Platform of Trust has taken the initiative to formulate Data Product Specification (DPS) to enhance data commoditization and monetization. We are formulating the commercial aspects related to monetizing data in a form of data products. The goal is to enable data rights holders to commercialize their data in order to accelerate the emerging data economy.
An underlying enabler is the universal information model (Platform of Trust Ontology) which allows data to be harmonized across business domains. Once data is harmonized it becomes critical that commercialization of the data is easy and transparent. Commercial transactions should be controlled and monitored so that trust between data publishers and customers can be achieved.
Platform of Trust is enabling an ecosystem where all aspects of data commercialization and transactions can be achieved in a controllable and trustworthy manner.
Platform of Trust Ontology
API (Connector) layer
Data Product Management layer
Data Product Specification
Data Product Specification has three main layers.
Data Product Model
Data Product Structure
The main principle of modeling entities and properties related to a data product is to enable flexible and extendable creation of data products. Some data products have simple commercial terms while other’s terms can be complex. Platform of Trust model provides easy creation of simple data products but sets no limits for including additional product related information.
Product covers properties that describe commercial information about the product such as commercial names, product codes, versions and life-cycle status.
PricePlan covers properties which describe pricing logic. Price related properties include rate, unit and currency. PricePlan also describes what are the possible mechanism to buy the product – mainly if its transaction or subscription based or combination of them. Note that one product can have multiple PricePlan options for example based on customer segmenting profile and/or areal policies.
Condition covers properties related product limitations which describe how a product can be used. For example, whether a product can be resold further, or for what purposes the product can be used, and by whom. Conditions can be divided in non-negotiable product conditions, and negotiable contract conditions. It is up to a data publisher to decide how the conditions are negotiated, and which of them negotiable. It is possible to set all contract conditions as non-negotiable, that means the product is offered in “take it or leave it” principle.
Services covers properties which describe service processes that are available for the product. They can be related to product design, production, selling/buying, delivery, support and invoicing. For example service can include information about supporting hours, contact methods and response times. Further, service quality information can be connected with the services and SLA clauses formulated based on that (see below more). Service or some services can also be priced separately and thus PricePlan can be connected to service.
Quality covers properties which describe qualitative parameters related to either product or services. If qualitative parameters can be monitored, it is possible to set target value ranges for the quality properties, and SLA clauses can be formulated into contracts between a data publisher and a customer. Product can contain these SLA options (product conditions) or they can be agreed in a contract between a publisher and a customer. The possible actions caused by breaching SLA clauses are also agreed in the contract. It is possible for a seller to define them as part of product conditions, and then they are non-negotiable on contract level.
Contract gathers together in one document all negotiated and non-negotiable information of a data product. A contract is formed when a data product customer purchases a product. A data publisher may set terms and conditions of a contract to be negotiable or non-negotiable. A contract defines all parties and their roles related to a data product. The parties must include at least a data publisher and a data product customer. If there are any other relevant parties, they must be defined. The permissions needed to access data are derived from roles and conditions of a contract. Other information defined in a contract is a term, limitations of use and payment related information, as well as what how to proceed if a party wants to stop using a data product.
Due to nature of data products, Platform of Trust is suggesting a data product license (DPL) as a default contract for data product market. In order to be compatible with systems relying on automation, a DPL should be a smart contract that means a computer program or a transaction protocol, which is intended to automatically execute, control or document legally relevant events and actions according to the terms of a DPL.
Connector covers properties required to utilize connector and access actual data product.
Monitoring covers properties related to monitoring services associated with data product.
Status contains actual status information of data product.
Platform of Trust has recognized ten attributes related to data quality. The attributes and their level of legal binding are defined in data product license (DPL). The quality attributes are the following:
Correctness verification level describes methods for data correctness verification. A simple verification is a data publisher’s assurance about data’s correctness and its origin in data product license (DPL).
Harmonization level describes the extent of data harmonization that means comparability of data values with other data values.
Interoperability level describes how interoperable a digital twin (identity network) of data is. The more accurately a digital twin is modelled with a common information model, the more interoperable it is.
Availability rate is uptime of the data source. It is defined in relation to time and presented as a percentage. Normally value is one of: 90%, 95%, 97.5%, 98%, 99.9%, 99.99%.
Update frequency means how the data is being updated. It is defined in relation to time.
Accuracy is also known as trueness. It tells how close the measured value is to the true value. Accuracy is about systematic margin of error.
Precision means closeness of the measured values to each other, if the measurement is repeated. Precision is about random margin of error.
Faultlessness rate is the rate of faultless values in all fields. It is presented as a percentage.
Completeness rate is the rate of full fields in all fields. It is presented as a percentage.
Security level describes data security solutions of the data source.
Response Time is targeted time in which data product will return payload response. Unit is milliseconds. This applies to request-response type of data products.