Automated processes validate that data values format conformance, data domain membership, or cross-column or table consistency, yet in the absence of an absolute “source of truth,” there is no way to automatically determine if a value is accurate. Despite efforts to ensure data quality, some data issues will require proactive attention and remediation. In this presentation, we examine the methods and protocols for data quality control, so that errors identified early in the process can be mitigated before their impacts are incurred.
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