A method for evaluating the reliability of data based on two aspects, Data Integrity and Data Provenance. Data Integrity measures quality through dimensions such as freshness (how up to date the data is), completeness (whether all expected properties are present), and validity (accuracy of representation). Data Provenance tracks lineage and transparency through origin (where the data came from) and verification (when it was last authenticated). Together, these provide a configurable score indicating overall trustworthiness. The goal of trust scoring is to provide a measurable level of confidence in the data, allowing organizations to make more informed decisions by using data that has been verified as trustworthy.
Why it matters: Trust scoring strengthens governance and accountability by providing a measurable way to assess data reliability before it is used in critical processes or AI systems.
