Data Migration in Application Management

Description

What does the Data Migration in Application Management course cover?

Data Migration in Application Management is covered here in 9 modules: Assessing Source Systems and Data Landscapes, Defining Migration Scope and Objectives, Designing Data Transformation and Mapping Strategies and 6 more. The outline lists 72 specific topics, opening with inventory legacy application schemas, including undocumented or deprecated fields used by downstream processes.

How do you approach Data Migration in Application Management step by step?

The work is sequenced in 9 stages. It starts with Assessing Source Systems and Data Landscapes, moves through Defining Migration Scope and Objectives and Designing Data Transformation and Mapping Strategies, and ends at Governing Ongoing Data Operations and Maintenance. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Data Migration in Application Management course?

Module 1 is Assessing Source Systems and Data Landscapes. It works through inventory legacy application schemas, including undocumented or deprecated fields used by downstream processes., determine data ownership across departments when source systems lack centralized stewardship., evaluate data quality issues such as missing primary keys, inconsistent timestamps, or orphaned records in operational databases. and 5 more.

How is the Data Migration in Application Management course delivered?

The Data Migration in Application Management course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Data Migration in Application Management course cost?

The Data Migration in Application Management course is $302 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the full lifecycle of enterprise data migration, comparable in scope to a multi-phase internal capability program that addresses technical, organisational, and compliance challenges across source assessment, pipeline development, cutover execution, and ongoing data governance.

Module 1: Assessing Source Systems and Data Landscapes

Inventory legacy application schemas, including undocumented or deprecated fields used by downstream processes.
Determine data ownership across departments when source systems lack centralized stewardship.
Evaluate data quality issues such as missing primary keys, inconsistent timestamps, or orphaned records in operational databases.
Map data dependencies between applications that rely on real-time or batch data feeds.
Classify data based on sensitivity and regulatory scope (e.g., PII, financial records) to inform migration priority and handling.
Decide whether to extract data directly from production systems or use staging environments to reduce operational risk.
Negotiate access permissions with system owners who may restrict data exports due to security policies.
Document technical debt in source systems, such as embedded business logic in stored procedures that must be replicated.

Module 2: Defining Migration Scope and Objectives

Select which data entities to migrate based on business criticality, usage frequency, and compliance requirements.
Establish data retention rules to exclude obsolete or redundant records from migration.
Define success criteria for data completeness, accuracy, and referential integrity post-migration.
Balance stakeholder demands for full historical data against storage and performance constraints in the target system.
Decide whether to migrate inactive user accounts or archived records based on legal hold requirements.
Identify shadow data sources such as spreadsheets or local databases used in parallel with official systems.
Set migration timelines in coordination with application decommissioning schedules and contract expirations.
Align migration scope with the functional capabilities of the target application to avoid data over-migration.

Module 3: Designing Data Transformation and Mapping Strategies

Resolve schema mismatches between source and target systems, such as differences in field length, data types, or enumeration values.
Develop transformation logic to standardize addresses, phone numbers, or product codes across disparate sources.
Handle hierarchical data structures (e.g., organizational charts) that require flattening or re-encoding for target compatibility.
Implement business rule translation when legacy systems encode logic in triggers or application code.
Decide whether to preserve original timestamps or reassign them to reflect migration time for audit consistency.
Create fallback mappings for fields with no direct equivalent in the target system, such as deprecated statuses.
Design transformation workflows that support incremental updates for multi-phase migrations.
Validate transformation outputs against sample datasets to detect logic errors before full execution.

Module 4: Building and Testing Migration Pipelines

Select ETL tools or custom scripts based on data volume, transformation complexity, and team expertise.
Implement error handling to log and quarantine records that fail transformation or validation rules.
Configure retry mechanisms for transient failures in network or database connectivity during extraction.
Test migration pipelines using anonymized production data to simulate real-world conditions.
Measure pipeline performance under load to identify bottlenecks in disk I/O, memory, or API rate limits.
Version control migration scripts and configuration files to enable rollback and auditability.
Integrate data profiling into the pipeline to generate pre- and post-migration quality reports.
Simulate partial failures to verify recovery procedures and data consistency after interruption.

Module 5: Ensuring Data Quality and Integrity

Define and automate validation rules for mandatory fields, unique constraints, and cross-table relationships.
Compare row counts, checksums, and aggregate metrics between source and target systems post-migration.
Investigate and resolve discrepancies in calculated fields that differ due to rounding or logic changes.
Implement reconciliation jobs to detect and report data drift during phased migration windows.
Use sampling techniques to audit data accuracy when full validation is computationally prohibitive.
Address referential integrity issues when parent records are migrated in a different sequence than children.
Document data quality exceptions and obtain business sign-off on acceptable deviation thresholds.
Monitor for duplicate records introduced by overlapping extraction windows or retry logic.

Module 6: Managing Security, Compliance, and Access Controls

Encrypt data in transit and at rest during migration to meet regulatory requirements (e.g., GDPR, HIPAA).
Apply role-based access controls to migration tools and intermediate storage locations.
Mask or redact sensitive data in test environments used for pipeline validation.
Retain audit logs of all migration activities, including who executed jobs and when.
Verify that data classification tags are preserved or re-applied in the target system.
Coordinate with legal teams to ensure data transfers across jurisdictions comply with data residency laws.
Decommission access credentials and temporary storage after migration completion.
Conduct security reviews of third-party tools or cloud services used in the migration workflow.

Module 7: Executing Cutover and Minimizing Downtime

Design a cutover window that aligns with business cycles to minimize user impact.
Implement final data sync processes to capture changes made during the migration blackout period.
Coordinate application freeze periods with business units to prevent data modifications during cutover.
Deploy parallel run environments to validate target system behavior with live data before full switch.
Prepare rollback procedures, including data restoration timelines and dependencies on other systems.
Monitor data synchronization latency between source and target during final delta migrations.
Communicate cutover status to stakeholders using real-time dashboards and escalation protocols.
Validate user access and permissions in the target system immediately after cutover.

Module 8: Post-Migration Validation and System Stabilization

Run end-to-end business process tests to confirm data usability in the new application.
Compare report outputs between old and new systems to detect discrepancies in aggregation or filtering.
Address user-reported data issues through a triage process that distinguishes migration errors from application bugs.
Retire source systems only after confirming data integrity and obtaining formal business sign-off.
Archive migration artifacts, including logs, scripts, and validation reports, for audit purposes.
Update data dictionaries and metadata repositories to reflect the new system's structure and lineage.
Monitor application performance to identify data-related bottlenecks, such as slow queries on migrated datasets.
Conduct a lessons-learned review to document technical decisions, failures, and process improvements.

Module 9: Governing Ongoing Data Operations and Maintenance

Establish ownership for data quality monitoring in the target application post-migration.
Integrate migrated data into existing backup and disaster recovery procedures.
Define SLAs for data refresh frequency if the target system relies on ongoing feeds from other sources.
Implement change management controls to prevent unauthorized schema modifications that break data lineage.
Set up alerts for anomalies in data volume, update frequency, or validation failure rates.
Plan for future data migrations by maintaining documentation of transformation logic and mapping decisions.
Review and update access controls periodically to reflect organizational changes and role transitions.
Evaluate the need for data archiving strategies in the new system based on growth patterns and retention policies.