Skip to main content

Usecases

All sectors are concerned at different scales.

• B2B & B2C can share data to unlock new insights and opportunities for businesses and customers • Communities can leverage collective intelligence and crowdsource data

Already in production: agriculture farm-to-fork insights

The agriculture supply chain relies on several interdependent actors, from the field to the fork. They need efficient information systems to better evaluate, manage and value the efforts made and the agri-environmental benefits associated to the practices. Moreover, they need to share data to create knowledge valuable at each link of the chain.

Crossing technical and accounting data from multiple providers (farmers, accountants, cooperatives, advisors...) can generate unique insights sold to industries, brands, retail and local authorities and provide transparency to end-consumers.

But knowledge created from these data can go way beyond traceability of practices. It can provide valuable insights for companies, public authorities or even new opportunities to develop new applications on top of the shared data. For exemple to feed agro-ecological machine learning models or business intelligence tools.

Other use cases

There are many opportunities to build community-focused use cases. For example:

  1. Data can be used towards a common community-based goal and share value based on the results.
  2. Data can be bought directly from the data provider.
  3. Data can be shared to access a decentralized custom service.
  4. Data-curation and Data Space governance can provide many new opportunities for communities to thrive. Some Data Space will combine all theses strategies and opportunities. But of the sake of simplicity, let's give examples for every category mentioned above.

1. DAO-powered hedge fund

The Data Space incentivize users to share data in order to predict price action. Shared data enables to gain an edge in the market by training a DAO-owned algorithm. The algorithm takes decisions on the market on how to manage the Data Space treasury. Data providers take a performance fee according to their performance. More data volume and diversity mean enables greater depth and breadth of analysis as well as enhanced originality.

2. & 3. Crypto-native Bloomberg terminal

What if we could know and compare VCs profiles to know what they usually do with unlocked tokens and tactically adapt investment strategies?

By combining many data feeds and services (a mix of many data providers and sources - including proprietary data) such as:

  • Dove metrics (fundraising database)
  • Unlocks Calendar (token unlocks database)
  • Nansen (on-chain wallet tracking)

The Data Space could provide a service where users trustlessly share their wallets' data (and the assets they own), and could have custom alerts and analytics. They are then incentivized to share their investment data and keep it updated, thus providing a highly valuable service and enabling new opportunities for users to profit from this data.

4. Curation Games

In addition to providing users the opportunity to contribute data, use new services and receive tokens, Data Space governors can use dsTokens (Data Space Tokens) to incentivize a specific data connection with web2 services (like Facebook or Google data) or with domain-related data (such as weather or annotated car images). Investors can then value the dsTokens of the various communities based on how valuable they view that specific data-pool to evolve and provide value.

Curation can also be done at the dataset and service level (not at the Data Space level), as explained in Curators.

The curation games will make new markets that will work as signals to show which datasets, services, data applications or even communities are or may be the most valuable in the future. As an example, Bob can speculate that a particular Data Space has reached a certain level of data maturity and will soon attract many users and builders, while shorting another declining/overvalued Data Space.