Data Mapping A Complete Guide
You're not behindyou're just working with systems that weren't built for clarity, speed, or control. Every day, data flows in from CRM, ERP, legacy sources, and third-party platforms. Without a rigorous map, that data becomes noise. Decisions slow down. Compliance risks rise. Projects stall before they launch. Silos aren't just inconvenient-they're costly. A single integration failure can delay go-live by weeks. A misconfigured field can compromise audit readiness. And if you're in healthcare, finance, or any regulated space, one inconsistency could trigger months of remediation. But what if you could transform confusion into confidence? What if every data point-from source to destination-had a documented journey, a clear owner, and a verified purpose? This isn’t hypothetical. It’s exactly what Data Mapping A Complete Guide enables: a repeatable, scalable process to turn unstructured chaos into board-ready clarity. In just days, this course helps you go from uncertain scoping to delivering a fully validated, enterprise-grade data map-complete with traceability, version control, and stakeholder alignment. You’ll walk away with a blueprint that’s not only technically precise but also governance-compliant and integration-ready. Like Sarah Kim, Senior Data Architect at a global bank, who used the methodology in this course to streamline a core banking migration. Her team reduced data reconciliation errors by 94%, cut testing cycles in half, and delivered the mapping documentation three weeks ahead of audit deadlines. Here’s how this course is structured to help you get there.
Course Format & Delivery Details Self-Paced. Immediate Access. Zero Time Lock-In.
Data Mapping A Complete Guide is designed for professionals who need real results-not scheduled lectures or rigid timelines. Once enrolled, you gain instant access to the full curriculum, structured for efficient, high-impact learning. No waiting. No delays. Just actionable knowledge when you need it. This is an on-demand course with no fixed start dates or weekly modules holding you back. Read, apply, and progress at your own pace-whether that’s 30 minutes a day or full immersion over a long weekend. Most learners complete the core framework in under 12 hours and begin applying techniques to live projects immediately. Lifetime Access & Ongoing Updates Included
Your enrollment includes lifetime access to all materials. This isn’t a time-limited window. As data standards evolve, regulations shift, and integration tools update, you'll receive future revisions at no extra cost. Your investment grows with you. All content is mobile-optimized and accessible 24/7 from any device. Whether you’re in the office, on-site, or traveling internationally, your progress syncs seamlessly. Resume exactly where you left off-whether it’s on your laptop, tablet, or phone. Structured for Real-World Application, Backed by Expert Guidance
You’re not learning in isolation. The course framework was developed by enterprise architects with over two decades of experience in large-scale data migrations, GDPR compliance programs, and system integration at Fortune 500 organizations. It includes direct, role-tailored guidance covering: - Data governance and ownership structures
- Mapping for API, ETL, and ELT pipelines
- Documentation standards for auditors and regulators
- Integration with metadata management and lineage tools
You’ll receive clear, written support pathways within each module-context-specific checklists, decision trees, and troubleshooting tables to help you push through blockers independently. Certificate of Completion Issued by The Art of Service
Upon finishing the course, you earn a verifiable Certificate of Completion issued by The Art of Service, a globally recognized credential trusted by professionals in over 140 countries. This certification is not a participation badge-it validates your mastery of a structured, repeatable data mapping methodology used in enterprise environments worldwide. Include it on your LinkedIn, resume, or internal promotion portfolio. It signals precision, compliance awareness, and technical rigor to hiring managers, audit committees, and cross-functional leaders. No Risk. No Hidden Fees. Full Confidence.
The pricing is straightforward. What you see is what you get-no recurring charges, surprise subscriptions, or upsells. The course accepts all major payment methods, including Visa, Mastercard, and PayPal. We offer a 30-day Satisfied or Refunded guarantee. If you follow the methodology and don’t find immediate value in the structure, clarity, and professional ROI, simply reach out for a full refund. No forms, no scripts, no hassle. After enrollment, you’ll receive a confirmation email. Once the course materials are prepared for access, your login and entry details will be sent in a separate notification. This ensures system integrity and consistent delivery across global time zones. This Course Works - Even If…
- You’ve never led a full-scale data mapping initiative before
- You’re not a data scientist or software engineer
- You work in a regulated industry with strict reporting requirements
- You’re supporting a migration, audit, or integration without dedicated tools
Unlike generic tutorials, this course gives you field-tested frameworks used in healthcare, banking, logistics, and SaaS-from business analysts to IT directors. Real people, real systems, real deadlines. This isn’t about theory. It’s about delivering a reliable, defensible, reusable data map that aligns technical execution with business outcomes. You gain clarity. You reduce risk. You gain career leverage.
Module 1: Foundations of Data Mapping - Defining data mapping in modern enterprise environments
- Understanding the difference between data mapping, transformation, and integration
- The role of data mapping in compliance and governance
- Key stakeholders in a data mapping initiative
- Data sources and destinations: Identifying upstream and downstream systems
- Common data types and formats encountered in mapping
- Metadata fundamentals and why they matter
- Principles of traceability, lineage, and audit readiness
- Mapping in Agile, Waterfall, and hybrid project frameworks
- Recognizing anti-patterns: When mapping fails silently
Module 2: Use Cases and Business Justification - Migrating legacy systems: How mapping reduces downtime
- Data warehouse and data lake schema alignment
- Supporting ETL/ELT pipelines with accurate field-level definitions
- Compliance requirements under GDPR, CCPA, and HIPAA
- Due diligence for M&A: Assessing data compatibility
- Preparing for system decommissioning with data retention rules
- API integration: Documenting request and response mappings
- Synchronizing CRM and marketing automation platforms
- Supporting ML models with reliable input data
- Creating justifiable business cases for stakeholders
Module 3: Scoping and Planning Methodology - Defining the scope: What to include and exclude
- Setting clear success criteria and deliverables
- Estimating effort based on source system complexity
- Identifying data ownership and subject matter experts
- Creating a schedule with built-in review cycles
- Risk assessment for high-impact or high-volume mappings
- Defining version control and change management rules
- Selecting appropriate documentation formats
- Mapping teams: RACI models and role clarity
- Aligning timelines with broader project milestones
Module 4: Frameworks for Consistent Execution - The Five-Step Data Mapping Process
- Standardizing input and output nomenclature
- Classifying data by sensitivity and regulatory exposure
- Using a master mapping register for organization-wide consistency
- Documenting transformation logic: Arithmetic, concatenation, lookups
- Handling NULLs, defaults, and conditional logic
- Common transformation patterns and their documentation
- Designing reusability into your mappings
- Decoupling business rules from technical implementation
- Establishing naming conventions for fields and systems
Module 5: Source System Analysis Techniques - Extracting database schemas and metadata
- Reading table definitions and primary keys
- Identifying redundant or deprecated fields
- Understanding source system constraints and triggers
- Engaging with legacy system owners and custodians
- Handling undocumented or poorly documented systems
- Sampling data to infer field purpose and content
- Mapping flat files, spreadsheets, and JSON structures
- Reverse-engineering field usage from reports and interfaces
- Detecting hidden business logic in source applications
Module 6: Target System Requirements and Constraints - Understanding destination schema limitations
- Field length, data type, and nullability constraints
- Handling required fields with no source equivalent
- Managing auto-generated and system-managed fields
- Compliance and encryption requirements at destination
- Indexing and performance implications of mapped data
- Validating data acceptance rules and parsing logic
- Mapping to cloud data warehouses: Snowflake, BigQuery, Redshift
- Integration with CRM platforms like Salesforce and HubSpot
- Aligning with identity and access management policies
Module 7: Field-Level Mapping Techniques - One-to-one, one-to-many, many-to-one mappings
- Concatenation and string manipulation with real examples
- Date and time zone conversions across regions
- Currency conversion and formatting standards
- Standardizing codes using lookup tables and codelists
- Handling yes/no, true/false, and coded boolean fields
- Mapping enumerated values with transformation logic
- Dealing with free-text fields and data cleansing
- Using regular expressions to standardize formats
- Documenting complex business rules step by step
Module 8: Data Transformation Logic - Writing clear, auditable transformation expressions
- Arithmetic operations: Aggregations, calculations, ratios
- Conditional statements using IF-THEN-ELSE logic
- Handling data quality issues during transformation
- Escalation paths for unresolved transformation rules
- Documenting fallback and default value strategies
- Versioning transformation logic for audit trails
- Mapping lookup tables and dimension tables
- Preserving source system logic without hardcoding
- Tool-agnostic transformation design principles
Module 9: Validation and Testing Strategies - Defining validation checkpoints and success criteria
- Sample-based vs. full-volume data verification
- Using checksums, row counts, and hash validations
- Reconciliation techniques between source and target
- Handling discrepancies and failed validations
- Root cause analysis for mapping errors
- Testing transformation logic in staging environments
- Automatable validation rules for repeatable checks
- Peer review and sign-off workflows
- Preparing for UAT with business stakeholders
Module 10: Documentation Standards and Templates - Designing a centralized data mapping document
- Standard columns: Source Field, Data Type, Target Field, Transformation
- Adding notes, owner, version, and audit fields
- Using color coding and conditional formatting effectively
- Version control and change tracking best practices
- Choosing between Excel, Google Sheets, and databases
- Exporting documentation for audit submission
- Creating summaries for non-technical audiences
- Automating documentation updates with macros and scripts
- Integrating with Confluence, SharePoint, or Notion
Module 11: Tools and Platforms for Data Mapping - Selecting tools based on team size and complexity
- Open-source vs. commercial mapping software comparison
- Integration with Informatica, Talend, and Microsoft SSIS
- Using Alteryx and Power Query for transformation design
- Leveraging built-in mapping features in ETL tools
- Metadata management tools: Collibra, Atlan, Alation
- Data lineage platforms and how they use mappings
- Using low-code platforms for rapid mapping design
- Mapping within iPaaS tools like MuleSoft and Boomi
- Tool-agnostic templates for maximum flexibility
Module 12: Governance, Compliance, and Audit Readiness - Mapping data flows for GDPR Article 30 records
- Supporting Data Protection Impact Assessments (DPIAs)
- Documenting data minimization and retention rules
- Ensuring PII and sensitive data are traceable
- Compliance with SOX, HIPAA, and PCI-DSS requirements
- Preparing for internal and external audits
- Creating auditor-friendly summary reports
- Retention, access controls, and data subject rights
- Legal basis for data processing documentation
- Handling cross-border data transfer mappings
Module 13: Stakeholder Communication and Collaboration - Translating technical mappings for business users
- Running effective mapping review meetings
- Using visual diagrams to show data flow
- Managing conflicting interpretations across teams
- Getting buy-in from legal, compliance, and finance
- Handling escalation paths for unresolved mappings
- Writing clear, non-technical field definitions
- Collaborative editing with version history
- Using feedback loops to refine documentation
- Presenting mapping outcomes to executive sponsors
Module 14: Advanced Data Mapping Scenarios - Mapping hierarchical or nested data structures
- Handling parent-child relationships in CRM systems
- Transforming XML and JSON payloads
- Mapping semi-structured and unstructured data
- Time-series and event-based data alignment
- Mapping for change data capture (CDC) pipelines
- Synchronizing real-time vs. batched data flows
- De-duplication and merging logic across sources
- Mapping multi-language and localized content
- Handling timezone-aware timestamps in global systems
Module 15: Change Management and Version Control - Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- Defining data mapping in modern enterprise environments
- Understanding the difference between data mapping, transformation, and integration
- The role of data mapping in compliance and governance
- Key stakeholders in a data mapping initiative
- Data sources and destinations: Identifying upstream and downstream systems
- Common data types and formats encountered in mapping
- Metadata fundamentals and why they matter
- Principles of traceability, lineage, and audit readiness
- Mapping in Agile, Waterfall, and hybrid project frameworks
- Recognizing anti-patterns: When mapping fails silently
Module 2: Use Cases and Business Justification - Migrating legacy systems: How mapping reduces downtime
- Data warehouse and data lake schema alignment
- Supporting ETL/ELT pipelines with accurate field-level definitions
- Compliance requirements under GDPR, CCPA, and HIPAA
- Due diligence for M&A: Assessing data compatibility
- Preparing for system decommissioning with data retention rules
- API integration: Documenting request and response mappings
- Synchronizing CRM and marketing automation platforms
- Supporting ML models with reliable input data
- Creating justifiable business cases for stakeholders
Module 3: Scoping and Planning Methodology - Defining the scope: What to include and exclude
- Setting clear success criteria and deliverables
- Estimating effort based on source system complexity
- Identifying data ownership and subject matter experts
- Creating a schedule with built-in review cycles
- Risk assessment for high-impact or high-volume mappings
- Defining version control and change management rules
- Selecting appropriate documentation formats
- Mapping teams: RACI models and role clarity
- Aligning timelines with broader project milestones
Module 4: Frameworks for Consistent Execution - The Five-Step Data Mapping Process
- Standardizing input and output nomenclature
- Classifying data by sensitivity and regulatory exposure
- Using a master mapping register for organization-wide consistency
- Documenting transformation logic: Arithmetic, concatenation, lookups
- Handling NULLs, defaults, and conditional logic
- Common transformation patterns and their documentation
- Designing reusability into your mappings
- Decoupling business rules from technical implementation
- Establishing naming conventions for fields and systems
Module 5: Source System Analysis Techniques - Extracting database schemas and metadata
- Reading table definitions and primary keys
- Identifying redundant or deprecated fields
- Understanding source system constraints and triggers
- Engaging with legacy system owners and custodians
- Handling undocumented or poorly documented systems
- Sampling data to infer field purpose and content
- Mapping flat files, spreadsheets, and JSON structures
- Reverse-engineering field usage from reports and interfaces
- Detecting hidden business logic in source applications
Module 6: Target System Requirements and Constraints - Understanding destination schema limitations
- Field length, data type, and nullability constraints
- Handling required fields with no source equivalent
- Managing auto-generated and system-managed fields
- Compliance and encryption requirements at destination
- Indexing and performance implications of mapped data
- Validating data acceptance rules and parsing logic
- Mapping to cloud data warehouses: Snowflake, BigQuery, Redshift
- Integration with CRM platforms like Salesforce and HubSpot
- Aligning with identity and access management policies
Module 7: Field-Level Mapping Techniques - One-to-one, one-to-many, many-to-one mappings
- Concatenation and string manipulation with real examples
- Date and time zone conversions across regions
- Currency conversion and formatting standards
- Standardizing codes using lookup tables and codelists
- Handling yes/no, true/false, and coded boolean fields
- Mapping enumerated values with transformation logic
- Dealing with free-text fields and data cleansing
- Using regular expressions to standardize formats
- Documenting complex business rules step by step
Module 8: Data Transformation Logic - Writing clear, auditable transformation expressions
- Arithmetic operations: Aggregations, calculations, ratios
- Conditional statements using IF-THEN-ELSE logic
- Handling data quality issues during transformation
- Escalation paths for unresolved transformation rules
- Documenting fallback and default value strategies
- Versioning transformation logic for audit trails
- Mapping lookup tables and dimension tables
- Preserving source system logic without hardcoding
- Tool-agnostic transformation design principles
Module 9: Validation and Testing Strategies - Defining validation checkpoints and success criteria
- Sample-based vs. full-volume data verification
- Using checksums, row counts, and hash validations
- Reconciliation techniques between source and target
- Handling discrepancies and failed validations
- Root cause analysis for mapping errors
- Testing transformation logic in staging environments
- Automatable validation rules for repeatable checks
- Peer review and sign-off workflows
- Preparing for UAT with business stakeholders
Module 10: Documentation Standards and Templates - Designing a centralized data mapping document
- Standard columns: Source Field, Data Type, Target Field, Transformation
- Adding notes, owner, version, and audit fields
- Using color coding and conditional formatting effectively
- Version control and change tracking best practices
- Choosing between Excel, Google Sheets, and databases
- Exporting documentation for audit submission
- Creating summaries for non-technical audiences
- Automating documentation updates with macros and scripts
- Integrating with Confluence, SharePoint, or Notion
Module 11: Tools and Platforms for Data Mapping - Selecting tools based on team size and complexity
- Open-source vs. commercial mapping software comparison
- Integration with Informatica, Talend, and Microsoft SSIS
- Using Alteryx and Power Query for transformation design
- Leveraging built-in mapping features in ETL tools
- Metadata management tools: Collibra, Atlan, Alation
- Data lineage platforms and how they use mappings
- Using low-code platforms for rapid mapping design
- Mapping within iPaaS tools like MuleSoft and Boomi
- Tool-agnostic templates for maximum flexibility
Module 12: Governance, Compliance, and Audit Readiness - Mapping data flows for GDPR Article 30 records
- Supporting Data Protection Impact Assessments (DPIAs)
- Documenting data minimization and retention rules
- Ensuring PII and sensitive data are traceable
- Compliance with SOX, HIPAA, and PCI-DSS requirements
- Preparing for internal and external audits
- Creating auditor-friendly summary reports
- Retention, access controls, and data subject rights
- Legal basis for data processing documentation
- Handling cross-border data transfer mappings
Module 13: Stakeholder Communication and Collaboration - Translating technical mappings for business users
- Running effective mapping review meetings
- Using visual diagrams to show data flow
- Managing conflicting interpretations across teams
- Getting buy-in from legal, compliance, and finance
- Handling escalation paths for unresolved mappings
- Writing clear, non-technical field definitions
- Collaborative editing with version history
- Using feedback loops to refine documentation
- Presenting mapping outcomes to executive sponsors
Module 14: Advanced Data Mapping Scenarios - Mapping hierarchical or nested data structures
- Handling parent-child relationships in CRM systems
- Transforming XML and JSON payloads
- Mapping semi-structured and unstructured data
- Time-series and event-based data alignment
- Mapping for change data capture (CDC) pipelines
- Synchronizing real-time vs. batched data flows
- De-duplication and merging logic across sources
- Mapping multi-language and localized content
- Handling timezone-aware timestamps in global systems
Module 15: Change Management and Version Control - Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- Defining the scope: What to include and exclude
- Setting clear success criteria and deliverables
- Estimating effort based on source system complexity
- Identifying data ownership and subject matter experts
- Creating a schedule with built-in review cycles
- Risk assessment for high-impact or high-volume mappings
- Defining version control and change management rules
- Selecting appropriate documentation formats
- Mapping teams: RACI models and role clarity
- Aligning timelines with broader project milestones
Module 4: Frameworks for Consistent Execution - The Five-Step Data Mapping Process
- Standardizing input and output nomenclature
- Classifying data by sensitivity and regulatory exposure
- Using a master mapping register for organization-wide consistency
- Documenting transformation logic: Arithmetic, concatenation, lookups
- Handling NULLs, defaults, and conditional logic
- Common transformation patterns and their documentation
- Designing reusability into your mappings
- Decoupling business rules from technical implementation
- Establishing naming conventions for fields and systems
Module 5: Source System Analysis Techniques - Extracting database schemas and metadata
- Reading table definitions and primary keys
- Identifying redundant or deprecated fields
- Understanding source system constraints and triggers
- Engaging with legacy system owners and custodians
- Handling undocumented or poorly documented systems
- Sampling data to infer field purpose and content
- Mapping flat files, spreadsheets, and JSON structures
- Reverse-engineering field usage from reports and interfaces
- Detecting hidden business logic in source applications
Module 6: Target System Requirements and Constraints - Understanding destination schema limitations
- Field length, data type, and nullability constraints
- Handling required fields with no source equivalent
- Managing auto-generated and system-managed fields
- Compliance and encryption requirements at destination
- Indexing and performance implications of mapped data
- Validating data acceptance rules and parsing logic
- Mapping to cloud data warehouses: Snowflake, BigQuery, Redshift
- Integration with CRM platforms like Salesforce and HubSpot
- Aligning with identity and access management policies
Module 7: Field-Level Mapping Techniques - One-to-one, one-to-many, many-to-one mappings
- Concatenation and string manipulation with real examples
- Date and time zone conversions across regions
- Currency conversion and formatting standards
- Standardizing codes using lookup tables and codelists
- Handling yes/no, true/false, and coded boolean fields
- Mapping enumerated values with transformation logic
- Dealing with free-text fields and data cleansing
- Using regular expressions to standardize formats
- Documenting complex business rules step by step
Module 8: Data Transformation Logic - Writing clear, auditable transformation expressions
- Arithmetic operations: Aggregations, calculations, ratios
- Conditional statements using IF-THEN-ELSE logic
- Handling data quality issues during transformation
- Escalation paths for unresolved transformation rules
- Documenting fallback and default value strategies
- Versioning transformation logic for audit trails
- Mapping lookup tables and dimension tables
- Preserving source system logic without hardcoding
- Tool-agnostic transformation design principles
Module 9: Validation and Testing Strategies - Defining validation checkpoints and success criteria
- Sample-based vs. full-volume data verification
- Using checksums, row counts, and hash validations
- Reconciliation techniques between source and target
- Handling discrepancies and failed validations
- Root cause analysis for mapping errors
- Testing transformation logic in staging environments
- Automatable validation rules for repeatable checks
- Peer review and sign-off workflows
- Preparing for UAT with business stakeholders
Module 10: Documentation Standards and Templates - Designing a centralized data mapping document
- Standard columns: Source Field, Data Type, Target Field, Transformation
- Adding notes, owner, version, and audit fields
- Using color coding and conditional formatting effectively
- Version control and change tracking best practices
- Choosing between Excel, Google Sheets, and databases
- Exporting documentation for audit submission
- Creating summaries for non-technical audiences
- Automating documentation updates with macros and scripts
- Integrating with Confluence, SharePoint, or Notion
Module 11: Tools and Platforms for Data Mapping - Selecting tools based on team size and complexity
- Open-source vs. commercial mapping software comparison
- Integration with Informatica, Talend, and Microsoft SSIS
- Using Alteryx and Power Query for transformation design
- Leveraging built-in mapping features in ETL tools
- Metadata management tools: Collibra, Atlan, Alation
- Data lineage platforms and how they use mappings
- Using low-code platforms for rapid mapping design
- Mapping within iPaaS tools like MuleSoft and Boomi
- Tool-agnostic templates for maximum flexibility
Module 12: Governance, Compliance, and Audit Readiness - Mapping data flows for GDPR Article 30 records
- Supporting Data Protection Impact Assessments (DPIAs)
- Documenting data minimization and retention rules
- Ensuring PII and sensitive data are traceable
- Compliance with SOX, HIPAA, and PCI-DSS requirements
- Preparing for internal and external audits
- Creating auditor-friendly summary reports
- Retention, access controls, and data subject rights
- Legal basis for data processing documentation
- Handling cross-border data transfer mappings
Module 13: Stakeholder Communication and Collaboration - Translating technical mappings for business users
- Running effective mapping review meetings
- Using visual diagrams to show data flow
- Managing conflicting interpretations across teams
- Getting buy-in from legal, compliance, and finance
- Handling escalation paths for unresolved mappings
- Writing clear, non-technical field definitions
- Collaborative editing with version history
- Using feedback loops to refine documentation
- Presenting mapping outcomes to executive sponsors
Module 14: Advanced Data Mapping Scenarios - Mapping hierarchical or nested data structures
- Handling parent-child relationships in CRM systems
- Transforming XML and JSON payloads
- Mapping semi-structured and unstructured data
- Time-series and event-based data alignment
- Mapping for change data capture (CDC) pipelines
- Synchronizing real-time vs. batched data flows
- De-duplication and merging logic across sources
- Mapping multi-language and localized content
- Handling timezone-aware timestamps in global systems
Module 15: Change Management and Version Control - Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- Extracting database schemas and metadata
- Reading table definitions and primary keys
- Identifying redundant or deprecated fields
- Understanding source system constraints and triggers
- Engaging with legacy system owners and custodians
- Handling undocumented or poorly documented systems
- Sampling data to infer field purpose and content
- Mapping flat files, spreadsheets, and JSON structures
- Reverse-engineering field usage from reports and interfaces
- Detecting hidden business logic in source applications
Module 6: Target System Requirements and Constraints - Understanding destination schema limitations
- Field length, data type, and nullability constraints
- Handling required fields with no source equivalent
- Managing auto-generated and system-managed fields
- Compliance and encryption requirements at destination
- Indexing and performance implications of mapped data
- Validating data acceptance rules and parsing logic
- Mapping to cloud data warehouses: Snowflake, BigQuery, Redshift
- Integration with CRM platforms like Salesforce and HubSpot
- Aligning with identity and access management policies
Module 7: Field-Level Mapping Techniques - One-to-one, one-to-many, many-to-one mappings
- Concatenation and string manipulation with real examples
- Date and time zone conversions across regions
- Currency conversion and formatting standards
- Standardizing codes using lookup tables and codelists
- Handling yes/no, true/false, and coded boolean fields
- Mapping enumerated values with transformation logic
- Dealing with free-text fields and data cleansing
- Using regular expressions to standardize formats
- Documenting complex business rules step by step
Module 8: Data Transformation Logic - Writing clear, auditable transformation expressions
- Arithmetic operations: Aggregations, calculations, ratios
- Conditional statements using IF-THEN-ELSE logic
- Handling data quality issues during transformation
- Escalation paths for unresolved transformation rules
- Documenting fallback and default value strategies
- Versioning transformation logic for audit trails
- Mapping lookup tables and dimension tables
- Preserving source system logic without hardcoding
- Tool-agnostic transformation design principles
Module 9: Validation and Testing Strategies - Defining validation checkpoints and success criteria
- Sample-based vs. full-volume data verification
- Using checksums, row counts, and hash validations
- Reconciliation techniques between source and target
- Handling discrepancies and failed validations
- Root cause analysis for mapping errors
- Testing transformation logic in staging environments
- Automatable validation rules for repeatable checks
- Peer review and sign-off workflows
- Preparing for UAT with business stakeholders
Module 10: Documentation Standards and Templates - Designing a centralized data mapping document
- Standard columns: Source Field, Data Type, Target Field, Transformation
- Adding notes, owner, version, and audit fields
- Using color coding and conditional formatting effectively
- Version control and change tracking best practices
- Choosing between Excel, Google Sheets, and databases
- Exporting documentation for audit submission
- Creating summaries for non-technical audiences
- Automating documentation updates with macros and scripts
- Integrating with Confluence, SharePoint, or Notion
Module 11: Tools and Platforms for Data Mapping - Selecting tools based on team size and complexity
- Open-source vs. commercial mapping software comparison
- Integration with Informatica, Talend, and Microsoft SSIS
- Using Alteryx and Power Query for transformation design
- Leveraging built-in mapping features in ETL tools
- Metadata management tools: Collibra, Atlan, Alation
- Data lineage platforms and how they use mappings
- Using low-code platforms for rapid mapping design
- Mapping within iPaaS tools like MuleSoft and Boomi
- Tool-agnostic templates for maximum flexibility
Module 12: Governance, Compliance, and Audit Readiness - Mapping data flows for GDPR Article 30 records
- Supporting Data Protection Impact Assessments (DPIAs)
- Documenting data minimization and retention rules
- Ensuring PII and sensitive data are traceable
- Compliance with SOX, HIPAA, and PCI-DSS requirements
- Preparing for internal and external audits
- Creating auditor-friendly summary reports
- Retention, access controls, and data subject rights
- Legal basis for data processing documentation
- Handling cross-border data transfer mappings
Module 13: Stakeholder Communication and Collaboration - Translating technical mappings for business users
- Running effective mapping review meetings
- Using visual diagrams to show data flow
- Managing conflicting interpretations across teams
- Getting buy-in from legal, compliance, and finance
- Handling escalation paths for unresolved mappings
- Writing clear, non-technical field definitions
- Collaborative editing with version history
- Using feedback loops to refine documentation
- Presenting mapping outcomes to executive sponsors
Module 14: Advanced Data Mapping Scenarios - Mapping hierarchical or nested data structures
- Handling parent-child relationships in CRM systems
- Transforming XML and JSON payloads
- Mapping semi-structured and unstructured data
- Time-series and event-based data alignment
- Mapping for change data capture (CDC) pipelines
- Synchronizing real-time vs. batched data flows
- De-duplication and merging logic across sources
- Mapping multi-language and localized content
- Handling timezone-aware timestamps in global systems
Module 15: Change Management and Version Control - Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- One-to-one, one-to-many, many-to-one mappings
- Concatenation and string manipulation with real examples
- Date and time zone conversions across regions
- Currency conversion and formatting standards
- Standardizing codes using lookup tables and codelists
- Handling yes/no, true/false, and coded boolean fields
- Mapping enumerated values with transformation logic
- Dealing with free-text fields and data cleansing
- Using regular expressions to standardize formats
- Documenting complex business rules step by step
Module 8: Data Transformation Logic - Writing clear, auditable transformation expressions
- Arithmetic operations: Aggregations, calculations, ratios
- Conditional statements using IF-THEN-ELSE logic
- Handling data quality issues during transformation
- Escalation paths for unresolved transformation rules
- Documenting fallback and default value strategies
- Versioning transformation logic for audit trails
- Mapping lookup tables and dimension tables
- Preserving source system logic without hardcoding
- Tool-agnostic transformation design principles
Module 9: Validation and Testing Strategies - Defining validation checkpoints and success criteria
- Sample-based vs. full-volume data verification
- Using checksums, row counts, and hash validations
- Reconciliation techniques between source and target
- Handling discrepancies and failed validations
- Root cause analysis for mapping errors
- Testing transformation logic in staging environments
- Automatable validation rules for repeatable checks
- Peer review and sign-off workflows
- Preparing for UAT with business stakeholders
Module 10: Documentation Standards and Templates - Designing a centralized data mapping document
- Standard columns: Source Field, Data Type, Target Field, Transformation
- Adding notes, owner, version, and audit fields
- Using color coding and conditional formatting effectively
- Version control and change tracking best practices
- Choosing between Excel, Google Sheets, and databases
- Exporting documentation for audit submission
- Creating summaries for non-technical audiences
- Automating documentation updates with macros and scripts
- Integrating with Confluence, SharePoint, or Notion
Module 11: Tools and Platforms for Data Mapping - Selecting tools based on team size and complexity
- Open-source vs. commercial mapping software comparison
- Integration with Informatica, Talend, and Microsoft SSIS
- Using Alteryx and Power Query for transformation design
- Leveraging built-in mapping features in ETL tools
- Metadata management tools: Collibra, Atlan, Alation
- Data lineage platforms and how they use mappings
- Using low-code platforms for rapid mapping design
- Mapping within iPaaS tools like MuleSoft and Boomi
- Tool-agnostic templates for maximum flexibility
Module 12: Governance, Compliance, and Audit Readiness - Mapping data flows for GDPR Article 30 records
- Supporting Data Protection Impact Assessments (DPIAs)
- Documenting data minimization and retention rules
- Ensuring PII and sensitive data are traceable
- Compliance with SOX, HIPAA, and PCI-DSS requirements
- Preparing for internal and external audits
- Creating auditor-friendly summary reports
- Retention, access controls, and data subject rights
- Legal basis for data processing documentation
- Handling cross-border data transfer mappings
Module 13: Stakeholder Communication and Collaboration - Translating technical mappings for business users
- Running effective mapping review meetings
- Using visual diagrams to show data flow
- Managing conflicting interpretations across teams
- Getting buy-in from legal, compliance, and finance
- Handling escalation paths for unresolved mappings
- Writing clear, non-technical field definitions
- Collaborative editing with version history
- Using feedback loops to refine documentation
- Presenting mapping outcomes to executive sponsors
Module 14: Advanced Data Mapping Scenarios - Mapping hierarchical or nested data structures
- Handling parent-child relationships in CRM systems
- Transforming XML and JSON payloads
- Mapping semi-structured and unstructured data
- Time-series and event-based data alignment
- Mapping for change data capture (CDC) pipelines
- Synchronizing real-time vs. batched data flows
- De-duplication and merging logic across sources
- Mapping multi-language and localized content
- Handling timezone-aware timestamps in global systems
Module 15: Change Management and Version Control - Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- Defining validation checkpoints and success criteria
- Sample-based vs. full-volume data verification
- Using checksums, row counts, and hash validations
- Reconciliation techniques between source and target
- Handling discrepancies and failed validations
- Root cause analysis for mapping errors
- Testing transformation logic in staging environments
- Automatable validation rules for repeatable checks
- Peer review and sign-off workflows
- Preparing for UAT with business stakeholders
Module 10: Documentation Standards and Templates - Designing a centralized data mapping document
- Standard columns: Source Field, Data Type, Target Field, Transformation
- Adding notes, owner, version, and audit fields
- Using color coding and conditional formatting effectively
- Version control and change tracking best practices
- Choosing between Excel, Google Sheets, and databases
- Exporting documentation for audit submission
- Creating summaries for non-technical audiences
- Automating documentation updates with macros and scripts
- Integrating with Confluence, SharePoint, or Notion
Module 11: Tools and Platforms for Data Mapping - Selecting tools based on team size and complexity
- Open-source vs. commercial mapping software comparison
- Integration with Informatica, Talend, and Microsoft SSIS
- Using Alteryx and Power Query for transformation design
- Leveraging built-in mapping features in ETL tools
- Metadata management tools: Collibra, Atlan, Alation
- Data lineage platforms and how they use mappings
- Using low-code platforms for rapid mapping design
- Mapping within iPaaS tools like MuleSoft and Boomi
- Tool-agnostic templates for maximum flexibility
Module 12: Governance, Compliance, and Audit Readiness - Mapping data flows for GDPR Article 30 records
- Supporting Data Protection Impact Assessments (DPIAs)
- Documenting data minimization and retention rules
- Ensuring PII and sensitive data are traceable
- Compliance with SOX, HIPAA, and PCI-DSS requirements
- Preparing for internal and external audits
- Creating auditor-friendly summary reports
- Retention, access controls, and data subject rights
- Legal basis for data processing documentation
- Handling cross-border data transfer mappings
Module 13: Stakeholder Communication and Collaboration - Translating technical mappings for business users
- Running effective mapping review meetings
- Using visual diagrams to show data flow
- Managing conflicting interpretations across teams
- Getting buy-in from legal, compliance, and finance
- Handling escalation paths for unresolved mappings
- Writing clear, non-technical field definitions
- Collaborative editing with version history
- Using feedback loops to refine documentation
- Presenting mapping outcomes to executive sponsors
Module 14: Advanced Data Mapping Scenarios - Mapping hierarchical or nested data structures
- Handling parent-child relationships in CRM systems
- Transforming XML and JSON payloads
- Mapping semi-structured and unstructured data
- Time-series and event-based data alignment
- Mapping for change data capture (CDC) pipelines
- Synchronizing real-time vs. batched data flows
- De-duplication and merging logic across sources
- Mapping multi-language and localized content
- Handling timezone-aware timestamps in global systems
Module 15: Change Management and Version Control - Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- Selecting tools based on team size and complexity
- Open-source vs. commercial mapping software comparison
- Integration with Informatica, Talend, and Microsoft SSIS
- Using Alteryx and Power Query for transformation design
- Leveraging built-in mapping features in ETL tools
- Metadata management tools: Collibra, Atlan, Alation
- Data lineage platforms and how they use mappings
- Using low-code platforms for rapid mapping design
- Mapping within iPaaS tools like MuleSoft and Boomi
- Tool-agnostic templates for maximum flexibility
Module 12: Governance, Compliance, and Audit Readiness - Mapping data flows for GDPR Article 30 records
- Supporting Data Protection Impact Assessments (DPIAs)
- Documenting data minimization and retention rules
- Ensuring PII and sensitive data are traceable
- Compliance with SOX, HIPAA, and PCI-DSS requirements
- Preparing for internal and external audits
- Creating auditor-friendly summary reports
- Retention, access controls, and data subject rights
- Legal basis for data processing documentation
- Handling cross-border data transfer mappings
Module 13: Stakeholder Communication and Collaboration - Translating technical mappings for business users
- Running effective mapping review meetings
- Using visual diagrams to show data flow
- Managing conflicting interpretations across teams
- Getting buy-in from legal, compliance, and finance
- Handling escalation paths for unresolved mappings
- Writing clear, non-technical field definitions
- Collaborative editing with version history
- Using feedback loops to refine documentation
- Presenting mapping outcomes to executive sponsors
Module 14: Advanced Data Mapping Scenarios - Mapping hierarchical or nested data structures
- Handling parent-child relationships in CRM systems
- Transforming XML and JSON payloads
- Mapping semi-structured and unstructured data
- Time-series and event-based data alignment
- Mapping for change data capture (CDC) pipelines
- Synchronizing real-time vs. batched data flows
- De-duplication and merging logic across sources
- Mapping multi-language and localized content
- Handling timezone-aware timestamps in global systems
Module 15: Change Management and Version Control - Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- Translating technical mappings for business users
- Running effective mapping review meetings
- Using visual diagrams to show data flow
- Managing conflicting interpretations across teams
- Getting buy-in from legal, compliance, and finance
- Handling escalation paths for unresolved mappings
- Writing clear, non-technical field definitions
- Collaborative editing with version history
- Using feedback loops to refine documentation
- Presenting mapping outcomes to executive sponsors
Module 14: Advanced Data Mapping Scenarios - Mapping hierarchical or nested data structures
- Handling parent-child relationships in CRM systems
- Transforming XML and JSON payloads
- Mapping semi-structured and unstructured data
- Time-series and event-based data alignment
- Mapping for change data capture (CDC) pipelines
- Synchronizing real-time vs. batched data flows
- De-duplication and merging logic across sources
- Mapping multi-language and localized content
- Handling timezone-aware timestamps in global systems
Module 15: Change Management and Version Control - Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- Why versioning is critical in live environments
- Documenting change requests and approvals
- Using version numbers and effective dates
- Change logs: Who changed what and why
- Communicating updates to downstream teams
- Handling rollback scenarios and emergency fixes
- Integrating with change management systems
- Using Git for managing mapping documentation
- Configuring access permissions and edit rights
- Auditing version history for compliance
Module 16: Integration with Data Governance Programs - Linking data maps to data dictionaries and catalogs
- Establishing data ownership and stewardship rules
- Embedding mapping standards into onboarding
- Connecting lineage to data quality KPIs
- Using maps to support data classification efforts
- Tying mapping metadata to SLAs and incident response
- Integrating with enterprise architecture frameworks
- Supporting data subject access requests (DSARs)
- Automating data impact analysis across systems
- Creating a culture of documentation and accountability
Module 17: Real-World Projects and Case Studies - Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints
Module 18: Certification, Next Steps, and Career Advancement - Preparing for your Certificate of Completion assessment
- Submitting your final data mapping project for evaluation
- What the certification validates and how to use it
- Adding your achievement to LinkedIn and job profiles
- Leveraging the certification in salary negotiations
- Using completed projects as portfolio pieces
- Transitioning into roles like Data Analyst, Architect, or Governance Lead
- Leading future mapping initiatives with authority
- Accessing alumni resources and community forums
- Next steps: Data modeling, lineage, and automation
- Healthcare patient record migration across systems
- Financial data integration from ERPs to reporting platforms
- Retail inventory sync across supply chain tools
- Marketing attribution mapping from digital channels
- HRIS to payroll system field alignment
- API request-response mapping in SaaS integrations
- Legacy system retirement with data archiving rules
- Cloud migration data mapping for AWS and Azure
- Replatforming e-commerce order management logic
- Mapping customer journey data across touchpoints