What quality is made of
Data quality has four measurable properties. Problems in any one make the data harder to act on.
Dimension 1
Accuracy
Does the data reflect what is actually true? An email address that no longer works. A deal stage not updated after a conversation moved forward. A contact name spelled inconsistently. Accuracy failures make the data wrong, not just incomplete. They are the most damaging because wrong data drives wrong decisions with confidence.
Dimension 2
Completeness
Are the fields that matter filled in? A required field left blank because intake did not enforce it. A follow-up note that stayed in email instead of the CRM. An account created without an owner. Completeness failures make the data partial, which produces partial decisions, or the appearance of a complete picture when information is missing.
Dimension 3
Consistency
Does the same field mean the same thing everywhere it appears? A status field used differently by sales and marketing. A source field with seven values where only three follow any defined rule. A date field where some records use the meeting date and others use creation date. Consistency failures make data ambiguous, numbers that cannot be compared across records, teams, or time periods.
Dimension 4
Timeliness
Is the data current enough to act on? A lead status reflecting a conversation from three months ago. An account health score calculated from six-week-old data. A dashboard that refreshes weekly when decisions need to happen daily. Timeliness failures drive decisions based on a state that no longer exists, often without the decision-maker knowing the data is stale.
