Delivering trusted data: A modern approach to data quality
Today 80% of a data team’s job is to prepare the data for downstream use. But as data volumes and data complexity increase, traditional approaches to data quality are not effective or sustainable.
In this on-demand webinar, Kirk Haslbeck, VP of Engineering at Collibra and founder of OwlDQ, discusses how a modern and predictive data quality approach can revolutionize how an organization ensures data quality.
Learn data quality best practices, so your data teams can move beyond manual rule writing and data silos to ensure the delivery of trusted data and analytics in a scalable way. Watch the webinar to learn why you should move from:
Static rules to adaptive rules and rule management
Reactive data quality to predictive data quality
No data ownership to an integrated data ownership model
Bespoke data quality to spark-distributed data mesh
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