Martin Saps

Data Marketplaces: Value Capture in Web 3.0  

June 6, 2018


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By Lawrence Lundy-Bryan

Surely at this point, if you are reading this article you have heard of the Convergence Ecosystem. This excerpt is the latest in a series from Convergence Ecosystem vision paper. Go and read the full thing here which can be read here. Or take a look at the previous excerpts which have covered core themes from the paper:  

Marketplaces are critical elements of the entire Convergence ecosystem; the element that incentivises data to be collected, shared and utilised. We now have the ability to open up the machine economy and need to think of ‘trading’ beyond the scope of human interaction. The sorts of marketplaces that are being developed in the crypto community are decentralised, automated and tokenized. These marketplaces are made possible because of the distributed ledgers, consensus mechanisms and interoperability protocols at the lower levels.

[ctt template=”3″ link=”ro7Ef” via=”no” ]We will see the emergence of a whole host of new types of marketplaces beyond just today’s cryptocurrency exchanges like Binance or Coinbase. [/ctt]

We are seeing the emergence of data exchanges that work with specific types of data; machine data from IoT networks, artificial intelligence data, personal data, and complex digital assets like crypto-collectables (pioneered by ERC 721 non-fungible tokens like CryptoKitties) and bots. It’s likely that over time marketplaces will expand into enabling all types of data and if that does occur we could end up with a dominant data and digital asset marketplace for Web 3.0 like Amazon for the Web 2.0. It’s interesting to think where the points of leverage will be in the Web 3.0 especially if value and data are interoperable across blockchains. Anyway at least for now, we see four types of decentralised data marketplace.

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IoT Data Marketplaces

IoT data is already being collected in vast quantities, but the sprawl of devices has created a fragmented ecosystem. On the consumer side, operating system providers like Apple, Google and Amazon are attempting to leverage their dominant positions in smartphones and retail to sell more devices to collect more data. The Apple Watch and CarPlay, Google Home and Next, Amazon Echo and Dot; these are all attempts to grow their walled gardens of data. Smaller consumer IoT device makers like Fitbit, Wink, or GreenIQ struggle to collect enough data to make do meaningful machine learning to improve their products as quickly as the tech giants.

On the enterprise side, the same dynamics are at work. The internet of things (IoT) and industrial internet in the United States, Industrie 4.0 in Germany, and 物联网 (wù lián wăng) in China all promise to use low-cost sensors and big data analytics to dramatically improve productivity and usher in a new age of data-driven manufacturing. But the promise has not been realized for a number of reasons. Core to the failure has been the lack of data sharing. This lack of data sharing has been the case across all industries that are trying to utilise IoT technologies including aviation, agriculture, and utilities. The problem, as we have already highlighted, is that there is no incentive to share data because it is seen as the competitive advantage to be protected.

[ctt template=”3″ link=”QPSi8″ via=”no” ]Current data infrastructure is coarse: data is either hoarded and valuable, or shared with limited commercial viability.[/ctt]

IoT marketplaces begin to offer new business models for the monetisation of machine data. The IOTA data marketplace, Streamr, Datum and Databroker DAO are all examples of these marketplace emerging to enable the sharing of sensor and machine data.


AI Data Marketplaces

Just like IoT data, or any data for that matter, data for AI algorithms tend to be accumulated by the largest companies. Society is becoming reliant on data, and as it applied to AI algorithms, we are facing a situation in which a select group of organisations are amassing vast datasets and building unassailable AI capabilities. With the emergence of deep learning as the most useful machine learning technique for a range of AI applications like computer vision and natural language processing, data has become like digital oil. Digital monopolies like Facebook, Google and Amazon, today get data from users for free. Every like, search and purchase feeds the learning system to further improve the algorithms; in turn bringing more customers and engagement. In value chain terms, data is supply, and AI algorithms are demand. Digital monopolies are searching everywhere for more and more data to feed their algorithms: Facebook buying WhatsApp and Instagram, Google with self-driving cars and Google Home, and Amazon with Alexa Echos and Dots.

“Traditionally proprietary data and technology have been significant defensibility mechanisms for companies. In the blockchain industry this is all open source, leading to an incredibly rapid innovation cycle, but also shifting defensibility more towards the sheer size of the community and thus the distribution power. This is an industry where increasingly users decide what technologies they want to use.”

Teemu Paivinen, Founder, Equilibrium Labs & Author of Thin Protocols

Decentralised AI data marketplaces will reduce, and eventually remove, the competitive advantage of hoarding private data by enabling anybody to monetise data. Again in value chain terms, these marketplaces increase supply. An AI data marketplace will make it easy for people and increasingly agents and bots to recommend, price and therefore find value in different types of data. A market for data will lead to more efficient allocation of data, rather than giving it away for free or not using it at all. As more and more machines, individuals and organisations upload data to sell on a data marketplace, it becomes more attractive to data buyers. As this data commons grows with more datasets, it will attract more data buyers, creating powerful network effects. More than anything, decentralised AI data marketplaces are a bulwark to the rapacious AI data monopolies that have the potential to become the most powerful organisations ever built (if they aren’t already), controlling ever-increasing numbers of industries and markets with their superior AI capabilities. It is, for this reason, we invested in the Ocean Protocol, whose mission is “to unlock data, for more equitable outcomes for users of data, using a thoughtful application of both technology and governance.”

“The aim of Ocean Protocol to to equalize the opportunity to access data, so that a much broader range of AI practitioners can create value from it, and in turn spread the power of data. To respect privacy needs, we must include privacy-preserving compute. Our practical goal is deploy a tokenised ecosystem that incentivizes for making AI data & services available. This network can be used as a foundational substrate to power a new ecosystem of data marketplaces, and more broadly, data sharing for the public good.”

Trent McConaghy, Co-Founder, BigChainDB & Ocean Protocol

We also expect to see these marketplaces become ever more automated and efficient. Another of our portfolio companies, Fetch, is building a solution that uses decentralised machine learning to enable marketplaces to self-evolve around popular or valuable datasets, improving discoverability. In some senses they are embedding marketplaces directly into the ledger to truly enable the machine economy.


Personal Data Marketplaces

After peer-to-peer payments, control of personal data has been one of the most talked about applications for blockchains. This is related to but separate from self-sovereign identity and SII networks like Sovrin, in the sense that once an individual controls their own identity, they can choose who can have access to it. The same principle can be applied to other personal data. This choice puts the individual in the position of the seller and the party who wants access to the data as the buyer. Personal data is an economic asset that we currently give up in return for services. Some data is handed over consciously, like entering an email address or a telephone number; other data is captured without us knowing about it: likes, tweets, our online behaviour and other forms of digital data exhaust. The value comes (albeit it is much less understood by individuals) when different datasets are aggregated, and an individual psycho-demographic profile is created and sold to all sorts of organisations like insurers, market researchers, and political organisations. A multi-billion dollar data industry exists just to trade personal data.

Individual pieces of personal data are not particularly valuable on their own. According to the Financial Times, general information such as age, gender or location is worth just 0.0005 dollars per person. Buyers will have to fork out 26 cents per person for lists of people with specific health conditions. Genomic data would likely fetch much more.

[ctt template=”3″ link=”8851i” via=”no” ]The challenge is thatat an individual level, there is very little economic value. Value comes in aggregate. This is where blockchains, self-sovereign identity, and personal data wallets combine.  [/ctt]

In today’s Web 2.0 paradigm, Google, Facebook and other data monopolists capture the profit. In the future, blockchain infrastructure, self-sovereign identity and personal data marketplaces will empower individuals. They can choose to allow Google and Facebook to use their data, or they can auction it off to get the best price. They might decide to only sell general information, but not their genomic data. Others will rent access to genomic data to cancer research charities but not insurers. New business models will emerge as buyers give sellers discounts based on aggregating family data for instance and new startups will emerge differentiating on consumer trust. Metâme is a UK-based startup working on creating a universal unit of trade enabling bundles of personal data to be packaged and exchanged. A data marketplace is not necessarily about making the most money. It is about giving individuals choice and control of how they want to invest their most valuable economic asset.

   “Self-sovereign personal data marketplaces need to address two key hurdles before they can take off: 1) the need for a universal unit of trade that transforms personal data into assets which people can tangibly trade and own, 2) ensuring anonymity and then incentivising consented identifiability as new legislation like GDPR effectively calls for anonymity by default. Without solutions to these problems personal data marketplaces cannot scale sustainably.”

Dele Atanda, CEO, Metâme Labs


Digital Assets Marketplaces

The final category of marketplace we expect to evolve are digital asset marketplaces. Unlike traditional physical assets or money, distributed ledger-based crypto-tokens can be programmable. This gives them more flexibility and variety than their physical counterparts. Cryptocurrencies, or tokens designed to be a medium of exchange, are already reasonably well-defined and projects are innovating around how to create the optimal token for this use in mind with rules around supply, distribution, privacy, and other attributes being tweaked. Cryptocurrencies confer the fact that the crypto-token is a medium of exchange. Most tokens are incorrectly referred to as cryptocurrencies. This is because Bitcoin began life as a cryptocurrency and has over the last ten years become more of a crypto-asset, predominantly because of the programmed deflationary economics (Layer 2 solutions like Lightning may change this classification however). However, currencies and assets require different economic designs. Currencies need to have a high velocity; assets need to retain and ideally increase value resulting in low velocity.  

Broader than cryptocurrencies, digital assets will come to include all digitals assets that use distributed ledgers to create scarcity. Today there isn’t a clear distinction between cryptocurrencies and crypto-assets, but as the market matures, it will become more evident which tokens are designed to be a medium of exchange and which are designed to be a store of value. It is challenging to be both. Ether, for example, is intended to be used as a medium of exchange to redeem decentralised services from applications. But as its price rises, it becomes more of a store of value and less of a medium of exchange as holders refrain from redeeming Ether in anticipation of value appreciation. This non-fungible subclass of crypto-assets will be designed to be collectables and derive value through exclusivity and proof-of-ownership. Tooling for this is already emerging with the ERC 721 NFTs.

We expect to see a whole new ecosystem of digital assets like in-game weapons or costumes for gaming, AI bots and virtual avatar templates, such as those provided by SEED. Virtual reality land such as Decentraland; objects with real-world counterparts like digital twins from Spherity; and even digital to physical assets like 3D printed items, many of which will be collaboratively made and collectively owned. [ctt template=”3″ link=”N84cy” via=”no” ]With digital scarcity comes the ability to artificially limit supply which has up until now been almost impossible with existing digital and Internet technologies. [/ctt]

The possibilities are endless and we are at the very beginning of a whole new age of digital assets created, bought, licensed, rented and sold in decentralised peer-to-peer marketplaces.

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