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The IAB Technology Laboratory today announced the release of Data Transparency Framework 1.0 that outlines sourcing disclosures for those collecting data used to target, personalize, and measure digital advertising.
According to IAB, the Data Transparency Framework 1.0, available for public comment until July 16, will introduce ID-level transparency into a largely opaque marketplace where data segment composition is difficult to evaluate—and collection and usage practices are increasingly scrutinised via legislation such as the EU’s General Data Protection Regulation (GDPR).
“The Data Transparency Framework enables meaningful understanding of attributes and sourcing practices across data providers,” said Dennis Buchheim, Senior Vice President and General Manager, IAB Tech Lab. “In parallel, we’re developing standards to bring similar transparency to identity management offerings, and we acquired the DigiTrust ID service to improve efficiency and safety in audience recognition. Together, these efforts enable more effective use of data in marketing and help improve consumer ad experiences – ultimately supporting digital marketing’s ongoing role in funding content and services.”
The Data Transparency Framework 1.0 includes a new set of minimum disclosure requirements for data sellers; an open API to structure and communicate information among supply chain participants and ease implementation requirements; supporting compliance programs; the minimum disclosure requirements apply to each organization that may contribute to data segment generation. Disclosure includes quality signals such as attribute provenance and age, and whether attributes are associated with individuals, households, or businesses. IAB says that the resulting awareness of data characteristics will benefit consumers by supporting more relevant ad experiences that can also be aligned with forthcoming consent requirements.
IAB Tech Lab is also introducing a companion resource for public comment: Audience Taxonomy 1.0. As one of the disclosure requirements for Data Transparency, the taxonomy provides standard segment names that data sellers can reference, making it easier for buyers and platforms to compare and consider conceptually similar data segments.