# Quick Start Guide

> Get from zero to your first data quality insights in 10 minutes. Step-by-step guide to getting started.

Source: https://dataqualitysense.com/resources/getting-started/quick-start-guide/
Last updated: 2026-06-03

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## Get Started in 10 Minutes

This guide takes you from zero to your first data quality insights. Follow these steps to understand your current state and identify where to focus first.

## Step 1: Take the AI Readiness Assessment

Start with the free assessment to establish your baseline.

**What you'll get:**
- Score across key data quality dimensions
- Specific recommendations for improvement
- Comparison to industry benchmarks
- Priority areas to address first

**How to take it:**
1. Go to [AI Readiness Assessment](/ai-readiness/)
2. Answer 10 questions about your Salesforce data practices
3. Get your score in 3 minutes

> **Tip:** Be honest in your answers. The assessment is for your benefit, not a test to pass. Accurate answers give you accurate recommendations.

## Step 2: Understand Your Score

The assessment provides scores across five data quality dimensions:

| Dimension | What It Measures | Low Score Means |
|-----------|------------------|-----------------|
| Completeness | Required fields populated | Missing data in critical fields |
| Validity | Correct formats | Malformed emails, phones, etc. |
| Uniqueness | No duplicates | Duplicate records fragmenting data |
| Timeliness | Current information | Stale records need updating |
| Consistency | Uniform values | Inconsistent formats across records |

### Score Tiers

| Score | Tier | What It Means |
|-------|------|---------------|
| 80-100 | Strong | Your data foundation is solid. Focus on maintaining it. |
| 60-79 | Developing | Good progress, but specific areas need attention. |
| 40-59 | Emerging | Multiple dimensions need improvement before AI initiatives. |
| 0-39 | Critical | Significant data quality issues exist. Start with fundamentals. |

## Step 3: Identify Priority Areas

Based on your score, identify 2-3 areas to focus on first. Do not try to fix everything at once.

### Prioritization Framework

| If Your Score Is Low In... | Start With... |
|----------------------------|---------------|
| Completeness | Identify top 5 critical fields, measure fill rates |
| Validity | Audit email and phone formats, add validation rules |
| Uniqueness | Run duplicate detection, establish merge process |
| Timeliness | Define freshness thresholds, create update workflows |
| Consistency | Standardize picklist values, clean up variations |

### High-Impact Starting Points

For most organizations, these fields have the highest impact:

**Contacts:**
- Email (validity, completeness)
- Phone (validity, completeness)
- Title (completeness, consistency)

**Accounts:**
- Industry (completeness, consistency)
- Annual Revenue (timeliness, completeness)
- Billing Address (validity, completeness)

**Opportunities:**
- Close Date (timeliness)
- Amount (completeness)
- Stage (consistency)

## Step 4: Install DQS

When you're ready to measure your actual Salesforce data, install Data Quality Sense.

> **Get Data Quality Sense:** [Install from Salesforce AppExchange](https://appexchange.salesforce.com/appxListingDetail?listingId=e1742ac7-7ec3-4dfb-815e-7922082524c4)

### Installation Steps

1. Go to Salesforce AppExchange
2. Search for "Data Quality Sense"
3. Click "Get It Now"
4. Follow the installation wizard
5. Assign permissions to users who will configure and run scans

### What DQS Adds

After installation, you have access to:

| Feature | Description |
|---------|-------------|
| Definition Builder | Configure what to analyze |
| Scan Execution | Run data quality checks |
| Results Dashboard | View metrics and drill-down |
| Export | Download affected records for cleanup |

## Step 5: Create Your First Definition

A Definition tells DQS what to analyze. Start with a focused scope.

> **See it in action:** [Watch this step in the demo (0:29)](#dqs-demo-29)

### Recommended First Definition

For your first scan, focus on one object with high business impact:

**Option A: Contact Data Quality**
- Object: Contact
- Fields: Email, Phone, MailingCity, MailingState, MailingCountry
- Dimensions: Completeness, Validity, Consistency

**Option B: Account Health Check**
- Object: Account
- Fields: Industry, AnnualRevenue, BillingCity, BillingState
- Dimensions: Completeness, Consistency, Timeliness

**Option C: Opportunity Pipeline**
- Object: Opportunity
- Fields: Amount, CloseDate, StageName
- Dimensions: Completeness, Timeliness

### Definition Builder Steps

1. Click **New Definition** in DQS
2. Select **Capabilities** (which dimensions to measure)
3. Select **Object** and **Fields** to analyze
4. Optionally add **Filters** to narrow scope
5. **Configure** thresholds for each dimension
6. **Review** and save

For detailed guidance, see the [Definition Builder Guide](/resources/using-dqs/definition-builder/).

## Step 6: Run Your First Scan

With your Definition saved, run your first scan.

> **See it in action:** [Watch this step in the demo (2:32)](#dqs-demo-152)

1. Open the Definition
2. Click **Run Scan**
3. Wait for processing to complete (time depends on record count)
4. Review results in the dashboard

### Schedule Recurring Scans

With DQS, you can automate scans so your data quality is monitored continuously without manual effort.

1. Open your Definition
2. Navigate to the **Schedule** tab
3. Set the frequency (daily, weekly, or monthly)
4. Choose the preferred time and day
5. Save the schedule

For a walkthrough, see [Running Scans](/resources/using-dqs/running-scans/).

Scheduled scans run automatically in the background and update your dashboard with fresh results. This is the recommended approach for ongoing monitoring — you catch data quality degradation early without having to remember to run scans manually.

### What to Expect

First scans often reveal more issues than expected. This is normal. Your goal is visibility, not perfection.

Common first-scan findings:
- 10-30% of records have at least one issue
- Certain fields have much lower fill rates than expected
- Duplicate detection finds records you did not know existed
- Format validation reveals inconsistencies in data entry

## What to Do Next

### Week 1: Understand the Baseline

- Review scan results
- Identify the top 3 issues by volume
- Understand which records are affected

### Week 2-4: Address Priority Issues

- Start with the highest-impact, easiest-to-fix issues
- Create a cleanup plan for affected records
- Add validation rules to prevent new issues

### Ongoing: Monitor and Maintain

- Schedule recurring scans
- Track improvement over time
- Expand scope to additional objects and fields

## Next Steps

- [Understanding Results](/resources/using-dqs/understanding-results/): How to interpret your scan data
- [Definition Builder Guide](/resources/using-dqs/definition-builder/): Detailed configuration guidance
- [Agentforce Preparation](/resources/ai-readiness/agentforce-preparation/): Preparing data for Agentforce
- [Best Practices](/resources/best-practices/common-pitfalls/): Avoid common data quality mistakes