Delpha Score Meter

The Delpha Score Meter provides a clear 0–100 quality score based on six dimensions, helping you assess, prioritize, and improve Salesforce data accuracy and reliability at a glance.

What You Can Do in the Score Meter

The Score Meter is your all-in-one command center for data quality. It helps you understand, review, and apply improvements — all in a single, intuitive interface.

1. Understand Your Data Quality at a Glance

• See your overall quality score for the selected object (e.g. Accounts, Contacts).

• The score is visually represented with a color-coded gauge for quick assessment.

• The number of available data quality recommendations and the number of duplicates are clearly listed.

2. Data Quality: Review and Apply Suggestions Inline

Click on the 'x recommendations' link and view AI-generated recommendations directly from the Score Meter.

• Each recommendation includes:

  • A confidence score based on the data quality dimensions

  • Extra information to help the decision

• You can expand or collapse details for quick scanning or deep inspection.

You access this view clicking on the number of recommendations link or navigating in the Score Meter component using the 'next page' sign (>).

• Choose how to apply recommendations:

  • One by one

  • Bulk select multiple items

  • Accept all suggestions in one click

• The Show more button appears when multiple options are available.

Delpha showcases the data quality recommendation with the highest confidence score in the score meter. After the data quality analysis, additional data is accessible.

If Delpha's recommendation isn't suitable, click 'Show more' to view all options. Apply a different value by clicking on the green tag.

3. Duplicate: Review and fix potential duplicates

Click on the 'x duplicate' link to open the Duplicate Merge Editor and start the deduplication process.

Check this article for more info on the manual deduplication process.

What the Quality Score Represents

The score is based on six key data quality dimensions, evaluated field by field:

Dimension	Meaning
Completeness	Is the value present or missing?
Validity	Does the data follow the correct format or rules?
Uniqueness	Is this value duplicated elsewhere in the system?
Consistency	Is the data coherent across fields or systems?
Accuracy	Is the value correct (factually or logically)?
Timeliness	Is the information still relevant and up-to-date?

Each field within the record is evaluated and color-coded:

• Red – Poor

• Orange – Warning

• Green – Good

• Grey – Not applicable / no data

Why It Matters

The score helps you:

• Quickly assess trustworthiness of a record

• Decide whether to clean, enrich, or reject the data

• Track data quality improvements over time