The dimensions of data quality are the vocabulary RevOps, sales, and data teams use when they ask: can we trust this record? Instead of one vague “quality score,” dimensions break quality into specific traits you can measure, fix, and defend in audits.

This listicle sticks to eight dimensions that show up again and again in real B2B work—CRM, marketing automation, enrichment pipelines, and reporting. We picked them because they’re actionable: each one maps to checks you can run, metrics you can trend, and owners you can assign. For frameworks, scoring, and prioritization, see our in-depth guide to data quality dimensions.

1. Accuracy — does the data reflect the real world?

Accuracy means the value matches reality: the right person, the right company, the right title, the right number. A record can be valid (correct format) and still be wrong—think a perfectly formatted email for someone who left the company last year.

It matters because bad accuracy quietly poisons downstream work. Routing rules, territory plans, and AI features all assume “true” inputs. One stale title can send a VP-level lead to an SDR queue built for SMB.

How to measure it: sample records against a trusted source (HR file, billing system, verified enrichment), or track operational signals like email bounces, wrong-number rates, and CRM “correction” tickets. Pair accuracy checks with a data quality assessment so you’re not guessing where the fire is.

Example: “Director of Sales” in the CRM while LinkedIn shows “CRO” — accurate format, inaccurate job reality. Fix with periodic re-verification, not just mandatory fields.

2. Completeness — are the fields you actually need filled in?

Completeness is about required for the job, not “every optional attribute populated.” If outbound needs email + company domain + persona tag, completeness is the share of records that have all three—not whether hobby fields are filled.

Incomplete data creates hidden shrinkage: campaigns silently skip people, reports exclude segments, and reps improvise instead of selling. It’s one of the fastest dimensions to improve if you define “required” honestly per workflow.

How to measure it: rule-based coverage: % of records with each required field, broken down by source and team. Trend it weekly during cleanup sprints.

Example: 40% of new leads missing country breaks regional routing. The fix is usually upstream (form, enrichment, integration mapping)—not “train reps to try harder.”

3. Consistency — does the same fact read the same everywhere?

Consistency means one canonical truth across systems and tables: same company name spelling, same lifecycle stage definitions, same currency. It’s the dimension that explodes when marketing, sales, and finance each maintain a “shadow” definition of “customer.”

When consistency breaks, you get reconciliation meetings instead of decisions. Dashboards disagree, handoffs fail, and automation fires on the wrong trigger.

How to measure it: cross-system joins on stable keys (domain, account ID) and mismatch reports: count of accounts where ARR, employee count, or owner differs beyond a tolerance. A solid data quality framework names who owns those definitions.

Example: “Acme Inc.” vs. “Acme, Inc.” vs. “acme.com” as account names—three records, one company, three forecasts.

4. Timeliness — is the data fresh enough for the decision you’re making?

Timeliness is freshness relative to use. Real-time chat support needs different SLAs than annual planning headcount. B2B contact data decays constantly as people change roles; a “good” record from 18 months ago may be useless for outbound today.

Stale data wastes cycles and erodes trust. Reps stop believing the CRM; marketers build segments on ghosts.

How to measure it: age of key fields (last verified date, last activity, last successful delivery), plus SLA breaches: % of active opportunities with contacts not verified in N days.

Example: High-intent webinar leads enriched weekly; static newsletter subscribers refreshed quarterly. Same database, different timeliness rules—both intentional.

5. Validity — does the data obey format and business rules?

Validity checks structure and constraints: email looks like an email, dates parse, enums are from the allowed list, amounts are non-negative. Invalid values often mean integration bugs or rushed imports, not “bad salespeople.”

Invalid data breaks automations at the worst moment—sync errors, failed API calls, silent drops in ETL.

How to measure it: validation rule pass rates at ingestion; regex and schema checks; % of records failing each rule. Operationalize the counts as data quality metrics on a dashboard.

Example: Phone numbers stored as text with inconsistent country codes—valid to a human eye, invalid to a dialer.

6. Uniqueness — is each entity represented once (enough)?

Uniqueness targets duplicate and near-duplicate records: the same person or account appearing multiple times with small variations. Duplicates inflate pipeline, skew attribution, and annoy customers who get three sequences from three “different” contacts.

It’s rarely about perfect dedupe everywhere—it’s about golden records for the workflows that matter.

How to measure it: duplicate rate by match keys (email, domain + name, CRM dedupe tool); count of merges per month; % of accounts with >1 primary contact in the same buying group.

Example: Two leads for “j.smith@acme.com” and “jsmith@acme.com” — uniqueness tooling and governance catch what the eye misses.

7. Integrity — do relationships between records still make sense?

Integrity (sometimes called relational integrity) means foreign keys resolve, rollups add up, and hierarchies aren’t broken: every contact points to a real account, every line item to a real order, no orphaned custom objects after a migration.

Low integrity is sneaky—reports run, but numbers are quietly wrong because orphaned rows are excluded or double-counted.

How to measure it: orphan counts (contacts without accounts, tasks without owners), referential integrity checks in the warehouse, reconciliation between subtotals and parents.

Example: After a CRM merge, 12% of contacts still reference deleted account IDs until a batch job fixes links.

8. Precision — is the data granular enough for how you use it?

Precision is about the right level of detail—not more, not less. A “United States” country value may be valid but too coarse for territory carving; “VP” without function is too vague for persona scoring.

Imprecise data forces teams to bucket guesses into strategy: wrong territories, wrong ICP filters, wrong prioritization models.

How to measure it: distribution checks (how often is field X the generic default?), granularity coverage (state filled when country = US), and model feature completeness for ML or scoring.

Example: Job title “Manager” with no department—valid, consistent, yet too imprecise for ABM plays that depend on function.

Putting the dimensions to work

You don’t need to “fix” all eight at once. Start with the two or three dimensions that hurt your highest-stakes workflow this quarter—usually accuracy, completeness, and timeliness for outbound GTM teams—and expand from there. If CRM data quality is your bottleneck, run your dimensions audit on production CRM objects first, not a theoretical enterprise-wide catalog.

For a deeper walkthrough of definitions, tradeoffs, and how dimensions connect to governance and tooling, read our in-depth guide to data quality dimensions. When you’re ready to improve contact-level accuracy and completeness at scale, teams also use waterfall enrichment to validate and fill gaps across multiple sources—learn how waterfall enrichment works and try FullEnrich with 50 free credits, no credit card required.