Analytics

The Analytics Module is designed to visualize the relationship between DIDs (Decentralized Identifiers) and Credentials. It allows for data ingestion from various sources, including the Analytics Platform API, and provides a comprehensive view of the data in a graph format. This module is particularly useful for analyzing connections between different entities and understanding data flow within the ecosystem.

At its core, the system is a tool for linked-data analysis, allowing you to uncover hidden relationships between trust chains, trace historical records, and compare and validate claims to their root of trust. The most straightforward example of this is the relationship between a DID and a Verifiable Credential (VC) or Verifiable Presentation (VP). Each Credential has an issuer and usually also a holder (though this is not always required). By ingesting the Credential into the system (either through the API or manually), the system automatically creates a connection between the DID of the issuer and the Credential. This connection is then displayed in the graph view. In the case of the issuer, it means that the issuer's DID has been published on some VDR (Verifiable Data Registry) and should therefore be public. The analytics platform scans the supported networks for new DIDs constantly and builds a comprehensive database. It is therefore able to draw a line between the Credential node and the DID node on the graph.

Up to this point, this isn't very different from what any verifier of a Credential would do. They also need to resolve the issuer's DID using some kind of resolver to get the public keys for verification purposes. However, the analytics platform goes one step further: it is also able to analyze all related information of that issuer's DID. Does it have data in common with other issuers? Have other Credentials in the tenant's data pools also been issued by this DID? The analytics platform can present all this information visually. This allows users to make sense of the data. It is especially useful when you have a large dataset and want to find patterns or anomalies.

The platform also excels when dealing with intertwined data, where one entity delegates trust to another, and multiple Credentials are used to model complex relationships. The graph view can show all of this visually. Simply inspecting individual Credentials or DIDs would be time-consuming and error-prone. If you've ever encoded a JWT by hand, you know how complex the data can be and how easy it is to overlook something. The graph view makes this process easier and more reliable.