplaces being established and raises some of the possible challenges of implementation.

It's well-known that data is the new oil, a particularly interesting idea driving the monetization of the Internet of Things. We've seen how data can save companies millions in terms of running costs through predictive maintenance insights, waste reduction and reduced down times. We've also seen a layer of monetization where connected devices will be layered with various software-as-a-service options, from energy cost insights (such as knowing a refrigerator is not cold enough or that a home device is using more energy than normal and costing more; to subscription models such as a coffee machine that orders extra coffee beans when running low or a refrigerator connected to a food delivery service.

But we're moving even further, thanks to the sheer scale of connected devices spewing forth data. What's emerging is the evolutionary creation of a 'Machine Economy' where devices will trade everything from storage, computation/analytics to electricity and sensor data.

A suite of platforms, marketplaces, and trading spaces are emerging with the aim to enable companies to sell or exchange data:

Data analytics company Dawex has created a platform that enables businesses to monetize their data or exchange it free of charge. Each organization is rigorously vetted with a strict process to ensure its identity. Companies can monetize datasets & APIs, raw data, refined data & insights. Examples of traded data include customer and product-related data, financial data, IoT data, licensed as one-off deals or subscriptions.

In July 2017, Samsung ARTIK unveiled their Smart IoT platform: ARTIK Cloud Monetization, a service they created to monetize the data shared by IoT devices and applications and kick-start new business models, such as Hardware as a Service. They suggest that device manufacturers can offer pricing tailored to different consumption patterns. For example, they can offer some end users a higher up-front fee with a more generous free-use tier, or they can charge a smaller up-front hardware fee with a higher ongoing usage charge.

Then, in December last year, The IOTA Foundation announced the beginning of a pilot project termed The Data Marketplace. They aim to create a data marketplace, essentially a platform where firms can buy and sell data from sensors and IoT-connected devices. They contend that their public Distributed Ledger architecture, in opposition to the Blockchain (known as Tangle), ensures data authenticity and an audit trail of data as their Ledger enables tamper-proof data. According to IOTA:

"The sheer magnitude and influence data has and increasingly will have on our society automatically come with huge business opportunities in the tens of billions in the next few years. Trading data will be a mutually beneficial exercise that both boost innovation for companies as well as create entirely new revenue streams of data that would otherwise just go to waste."

Over 20 organizations have signed up including Deutsche Telekom, EWE, Microsoft, Bosch, Tine, PwC, Accenture, Fujitsu, Schneider Electric, Orange and DNV GL. Their use cases are diverse, ranging from utility companies to autonomous vehicles, smart cities, and telcos.

With new revenue models comes to question the importance of data authenticity
The authenticity of data is arguably something those who've incorporated connected devices and service platforms into their workplace structure have not really had cause to ponder, not unreasonably trusting their platform provider. The inherent underlying assumption is that as data generated becomes something that can be used to make money, or is used in cross-device and cross-industry use cases, it may be subject to fraud. We can expect to see greater attention paid to data sovereignty: blockchain related technologies (such as IOTA's marketplace) are being rolled out by some.

The term GDPR (General Data Protection Regulation) is usually mentioned in the same paragraph along with ownership rights, regulatory requirements and international handling. It's likely that data handling will become more discerning (a supply company may not want to provide its data to its competitor for example), and whether data can be withdrawn or sold on again may need to be determined.

While we're more at a pilot stage than a solid marketplace, there are questions emerging. We have to wonder about the burgeoning growth in data marketplace and exchanges, how will they contrast and compare and importantly, connect with each other? What relationship will they have with existing IoT data platforms? How will each leverage their competitive advantage and will their benefits be easier to ascertain to a company undergoing digital transformation than that of our current explosion of IoT platforms? What happens to a company's data if an exchange is closed or sold to another competitor?


Want to learn more. Read Part Two to learn about specific use cases of data monetization in IoT and greater discussion about what comes next.  

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