Understanding the Five Dimensions
Data quality is measured across five key dimensions. Each dimension answers a specific question about your data, and together they determine whether your data is fit for purpose.
DQS measures all five dimensions natively within Salesforce.
| Dimension | Key Question | Example Issue |
|---|---|---|
| Completeness | Is the data present? | Missing email addresses |
| Validity | Is the format correct? | Malformed phone numbers |
| Uniqueness | Is each record distinct? | Duplicate contacts |
| Timeliness | Is the data current? | Stale opportunity dates |
| Consistency | Is the data uniform? | ”USA” vs “United States” |
1. Completeness
Completeness measures whether required data values are present and not missing.
What DQS Measures
- Fill Rate: Percentage of records with non-empty values
- Blank Detection: Empty strings and whitespace-only values
- Placeholder Detection: Values like “N/A”, “TBD”, or “Unknown”
Why Completeness Matters
Incomplete data breaks automation. When a workflow requires an email address that does not exist, the workflow fails. When a report filters on Industry but half your records lack Industry values, your analytics show only part of the picture.
Common Completeness Issues
| Issue | Example | Impact |
|---|---|---|
| Blank fields | Empty Contact Phone | Cannot call prospects |
| Placeholder values | ”TBD” in Company Name | Invalid for reporting |
| Whitespace-only | ” ” in Description | Appears filled but is not |
Tip: Start with your most critical fields. You do not need 100% completeness on every field, only on the ones that matter for your use case.
2. Validity
Validity measures whether data values conform to expected formats and patterns.
What DQS Measures
- Format Validation: Email, phone, URL patterns
- Pattern Matching: Custom regex patterns
- Domain Validation: Allowed value lists
Validity vs Accuracy
Validity and accuracy are different. An email address can be valid (correct format) but inaccurate (wrong person). DQS measures validity because format validation can be automated. Accuracy requires external verification.
| Check | Valid? | Accurate? |
|---|---|---|
| john@company.com | Yes | Unknown without verification |
| john@company | No | N/A (format is wrong) |
| john.smith@oldcompany.com | Yes | No (person left the company) |
Common Validity Issues in Salesforce
- Email addresses missing ”@” or domain
- Phone numbers with wrong digit count
- URLs without protocol (http/https)
- Free-text where picklist values are expected
3. Uniqueness
Uniqueness measures whether data values are distinct and non-duplicated.
What DQS Measures
- Duplicate Detection: Exact matches across records
- Distinct Count: Total unique values per field
- Entropy Analysis: Value diversity and distribution
The Cost of Duplicates
Duplicate records waste storage, confuse users, and produce inflated metrics. When Sales has two records for the same company, they lose context and risk embarrassing duplicate outreach.
Research shows:
- B2B databases contain 10-30% duplicate records on average
- Each duplicate costs organizations in wasted email sends, confused reporting, and fragmented customer history
Duplicate Prevention vs Detection
DQS focuses on detection, which helps you identify duplicates that already exist. Prevention (stopping duplicates at creation) requires validation rules and matching rules in Salesforce setup.
4. Timeliness
Timeliness measures whether data is current enough for its intended use.
What DQS Measures
- Freshness Rate: Percentage of records within acceptable age
- Staleness Detection: Records exceeding your threshold
- Average Age: Mean age of date field values
- Future Date Detection: Invalid dates in the future
Setting Freshness Thresholds
Different data types have different freshness requirements:
| Data Type | Typical Threshold | Why |
|---|---|---|
| Lead Last Activity | 30 days | Cold leads lose value |
| Contact Info | 90 days | People change jobs |
| Opportunity Close Date | Current quarter | Forecasting accuracy |
| Account Annual Revenue | 1 year | Annual updates expected |
Why Timeliness Matters for AI
AI and machine learning models learn from your data. When your data is stale, the AI learns outdated patterns. Agentforce, for example, uses your Salesforce data to inform responses. Outdated contact information leads to incorrect recommendations.
5. Consistency
Consistency measures whether data values are uniform and standardized.
What DQS Measures
- Conformance Rate: Percentage matching expected patterns
- Variant Detection: Different representations of the same value
- Dominant Value Analysis: Most common value per field
Types of Inconsistency
| Type | Example | Problem |
|---|---|---|
| Format variation | ”USA” vs “United States” vs “US” | Breaks filtering and grouping |
| Spelling variation | ”Acme Corp” vs “ACME Corporation” | Creates false duplicates |
| Case variation | ”new york” vs “New York” | Looks unprofessional in reports |
Why Consistency Matters for AI
AI models treat “USA” and “United States” as different values. If your records use both, the AI cannot group them correctly. Inconsistent data fragments your AI’s understanding and produces unreliable outputs.
Achieving Consistency
- Use picklists instead of free text where possible
- Standardize on a single format for each field
- Import from picklist values when configuring DQS consistency checks
Balancing the Dimensions
Not every dimension is equally important for every use case. Consider what you need:
| Use Case | Priority Dimensions |
|---|---|
| Marketing campaigns | Completeness, Validity (for deliverability) |
| Sales forecasting | Timeliness, Completeness |
| Customer service | Uniqueness (one source of truth) |
| AI/Agentforce | All five, plus AI Readiness checks |
DQS Dimension Coverage
DQS measures all five dimensions with comprehensive capabilities:
| Dimension | What DQS Measures |
|---|---|
| Completeness | Fill rate, null detection, blank detection, placeholder detection, contextual logic |
| Validity | Format validation, custom regex patterns, invalid record identification |
| Uniqueness | Duplicate detection, entropy analysis, frequency distribution |
| Timeliness | Freshness rate, staleness detection, average age, future date detection |
| Consistency | Conformance checking, variant discovery, dominant value analysis |
Next Steps
Now that you understand the five dimensions:
- See how they apply in your CRM: Data Quality in Salesforce
- Take the AI Readiness Assessment to see your current scores
- Read about Agentforce Preparation for additional checks beyond basic data quality
- Learn how to use the Definition Builder to configure your first scan