Completeness: Configuration Scenarios

What These Scenarios Cover

This page walks through three real-world configurations of DQS completeness analysis. Each scenario covers a specific business problem, shows the exact settings to use, and explains how to read the results.

These walkthroughs build on the concepts from the main Completeness article. Read that first if you are new to completeness metrics or the diagnostic funnel.

Scenario 1: Email Hygiene on Contacts

The Problem

Your sales team reports that email campaigns have low delivery rates. The marketing operations team blames the data, but nobody knows how much email data is actually missing. You need a clear count of Contacts without a usable email address.

Configuration

This is a straightforward fill rate check. Use Basic Completeness mode on the Contact object, targeting the Email field.

The Email field is a text-based field in Salesforce, so DQS produces both null and blank breakdowns.

Sample Results

Total Contact records: 10,000.

Reading the Results

Start with the headline: 73% completeness. That means 2,700 Contacts have no email address. Your email campaigns can only reach 7,300 of 10,000 contacts at best.

Now walk the diagnostic funnel to understand why 2,700 records are empty.

Null Count: 1,800. These Contacts never had an email entered. The field was never touched. This pattern is common with manually created records where reps skip the email field during fast data entry, or with legacy records imported from a system that did not capture email.

Blank Count: 900. These Contacts have an empty string in the email field. The field was written to, but with no value. This pattern points to a different root cause: web form integrations that submit the record even when the email field is left empty. The integration writes '' (empty string) instead of leaving the field null.

Two root causes require two different fixes:

• For the 1,800 nulls: Address the data entry gap. Make the email field required on the Contact page layout, or add a prompt during record creation.
• For the 900 blanks: Fix the integration. Add client-side validation to the web form so empty email fields are not submitted, or add a Salesforce validation rule that rejects empty strings on email.

What to Do Next

Use the Empty Count (2,700) to scope a data enrichment project. If you work with a data vendor, this is your record count for cost estimation. Track Completeness Rate over time to measure whether the fixes are working.

Scenario 2: Industry Placeholder Detection on Accounts

The Problem

Your Account segmentation reports show 94% of Accounts have an Industry value. Marketing trusts this number and uses it for campaign targeting. You suspect the 94% is inflated by placeholder values like “N/A” and “Unknown” that look like data but carry no information.

Configuration

Use Contextual Completeness mode on the Account object, targeting the Industry field. This mode enables placeholder detection, which is what you need to test your theory.

Turn off case sensitivity for this scan. Users enter placeholders in all kinds of capitalization: “n/a”, “N/a”, “N/A”. Catching all variants gives you the true picture.

Sample Results

Total Account records: 10,000.

Reading the Results

The headline number looks healthy: 94% completeness. But this is exactly what you suspected was misleading.

Look at the gap between Empty Count and Incompleted Count. Empty Count says 600 records have no value at all. Incompleted Count says 2,400 records have no usable value. The difference is 1,800 records with placeholder values.

Here is the math:

Incompleted Count (2,400) = Empty Count (600) + Placeholder Count (1,800)

600 records are visibly empty. Anyone running a standard Salesforce report would spot these. But 1,800 records contain values like “N/A”, “Other”, or “Unknown” that inflate the completeness rate without providing real segmentation data.

The true usable completeness is closer to 76%, not 94%. That 18-point gap is the hidden incompleteness that standard reports miss.

Why this matters for segmentation: If marketing runs a campaign targeting “Technology” industry Accounts, the segment count is accurate. But if they run a report showing total coverage by industry, the 94% headline hides the fact that nearly 1 in 5 “populated” records carry no usable industry information. Territory assignments, industry-based routing, and executive dashboards all inherit this distortion.

What to Do Next

Scope your data enrichment project to 2,400 records, not 600. The cleanup target is the Incompleted Count, not the Empty Count. Work with your Account managers to fill in real industry values, or use an enrichment service. Run the scan again after cleanup to measure improvement.

Scenario 3: Case Description Depth for AI Readiness

The Problem

Your company is evaluating an AI tool that summarizes Case descriptions for support agents. The vendor says the AI needs “rich text data” to work effectively. Before investing in the tool, you need to assess whether your Case Description field has enough substance for the AI to produce useful summaries.

Configuration

Use Contextual Completeness mode on the Case object, targeting the Description field. You need the full set of contextual metrics: placeholder detection and the text quality metrics (Rich Text Ratio, Text Field Utilization, Average Utilization).

The placeholder list here reflects how support agents actually fill in the Description field. Instead of writing a real description, they type a quick shorthand. These entries are technically populated but give the AI nothing to summarize.

Sample Results

Total Case records: 4,200 (estimated from the empty + populated counts, where 500 empty out of ~4,200 total gives the 88% completeness rate).

Reading the Results

Start with the headline: 88% completeness. That sounds healthy for a text field. But this analysis is about AI readiness, not data hygiene. The headline number is not enough.

Incompleted Count vs Empty Count. 500 records have no description at all. But 1,800 records are incomplete when you include placeholders. The gap of 1,300 records contains entries like “See email”, “Call back”, and “Pending.” These records pass a basic completeness check but give the AI nothing to work with.

Rich Text Ratio: 31%. This is the number that answers your question. Only 31% of Case descriptions have meaningful content above the character threshold. The other 69% of “populated” descriptions are either placeholders (already counted above) or entries too short and shallow for the AI to summarize, things like “issue reported”, “customer called”, or “escalated.”

Text Field Utilization: 12%. The Description field is a Long Text Area with large character capacity. Records are using only 12% of that capacity on average across the dataset. This confirms that most entries are very short.

Average Utilization: 8.4%. The mean usage across all records is 8.4% of field capacity. Most descriptions are a few words, not paragraphs.

The AI readiness verdict: The AI summarization tool will produce useful results for about 31% of Cases. For the remaining 69%, the AI will either fail to generate a summary or produce something based on a sentence fragment. The tool will underperform on more than two-thirds of your case volume.

What to Do Next

Present this data to stakeholders before committing to the AI tool. The numbers make the case clearly: the AI project needs a data enrichment phase first. Define a target Rich Text Ratio (start with 60% or higher) and build a plan to improve description quality. Options include:

• Update Case creation processes to require a minimum description length
• Train support agents on writing useful descriptions
• Add screen flows that prompt for detailed information during case intake

Run the scan again after each improvement cycle. Track Rich Text Ratio as your primary progress metric for AI readiness.

Choosing Your Configuration

Use this table to pick the right starting point for your completeness analysis.

For a full reference of all 13 completeness metrics and how they fit into the diagnostic funnel, return to the main Completeness article.

To see how completeness and other data quality dimensions affect your AI readiness, take the AI Readiness Assessment.