Data Migration Strategy & Approach

The discipline that derails more Oracle projects than any other. Oracle data migration strategy from first principles — object prioritisation, data quality, mapping templates, load sequencing, and reconciliation. Built from real project experience. 6 hrs · 2 sessions. Overview Data migration is the most underestimated workstream on any Oracle implementation. Teams routinely discover this at the worst possible time — six weeks before go-live, when the data quality assessment reveals problems that will take months to fix, or at the first mock migration, when a load sequence error means the entire overnight run fails and has to be restarted from scratch. The consequence is almost always a delayed go-live, usually preceded by a period of intense panic. This course teaches data migration as a discipline with its own methodology, its own deliverables, and its own quality gates — not as a technical task that starts when everything else is finished. We cover object prioritisation: which data objects to migrate and in what sequence. Data quality assessment: how to identify problems in source data before they become migration problems. Mapping template design: how to document the transformation from source to target in a way that is usable by a developer and traceable at audit. Load sequencing: why getting the order wrong causes cascade failures. And reconciliation: how to confirm that what went in is what came out. The examples in this course are drawn from real migrations — EBS to Fusion, NetSuite to Fusion, Excel spreadsheets to Fusion, and legacy African ERP systems to Fusion. The data quality problems we use as examples are real data quality problems. The sequencing errors we show are errors that have caused actual go-live delays. Learning outcomes • Produce a data migration plan that identifies all objects, their sequence, quality requirements, and the timeline for each activity • Design a data quality assessment process that identifies migration risks early enough for the project to address them • Create mapping templates that document source-to-target transformations in a way that is usable by developers and traceable at audit • Structure a mock migration programme that progressively improves data quality and load reliability before go-live • Design a cutover data loading plan that correctly sequences data activities within the overall cutover weekend • Conduct a three-level reconciliation — record count, control total, and sample validation — and interpret the results Modules 1. Object prioritisation, data quality assessment, and mapping design (3 hours) We begin with object prioritisation: the process of identifying which data objects need to be migrated, in what sequence, and with what level of completeness. We work through the standard Oracle Fusion object hierarchy — from the foundational setup objects (chart of accounts, suppliers, customers) through the transactional open items (open invoices, open purchase orders, asset registers) — and the dependencies between them that determine load sequence. We then cover data quality assessment: how to extract source data early enough to fix problems, what to look for, and how to quantify the effort required. We end with mapping template design: the document that connects source system fields to target Oracle fields, records the transformation logic, and provides the basis for both development and reconciliation. Topics: Object prioritisation framework; Load sequence dependencies; Data quality assessment methodology; Common data quality problems; Mapping template design 2. Mock migrations, reconciliation, and cutover data loading (3 hours) The second session covers the operational side of data migration: how to run a mock migration, what to do when it fails, and how to use mock migration results to improve data quality and loading scripts before go-live. We cover the reconciliation framework — the process of confirming that data loaded into Oracle matches the source system — and why reconciliation needs to happen at three levels: record count, control total, and sample validation. We then cover cutover data loading: the specific data migration activities that happen during the go-live cutover weekend, why cutover data is different from the mock migration data, and how to plan the cutover data load as part of the overall cutover plan. We end with a reconciliation case study where participants work through a migration that did not reconcile and identify the cause. Topics: Mock migration process; Failure recovery; Three-level reconciliation; Cutover data loading; Reconciliation case study Important The project manager's most dangerous assumption The single most dangerous assumption on any Oracle project is that data migration is something that happens near the end, after the system is configured. Data migration should begin in the design phase and run in parallel with build. Organisations that start data migration activities late almost always miss their go-live date — and the delay is proportional to how late they started. Delivery Two sessions, fully self-contained. Works well as a standalone course or as part of a Track 03 programme alongside Oracle Project Methodology. For clients preparing for an imminent go-live, individual coaching is available for immediate practical application on the live project. Certification Certificate of completion Price USD 600 · per participant per person · indicative · group pricing available HRDC eligibility Up to 75% refundable Mauritius-registered employers may claim this training cost via the HRDC levy. We provide full documentation to support your claim. Languages English Provider reference SIMPLIT-016-DATA_MIGRATION_STRATEGY

• Project managers and programme leads on Oracle implementation projects • Consultants who are responsible for the data migration workstream and have not done it before • Functional leads who need to understand what data migration requires from the business • Client-side data owners who will be responsible for extracting and validating source data • Anyone who has been on an Oracle project where data migration caused problems • New consultants who will be supporting a data migration workstream