Salesforce data quality · The six dimensions
The six data quality dimensions — and how to measure them in Salesforce
Completeness, validity, consistency, uniqueness, timeliness, accuracy. These six dimensions are how you turn “our CRM data is messy” into something specific enough to fix. Here’s what each one means, with a real Sales Cloud example — and how they combine into a single score you can act on.
Start here
What a “data quality dimension” actually is
A data quality dimension is one specific way data can be good or bad. “Bad data” is too vague to fix; “this Account is a duplicate” or “this Opportunity has no close date” is specific enough to act on. The six dimensions below are the standard lens data teams use — and every one of them shows up plainly in Salesforce.
The six dimensions
Six ways your Salesforce data can let you down
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Completeness
Are the fields that matter actually filled in? In Sales Cloud: an Opportunity with no close date, or a Lead with no email, can’t be worked or forecasted.
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Validity
Does the value fit the rules and format it should? In Sales Cloud: a phone number sitting in an email field, or a stage value that no longer exists in your sales process.
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Consistency
Does the same fact agree everywhere it appears? In Sales Cloud: an Account’s industry disagreeing with the same company recorded on a related Contact.
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Uniqueness
Is each real-world thing stored exactly once? In Sales Cloud: the same company saved as three Accounts inflates coverage and splits activity history.
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Timeliness
Is the data current, or has the world moved on? In Sales Cloud: a Contact who changed jobs months ago is a dead lead dressed up as a live one.
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Accuracy
Does the data match reality? In Sales Cloud: a deal marked Commit that the customer has already postponed is the most expensive kind of inaccurate.
Putting it together
One score, not six arguments
Measured one at a time, the dimensions are an academic exercise. Measured together, per object, they become a number a team can own. ForecastGuard scores each of your five core Sales Cloud objects — Leads, Contacts, Accounts, Opportunities and Cases — across all six dimensions to produce a DQ Health Score Card, then ranks what to remediate first. See the bigger picture in the Salesforce data quality guide, or see the scorecard in the interactive preview →
FAQ
Common questions about data quality dimensions
What are the six dimensions of data quality?
The six commonly used data quality dimensions are completeness, validity, consistency, uniqueness, timeliness and accuracy. Together, they help organizations measure whether data is usable, trustworthy and fit for business decisions.
Why are data quality dimensions important?
Data quality dimensions give teams a structured way to evaluate the health of business data. Instead of saying data is “bad,” teams can identify whether the issue is missing fields, invalid values, duplicate records, stale data, inconsistent formats or inaccurate information.
How do you measure completeness in data quality?
Completeness measures whether required fields are populated. For example, in Salesforce, an Opportunity record may be incomplete if it is missing close date, stage, amount, account, owner or next-step information.
What is the difference between accuracy and validity?
Validity checks whether data follows the required format or business rule, while accuracy checks whether the data reflects the real-world truth. For example, an email address may be valid because it follows the correct format, but inaccurate if it belongs to the wrong person.
How does uniqueness relate to duplicate records?
Uniqueness measures whether each real-world entity is represented only once. Duplicate Leads, Contacts, Accounts, Opportunities or customer records reduce uniqueness and can create reporting errors, sales confusion and poor customer experience.
How can businesses improve data quality across these dimensions?
Businesses can improve data quality by profiling key data objects, scoring records against each dimension, prioritizing high-impact issues, standardizing formats, removing duplicates, assigning stewardship ownership and monitoring quality continuously.
How does ForecastGuard use data quality dimensions?
ForecastGuard helps Salesforce teams assess CRM data quality across core objects such as Leads, Contacts, Accounts, Opportunities and Cases. It helps identify issues like missing fields, duplicate records, stale opportunities and pipeline hygiene risks so teams can prioritize cleanup based on business impact.
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