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Quick Start Guide

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

Updated

Quick Start Guide

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
  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

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)

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.

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)

  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.

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