Consistency

What is Consistency?

Consistency measures whether your data values conform to an agreed-upon standard. A field is consistent when every record uses the same term for the same concept. A field is inconsistent when variations exist that mean the same thing but are expressed differently.

“USA”, “United States”, and “US” all refer to the same country. To Salesforce, they are three distinct values. A report grouped by Country produces three rows instead of one. An automation filtering for “USA” misses 230 records containing “United States.”

Conformance Rate = (Conforming Records / Total Records) x 100

If 850 of 1,000 Account records have a Country value that matches your allowed values list, your Country conformance rate is 85%. The remaining 15% contain variations that need standardization.

Why Consistency Matters

Reporting

Inconsistent data fragments your reports. When your Country field contains five different spellings of the same country, a report grouped by Country shows five rows instead of one. The total is correct, but the grouping is wrong. Executive dashboards built on these reports tell a misleading story.

Automation

Salesforce automation depends on exact value matches. A workflow filtering for Country = "USA" misses records with “United States” and “US.” The automation runs correctly on matching records but silently skips everything else.

AI and Agentforce

AI models treat each variation as a separate concept. “USA” and “United States” become two different countries in the model’s view. Agentforce generates responses using your field values. Inconsistent values produce inconsistent AI outputs.

How DQS Measures Consistency

DQS produces 6 consistency metrics organized around a diagnostic question: “Do your field values conform to a standard, and if not, what are they doing instead?”

Think of these metrics as a diagnostic flow. Each step reveals a different layer of the problem.

Step 1: How Consistent Is It?

Conformance Rate is the headline metric. It calculates the percentage of records where the field value matches one of your defined allowed values. This is the number you put on a dashboard.

You run a scan on the Account object. The Country field shows a Conformance Rate of 72%. That means 28% of your Accounts contain country values outside your allowed list. Your territory assignment rules, regional dashboards, and localization logic all operate on incomplete data.

Every other consistency metric exists to explain what the non-conforming 28% looks like.

Step 2: What Is the Scale?

Rates tell you severity. Counts tell you workload. Two metrics answer the scale question:

These two numbers always add up to total records. If your Conformance Count is 720 and Non-Conforming Count is 280, you have exactly 1,000 records evaluated.

Example: Your data steward needs to clean up the Industry field. Non-Conforming Count is 3,400. She now knows the scope of the project, can assign resources, and can set a realistic timeline for standardization.

Step 3: How Fragmented Is the Data?

Variant Count measures the number of distinct values found in a field across all records. It answers: “How many different ways are people expressing this data?”

This metric works as a schema health indicator. A Country field with 5 distinct values is likely well-controlled. A Country field with 47 distinct values signals serious fragmentation, even before you check conformance.

Example: During an org audit, you scan Lead_Source__c on Leads. Variant Count returns 34. Your marketing team defined 8 lead sources. The remaining 26 variants are misspellings, abbreviations, and ad-hoc entries from integrations. The picklist is technically enforced at the UI, but API-created records bypass it.

Step 4: What Are the Actual Values?

Dominant Values returns the top N most frequent values in the field, along with each value’s frequency count. This tells you what your data actually looks like, not what you expect it to look like.

This table reveals three things. First, the de facto standard: “United States” at 45% is what most users enter. Second, the common variations: “USA” and “US” account for another 41%. Third, the long tail: “U.S.A.” and “United States of America” are rare but present.

Example: Your team is debating whether to standardize Country values to ISO codes (“US”) or full names (“United States”). Dominant Values shows that 45% of records already use “United States.” Standardizing to that value requires changing 55% of records. Standardizing to “US” requires changing 82%. The data tells you the cheaper path.

Why Rates and Counts Come in Pairs

Conformance metrics come as a rate (Conformance Rate) and its inverse (Non-Conformance Rate), plus absolute counts for both sides. This is intentional:

• Rates are for dashboards, executive reporting, and trend tracking. “Conformance improved from 72% to 94% this quarter.”
• Counts are for project planning, workload estimation, and cleanup scoping. “We have 3,400 non-conforming records to fix.”

Use rates to communicate progress. Use counts to plan work.

Metric Reference

Foundation Metrics

These 2 metrics form the base of every consistency analysis. They tell you the conformance rate and the number of records that match.

Advanced Metrics

These 4 metrics go beyond “does it match?” to ask “what does the non-matching data look like?” They require the Advanced Conformance Analysis mode.

Field Type Coverage

DQS supports consistency checks on the following Salesforce field types:

Two Analysis Modes

DQS offers two consistency analysis modes:

Conformance Check answers the question: “Do field values match my allowed list?” It produces the 2 foundation metrics and covers the essentials for a quick conformance audit.

Advanced Conformance Analysis goes deeper. It produces all 6 metrics, including non-conforming counts, variant detection, and dominant value distribution. Use this mode when you need to understand the full landscape of value fragmentation, not just the conformance score.

Configuring Consistency

DQS provides four configuration inputs for consistency. Each can be set at the global level (applies to all fields) and overridden at the individual field level.

Tip: Start by using Import from Field to see what values actually exist in your data before defining your allowed values list.

Import from Field: Discovery-First Configuration

A common challenge with consistency is knowing which values to expect. You cannot define allowed values if you do not know what your data contains.

Import from Field solves this. It queries the actual field data, groups values by frequency, and displays the results as a checklist.

How it works:

1. Open the Expected Values configuration.
2. Click Import from Field. DQS queries the live data and returns distinct values sorted by frequency.
3. Review the checklist. Each value shows how many records contain it.
4. Check the values you consider correct. Leave incorrect, malformed, or placeholder values unchecked.
5. Click Add Selected. The checked values populate your allowed values list.

Example: You configure consistency for a Rating__c field. Import from Field returns:

The first three values are your real ratings. You check those. “Very High” is a data entry error. “240 km/h” is clearly wrong-field data. “N/A” is a placeholder. You leave those unchecked. When the scan runs, those 43 records appear as non-conforming, and your allowed values list is built from what your data actually contains.

This workflow inverts the traditional “guess first, fix later” approach. You discover first, then define your standard.

Common Consistency Issues

Country and State Variations

The most common inconsistency in Salesforce data. Without standardization, a single concept like “United States” appears as 5 or more distinct values. Reports fragment. Filters miss records. Territory rules fail.

Fix: Enable Salesforce State and Country Picklists for all address fields. Use DQS to find and clean up existing non-conforming values.

Free-Text Fields Without Governance

Text fields that lack picklist constraints accumulate variations over time. Industry, Job Title, Lead Source, and Department fields are frequent offenders when implemented as free text.

Fix: Convert high-value free-text fields to picklists. Use Import from Field to see your current value distribution before defining the picklist options.

Integration-Created Variations

External systems and APIs write records that bypass Salesforce UI validation. A marketing automation platform writes “Info Technology” while your standard is “Information Technology.” These variations accumulate silently.

Fix: Apply value mapping rules in your integration layer. Run periodic consistency scans to catch new variations from data sources you do not control.

Casing Inconsistencies

Users enter “Active”, “active”, and “ACTIVE” across different records. With case-sensitive matching disabled, DQS counts all three as conforming. But the field still contains three different spellings in the raw data.

Fix: Decide whether casing matters for your use case. For display-facing fields, standardize casing through data cleanup. For matching purposes, disable case sensitivity in your DQS configuration.

Best Practices

Define Standards Before Scanning

Document the expected value for each constrained field before running your first scan. Without a clear standard, you have no baseline to measure against.

Use the Discovery Workflow for Unknown Fields

For fields where you do not have a predefined standard, use Import from Field first. Let the data tell you what the de facto standard is. The value with the highest frequency is often the right choice as your canonical value.

Track Conformance Over Time

A single conformance score is a snapshot. Track scores across multiple scans to detect degradation early, measure cleanup progress, and identify data sources that introduce new variations.

Use Variant Count as an Early Warning

Monitor Variant Count across scans. A field that jumps from 12 to 28 distinct values between scans has a new source of variation. Investigate before the problem scales.

Prioritize by Business Impact

Not every field needs 100% conformance. Focus on fields that drive reporting (Country, Industry), feed automation (Status, Stage), or supply data to AI and Agentforce.

Next Steps

You have now completed all five data quality dimensions. Continue your learning:

• In Salesforce: Data Quality in Salesforce - standardize picklist and reference values in Salesforce
• Next: Agentforce Preparation - Learn about AI-specific data requirements
• Previous: Timeliness - Measure data freshness and currency
• Overview: The Five Dimensions - Review all dimensions together
• Action: AI Readiness Assessment - See your consistency scores and more