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Presenters also agreed that data quality is important, especially for critical data attributes. However, not all stakeholders and use cases consider the same data attributes critical. Despite these differences, there exists great opportunities to reduce effort and increase efficiency and quality by coordinating initiatives. Presenters described many models for managing data quality, from centralized data scrubbing processes, distributed processes for a centralized repository, and reliance on the processes of directory participants perhaps reinforced by policy. Health plans and insurers, in particular, have regulator mandates and reporting requirements concerning data quality.

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