Migrate master, reference, and transactional data from any source to SAP S/4 HANA.
ChainSys Smart Data Platform and proven methodology ensure data migration success, from any source to SAP S/4 HANA, in as little as one-third the time. Trust ChainSys Smart Data Platform to deliver your critical data migration projec
Uses Cases, Key Features and Highlights
- Migrate master, reference, and transaction data from any source to SAP S/4 HANA.
- Consolidate multiple source systems and/or subsystems onto a single SAP S/4 HANA instance.
- Support mergers, acquisitions, and divestitures in SAP S/4 HANA.
- Bifurcate a single SAP S/4 HANA instance into multiple instances.
- Inline data profiling and improved data quality
- Inline master data deduping, cleansing, enrichment
- Inline data pre- and post-load validation
- End-to-end data lineage and reconciliation
- Parallel processing
- Reduced Migration Project Risk and Timeline
- Smooth Cutovers with Reduced Cutover Time
- Satisfied Auditors
ChainSys Smart Data Platform leverages pre-built object-level extract adaptors for most common enterprise applications, as well as pre-built object-level load adaptors to SAP S/4 HANA, to accelerate your extraction, mapping, transformation, and loading.
ChainSys Smart Data Platform rapidly extracts master, reference, and transactional data to assess and profile it, then configurable business rules are applied to match, merge, cleanse and enrich each data object as part of its data flow. Each data flow is then orchestrated to execute sequentially or in parallel, as required by the target system. Source data objects are loaded into the ChainSys data mart for pre-validation, transformation, corrections, before final loading into the target. End-to-end orchestrated data migrations (test cycles) are typically performed 3 to 5 times, prior to production cutover. With an increased number of test cycles and the first test cycle happening as early as one month into the data migration effort, the data quality improves significantly from one test cycle to the next. Full end-to-end data reconciliation of all data sources to all targets is performed with each iteration.