A new guide offers practical steps for building a data quality framework quickly and sustainably, crucial for teams lacking extensive resources or time. It emphasizes defining specific use-case requirements, prioritizing critical data elements, establishing ownership, and implementing automated rules to ensure continuous improvement.
This approach helps organizations avoid scope overload and demonstrates immediate value, fostering trust in the initiative before scaling up.
Read the full article at DEV Community
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