ID-Level Data Transparency Standards Released for Public Comment
Download Data Transparency Standards (Draft in Public Comment until 7/16/2018)
Overview of ID-Level Data Transparency Standards
The framework establishes: 1) a baseline expectation for data sellers regarding the additional information that buyers need to make an informed purchase decision, 2) a standard audience taxonomy so buyers can compare like segments across vendors, and 3) software to deliver this data to buyers in their platform of choice via an open source API. It is in accordance with the operational and technical realities of segment development and generation, and within a flexible framework that can evolve over time, based on marketplace needs.
The framework is made up of four components. This document makes available the first two components for public comment–the labeling requirements and audience taxonomy–while the latter two supporting components will be introduced into the market by the end of 2018. The four components are:
- ID-level labeling requirements for data sellers
- Standard audience taxonomy / data segment naming convention that’s incorporated as one of the labeling requirements
- Open-source API to structure and communicate machine-readable label data between supply chain participants
- Compliance program to acknowledge transparent data sellers
When scaled and implemented across the ecosystem, the standards and supporting software will introduce ID-level metadata into the ecosystem for the first time. Very much akin to how OpenRTB largely shifted media buying philosophies from buying publishers as proxies for audiences toward buying individual impressions informed by data, ID-level visibility will refocus data buying from broad audience segments to buying individual ID-attribute pairs informed by metadata.
The framework will not only allow buyers to easily calculate segment composition against data provenance, age and ultimately determine segment applicability at the point of purchase, but also have numerous positive downstream effects, including but not limited to:
- Improved efficiency and liquidity of the data marketplace
- Shift of human capital to higher value tasks made possible by automated segment composition analysis
- More efficient, effective digital ad campaigns and improved consumer ad experiences
- Growth and differentiation within the data industry at large by removing perceptions of data commoditization and encouraging price premiums / incremental investment for rigorous and accurately defined data segments that play an outsized role in delivering marketing outcomes
- Transformation of metadata into a machine-readable format that will:
- Facilitate use of calculated fields to understand segment composition and applicability
- Allow bidding platforms to learn over time which signals have more or less of an impact on KPI performance and naturally skew investment into those higher performing areas
- Enable more sophisticated application of audience data for campaign efficacy and attribution analysis via application of robust metadata signals
The labeling requirements and audience taxonomy are available for 60-day public comment through Monday July 16, 2018. The labeling requirements can be reviewed here and the Standardized Audience Taxonomy can be found here.
Please submit written feedback to email@example.com.
Primary IAB Tech Lab Contact for Data Transparency
Director of Product, Data
Working Group Members
List of Data Transparency Standards Members