Completeness

What is Completeness?

Completeness measures whether your data is actually there. A field is complete when it contains meaningful data. A field is incomplete when it is null, blank, or filled with a placeholder like “N/A” or “TBD.”

Completeness is the most fundamental data quality dimension. Without data, you have nothing to validate, deduplicate, or analyze.

Completeness Rate = (Records with Data / Total Records) x 100

If 850 of 1,000 Contact records have an Email value, your Email completeness rate is 85%. This metric (sometimes called fill rate) is the headline number for any field.

Why Completeness Matters

Reporting

Incomplete data skews your analytics. When 40% of Account records lack an Industry value, any report grouped by Industry shows only partial truth. Dashboards become unreliable. Executive decisions rest on a fraction of the picture.

Automation

Salesforce automation depends on field values. A workflow that sends emails fails when Email is blank. A process that updates Account Owner fails when the lookup is null. Each missing value is a potential automation failure.

AI and Agentforce

AI models learn from your data. When fields are empty, the model has nothing to learn from. Agentforce uses your Salesforce data to generate responses and take action. Missing data means incomplete context and less useful AI outputs.

How DQS Measures Completeness

DQS produces 10 completeness metrics organized around a diagnostic question: “Where is the data missing, why, and is the data that exists actually useful?”

Think of these metrics as a diagnostic funnel. Each step builds on the previous one.

Step 1: How Complete Is It?

Completeness Rate is the headline metric. It calculates the percentage of records where the field contains a non-empty, non-null value. This is the number you put on a dashboard.

You run a scan on the Account object. The Industry field shows a Completeness Rate of 62%. That means 38% of your Accounts have no industry value, which means your segmentation reports, territory rules, and marketing campaigns that filter by industry all work with incomplete data.

Every other completeness metric exists to explain why this number is not 100%.

Step 2: What Is the Scale?

Rates tell you severity. Counts tell you workload. Populated Count answers the scale question: how many records actually have a value. Use it for coverage reporting and to size the gap against your total record count — the difference between your total and the Populated Count is the cleanup backlog.

Example: Your data steward needs to build a cleanup campaign. With 50,000 Contacts and a Populated Count of 35,800 on Phone, she knows 14,200 records need enrichment, can estimate cost with a data vendor, and can set a realistic timeline.

Step 3: Why Is It Incomplete?

Three metrics break down the cause of incompleteness. Each points to a different root problem.

Null Count and Null Rate measure records where the field has a true database null, meaning the field was never populated. In Salesforce, null and empty string are different states. A field that was never touched is null. A field that was explicitly cleared is an empty string. This distinction tells you whether data was never captured or was deliberately removed.

Example: After a data migration, the Fax field on Accounts shows a 45% Null Rate. The fax data was never migrated from the legacy system (null = never existed) rather than being captured and later cleared. A high Null Rate points you at the source system, not at user behavior.

Placeholder Count and Placeholder Rate measure records containing known placeholder values like “N/A”, “TBD”, “Unknown”, or any custom values you define. These values look like data but carry no real information.

Example: Your global Account data shows 94% Completeness Rate on Industry. Looks great on paper. But Placeholder Rate reveals that 18% of those “populated” values are actually “N/A”, “Other”, or “Unknown.” The true completeness is closer to 76%. This is the metric that turns a green dashboard red.

Step 4: Is the “Complete” Data Useful?

The first three steps identify what is missing. Step 4 asks a harder question: is the data that is there actually worth having?

Incompleted Count is the broadest measure of missing data. It combines all forms of incompleteness: nulls, blanks, and placeholder values. When placeholder detection is active, Incompleted Count is always greater than or equal to the Null Count alone, because it also captures whitespace-only and placeholder entries.

Example: Your Description field on Opportunities shows a Null Count of 500 but an Incompleted Count of 1,800. The difference? 1,300 records have descriptions like “TBD”, “N/A”, or ”---”. Those records are technically filled in but practically useless. Without this metric, you would think you only have 500 records to fix instead of 1,800.

Rich Text Ratio measures the percentage of text field records that contain substantial content above a character threshold. It separates fields with meaningful prose from fields with a few words. A Description field is “populated” whether it contains “Good customer” or a three-paragraph account plan. For AI readiness, the depth of content matters as much as its presence.

Example: Your company is evaluating an AI tool that summarizes Case descriptions. You scan the Description field on Cases: 88% Completeness Rate, but only 31% Rich Text Ratio. Only 31% of case descriptions have enough substance for the AI to work with. The rest are entries like “call back”, “see email”, or “issue reported.” The AI project needs a data enrichment phase before it can deliver value.

Text Field Utilization measures how much of a text field’s available character capacity is used. A Long Text Area with 32,000 character capacity where the average entry is 45 characters has very low utilization.

Average Utilization gives you the mean percentage of field length used across all records. Together with Text Field Utilization, it paints a complete picture of whether your text fields are right-sized.

Example: During an org assessment, Text Field Utilization reveals that Notes__c (a Long Text Area, 131,072 characters) averages 3.2% utilization, with most entries under 200 characters. Meanwhile, Short_Description__c (Text, 255 characters) shows 94% utilization with frequent truncation issues. The schema needs right-sizing: the Long Text Area is overkill, and the Text field is too small.

Note: Text Field Utilization and Average Utilization apply only to String and TextArea fields, because these are the field types with a defined character capacity to measure against.

Why Rates and Counts Come in Pairs

Most metrics come as a rate (percentage) and a count (absolute number). This is intentional:

• Rates are for dashboards, executive reporting, and trend tracking. “Completeness improved from 72% to 89% this quarter.”
• Counts are for project planning, workload estimation, and cleanup scoping. “We have 14,200 records to fix.”

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

Metric Reference

Foundation Metrics

These 5 metrics form the base of every completeness analysis. They work on virtually all field types.

Contextual Metrics

These 5 metrics go beyond “is it there?” to ask “is it meaningful?” They require the Contextual Completeness analysis mode.

Field Type Coverage

DQS supports completeness checks on all standard Salesforce field types:

Two Analysis Modes

DQS offers two completeness analysis modes:

Basic Completeness answers the question: “Are fields filled in?” It produces the 5 foundation metrics and covers the essentials every org needs for a data hygiene check or quick audit.

Contextual Completeness goes deeper. It produces all 10 metrics, including placeholder detection, rich text analysis, and field utilization. Use this mode when you need to distinguish between data that is present and data that is useful.

Configuring Completeness

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

Tip: Start with the common placeholders (“N/A”, “TBD”, “Unknown”, ”—”) and add org-specific values as you discover them in scan results.

Common Completeness Issues

Optional Fields Never Filled

When fields are optional, users skip them. Over time, valuable fields like Company Description or LinkedIn URL have near-zero completeness rates.

Fix: Make critical fields required, or create prompts during record editing.

Bulk Imports with Gaps

Data migrations and list imports often lack values for certain fields. A purchased contact list has no Account association. A legacy system export lacks standardized Industry values.

Fix: Audit imports before loading. Use DQS to establish baselines and track improvement after each import.

Placeholder Abuse

Users enter “N/A” or “TBD” to pass validation rules. The field appears complete but contains no usable data. Standard reports count these as populated.

Fix: Enable placeholder detection and define your placeholder values list. Review and update placeholder values during regular data maintenance.

Whitespace Padding

Some integrations or manual entry leaves fields with whitespace only. Salesforce counts these as “filled” but they contain nothing useful.

Fix: Enable blank detection to catch whitespace-only values.

Best Practices

Prioritize by Business Impact

Not every field needs high completeness. Focus on fields that drive automation, appear in executive dashboards, feed AI and Agentforce, or support compliance requirements.

Track Trends Over Time

A single completeness score is a snapshot. Track scores across multiple scans to detect degradation early, measure improvement initiatives, and identify problem data sources.

Address Root Causes

Low completeness signals a process issue. Investigate whether users are skipping fields, imports are missing data, or integrations are failing silently. Fix the source, not just the symptom.

Use the Diagnostic Funnel

Do not stop at Completeness Rate. Walk through the funnel: check the scale (Populated Count), identify the cause (Null vs Placeholder), then evaluate content quality (Rich Text Ratio, Utilization). Each step reveals a different type of problem with a different fix.

Next Steps

You now understand how to measure and improve completeness. Continue learning about the next dimension:

• In Salesforce: Data Quality in Salesforce - measure completeness across your CRM records
• Next: Validity - Ensure your data conforms to expected formats
• Related: The Five Dimensions - Overview of all dimensions
• Action: AI Readiness Assessment - See your current completeness scores