Processing services

A data space can have participants that do not offer their data and are not the end users of data. At its most basic level, these can be participants that are offering algorithms and code for processing data as a data contract to deliver code libraries, signed containers, or entire virtual machines to other participants. For very computation intensive or special hardware requiring workloads these participants might offer their own infrastructure as part of the contract and use policies to control the use of their resources.

Many data spaces can be built on top of the peer-to-peer model, such as a data supply chain where data assets pass through multiple processors before reaching the end user. The implementation and capability of these services again depends on the architecture, policies, and rules of the data space.

Data escrow, data trustee

For many applications, data assets and algorithms from multiple sources need to be combined to generate value. This will lead to trusted service providers collecting all necessary data, perform the calculations, and then distribute the results - while adhering to all contract policies and guaranteeing the execution of usage policies such as the enforcement of deletion rules. The business model for these participants will be only to provide trusted services and not to use the data.

Plenty of possible models are conceivable, from centralized, federated to decentralized offerings with different technical capabilities, trust levels and costs. Classic data aggregation platforms such as data lakes can also be a possible implementation and benefit from the trust which a data space provides.