What You’ll Learn
This guide covers the fundamentals of data quality and introduces Data Quality Sense (DQS), a Salesforce-native application that measures your data health.
By the end, you will understand:
- What data quality means and why it matters
- The five dimensions DQS measures
- How to get started with your first assessment
What is Data Quality?
Data quality measures how well your data serves its intended purpose. High-quality data is:
- Complete: Required fields are populated
- Valid: Values match expected formats
- Unique: No duplicate records
- Timely: Data is current and up-to-date
- Consistent: Values are uniform across records
When data lacks these qualities, problems cascade through your organization.
Why Data Quality Matters
Poor data quality costs organizations real money and creates operational friction:
| Impact Area | Example |
|---|---|
| Lost revenue | Missed opportunities from outdated contact information |
| Wasted resources | Hours spent manually cleaning data |
| Poor customer experience | Customers receive wrong information |
| Compliance risk | Inaccurate reporting triggers regulatory issues |
| AI failures | Models trained on bad data produce bad outputs |
The Numbers
Research shows the financial impact is significant:
- Organizations lose 15-25% of revenue annually due to poor data quality
- Over 25% of organizations lose more than $5 million per year (IBM 2025)
- Employees spend up to 27% of their time correcting data errors
For Salesforce users, duplicate records alone waste storage and fragment customer history across multiple records.
Introducing DQS
Data Quality Sense (DQS) is a Salesforce-native application that helps you:
- Measure data quality across five dimensions
- Identify specific records and fields with issues
- Prioritize which problems to fix first
- Monitor ongoing data health over time
Why Salesforce-Native Matters
DQS runs entirely within Salesforce. Your data never leaves the platform:
| Feature | Benefit |
|---|---|
| No data export | Your data stays secure |
| No external APIs | No integration complexity |
| No code required | Point-and-click configuration |
| Native UI | Familiar Salesforce experience |
The AI Readiness Dimension
Beyond traditional data quality, DQS also measures AI readiness. As organizations adopt Agentforce and other AI capabilities, data requirements increase:
| Traditional Data Quality | AI Readiness |
|---|---|
| Is the field populated? | Is there enough text content for AI to learn from? |
| Is the format valid? | Is the language consistent? |
| Are there duplicates? | Is PII protected before AI exposure? |
DQS measures both dimensions in a single scan.
Getting Started
Take these steps to begin your data quality journey:
Step 1: Assess Your Current State
Take the AI Readiness Assessment. In 3 minutes, you’ll get a score across key data quality dimensions and specific recommendations for improvement.
Step 2: Understand the Dimensions
Read The Five Dimensions of Data Quality to understand what DQS measures and why each dimension matters.
Step 3: Learn About AI Readiness
If you’re preparing for Agentforce or other AI initiatives, read the Agentforce Preparation Guide to understand additional requirements.
Step 4: Install DQS
When you’re ready to measure your actual Salesforce data, install DQS from the AppExchange and create your first Definition.
Next Steps
- Why Data Quality Matters: The business case for investing in data quality
- Quick Start Guide: Step-by-step first actions
- AI Readiness Assessment: Get your score in 3 minutes