DMA: Data and Marketing Association
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Council for Data Integrity

The future belongs to those who understand people the most

Data Quality Labeling Standards

Join forces with the industry’s top data management experts

The desire to improve the customer experience by delivering the most relevant and valuable offers to consumers using audience data drives billions in marketing and media spend. Yet the measures of data quality are often unknown. Understanding what drives reliable identity data across the myriad of data suppliers is daunting. And sifting through disparate data descriptors without the weight of independent verification calls into question claims of validity, recency, completeness and accuracy. With the future of marketing resting on high-quality identity data, the foundation of quality can no longer be guesswork.

That’s why DMA’s Council for Data Integrity is taking the lead in this cross-industry Data Quality Labeling Standards program. DMA’s Council for Data Integrity seeks to bring reputable organizations together to set themselves apart as responsible, high-quality data providers. In partnership with The Advertising Research Foundation (ARF) and Coalition for Innovative Media Measurement (CIMM) – two of the media industry’s leading data quality authorities -this important industry initiative enables the data services community to provide marketers and agencies with the insights they need to be assured that the data they are using is accurate, reliable and of the highest quality. And most importantly, backed by the Data & Marketing Association, the Data Quality Labeling Standards program will have the weight of independent verification, thus providing identity data users to power their marketing and media spend with full confidence.

Our goal is to define labeling standards for identity data similar to FDA food labeling requirements – so every marketer, agency, data provider and publisher understands the source ingredients and marketing value of their identity data


  • Data source
  • Data collection
  • Data integration
  • Data cleansing
  • Propensity for lookalike modeling


  • Recency and frequency
  • Consistency over time
  • Target assignment accuracy
  • Target coverage
  • Deterministic vs. probabilistic

This initiative will create tactical tools that produce measurable benefits and results for all data-driven marketing and media ecosystem companies

  1. An industry-agreed, data labeling standard for commercially available audience data that underlies tailored audience segments
  2. Consensus among critical industry organizations, representing all members of the ecosystem
  3. Peer reviewed validation methods that make data labels comparable, and raise confidence across all users

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