The visible problem is rarely the whole problem
A missing field or invalid value may look like a simple record-level defect. In a transformation, however, that record can participate in multiple processes, interfaces, reports, controls, and business decisions. The real cost is therefore not just correction; it is the chain of activities affected by the defect.
Quality has a business dimension
Not every quality issue deserves the same response. A field that is irrelevant to a particular process may be less important than a smaller number of records that affect revenue, procurement, inventory, compliance, or customer operations. Good assessment connects quality rules to business consequences.
From findings to action
A useful data-quality program moves beyond a list of errors. It quantifies the issue, identifies the affected objects and processes, establishes ownership, defines remediation rules, and validates the result. This creates a measurable path from assessment to improvement.
Axiums perspective
The goal is not perfect data for its own sake. The goal is data that is fit for the business outcomes the transformation is expected to deliver.
- Prioritize quality issues by business impact.
- Measure the scale and distribution of important defects.
- Connect remediation to accountable business owners.
- Validate improvements before data enters critical testing or migration activities.