A tailored course, built for your situation
Mastering Data Quality in Salesforce: A Step-by-Step System for Reliable, Actionable Insights
Eliminate dirty data, automate validation, and ensure trust in every report , built for Salesforce Solution Engineers
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Your Salesforce reports are only as good as the data behind them , and bad data is silently eroding trust in your insights.
The situation this course is for
Even small data inconsistencies snowball into major operational risks: sales teams misaligned on accounts, service cases misrouted, and leadership making decisions on flawed metrics. As a Salesforce Solution Engineer, you're expected to deliver clean, reliable systems , but without a structured approach to data quality, you're constantly firefighting instead of innovating. The tools exist, but knowing exactly when and how to apply them , especially across large, evolving orgs , is what separates functional setups from future-proof ones.
Who this is for
Salesforce Solution Engineers and architects who own data integrity in production environments and need a repeatable framework to enforce quality at scale.
Who this is not for
Beginners learning Salesforce basics or admins focused only on point-and-click customization without governance depth.
What you walk away with
- Implement a proactive data quality framework tailored to Salesforce
- Automate validation rules and duplicate prevention without breaking user workflows
- Design scalable monitoring systems for ongoing data health
- Reduce manual cleanup cycles by at least 70%
- Align data governance with business outcomes across departments
The 12 modules (with all 144 chapters)
Module 1. The State of Data Quality in Salesforce Today
Understand why data decay happens in Salesforce environments and how top performers detect early warning signs before they escalate into reporting failures or compliance risks.
12 chapters in this module
- Data decay patterns
- Signal vs noise
- Trust erosion
- Org maturity model
- Governance gaps
- User behavior impact
- Field-level risks
- Integration drift
- Reporting blind spots
- Ownership confusion
- Tech debt traps
- Prevention mindset
Module 2. Core Principles of Trusted Data
Establish the foundational rules that govern reliable data in any Salesforce implementation, from field hygiene to relationship integrity and cross-object consistency.
12 chapters in this module
- Truth in source
- Uniqueness rules
- Completeness thresholds
- Accuracy checks
- Timeliness standards
- Consistency models
- Stewardship roles
- Lifecycle stages
- Validation hierarchy
- Error handling
- Audit readiness
- Recovery paths
Module 3. Designing for Data Integrity
Learn how to architect Salesforce objects, fields, and relationships from the start to resist corruption and support long-term scalability across teams and systems.
12 chapters in this module
- Schema planning
- Field type logic
- Picklist control
- Relationship design
- Naming standards
- Ownership models
- Sharing alignment
- Automation triggers
- Validation layers
- Error messaging
- User adoption
- Change readiness
Module 4. Automating Data Validation
Deploy targeted automation using validation rules, flows, and custom logic to catch bad data at entry without disrupting productivity.
12 chapters in this module
- Rule timing
- Error placement
- User context
- Bulk impact
- Flow triggers
- Criteria logic
- Message clarity
- Exception handling
- Testing strategy
- Deployment order
- Monitoring rules
- Rule retirement
Module 5. Duplicate Prevention Strategy
Go beyond standard matching rules to build layered defenses against duplicates across leads, contacts, accounts, and custom objects.
12 chapters in this module
- Match rules setup
- Duplicate rules
- Job timing
- Alert vs block
- Merge workflows
- Ownership transfer
- Reporting impact
- Custom logic
- Third-party tools
- User training
- Rule tuning
- Exception tracking
Module 6. Data Profiling and Assessment
Use native and lightweight tools to assess data health across key objects and identify high-risk areas before they impact business operations.
12 chapters in this module
- Field analysis
- Null rate tracking
- Pattern detection
- Outlier spotting
- Cross-field checks
- Historical drift
- Health scoring
- Sampling methods
- Tool selection
- Dashboard setup
- Trend alerts
- Remediation planning
Module 7. Data Cleansing at Scale
Execute safe, auditable cleanup campaigns using bulk tools, validation safeguards, and rollback strategies that protect production integrity.
12 chapters in this module
- Scope definition
- Backup protocols
- Batch sizing
- Validation gates
- Error logging
- Rollback plans
- User comms
- Change windows
- Post-cleanup audit
- Stakeholder review
- Success metrics
- Lessons captured
Module 8. Monitoring and Alerting Systems
Set up continuous monitoring for data health using reports, dashboards, and automated alerts tailored to specific stakeholder needs.
12 chapters in this module
- KPI selection
- Dashboard design
- Alert thresholds
- Escalation paths
- Daily checks
- Weekly reviews
- Ownership alerts
- Integration checks
- User behavior
- Field changes
- Permission drift
- Automated snapshots
Module 9. Governance and Stewardship Models
Define clear roles, responsibilities, and processes for maintaining data quality across departments and over time.
12 chapters in this module
- Steward roles
- RACI setup
- Review cycles
- Policy documentation
- Training plans
- Enforcement tactics
- Accountability tracking
- Change requests
- Version control
- Audit trails
- Compliance alignment
- Leadership reporting
Module 10. Change Management for Data Projects
Lead successful data initiatives by aligning technical changes with user behavior, training, and adoption strategies.
12 chapters in this module
- Impact mapping
- Stakeholder analysis
- Communication plan
- Training rollout
- Feedback loops
- Pilot design
- Go-live support
- Adoption tracking
- Resistance handling
- Success stories
- Iterative tuning
- Post-launch review
Module 11. Integrations and Data Flow Risks
Secure data quality at the boundaries where external systems connect to Salesforce, preventing corruption from APIs and sync jobs.
12 chapters in this module
- Inbound validation
- Field mapping
- Error queues
- Retry logic
- Sync frequency
- Data type mismatches
- Null handling
- Rate limits
- Logging needs
- Monitoring gaps
- Ownership clarity
- Breakpoint testing
Module 12. Scaling Data Quality Across Orgs
Extend your data quality framework across multiple Salesforce instances, sandboxes, and future projects with confidence.
12 chapters in this module
- Template creation
- Playbook reuse
- Sandbox alignment
- CI/CD integration
- Change sets
- Version control
- Org comparison
- Automated checks
- Audit readiness
- Team onboarding
- Knowledge transfer
- Future-proofing
How this maps to your situation
- You're implementing a new Salesforce org and need to bake in data quality from day one
- You're troubleshooting reporting inaccuracies caused by inconsistent data entry
- You're preparing for an audit or compliance review requiring clean historical records
- You're scaling automation and need to ensure data integrity doesn't break workflows
Before vs. after
Before
Spending hours cleaning data manually, reacting to duplicate records, and defending report accuracy under scrutiny.
After
Confidently delivering trusted reports, with automated safeguards preventing bad data before it enters the system.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3-4 hours per week over 12 weeks , designed to fit around real project timelines without disruption.
If nothing changes
Without a structured approach, data quality issues will continue to escalate , leading to broken automation, lost trust in analytics, compliance exposure, and increased technical debt that slows every future project.
How this compares to the alternatives
Unlike generic Salesforce admin courses or broad data management frameworks, this program focuses exclusively on actionable, field-tested tactics for maintaining data quality in live Salesforce environments , with templates and playbooks you can apply immediately.
Frequently asked
Who is this course for?
Salesforce Solution Engineers, architects, and senior admins responsible for data integrity in production environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, 30-day money-back guarantee if the course doesn't meet your expectations.
$199 one-time. Approximately 3-4 hours per week over 12 weeks , designed to fit around real project timelines without disruption..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours