Magic quadrant for data quality solutions

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Data quality has traditionally been mandated to fulfill compliance and governance requirements and to reduce operational risks and costs. It's a competitive advantage that data and analytics (D&A) leaders must continuously engage with in order to achieve those goals.

The data quality management practice has been maturing in recent years and the vendor landscape is focused on addressing many of the market requirements. It covers much more than just technology. It includes workflow, roles, collaboration and processes (such as those for monitoring, reporting and remediating data quality issues).

Download this paper to learn why evaluating and selecting data quality solutions is much less of a specialized IT task than it was formerly. It now requires greater collaboration with business leaders and users.

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