Thorough understanding of source systems is essential for successful migration. Our consultants map the legacy data landscape, gathering critical information about all source systems and data. Key activities include system inventory workshops, data profiling, documentation creation and establishing governance structures. Konexxia identifies each legacy data source corresponding to target Dynamics entities.

The Test & Reconcile phase ensures proper testing and reconciliation procedures during each migration cycle. Each iteration concludes with formal data validation (DV), including internal unit testing and external business validation. Testing approaches include volume, boundary, integration and user acceptance testing. Validation cycle quantity depends on data complexity, volumes, legacy systems, project maturity and timeframes, all integrated into the wider programme plan.

Source-to-target mapping requires collaboration between customer SMEs, database administrators, our team and the SI. We create comprehensive field mapping documents specifying transformation rules. Deliverables include mapping documentation, transformation specifications, quality standards and gap analysis reports. Through targeted workshops, we define object scope and field-level mapping rules, scheduling additional sessions for separate source systems as needed.

The Refine phase iteratively enhances mappings, builds and transformations through planned cycles (DVs). This maximises visibility of progress and ensures optimal data quality before go-live. Techniques include retrospective reviews, continuous improvement of transformation rules, progressive quality enhancement and incremental loading. This approach applies to all market engagements, aligned with the programme plan.

This phase implements appropriate build processes for each migration cycle, incorporating feedback from previous testing to refine transformation logic. Activities include developing extraction routines, creating transformation processes, building loading mechanisms, implementing validation checks, and establishing monitoring capabilities.

Our Data Cutover Plan, developed through DV Test Cycles, details all data activities including responsibilities, timing, sequences and interdependencies. The plan covers data dependencies, task durations, reconciliation checkpoints, entity classification and aligns with the overall project cutover. Non-volatile master data can be pre-loaded into D365 F&O when required. The data team lead develops this plan, which integrates with the overall project cutover managed by the Implementation Project Manager. ​ Post go-live, the data team provides support by reviewing data-related bugs, planning fixes, addressing queries and providing legacy-to-new mapping references. Support activities include quality monitoring, stakeholder feedback sessions and knowledge transfer. This support aligns with the wider project framework but typically reduces after successful month-end close. Importantly, validated production data errors should primarily be corrected manually by users with data team support.