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Delpha Data quality

Improve and manage Salesforce data with Delpha's data quality tools. Learn how to exclude records, fix bad data, track email validity. Understand Delpha’s token usage and the 6 data quality dimensions

The Delpha Data Quality section helps users understand, control, and enhance the quality of Salesforce data using Delpha’s intelligent recommendations and metrics.

What are the 6 data quality dimensions

Delpha uses six dimensions—completeness, validity, uniqueness, consistency, accuracy and timeliness—to score data quality in Salesforce. Each metric ensures your data is clean, consistent and accurate

Delpha Data Quality Dimensions Explained

Delpha evaluates each field using six core data quality dimensions to provide a comprehensive health check:

Dimension

Meaning

Completeness

Delpha Data Quality Field Pack for Salesforce

Learn how Delpha augments Salesforce with companion fields to assess six data-quality dimensions and surface normalized/recommended values with confidence and status.

Delpha Data Quality — Overview

Delpha evaluates data quality across six dimensions.
For every field monitored, Delpha adds a standard set of companion fields to the object.
- Labels start with “D”.
- API names start with delpha__DDQ_Quality.

Delpha Data Quality Analysis (per targeted field)

Existing records are assessed on the six dimensions, with one dedicated field per dimension on the target object.

For each field you monitor (shown here as {TargetedField}), Delpha creates one field per dimension plus an overall score:

delpha__DDQ_QualityTargetedFieldCompleteness__c — Boolean — Completeness
delpha__DDQ_QualityTargetedFieldValidity__c — Picklist (Valid, Invalid, Unknown) — Validity
delpha__DDQ_QualityTargetedFieldUniqueness__c — Number (count of occurrences in the org)

Delpha Recommendation

delpha__DDQ_QualityTargetedFieldNormalized__c — Same type as {TargetedField} — Value after normalization
delpha__DDQ_QualityTargetedFieldRecommended__c — Same type as {TargetedField} — Value recommended by Delpha
delpha__DDQ_QualityTargetedFieldRecommendedScore__c

Delpha Support fields

delpha__DDQ_QualityTargetedFieldLastUpdated__c — DateTime — Last analysis timestamp
delpha__DDQ_QualityTargetedFieldPreviousStatus__c — Number — Previous value of …RecommendedStatus__c

Delpha may add small, field-specific adjustments to improve fit:

Expanded picklists: Certain targeted fields can include extra values beyond the defaults.
- Examples:

Data Quality Recommended Status — Full Reference Guide

Delpha’s Data Quality Recommended Status shows the current state of each monitored field’s assessment, recommendation, and user or steward action. Critical for data quality automation and reporting.

The Data Quality Recommended Status is a core field used by Delpha to track the lifecycle of data-quality assessments for each monitored field in a record.

Every monitored field (Email, Website, Phone, etc.) automatically receives its own status field following the format:

Example:

delpha__DDQ_QualityEmailRecommendedStatus__c
delpha__DDQ_QualityWebsiteRecommendedStatus__c

How to exclude records from the analysis

Learn how to exclude Account and Contact records from Delpha's data quality assessment in Salesforce by using or customizing the "D Do Not Assess" field.

Exclude Records from Data Quality Analysis

Overview

In Delpha, you can exclude specific Account or Contact records from the Data Quality assessment process using the D Do Not Assess checkbox field.

How It Works

Each targeted object (Account, Contact) includes a checkbox field provided by Delpha named Do Not Assess.

✅ When the field is set to true, the record is excluded from any data quality scoring or recommendation process.
❌ When the field is false or left blank, the record will be processed normally during assessments.

Custom Exclusion Logic

For advanced scenarios, you can:

Open the Delpha Setup app in Salesforce.
Navigate to the for the relevant object (Account or Contact).
Replace the default D Do Not Assess field with your own custom formula field.
Use that formula to dynamically control which records should be excluded.

This allows for dynamic, rule-based exclusion, such as:

Only assess records created after a certain date.
Exclude inactive contacts.
Exclude accounts without a LinkedIn profile.

How to fix my data quality

Learn the 3 essential steps to fix data quality in Salesforce using Delpha: assess records, review scores, and apply smart recommendations with automation.

How to Fix Data Quality Issues in Delpha

Overview

Fixing data quality in Delpha follows a simple 3-step process: assess, review, and apply recommendations. Whether you're working across your entire org or on a single record, Delpha provides tools to streamline and automate improvements.

What is a Token?

Learn how Delpha tokens work as credits for evaluating and enriching Salesforce records. See how token usage scales with your data refresh schedule.

What Is a Token in Delpha?

In Delpha, a token represents a credit used to evaluate and enhance a single record (Account, Contact, or Lead). Token usage is tied to how frequently you assess or enrich your data.

How Token Consumption Works

Each record may consume up to one token per day, depending on how often you run assessments.
Token usage applies when Delpha performs:
- A data quality evaluation
- A field-level enrichment

Examples of Token Usage

Frequency

Tokens per Record per Year

The less frequently you refresh your data, the fewer tokens you'll use — allowing you to control costs and optimize efficiency.

More info on website

Do I consume a token when applying a Delpha recommendation?

Delpha only consumes a token when analyzing a record and generating recommendations — not when applying those recommendations.

Explanation

Delpha tokens are only consumed when a record is analyzed—that is, when Delpha runs an evaluation and generates data quality recommendations.

✅ Token is used:

When running a data quality assessment
When retrieving or refreshing recommendations

❌ Token is NOT used:

When applying a recommendation manually
When auto-copying a recommendation (if score threshold is met)

This allows users to review and apply changes freely without impacting their token balance once the initial assessment has been done.

What are the Data Quality dimensions for Contact Name

Learn how Delpha evaluates Name fields using six data quality dimensions: completeness, validity, uniqueness, consistency, accuracy, and timeliness.

Name analysis relies on the standard Contact.Name field

Understanding Delpha’s Data Quality Dimensions for Name

Delpha evaluates each field using key data quality dimensions. Below is how each dimension applies to Name field, helping users interpret scores and drive cleanup efforts.

Data Quality Dimensions Explained

Completeness

Question: Are both first and last names present?
Computation: 1 if both are present, 0 if either is missing or empty.

Validity

Question: Do both names conform to valid character and pattern rules?
Computation: 1 if both are valid, 0 if either is invalid.
Example:
Input: Jean → Valid
Input: J3an! → Invalid

Uniqueness - NA

Consistency

Question: Do the original and ?
Computation: 1 if both first and last names are unchanged after normalization, 0 otherwise.
Example:
Input: Jean → Normalized: Jean → Consistency: 1
Input: Jéan → Normalized:

Accuracy

Question: How likely is the name to be correct (not reversed or misspelled)?
Reversed Names Detection
Purpose: Detects if the first and last names are likely swapped.
Method:

Timeliness

Related to the last assessment date.

How Contact Name Normalization works?

Name analysis relies on the standard Contact.Name field

Normalization Strategies

Name normalization is modular and configurable. Each strategy can be set independently, and multiple strategies can be combined for robust normalization. The main strategies are:

What are the Data Quality fields for Contact Email

This page details key email data quality metrics including accuracy, completeness, and validity, ensuring effective email analysis.

Data Quality Fields for Email

fields ensuring accuracy and timeliness for effective communication.

Email analysis relies on the standard Contact.Email field

Fields for Current Quality Metrics

These fields describe the current state of the email field for a given record.

Fields for Delpha’s Recommendation

These fields represent Delpha’s suggested correction or improvement to the current email field.

All our recommended emails are tested
To get more insight on the Data Quality Dimensions for Email, check this

What are the Data Quality dimensions for Address

Delpha Address Data Quality standardizes addresses worldwide, validates them against real locations, and provides reliable recommendations for clean, consistent data in Salesforce.

Address analysis applies on the standard fields:

What Legal Ids are supported, in which country?

View the full list of countries and their supported legal ID types used by Delpha in its Legal ID assessment and recommendation engine. Ensure accurate entity identification.

Supported Countries & Legal ID Types

Below is a code-accurate list of supported countries and their recognized legal ID types.

Country

Legal ID Type

What are the Delpha Ultimate Parent Fields

Covers data-quality dimensions (accuracy, timeliness), recommendation metadata (status, score), and extended audit details (IDs, links, path, comments, JSON).

Here’s the Ultimate field set with label, API name, and a short explanation. It includes standard and (Fields marked [Calculated] are computed from existing values from object Account.)

Standard data quality field set can be found here.

Delpha Data Quality Analysis

Label

API name

Explanation

Delpha Recommendation

Label

API name

Explanation

Delpha Support fields

Label

API name

Explanation

Support Fields: Calculated from Account current data

Label

API name

Explanation

Delpha trusted sources for Ultimate Parent

Delpha identifies each company’s Ultimate Parent by automatically analyzing trusted sources like websites, registries, filings, and press releases with LLM.

Sources of Truth for Ultimate Parent Recommendation

Delpha determines the Ultimate Parent of a company by cross-checking multiple trusted and verifiable sources.

All these sources are automatically analyzed by specialized LLMs , which interpret and consolidate the information to produce a reliable recommendation.

Below, the list of sources sorted by order of importance

1. Company Website

The official company website is the main reference to identify ownership relationships and group structures.

2. Official Business Registries

Delpha continuously collects and analyzes data from national and regional company registries. These registries provide verified legal relationships such as parent-subsidiary links and ownership hierarchies.

3. Securities and Stock-Exchange Filings

For publicly listed entities, regulatory filings (e.g., 10-K, annual reports, prospectuses) are used to confirm corporate control and ownership information declared to investors and authorities.

4. Government Gazettes and Legal Publications

Official gazettes are monitored for legal events such as mergers, acquisitions, dissolutions, and reorganizations that may affect corporate structure.

5. Investor Relations and Press Releases

Validated announcements from investor relations pages or corporate press releases help confirm recent structural changes or acquisitions when registry data is not yet updated.

Delpha analyzes all the sources above, to This approach allows Delpha to accurately represent franchise and multi-brand networks, where multiple legal entities operate under a single brand identity, ensuring the Ultimate Parent recommendation aligns with sales, marketing, and go-to-market realities rather than pure legal ownership.

Ultimate - Delpha focuses on Brands and Operating structures

Delpha’s Ultimate model identifies brand-based parent relationships by analyzing company websites and communications, focusing on real-world operating structures rather than legal ownership.

Two Perspectives of Ultimate Parenting

The concept of Ultimate Parent can be understood in two complementary ways, depending on the business context. Each perspective serves different objectives and relies on different data sources.

Delpha Data quality

Delpha Data Quality Menu Overview

What are the 6 data quality dimensions

Delpha Data Quality Dimensions Explained

Delpha Data Quality Field Pack for Salesforce

Delpha Data Quality — Overview

Delpha Data Quality Analysis (per targeted field)

Delpha Recommendation

Delpha Support fields

Data Quality Recommended Status — Full Reference Guide

How to exclude records from the analysis

Exclude Records from Data Quality Analysis

Overview

How It Works

Custom Exclusion Logic

How to fix my data quality

How to Fix Data Quality Issues in Delpha

Overview

What is a Token?

What Is a Token in Delpha?

How Token Consumption Works

Examples of Token Usage

Do I consume a token when applying a Delpha recommendation?

Explanation

✅ Token is used:

❌ Token is NOT used:

What are the Data Quality dimensions for Contact Name

Understanding Delpha’s Data Quality Dimensions for Name

Data Quality Dimensions Explained

Completeness

Validity

Uniqueness - NA

Consistency

Accuracy

Timeliness

How Contact Name Normalization works?

Normalization Strategies

What are the Data Quality fields for Contact Email

Data Quality Fields for Email

Fields for Current Quality Metrics

Fields for Delpha’s Recommendation

What are the Data Quality dimensions for Address

What Legal Ids are supported, in which country?

Supported Countries & Legal ID Types

What are the Delpha Ultimate Parent Fields

Delpha Data Quality Analysis

Delpha Recommendation

Delpha Support fields

Support Fields: Calculated from Account current data

Delpha trusted sources for Ultimate Parent

Sources of Truth for Ultimate Parent Recommendation

Ultimate - Delpha focuses on Brands and Operating structures

Two Perspectives of Ultimate Parenting

What are the 6 data quality dimensions

Delpha Data Quality Dimensions Explained

Delpha Data quality

Delpha Data Quality Menu Overview

Do I consume a token when applying a Delpha recommendation?

Explanation

✅ Token is used:

❌ Token is NOT used:

How to fix my data quality

How to Fix Data Quality Issues in Delpha

Overview

Step 1: Run a Data Quality Assessment

Step 2: Review the Results

Step 3: Apply Delpha Recommendations

What are the Data Quality dimensions for Contact Name

Understanding Delpha’s Data Quality Dimensions for Name

Data Quality Dimensions Explained

Completeness

Validity

Uniqueness - NA

Consistency

Accuracy

Timeliness

How to exclude records from the analysis

Exclude Records from Data Quality Analysis

Overview

How It Works

Casing Strategy (default: `"identity"`)

Character Strategy (default: `"identity"`)

Spacing Strategy (default: `"identity"`)

Block Removal Strategy (default: `"identity"`)