Published: 29 July 2024
Generative AI has intensified the demand for data architects to provide high-quality data for their organizations. This research can help them develop a versatile and scalable framework that supports AI-ready data that looks beyond traditional standards of data quality.
Data professionals often underestimate the importance of metadata in ensuring data quality. Effective metadata management provides necessary context about data that can be utilized to mitigate shortcomings in data accuracy, better manage perceptions of poor data quality and enable AI-ready data.
Data and analytics (D&A) governance platforms that can leverage and orchestrate multiple technical capabilities provide opportunities to activate metadata. This in turn supports automation of tasks that align to the quality dimensions of accessibility, timeliness, relevance and accuracy.
Data quality is viewed by IT in terms of a narrow definition of data accuracy, as supported by data quality
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