Skip to main content
By Jonas AnderssonCo-founder
4 min read

Definition

What is data quality?

Data quality is how trustworthy data is for making decisions. Not whether it is technically complete or structurally valid, whether the people who depend on it can act on it correctly without first having to verify, correct, or supplement it from another source.

High-quality data does not require explanation before someone can use it. Low-quality data requires correction before anyone trusts it, multiple sources before anyone acts on it, or a specific person's knowledge to interpret it correctly. All three are failures of quality, even if the underlying records are technically present.

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.

In this articleTap to open the section map
  1. Overview
  2. What quality is made of
  3. The operational test

The operational test

If someone who did not create the data can act on it without asking for clarification, it is high quality.

It is more useful than any completeness check because it measures fitness for the actual decision, not whether the field passes validation. A field filled in wrong fails it; a technically optional but decision-critical field fails it too.

Key takeaways

  • The four dimensions: accuracy (is it correct?), completeness (is it all there?), consistency (does it mean the same thing everywhere?), timeliness (is it current?).
  • Data quality is about trustworthiness for decisions, not technical validity alone.
  • Problems in any one dimension make data harder to act on, even when the other three are fine.

If you only remember one thing

High-quality data does not require explanation before someone can use it.

Data & VisibilityData qualityCRM

Continue the thinking

Part of this guide

Related

CRM vs Spreadsheet: When to Move OnPrincipleExplores a neighboring systems problem from a different operational angle.5 min
Connections
How to Audit Your CRM Data in a DayMethodUses the definition of data quality as the yardstick the one-day audit measures against.7 minData Quality Is Ownership, Decay, and Contract DisciplineResearchUses operational trust as the standard the ownership and contract model must protect.8 minHow to Measure Manual Reporting CostMethodMakes the operational cost of unreliable data visible before a reporting fix is proposed.5 min

Sources

References behind this piece

  1. Government Data Quality Framework

    Government Digital Service

Jonas Andersson, co-founder of Mannvit Labs

Written by

Jonas Andersson

Co-founder, Mannvit Labs

Co-founder of Mannvit Labs, leading systems, data, automation, and technical delivery. He spent four years before this running e-commerce for a Swedish retailer, from SEO to campaigns.

He builds the internal tools and data workflows these guides come out of. See the builds.

  • Commercial data & reporting
  • CRM data quality
  • Process optimization
More from Jonas