Blockchains are the bedrock of secure IoT data nurtured for analytics
Data exchanges are where intermediaries purchase raw data and sell them as sets of data series worthy of analytics. Privacy and security concerns have prevented the enterprise from creating pools of interrelated series of IoT data and digital assets as resources for artificial intelligence. Data and digital assets from multiple sources can be purchased at the exchanges, where they are discoverable and shareable, and sold for the training of algorithms. Blockchains, the bedrocks of data exchanges, make data secure, and intelligible by keeping records of the context of data capture.
Shareable Data
Supply chains, for example, interconnect the activities of multiple partners — the producers, the logistics companies, the wholesalers and the retail companies. Each of the partners has an interest in learning how spoilage occurred en route to the sales outlet. They can assign responsibility, for any deterioration in quality, based on the data on temperature control and process compliance by each of them. Blockchains keep traceable records of the entire sequence of events in a supply chain for all partners to view and analyze to understand the causes of spoilage.
A recent report by McKinsey found that most IoT data remains largely unutilized because current use cases extract very little value from current use cases that track a single or few activities that trigger alerts when outcomes don’t match expected performance. Strategic uses of IoT data, such as the optimization of operations, will be far more valuable. Such use cases will require data and analysis for an entire cycle of activities inside the enterprise and extending to partners.
The reality is that a majority, fifty-four percent of the respondents, reported that the utilization rate for IoT data is only ten percent. Thirty-four percent experienced challenges determining the context of the data that they need for analysis.
Data marketplaces encourage participants in an ecosystem to voluntarily share their data for a monetary reward. Sellers are willing to pool data on legitimate exchanges as privacy regulations prevent them from using even their internal data for analytics purposes. “European banks want to use blockchains to analyze their customer data which they can’t do internally due to regulatory prohibitions,” Jernej Adamic, Co-founder, and CEO of Zenodys, a data exchange based in Holland and operations in South Korea, told us.
The verification of the immutable identity of participants on blockchains persuades sellers that data will remain secure on exchanges. Also, datasets stored on a distributed ledger are trustworthy; any attempt at tampering with the datasets will alert others in the blockchain.
Data exchanges use blockchains to keep records of data purchased from multiple IoT sources. Additionally, they organize lists of data series to be usable by analytics companies. They also create stores for associated digital assets such as algorithms, applications, and platforms.
Smart contracts on blockchains undergird the exchanges of data and assets between participants in the data marketplaces. They eliminate or supplement third parties like banks. As a result, it becomes possible to have automated transactions between machines such as stations charging electric cars purchasing solar electricity from the energy storage devices of households.
Blockchains keep records of contextual information gathered by sensors for an entire sequence of activities covering production, transportation, and delivery. The granular level of visibility helps to uncover machine vulnerabilities in factories, transportation choke points, surreptitious insertion of counterfeit products in supply chains and theft in stores.
Data marketplaces build their brands on the strength of their trustworthiness and the consistency in the quality of data series they sell. “Companies are hesitant to purchase data products unless intermediaries can provide service-level agreements to protect them against failures such as APIs getting overloaded,” Martin De Saulles, author of several books on IoT and the information business, told us based on his experience of researching companies in the UK. “Smart contracts help us to enter into conditioned agreements which base compensation on the achievement of KPIs such as latency and data availability,” George Saleh, CEO and co-founder of Paris-based Datapace.io, a data marketplace, told me.
Ethereum lets partners enter into contracts without third parties
Value of IoT Data
An amorphous mass of IoT data, described in multiple ways, is neither discoverable nor is it usable by artificial intelligence companies. It takes a description of the data to convey its value to analysts. “We created a standard taxonomy which is called the Universal Labeling Guidelines for the Internet of Things. The nomenclature to describe the data is from the industry, intelligible to business, not from IT. It describes things like the context, technology, provenance of the data. The signaling and control layer, with embedded metadata, is the source of the data feeds in real-time. An API of an AI engine can search the data series,” David Knight, the CEO of Terbine, a data exchange, told us.
The whole is greater than the sum of multiple data series that feed analytics as data network effects compound the value of each of them. Analysts can examine more scenarios when they can slice and dice several data sources. “Real-time availability of data for is the primary value we provide as a source,” George Saleh told us. “Our customers bring their business domain data and mesh it with the real-time data from our platform to gain action-worthy insights for prompt action,” George Saleh added.
The automotive industry is already a prospective customer for the data generated by the Datapace marketplace. “Automobile industry needs real-time environmental data on traffic and weather conditions to assess the risks of drivers’ behavior,” George Saleh explained to us.
Services from data
The demand for data grows when customers conceptualize services that can use it for creating business value. Jernej Adamic spends much of his time with large European companies to design new applications with IoT data. “Primarily, our European customers want to automate processes and need intelligible data to make decisions on operations in real-time,” he told us.
Currently, three sources of demand exist among European companies according to Jernej Adamic. “Predictive maintenance for manufacturing and utilities, insurance companies, need data for risk management, and micro-producers of renewable energy who want to aggregate data,” Jernej Adamic informed us. “We created a visual tool that helps our customers to depict the nature of the service they desire and map out where they will get their data,” Jernej Adamic told us. “We are cultivating a community of developers to build the applications,” he added.
Datapace is finding customers in the smart city and telecom space. In collaboration with Nokia, it uses sensor data, collected from cell towers, to gauge the state of the environment for each neighborhood, block, and street. Unlike the current practice of data collection in smart cities from street lights, sensor data from cell towers is granular. “Allergens are often concentrated in a few blocks. We sell this data to telecom companies or smart cities which are distributed by start-ups,” George Saleh informed us.
Similarly, fire mitigation authorities are alerted to the risk of fire in any neighborhood with help from the sensor data. “A faster response to fire risk is viable now with a command-and-control system that does not wait for 911 calls,” George Saleh surmised.
For the future, Datapace is negotiating business in the telecom sector where service providers are seeking more efficient use of their prohibitively expensive networks and spectrum. “Telecom companies want to share their networks and frequencies and want to balance loads in real-time. In the 5G environment, network slicing will be common,” George Saleh informed us.
“Local nodes will be responsible for the allocation of network capacity,” George Saleh added. “The implementation of decisions will happen at the speed of networks when blockchains, embedded at the management layer, keep records of network utilization and machines execute load balancing decisions directly without humans conferring,” George Saleh concluded.
5G will create a whole new segment of customers who engage in micro-transactions. “Augmented Reality is one application which will frequently consume data in small amounts. 5G brings data processing and storage to the edge where cell towers will host mini-datacenters and serve the needs for high-frequency, small volume data needs,” David Knight told us.
5G will provide the infrastructure for microtransactions of IoT data
Rewards for data contribution
Cryptocurrencies would be the ideal means to value and compensate the contributors of data and assets to blockchain supported data exchanges. They will be a faster and cheaper means to alternative digital payments or banking systems. Digital payments and banks are also not a viable means for machine-to-machine transactions since both need a human identity.
At this stage, data exchanges are improvising with paler versions of cryptocurrencies in order not to be whiplashed by the current volatility in their value and or get hurt by security intrusions. Also, the secondary markets for cryptocurrencies and assets are non-existent which poses challenges for their pricing.
The industry does not see the absence of liquid secondary markets for the monetization of crypto assets as a hindrance in the current state of development of data exchanges. “Secondary markets are one scenario for quantification of the value of cryptocurrencies for monetization. An alternative means is to encode criteria for payment into smart contracts with verifying data from the Internet of Things. Blockchains help to authenticate events that warrant a reward,” Jessica Groopman, Industry Analyst and Founding Partner at Kaleido Insights, who recently co-authored a report, “Internet of Trusted Things”, told us.
Companies have indeed adopted some version of this criteria-based valuation of data. “Terbine has spent considerable effort to craft a dynamic pricing system which factors in over twenty valuable attributes in the data,” David Knight told us.
David Knight, CEO of Terbine
The dynamic pricing system elicited interest from the automotive industry. Terbine launched its system, including the pricing mechanism, which it white-labeled for the Intelligent Transportation Society of America and some companies who licensed it for their own branded exchanges.
Terbine was unable to disclose the valuable attributes in the pricing system until it is granted patents for it. We learned about the kind of characteristics that go into pricing from Streamr based in Zug, Switzerland. In response to a written query, Shiv Malik, the head of communications and strategy at Streamr, informed us how one of its partners, Fysical, rewards each of the ten million mobile phone users who share data on its exchange. The value of the data each day as affected by the extent of the activity of the smartphones feeding data, whether they were at home (less valuable) or in commercial establishments (more valuable).
Criteria based valuation of data is not the only way for compensating data exchanges. Instead, a cryptocurrency can serve as book entries. “We have our stable coin for B2B transactions, conducted entirely within the exchange, whose value in fiat currency is determined by the sellers. The payment of the net amounts happens at the time of settlement in fiat currency,” George Saleh told us.
The security of cryptocurrency payments is still a concern. “We have adopted the hyper-ledger, a distributed ledger, which blends with Ethereum. We have separated the records of transactions from payments (with or without cryptocurrencies), to minimize the risk of a security breach,” David Knight told us.
Conclusion
Blockchains open a vast new opportunity for the monetization of IoT data that has been hard to tap without immunizing data sales from security and privacy concerns. They also augment the analytical value of the data by helping to put it into perspective with contextual information. Flawless implementation of cryptocurrencies will also make data exchanges scaleable.
