Consistency: Configuration Scenarios

What These Scenarios Cover

This page walks through three real-world consistency configurations, from initial setup to reading the scan results. Each scenario uses a different business context and analysis mode.

These scenarios build on the concepts and metrics covered in the main Consistency article. Read that first if terms like Conformance Rate, Variant Count, and Dominant Values are new to you.

Scenario 1: Country Field Standardization with Discovery

The Business Context

Your org has 15,000 Account records from 3 merged companies. The Country field is free text. Regional dashboards show fragmented data: “United States” appears as one row, “USA” as another, “US” as a third. Territory assignment rules miss records because they filter for a single spelling. You need to standardize, but you don’t know what values exist across all three legacy systems.

Configuration Walkthrough

Start with Import from Field to discover what your data actually contains before defining allowed values.

1. Open the Expected Values configuration for the Country field.
2. Click Import from Field. DQS queries the live data and returns distinct values sorted by frequency.
3. Review the checklist. The import reveals the full picture:

4. Decide on your standard. ISO country codes (“US”, “CA”, “UK”) are compact, industry-standard, and unambiguous. Check the ISO codes from the import list.
5. Click Add Selected to populate your allowed values.

Set the remaining configuration:

What the Scan Produces

Reading the Results

12% conformance is expected. You defined a new standard (ISO codes) that the data has never been normalized to. Only the 1,800 records already containing “US” match. This is not a bad score. It is your starting point.

47 variants reveals the scale of fragmentation. Three merged systems produced 47 different ways to express country names. Without this number, you would underestimate the cleanup effort.

Dominant Values shows where to focus. The top 3 variations (“United States”, “USA”, “US”) account for 8,600 records. Standardizing those three values alone lifts your conformance from 12% to 69%. Start there.

Non-Conforming Count (13,200) is your exact cleanup scope. Your data steward now has a concrete project size, not a guess.

Next Action

Build a value mapping table using the Dominant Values output. Map “United States” to “US”, “USA” to “US”, and so on. Run the data normalization. Rescan to verify your new Conformance Rate.

Scenario 2: Lead Rating Validation

The Business Context

Your Lead Rating field (Rating__c) is a text field that accepts “Hot”, “Warm”, or “Cold.” Sales managers report strange values in their pipeline reports. A filter for Rating = "Hot" returns fewer records than expected. You need a quick conformance audit to find out what is in the field and how many records need cleanup.

Configuration Walkthrough

Start with Import from Field to see the actual values before configuring your scan.

1. Open the Expected Values configuration for Rating__c.
2. Click Import from Field. The import returns:

The first three values are your real ratings. “Very High” comes from a different picklist (someone pasted from the wrong field). “240 km/h” is clearly data from the wrong field entirely. “N/A” is a placeholder.

3. Check “Hot”, “Warm”, and “Cold”. Leave the rest unchecked.
4. Click Add Selected.

Set the remaining configuration:

What the Scan Produces

Reading the Results

93.7% conforms. That means 43 records have garbage data. For a quick audit, the Conformance Check mode gives you the answer fast without computing advanced metrics.

The Import from Field step already told you what the garbage looks like. “Very High” (23 records from a wrong picklist value), “240 km/h” (12 records with wrong-field data), and “N/A” (8 placeholder entries). You don’t need Dominant Values here because the import gave you the breakdown before the scan even ran.

43 records is a manageable cleanup. This is not a data migration project. It is a 30-minute manual fix or a single data update job.

Next Action

Fix the 43 non-conforming records. Then convert Rating__c from a text field to a picklist to prevent future issues. API-created records bypass picklist validation, so run periodic consistency scans to catch new variations from integrations.

Scenario 3: Job Title Conformance for Persona Targeting

The Business Context

Your marketing team runs persona-based campaigns targeting “VP and above” Contacts. The Title field is free text with thousands of variations. Before every campaign, someone manually searches for title keywords, misses half the variations, and builds an incomplete audience list. The team needs a data-driven answer to two questions: “How many VP+ contacts do we have?” and “What titles do the rest of our contacts have?”

Configuration Walkthrough

1. Open the Expected Values configuration for the Title field on Contacts.
2. Click Import from Field. The import returns hundreds of values. Too many to check individually, but the frequency counts are useful for context.
3. Define your allowed values based on your persona mapping. Check or type the title values your team considers “VP and above”:

VP, Vice President, SVP, Senior Vice President, EVP,
Executive Vice President, Director, Senior Director,
CEO, CFO, CTO, CIO, CMO, COO, President

4. Click Add Selected.

Set the remaining configuration:

What the Scan Produces

Dominant Values (Top 20):

Reading the Results

34% conformance is not a failure. This is not a data quality problem. It means 34% of your Contacts hold VP+ titles, and that is your campaign target audience. The number answers the question your marketing team has been guessing at.

312 Variant Count confirms that free-text Title is highly fragmented. 312 distinct title values across 10,000 Contacts. This is normal for free-text fields and explains why manual searches miss people.

Dominant Values shows what titles your contacts actually have. Many of the top values are below VP level (Manager, Sales Rep, Account Executive). That is expected. These contacts are valid records with valid titles. They fall outside your target persona.

Non-Conforming Count (6,600) is NOT a cleanup scope. Unlike the Country scenario, these are not dirty records. They are contacts with titles outside your VP+ filter. “Manager” is a real title, not a data error. Treat Non-Conforming Count as “contacts outside this persona,” not “records to fix.”

The real insight: You now have a data-driven audience size. 3,400 VP+ contacts, verified by scanning the actual data. No more manual keyword searches.

Next Action

Use the Conformance Count (3,400) as your VP+ campaign audience size. Review the Dominant Values list for titles you missed. “Senior Manager” (275 records) and “Head of Operations” (180 records) are borderline. If those roles qualify for your campaigns, add them to the allowed values and rescan.

Choosing Your Configuration

For a full explanation of all 6 consistency metrics, analysis modes, and configuration inputs, return to the main Consistency article.

Ready to measure your own data quality? Take the AI Readiness Assessment to see your consistency scores and more.